Computer - Science topic

Abolfazl Ghoodjani

Thank you for your quick response and useful information. Can you share some resources about the interpretation and presentation of the results using predicted probabilities?

Dear Archimede Torretta ,

The Cytometer includes: ■ Analyzer that counts, measures and computes parameters. ■ Power Supply that provides voltages, vacuum and pressure. ■ MCL that positions the sample tubes in the carousel for sampling. ■ Laser(s) for fluorescence excitation.

Regards,

Shafagat

Quantum Computing by Mathematics: achieving tomorrow's technology with today's tools. In continuation of the previous posts, despite the advances to achieve the power of quantum computing at temperatures such as 1 Kelvin and efforts to use photonic and laser styles to achieve this power at room temperature, it is still far from reaching the level of using quantum computers in consumers' homes with reasonable costs. As mentioned, computing styles based on artificial intelligence and neural, cognitive, and neuromorphic computing are also being developed. Of course, these methods seek to imitate one hundred percent the behavior of human brain calculations in machines, so that they can convey most of the possible abilities and states. Meanwhile, using computational tools from various branches of mathematics such as algebra, analysis, and geometry is a completely different approach. Changing the basis of calculations from binary scalar states to vectors, matrices and other high-dimensional tools such as tensors and manifolds is a style that is very close to quantum states and gates. Of course, the origin of this style was trying to simulate states and quantum computing with mathematical algorithms on classical computers, as well as managing big data and multidimensional data with high dimensions.

For more information, please see:

Bruce Weaver A useful app but it appears that its focus (currently) is exclusively on power. I believe that a simulation that is run "from scratch" provides greater flexibility and yields a lot more information than just on power. For example, you can simulate the effects of non-normal and missing data and evaluate the performance of fit statistics, as well as parameter and standard error bias in addition to estimating power. Power estimations can be misleading when your SEM parameters or standard errors are biased due to, for example, an insufficient sample size.

Ввиду того, что время пребывания в аэротэнке около суток, а время удвоения биомассы автотрофов, например хлореллы четверо сутоко, автотрофов мало.

This error is due to the connection issue between your PC and the server. so your PC is not able to communicate with the server. Please verify the following

a) Restart your PC and try to run the simulation again.

2) Check the "server status" in License administrator (whether connected or not).

3) Check "server gateways" in server console (whether the server ip exists or not).

Hello Fadhaa Aditya Kautsar Murti. I don't know where you got "Add more covariates/predictors" in Babyak's article. The second suggestion in that section was to combine predictors if there is a sensible way to do so. And doing that would reduce the number of degrees of freedom eaten up by predictors. Indeed, noticed what the first sentence of that section says:

"One obvious approach to preserve degrees of freedom is to

reduce the number of predictors in the model."

You asked about hazard rates, which implies that you have a survival model. For survival models, the limiting sample size is the number of events. Babyak's recommendation is that you have at least 10-15 events per explanatory variable degree of freedom. Nowadays, Frank Harrell recommends at least 20 EPV.

I hope this helps.

Use the background area under peak 1 as shown in the figure. It is possible to perform iterative fitting to refine the background, but unlikely you will reduce the uncertainty in the estimate to an important degree.

One solution is to use: https://converter.app/docx-to-latex/result.php?lang=en to convert docx file to latex.

Daniel Jacob Bilar . Thanks for your insights. I will try your suggestions.

Quantifying the dielectrophoretic force in COMSOL involves setting up a simulation that includes the relevant physics and boundary conditions. Dielectrophoresis (DEP) is the phenomenon where a non-uniform electric field exerts a force on a dielectric particle. Here's a general guide on how to quantify the dielectrophoretic force using COMSOL:

By following these steps and utilizing COMSOL's capabilities for simulating dielectrophoresis, you can effectively quantify the dielectrophoretic force in your system.

To accurately determine the reason for the yellow light and ensure the proper functioning of your NanoDrop 8000, I recommend consulting the instrument's user manual or contacting the manufacturer's technical support for assistance. They can provide specific guidance based on the instrument's design and any error codes or messages displayed.

The quantitative cycle is proportional to the bacterial density...

1. Technical problems: The platform might be going through maintenance or having technical problems, which could interfere with the login procedure. It is best to ask for help from the platform's support staff in such circumstances.

2. Incorrect login information: Verify that the username and password you are entering are correct. In the event that you lose your login information, you can typically utilise the platform's password recovery feature to get back in.

3. Account activation: To use certain platforms, users must go through a verification process, like clicking on an email link, to activate their accounts. Your ability to log in may be blocked if the activation process is not finished.

4. Browser or device compatibility: Some platforms might need particular web browsers or devices to work properly, or they might have compatibility problems. To see if that fixes the login problem, try utilising a different browser or device.

5. Security measures: A platform may occasionally make the login process more difficult if it has added extra security measures like two-factor authentication (2FA). In order to successfully finish the authentication process, make sure you adhere to the given instructions.

For help, I suggest contacting Qeios customer support if you are having trouble logging in consistently. They will be able to answer any questions you might have and offer you specific advice.

@salomebukachi

You don't need to install on your computer. The easiest option would be ColabFold.

plz download and use

@Jennifer Santos , As I did not use chat GPT, I do not have access to this platform at all. Because it is banned and sanctioned in our country. But I want to know what is your problem?

The question is a bit older, but nonetheless :)

A few comments: Products have acquisition and disposal costs, but also for maintenance, support, updates/modifications and obsolescence. This is also part of the function. Products consist of parts (HW and SW) that can fail in several ways. The fixing causes the costs. Costs also come from lost revenue because of non-availability. So, I suggest to reformulate as Asset management question. Costs can be calculated via tasks/services that are required to restore a function.The functions of systems/parts drive the costs. A part failure, may not result in function failure, so it is irrelevant.

For methodology, have a look at www.s3000L.org, www.s4000P.org and the downloads of www.sx000i.org. Specs are for free, updates scheduled mid this year.

Tools are from TFD in UK (Vmetric) or Systecon Toolsuite from Sweden (OPUS10, etc). The math is not very complex of you know math, focus on costs vs availability. You may find some tools in the US as well.

ISO15288 on Systems Engineering provides the engineering perspective.

Hi Martin, funnily enough, someone just pointed me to this paper:

The godspeed questionnaire or some of its scales might be useful for what you plan.

LG aus Bonn :-)

Dear Naous Mohamed Have a look at https://www.researchgate.net/post/Best-graphics-program-for-making-scientific-illustrations-for-journal-articles for various good (and bad) suggestions.

Personally I used Biorender recently. It is a rather user-friendly tool (with limitations). You can try for free if it suits you. The only real drawback, it is not for free if you need publication quality pictures.

Best regards.

R + emacs

There could be several reasons why you're encountering difficulties calculating the K/eTh ratio or F-parameter in your gamma-ray spectrometry data for K-enrichment delineation. Here are some possibilities to explore:

By systematically investigating these potential causes and seeking additional resources, you should be able to identify the source of your problem and successfully calculate the K/eTh ratio or F-parameter for your K-enrichment delineation project.

Some ideas and associations:

You state “human can only aspire to fluency in so many different languages before mixing up words due to code switching”. I don’t know if this is true at all. On what research is it based? In my own situation, I am fluent in 5 languages and do not mix up words or get confused to which language a word belongs to.

Fluency in language corresponds to numeracy, that has been demonstrated. Children who are read a lot of stories in pre-school, for example, were no better in general subjects compared to other children, but they were better at maths later on. Both implicate logical thinking. Without logical thinking humans cannot string words into a longer narrative either.

People choosing coding or computer sciences may prefer to work individually and not in groups. That is a different dynamic, social vs solo, than proficiency at languages.

Hello, I suggest you start with the basics of OpenFOAM. You can find lots of resources on the official pages. See https://www.openfoam.com/documentation/user-guide or https://doc.cfd.direct/openfoam/user-guide-v11/index. There is many more when you google it.

As regards the solver, for your application you could use some of the multiphase solvers, e.g. interFoam. Look for a dam break tutorial, it could be the right solver for you and then adjust it to your needs.

Good luck! Jan

The elasticity of y with respect to x is defined as the percentage change in y resulting from a one-percent change in x, holding all else constant. In the context of your regression model, where you have regressed the growth rate of y (which can be thought of as the percentage change in y) against the growth rate of x (the percentage change in x), the estimated coefficient on the growth rate of x is an estimate of this elasticity directly.

Here's why: If you run the following regression:

Δ%y=a+b(Δ%x)+ϵ

where Δ%y is the growth rate of y (dependent variable), Δ%x is the growth rate of x (independent variable), a is the constant term, b is the slope coefficient, and ϵ is the error term, the coefficient b represents the change in Δ%y for a one-unit change in Δ%x. Because Δ%y and Δ%x are already in percentage terms, the coefficient b is the elasticity of y with respect to x.

So, if you have estimated the coefficient b from this regression, you have already estimated the elasticity. There is no need to recover or transform the coefficient further; the estimated coefficient b is the elasticity of y with respect to x.

It's important to note that this interpretation assumes that the relationship between y and x is log-linear, meaning the natural logarithm of y is a linear function of the natural logarithm of x, and the model is correctly specified without omitted variable bias or other issues that could affect the estimator's consistency.

The choice of tools or platforms for planning research works can depend on various factors, including the nature of your research, collaboration needs, and personal preferences. Here are some widely used and versatile tools/platforms for planning and organizing research:

Adnan, care to complete your 1/2 baked thought?

Good day all, please how do I request for a license file for materials studio software?

Yes, because what matters isn't the activation function, but the cost function.

Absolutely, there are some interesting parallels between how information flows in humans and in computers, although the mechanisms are quite different.

In humans, at the molecular biology level, information flow is governed by the Central Dogma, where DNA is transcribed into RNA, and then RNA is translated into proteins. These proteins are crucial for various cellular functions. In neurology, information flow involves neurons transmitting electrical and chemical signals across synapses, essentially how our brain communicates and processes information.

In computers, information flow happens through computer networking, where data is transferred using protocols over various types of physical and wireless networks. This is akin to a structured set of rules determining how data packets are sent, received, and interpreted.

The resemblance lies in the fundamental concept of transmitting information. In both humans and computers, information is encoded, transmitted, received, and then decoded. In humans, it's more about biochemical and electrical signals, while in computers, it's about digital data packets.

However, it's important to remember that the underlying processes and materials (biological vs. electronic) are fundamentally different. It's a fascinating area of study, with each field having its own complexities and advancements!

Dear Dr. Osama Sayaydeh

Here are some Scopus-indexed journals related to computer science that were generally well-regarded:

1. IEEE Transactions on Computers

2. ACM Transactions on Computer Systems

3. Journal of Computer Science and Technology

4. Information Sciences

5. Journal of Computer and System Sciences

6. Journal of Computer Science and Technology

7. Journal of Artificial Intelligence Research

8. Pattern Recognition

9. Expert Systems with Applications

10.Journal of Network and Computer Applications

It's advisable to check the latest Scopus database or journal rankings for the most up-to-date information. Additionally, the appropriateness of a journal may depend on the specific subfield within computer science that you're interested in. Always review the scope and focus of the journal to ensure it aligns with your research area.

■ A. Abusukhon, Z. Mohammad, A. Al-Thaher (2021) An authenticated, secure, and mutable multiple-session-keys protocol based on elliptic curve cryptography and text_to-image encryption algorithm. Concurrency and computation practice and experience. [Science Citation Index].

■ A. Abusukhon, N. Anwar, M. Mohammad, Z., Alghanam, B. (2019) A hybrid network security algorithm based on Diffie Hellman and Text-to-Image Encryption algorithm. Journal of Discrete Mathematical Sciences and Cryptography. 22(1) pp. 65- 81. (SCOPUS). https://www.tandfonline.com/doi/abs/10.1080/09720529.2019.1569821 ■ A. Abusukhon, B.Wawashin, B. (2015) A secure network communication protocol based on text to barcode encryption algorithm. International Journal of Advanced Computer Science and Applications (IJACSA). (ISI indexing). https://thesai.org/Publications/ViewPaper?Volume=6&Issue=12&Code=IJACSA&Seri alNo=9

■ A. Abusukhon, Talib, M., and Almimi, H. (2014) Distributed Text-to-Image Encryption Algorithm. International Journal of Computer Applications (IJCA), 106 (1). [ available online at : https://www.semanticscholar.org/paper/Distributed-Text-to-Image-Encryption-Algorithm-Ahmad-Mohammad/0764b3bd89e820afc6007b048dac159d98ba5326]

■ A. Abusukhon (2013) Block Cipher Encryption for Text-to-Image Algorithm. International Journal of Computer Engineering and Technology (IJCET). 4(3) , 50-59. http://www.zuj.edu.jo/portal/ahmad-abu-alsokhon/wpcontent/uploads/sites/15/BLOCK-CIPHER-ENCRYPTION-FOR-TEXT-TO-IMAGE ALGORITHM.pdf

■ A. Abusukhon, Talib, M. and Nabulsi, M. (2012) Analyzing the Efficiency of Text-to-Image Encryption Algorithm. International Journal of Advanced Computer Science and Applications ( IJACSA )(ISI indexing) , 3(11), 35 – 38. https://thesai.org/Publications/ViewPaper?Volume=3&Issue=11&Code=IJACSA&Seri alNo=6

■ A. Abusukhon, Talib M., Issa, O. (2012) Secure Network Communication Based on Text to Image Encryption. International Journal of Cyber-Security and Digital Forensics (IJCSDF), 1(4). The Society of Digital Information and Wireless Communications (SDIWC) 2012. https://www.semanticscholar.org/paper/SECURENETWORK-COMMUNICATION-BASED-ON-TEXT-TO-IMAGE-Abusukhon-Talib/1d122f280e0d390263971842cc54f1b044df8161

Check " https://optisystem.software.informer.com/13.0/ "

Maybe it will help you.

No. Can a computer or any other device smell? NO

Smell is one of the very important faculty.

Jolai Garcia

1. Set up your Excel spreadsheet: Create a table with the DMUs in the rows and the input and output variables in the columns.

2. Calculate the normalized values: Normalize the input and output variables to ensure that they are on a comparable scale. This can be done using various methods such as min-max normalization or z-score normalization. Apply the chosen normalization method to each variable column.

3. Set up the DEA model: Create a new worksheet or section in your spreadsheet to set up the DEA model. In this section, you will define the linear programming problem for each DMU.

4. Specify the objective function and constraints: In the DEA model section, set up the objective function and constraints based on the VRS assumptions. The objective function maximizes the efficiency score for each DMU, and the constraints ensure that the DMUs are efficient relative to each other. These constraints typically involve the normalized input and output values.

5. Solve the DEA model: Use Excel's Solver add-in or any linear programming solver tool to solve the DEA model. Set the solver to maximize the objective function while satisfying the defined constraints.

6. Retrieve the efficiency scores: Once the DEA model is solved, the efficiency scores will be obtained as the solution. These scores represent the technical efficiency of each DMU relative to the others. You can copy the efficiency scores back to the original table or create a new column to display the efficiency scores.

Good luck: partial credit AI

Visit the Scopus website (www.scopus.com)

Abdul-Sahib Hasani

1. Python: Python is often recommended as the first programming language for beginners. It has a clean and readable syntax, which makes it easier to understand and write code. It is used in fields such as web development, data analysis, artificial intelligence, and more.

2. Scratch: Scratch is a visual programming language developed by MIT. It uses a drag-and-drop interface that allows users to create interactive stories, animations, and games. Scratch is specifically designed for beginners and provides a fun and intuitive way to learn programming concepts. It helps students understand fundamental programming concepts like loops, conditionals, and variables.

Good luck

Manuele Martinelli, thank you for the notes. They help explore different ways to analyze it.

Dear Doctor

"Grounding it's done for safety in case current leaks out the power supply IF it's broken and it passes the current to case then the case itself becomes dangerous if you touch it. If you have grounding the current will go to the ground instead of you."

Reference: Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York: Wiley.

The book "Signal Detection Theory and Psychophysics" by Green and Swets is a classic reference in the field of psychophysics. It provides a comprehensive explanation of signal detection theory, including the computation of d-prime (d') for 2AFC/2IFC designs. It covers various topics related to signal detection theory, including threshold estimation, receiver operating characteristic (ROC) analysis, and the interpretation of d-prime values.

While the book was published in 1966, it remains a widely recognized and influential resource in the field. It may be helpful to consult more recent literature for any advancements or refinements in the computation of d-prime specifically for 2AFC/2IFC designs, as the field of psychophysics has evolved since the book's publication.

Hope it helps:credit AI

I want, when

Thank you so much! Let me relook into this problem

Dear Justine Praxidio

Without knowing more details about your research project, it is difficult to make meaningful statements here. Which research approaches in which domain do you want to test empirically? It cannot just be about the number of participants in the group. As a rule, certain preconditions and contextual conditions must be taken into account: What is the research question? What are the scientific objectives? Which specific teaching/ learning settings are to be evaluated: combined vs. shared, synchronous vs. asynchronous, individual or cooperative, etc.? Should it be a comparative study? Is a control group planned? These and other requirements must be clarified and defined. Then the research design can be precisely conceptualized.

Dear Doctor

[A computer virus is a type of malicious software, or malware, that spreads between computers and causes damage to data and software. Computer viruses aim to disrupt systems, cause major operational issues, and result in data loss and leakage.]

[Information is stimuli that has meaning in some context for its receiver. When information is entered into and stored in a computer, it is generally referred to as data. After processing -- such as formatting and printing -- output data can again be perceived as information. When information is compiled or used to better understand something or to do something, it becomes knowledge.]

[A computer is a machine that can be programmed to carry out sequences of arithmetic or logical operations automatically. Modern digital electronic computers can perform generic sets of operations known as programs. These programs enable computers to perform a wide range of tasks.]

Thank you very much. We will adress all of them whether they will examine PLAI. The last point we do since 2010 due to our own mindset and the influence of Prof. Rupert Lay. We started implementing this quite a time before the discussions about rules began. But we want an independent proof as assurance for investors. Even though our system needs much less money, energy, but affords a different way to program, ordinary investors are not interested.

Starting a PhD in Computer and Communication Engineering with a cybersecurity and deep learning concentration is thrilling. These cutting-edge subjects match your interests and include reading suggestions:

1. AI-Driven Cyber Threat Intelligence and Prediction: - Develop AI models to identify emerging cyber risks. This requires searching vast datasets for new attack patterns.

"Artificial Intelligence for Cybersecurity" by Yang et al.

2. Deep Learning for Malware Detection and Analysis: - Examining how deep learning might enhance malware detection, especially zero-day threats, by analysing code patterns and anomalies.

- "Malware Data Science: Attack Detection and Attribution" by Joshua Saxe and Hillary Sanders.

3. AI in Network Security and Anomaly Detection: - Creating deep learning models to identify network traffic anomalies, perhaps detecting intrusion attempts or odd activities.

- See "Network Traffic Anomaly Detection and Prevention" by Monowar H. Bhuyan, Dhruba K. Bhattacharyya, and Jugal K. Kalita.

4. Deep Learning in Cryptography and Secure Communications: - Investigating AI's role in cryptanalysis and inventing new cryptographic algorithms.

See Jean-Philippe Aumasson's "Serious Cryptography: A Practical Introduction to Modern Encryption".

5. AI-Enhanced IoT and Edge Computing Security Research: - Investigating AI-based security for IoT devices and infrastructure.

- See "Security and Privacy in Internet of Things (IoTs): Models, Algorithms, and Implementations" by Hu.

6. Privacy-Preserving AI in Cybersecurity: - Examining federated learning and differential privacy strategies to improve cybersecurity without compromising user privacy.

"Privacy-Preserving Machine Learning: Methods, Challenges, and Opportunities" by Xinjian Luo and Qiang Yang.

7. Addressing 5G Network Security Challenges with AI: - Examining AI solutions.

"5G Security: Fundamentals, Standards, and Practical Implementation" by Anand R. Prasad and Seung-Woo Seo.

8. Research on Explainable AI (XAI) in Cybersecurity: - Make AI judgements in cybersecurity more visible and explainable.

"Explainable AI: Interpreting, Explaining and Visualising Deep Learning" by Wojciech Samek et al.

9. Cyber-Physical Systems Security: - Investigating deep learning-based security solutions for critical infrastructures.

Houbing Song et al. edited "Cyber-Physical Systems: Foundations, Principles and Applications".

Find academics or professionals with good backgrounds in these areas as co-supervisors. They are available through academic departments, research papers, and cybersecurity and AI conferences and seminars.

These cutting-edge computer and communication engineering fields provide many PhD research options. The suggested academic library or journal database references might provide a theoretical and practical grasp of these domains.

In fluid dynamics, Green's functions play a crucial role in solving steady flow problems governed by the Euler and Navier-Stokes equations. These functions, denoted by G(x, y) for a point source at (x, y), represent the perturbation in the velocity and pressure fields induced by a unit point force acting at that location.

For the steady Euler equation, which describes inviscid incompressible fluid flow, the Green's function satisfies the following equation:

∇² G(x, y) + δ(x, y) = 0

where δ(x, y) is the Dirac delta function representing the point source. The solution to this equation is given by:

G(x, y) = -1/(2π) ln(|x - y|)

This Green's function represents the velocity potential due to a unit point source in the steady Euler flow.

For the steady Navier-Stokes equation, which incorporates the effects of viscosity, the Green's function satisfies a modified equation:

∇² G(x, y) + δ(x, y) - Re∇² G(x, y) = 0

where Re is the Reynolds number, a dimensionless parameter characterizing the flow regime. The solution to this equation is given by:

G(x, y) = K_0(Re |x - y|)

where K_0 is the modified Bessel function of the second kind of order zero. This Green's function represents the velocity perturbation due to a unit point force in the steady Navier-Stokes flow.

Green's functions are widely used in various fluid dynamics applications, including:

In summary, Green's functions serve as powerful tools for understanding and analyzing steady fluid flows governed by the Euler and Navier-Stokes equations. Their applications span a wide range of fluid dynamics problems, from fundamental theoretical studies to practical engineering applications.

tuneshare

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Hi

There are several reasons why statistical tests might not be applied:

1. Sample Size and Variability.

2. Marginal Improvements.

3. Computational Complexity.

4. Focus on Other Metrics.

5. Methodological Preference.

6. Lack of Standardization.

generally considered good practice to apply statistical tests in such scenarios to rigorously validate the results

Example of a Python program structure to calculate a Sustainable Reporting Score:

```python

def calculate_sustainable_reporting_score(data):

# Define your scoring methodology and calculation logic here

score = 0

# Perform calculations and update the score based on your criteria

# Example: Add scores based on different factors or indicators

score += data['factor1'] * weight_factor1

score += data['factor2'] * weight_factor2

score += data['factor3'] * weight_factor3

return score

# Example usage

data = {

'factor1': 0.75,

'factor2': 0.85,

'factor3': 0.9

}

# Define the weights for each factor based on your scoring methodology

weight_factor1 = 0.4

weight_factor2 = 0.3

weight_factor3 = 0.3

# Calculate the Sustainable Reporting Score

score = calculate_sustainable_reporting_score(data)

# Print the result

print("Sustainable Reporting Score:", score)

```

In the above example, the `calculate_sustainable_reporting_score()` function takes in a `data` dictionary as input, which contains the factors or indicators you want to consider in your scoring methodology. You can define your own factors and assign weights to each factor based on their importance in the overall score calculation.

The function performs the calculations based on your specific methodology and returns the resulting score.

In the example usage section, I've provided sample data and weights for three factors (`factor1`, `factor2`, and `factor3`). You can modify this section to input your own data and weights.

Hope it helps

example of how you can achieve this using the R programming language:

```R

# Install and load the required packages

install.packages("changepoint")

library(changepoint)

# Generate example ELISA assay data

# Replace with your actual ELISA assay data

data <- c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5)

# Perform change-point analysis

cpt <- cpt.mean(data, method = "BinSeg")

# Get the estimated change-point

change_point <- cpt@cpts[1]

# Print the estimated change-point

cat("Estimated Change-Point:", change_point, "\n")

```

`changepoint` package is used to perform change-point analysis on the ELISA assay data. First, you need to install the package using `install.packages("changepoint")`. Then, you can load the package with `library(changepoint)`.

Replace the `data` vector with your actual ELISA assay data. This vector should contain the measured values from your assay.

The `cpt.mean` function is used to perform the change-point analysis using the "BinSeg" method, which is a popular algorithm for detecting changepoints in data. You can explore other available methods in the `changepoint` package documentation.

The estimated change-point is extracted from the analysis result using `cpt@cpts[1]`, assuming there is a single change-point detected. If you expect multiple change-points, you can access them accordingly.

Hope it helps

you need a dataset that contains the free energy values at different time points. Assuming you have such a dataset, here's an example Python code that calculates the auto-correlation function using the `numpy` library:

```python

import numpy as np

def auto_correlation(data):

"""

Calculates the auto-correlation function of a given dataset.

Parameters:

data (numpy array): 1D array of free energy values.

Returns:

acf (numpy array): Auto-correlation function.

"""

n = len(data)

mean = np.mean(data)

data_normalized = data - mean

acf = np.correlate(data_normalized, data_normalized, mode='full')[-n:]

acf /= (n * np.var(data))

return acf

# Example dataset (replace with your own data)

free_energy_data = np.array([1.2, 1.5, 2.1, 2.5, 2.8, 2.9, 2.7, 2.4, 2.0, 1.8])

# Calculate auto-correlation function

acf = auto_correlation(free_energy_data)

# Print the auto-correlation values

print(acf)

```

Replace as needed. Hope it helps

Organize Data into Separate Files:

After organizing your data into separate files, you can use xmgrace to plot the band structures for different l-values.

To incorporate the l-character or weightage into the plot, you can utilize xmgrace's features such as:

Plot Multiple Datasets: Plot each l-value as a separate dataset (using different colors or line types for distinction).

Legend: Label each dataset with its respective l-value for clear identification.

Error Bars/Additional Data Columns: Utilize extra columns to represent the l-character value, for instance, by varying symbol size or color according to this value.

Customize the plot appearance, including axis labels, title, legends, and other formatting options as needed to enhance readability and presentation.

Once you have set up the plot to represent each l-value separately, you can compare and analyze the band structures for different l-values.i hope i helped.

Dear Charles Waruru,

the computer had many fathers. Of these are particularly noteworthy:

Konrad Zuse, John von Neumann, Alan Turing, Charles Babbage, John Atanasoff, Henry Edward Roberts

For more information about these computer fathers see the added addresses.

Best regards

Anatol Badach

Henry Edward Roberts; Father of Personal Computer

Contacter sur email

Ambient Intelligence (AmI) and Internet of Things (IoT) are two concepts that have gained significant attention in the field of technology. While they share some similarities, there are also distinct differences between the two.

Ambient Intelligence refers to a computing environment that is sensitive and responsive to human presence. It aims to create an intelligent and intuitive system that can adapt to users' needs without explicit instructions. AmI systems utilize sensors, data analysis, and machine learning algorithms to provide personalized services in a seamless manner. For example, smart homes equipped with AmI technology can adjust lighting, temperature, and music preferences based on individual preferences.

On the other hand, the Internet of Things refers to a network of physical objects embedded with sensors, software, and connectivity capabilities. IoT enables these objects to collect and exchange data over the internet without human intervention. The main goal of IoT is to connect various devices for efficient communication and automation. For instance, IoT can be seen in applications like smart cities where streetlights automatically adjust their brightness based on real-time traffic conditions.

Although both AmI and IoT involve interconnected devices and rely on data collection for decision-making processes, there are key differences between them. Firstly, while AmI focuses on creating an intelligent environment that adapts to humans' needs seamlessly, IoT emphasizes connecting devices for efficient communication without direct human involvement.

Secondly, AmI systems primarily rely on local processing capabilities within the environment itself. This means that most of the data processing occurs within the immediate vicinity of users or devices. In contrast, IoT systems often rely on cloud computing for storing and analyzing large amounts of data collected from multiple sources.

Lastly, another difference lies in their scope of application. Ambient Intelligence has a more personal focus as it aims at providing personalized services tailored specifically for individuals or small groups. On the other hand, IoT has broader applications ranging from industrial automation to healthcare monitoring systems.

In conclusion, Ambient Intelligence (AmI) and Internet of Things (IoT) are two distinct concepts in the field of technology. While they share similarities in terms of interconnected devices and data collection, their focus, processing capabilities, and scope of application differ significantly. Both concepts have the potential to revolutionize various industries and improve our daily lives.

Reference:

Kidd, C.D., Orr, R.J., Abowd, G.D., Atkeson, C.G., Essa, I.A., MacIntyre, B., Mynatt E.D. & Starner T.E. (1999). The Aware Home: A Living Laboratory for Ubiquitous Computing Research. In Proceedings of the Second International Workshop on Cooperative Buildings (CoBuild '99), 191-198.

Hi Noelle,

Please take a look on my papers (Hope these papers may help you deciding on a specific topic):

■ A. Abusukhon Intelligent Shoes for Detecting Blind Falls Using the Internet of Things. KSII Transactions on Internet and Information Systems. Vol. 17, Issue 9. 2023

■ A. Abusukhon, A. Al-Fuqaha, B. Hawashin, A Novel Technique for Detecting Underground Water Pipeline Leakage Using the Internet of Things. Journal of Universal Computer Science (JUCS). Vol. 29, No. 8.

■ A. Abusukhon, IOT Bracelets for Guiding Blind People in an Indoor Environment, in Journal of Communications Software and Systems, vol. 19, no. 2, pp. 114-125, April 2023, doi: 10.24138/jcomss-2022-0160.

■ A. Abusukhon (2021) Towards Achieving a Balance between the User Satisfaction and the Power Conservation in the Internet of Things, IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3051764. impact factor 9.936. Published by IEEE. https://ieeexplore.ieee.org/document/9326414. [Science Citation Index].

■ Ahmad Abusukhon, Bilal Hawashin and Mohammad Lafi (2021) An Efficient Algorithm for Reducing the Power Consumption in Offices Using the Internet of Things, International Journal of Advances in Soft Computing and its Applications (IJASCA). http://ijasca.zuj.edu.jo/Volumes.aspx

■ A. Abusukhon, Z. Mohammad, A. Al-Thaher (2021) An authenticated, secure, and mutable multiple-session-keys protocol based on elliptic curve cryptography and text_to-image encryption algorithm. Concurrency and computation practice and experience. [Science Citation Index].

■ B. Hawashin, A. Abusukhon An Efficient Course Recommender Using Deep Enriched Hidden Student Aptitudes. ICIC Express Letters, Part B: Applications, 2022.

■ A. Abusukhon, N. Anwar, M. Mohammad, Z., Alghanam, B. (2019) A hybrid network security algorithm based on Diffie Hellman and Text-to-Image Encryption algorithm. Journal of Discrete Mathematical Sciences and Cryptography. 22(1) pp. 65- 81. (SCOPUS). https://www.tandfonline.com/doi/abs/10.1080/09720529.2019.1569821

■ A. Abusukhon, B.Wawashin, B. (2015) A secure network communication protocol based on text to barcode encryption algorithm. International Journal of Advanced Computer Science and Applications (IJACSA). (ISI indexing). https://thesai.org/Publications/ViewPaper?Volume=6&Issue=12&Code=IJACSA&Seri alNo=9

■ A. Abusukhon, Talib, M., and Almimi, H. (2014) Distributed Text-to-Image Encryption Algorithm. International Journal of Computer Applications (IJCA), 106 (1). [ available online at : https://www.semanticscholar.org/paper/Distributed-Text-to-Image-Encryption-Algorithm-Ahmad-Mohammad/0764b3bd89e820afc6007b048dac159d98ba5326]

■ A. Abusukhon (2013) Block Cipher Encryption for Text-to-Image Algorithm. International Journal of Computer Engineering and Technology (IJCET). 4(3) , 50-59. http://www.zuj.edu.jo/portal/ahmad-abu-alsokhon/wpcontent/uploads/sites/15/BLOCK-CIPHER-ENCRYPTION-FOR-TEXT-TO-IMAGE ALGORITHM.pdf

■ A. Abusukhon, Talib, M. and Nabulsi, M. (2012) Analyzing the Efficiency of Text-to-Image Encryption Algorithm. International Journal of Advanced Computer Science and Applications ( IJACSA )(ISI indexing) , 3(11), 35 – 38. https://thesai.org/Publications/ViewPaper?Volume=3&Issue=11&Code=IJACSA&Seri alNo=6

■ A. Abusukhon, Talib M., Issa, O. (2012) Secure Network Communication Based on Text to Image Encryption. International Journal of Cyber-Security and Digital Forensics (IJCSDF), 1(4). The Society of Digital Information and Wireless Communications (SDIWC) 2012. https://www.semanticscholar.org/paper/SECURE NETWORK-COMMUNICATION-BASED-ON-TEXT-TO-IMAGE-Abusukhon-Talib/1d122f280e0d390263971842cc54f1b044df8161

Dear Malik Stalbert ,

Human technology in health care includes managerial knowledge required to marshal a health care workforce, operate hospitals and equipment, obtain and administer funds, and, increasingly, identify and establish markets.

Regards,

Shafagat

Thanks a lot

This paper: http://cedric.cnam.fr/~bentzc/INITREC/Files/DW10.pdf might be a good starting point.

Determine how to categorize or compute ranges within a Likert scale that is used to assess frequency or some other ordinal variable, which is ranging from 0 ("never") to 4 ("most of the time").

In most cases, computing range or defining categories in a Likert scale is primarily dependent on your research question and how detailed you want your analysis to be.

Understanding the Likert Scale:

Basic Approach to Compute Range:

If you're considering dividing the scale into specific ranges, you would generally consider the whole numbers as your primary split points, particularly because your categories are predefined (0, 1, 2, 3, 4).

If you Want to Compute Sub-ranges:

If for some reason you want to create sub-ranges within these primary categories, you'll likely need a more detailed scale or continuous data. Creating sub-ranges within a Likert scale might be challenging and somewhat contrary to its typical use, given it's an ordinal scale where each number represents a specific category, rather than a continuous scale.

However, if this is necessary for your analysis, one approach might be:

Important Considerations:

Your approach should be theoretically and methodologically sound. If in doubt, consult with a statistician or a research methods expert, and always pilot test your scale and approach to ensure they are valid and reliable for your specific research context. If you provide this scale to respondents, be sure to clearly communicate how they should select their response to avoid confusion.

yes i have

1. Check ResearchGate's Policies

- Visit ResearchGate's official website and review their policies regarding file uploads and storage.

- If you're unable to upload files consider contacting ResearchGate's support team for assistance.

- If you plan to sell or dispose of the computer where your research files are stored, make sure to back up your important research files and data to an external storage device or a cloud-based service.

Alternatively, as Dennis Hamilton mentioned, consider the GitHub platform as a clouds torage service.

One possible method of determining the enthalpy of mixing between two elements theoretically is to use the regular solution model, which assumes that the atoms are randomly distributed and that the enthalpy of mixing depends on the difference in the atomic sizes and the bond energies of the elements. The regular solution model can be expressed as follows:

ΔH mix = zNε(1 - x)x

where ΔH mix is the enthalpy of mixing per mole of solution, z is the coordination number, N is the Avogadro’s number, ε is the interaction energy between unlike atoms, and x is the mole fraction of one of the elements.

The interaction energy ε can be estimated from the bond energies of the pure elements using the following formula:

ε = (E A + E B )/2 - E AB

where E A and E B are the bond energies of the pure elements A and B, and E AB is the bond energy of the compound AB.

The bond energies can be obtained from experimental data or calculated from theoretical methods, such as density functional theory or molecular dynamics.

For more details and examples of this method, you can refer to these sources:

Enthalpy of Mixing in Binary Alloys: A Simple Model

Thermodynamic Modeling of Alloys

there are other possible methods to determine the enthalpy of mixing between two elements theoretically. Some of them are:

These are some examples of alternative methods to calculate the enthalpy of mixing theoretically. However, each method has its own assumptions, limitations, and parameters that need to be determined or fitted from experimental data or first-principles calculations. Therefore, no single method can be universally applicable or accurate for all kinds of solutions.

Here is a summary of the methods:

ΔH mix = zNε(1 - x)x

where ΔH mix is the enthalpy of mixing per mole of solution, z is the coordination number, N is the Avogadro’s number, ε is the interaction energy between unlike atoms, and x is the mole fraction of one of the elements.

The source for this method is Lecture 3: Models of Solutions - University of Cambridge.

ΔH mix = zNε(1 - x)x + zNβ(1 - x)x(1 - 2x)

where ΔH mix is the enthalpy of mixing per mole of solution, z is the coordination number, N is the Avogadro’s number, ε is the average interaction energy between unlike atoms, β is a parameter that measures the deviation from regularity, and x is the mole fraction of one of the elements.

The source for this method is Lecture 7: Quasichemical Solution Models - University of Cambridge.

ΔH mix = −zN(NA2AA + NB2BB − NABω)

where ΔH mix is the enthalpy of mixing per mole of solution, z is the coordination number, N is the Avogadro’s number, NA , NB , and NAB are the numbers of AA , BB , and AB bonds per atom, respectively, and ω = 2AA + 2BB − 22AB is a parameter that measures the bond energy difference between like and unlike atoms.

The source for this method is Cluster Variation Method Analysis of Correlations and … - Springer.

ΔH mix = −zN(NA2AA + NB2BB − NABω) − zN(NABCωABC + NABDωABD + …)

where ΔH mix is the enthalpy of mixing per mole of solution, z is the coordination number, N is the Avogadro’s number, NA , NB , NAB , NABC , NABD , etc. are the numbers of AA , BB , AB , ABC , ABD , etc. clusters per atom, respectively, ω = 2AA + 2BB − 22AB is a parameter that measures the bond energy difference between like and unlike atoms, and ωABC , ωABD , etc. are parameters that measure the cluster interaction energies.

The source for this method is Cluster Variation Method | SpringerLink.

good luck

Dear Asmita Phadke ,

Then use this formula: VO2 max = 132.853 - (0.0769 x your weight in pounds) - (0.3877 x your age) + (6.315 if you are male or 0 if you are female) - (3.2649 x your walking time) - (0.1565 x your heart rate at the end of the test). Losing weight won't necessarily increase your VO2 max.

https://www.verywellfit.com/what-is-vo2-max-3120097#:~:text=Then%20use%20this%20formula%3A%20VO2,the%20end%20of%20the%20test).&text=Losing%20weight%20won't%20necessarily%20increase%20your%20VO2%20max.

Regards,

Shafagat

Dear Abdulhameed Qahtan Abbood Al-Tai ,

The modified weak Galerkin method does not require the penalty parameter by comparing with traditional DG methods.

Regards,

Shafagat

Dear Abdulhameed Qahtan Abbood Al-Tai ,

The modified weak Galerkin method does not require the penalty parameter by comparing with traditional DG methods.

Regards,

Shafagat

Mohammad Imam its good that you elaborated the process.

in step-3, I see that the curvature is assumed to be d²y/dx² = M/EI. This it self is an assumption for small angle deflection. Is it valid for large angle deflection?

Thanks..

Dr Mohammad Imam thank you for your contribution to the discussion

Greetings Melody,

I would use an environment manager, such as Anaconda (which runs the open-source Conda engine under the hood). Unlike closed-source software computer languages (e.g., MATLAB), Python is maintained primarily by the community. That means that all of the language's packages/libraries/dependencies are constantly changing and being updated and, therefore, not always compatible with each other. Python environment managers make sure that all packages can operate with each other.

So, I suggest downloading Anaconda, installing it, and opening the Anaconda Navigator application. It will open in the "base" environment. Press the "Environment" tab on the left and then press the "Create" button from the bottom (it is recommended to create a new environment and not use the "base" environment for coding). You will be prompted to choose the version of Python that the environment will use and to give the environment a name. I suggest choosing the latest version (3.11). Anaconda will now create a new environment.

Go back to the "Home" tab. Make sure that the new environment's name is selected on the dropdown menu from the top next to "on". You will see several icons of popular Python data analysis packages. Among them, you will see "Jupyter Lab". Press "Install". Anaconda will install all the required dependencies. After that, you can press "Launch" next to Jupyter Lab's icon and it will launch on you browser.

Enjoy,

Eli