Python - Science topic

For sentinel-1. Have a look at PySAR, geopandas or SARpy,

openCV features a detection module, more generally tensorFlow/PyTorch for the ML approach.

Briefly on the inital segmentation.

With radar surface data you need image recognition to locate whatever you want to trace. So you can either attach a machine learning module and train it on the necessary data or go with a fuzzy image recognition method, and tune the parameters of that until it matches. Decide if you want to create result imagery or numerical data without presentation layer. At minimum you create your own timeseries data with timestamps and absolute geoposition data of the objects ( most simple: center of object vs. inserted geometric approximations of the shape with rough orientation and position data, depends on what is tracked) . You have a related problem to solve, now - satellite positions at different times = different absolute positioning in the image vs. geoposition. So you can choose distinct objects as reference or compute the sat position and calculate the expected position of target objects relative to the timestamp of each image. How many images you have to compare has an upper limit in the orbital parameters and the lower limit depends on the time resolution you need for this. From the mission data I derive they have an orbital cycle of 27 days with 385 orbits per cycle ~1.7 hours per orbit but with a phase of 140deg, see the sentinel-1 website for the mission data. There is XML SAFE format metadata available with the exact orbital position with each image, you will need that to compensate the phase shift.

Best of luck

ThS

Gökhan Ergen Dear Gökhan, Thank you for the detailed and thoughtful answer this resonates strongly with my experience as well. Thanks for sharing your workflow and tools; this is a very practical perspective.

Tools for Medical Image Segmentation in Research

When segmenting medical images for research purposes, you can choose from various platforms, tools, and programming languages. The optimal choice depends on your specific tasks, skill level, and preferred development ecosystem.

Python

Python is a popular choice for medical image processing due to its rich ecosystem of libraries and frameworks. Notable tools include:

Additionally, Python offers libraries like SimpleITK (for image processing, including segmentation) and NumPy (for array manipulation). 11 10

MATLAB

MATLAB provides tools for medical image segmentation, covering both classical image processing techniques and deep learning approaches. Key features include:

MATLAB is suitable for researchers already familiar with this environment or those requiring integration with other MathWorks tools. 18

Other Platforms

Comparison and Recommendations

CriterionPythonMATLABOther PlatformsFlexibility and EcosystemRich ecosystem with numerous frameworks and librariesIntegrated environment with ready‑made toolsVaries by tool (e.g., 3D Slicer is open and extensible) 6 7 9 17 18 2ComplexityRequires knowledge of Python and librariesMay be easier for MATLAB usersVaries (ITK‑SNAP has a simpler interface; 3D Slicer is more complex) 6 7 18 2Community SupportActive community with many resources and code examplesStrong community in the medical fieldVaries (e.g., 3D Slicer has an active community) 6 7 17 2Deep Learning IntegrationDirect support via frameworks (PyTorch, TensorFlow)Supported via Deep Learning Toolbox and Computer Vision ToolboxVaries (some tools support DL integration) 7 9 17

When selecting a tool, also consider data availability (formats, volume), required segmentation methods, and available resources for model training.

And sensitivity analysis and ask the journal what they require :)

Dear Researchers, in my case the problem is as follows: I want to keep Google Colab running for a long time, but the session keeps disconnecting and I experience RAM crashes. Colab Pro or Pro+ helps a bit but does not completely solve the issue. Therefore, for long or heavy computations, local environments like VS Code, Anaconda, or Jupyter Notebook are much safer and more stable.

Hi Muhammad,

I hope you're doing great!

The key challenges of using Python for scalable and performance-critical applications primarily stem from its design as an interpreted, dynamically typed language with a global lock.

To mitigate these, developers often rely on C-extensions (like NumPy), multiprocessing (to bypass the GIL), or asynchronous programming (for I/O-bound tasks), though these add complexity to the architecture.

Multi-threading is a myth in Python | GIL - https://www.youtube.com/watch?v=zvXU8jOMxoA

This YouTube video provides a quick, visual explanation of how the Global Interpreter Lock (GIL) prevents true parallelism in Python threads, which is central to understanding the language's performance limitations.

Adnan Majeed

I’m a bit confused about how MATLAB relates to WSI annotations in label efficiency for histopathology, and also how this ties into presenting on-site at the conference.

Your XML is invalid for REMD because:

You defined two <ensemble> sections in a single <system>

i-PI expects one ensemble per system

Each replica must be a separate <system>, each connected to its own CP2K instance

Model correctness depends on proper preprocessing (trend removal, seasonal adjustment), robustness checks (rolling forecasts, cross-validation), residual analysis, and sensitivity to policy or economic shocks; failure to address nonstationarity, structural breaks, or omitted climate drivers often leads to misleading inference and poor forecasts.

Public health in terms of most distinguished patterns of diseases, causes, remedies all such medical area will be ML most likely to be helpful for help in such a

Did you tried Kubelka-Munk Function ?

I recommend using a systems dynamics (SD) approach to create a simplified but realistic model of a hypothetical country. SD allows you to capture the fundamental interactions between sectors, populations, and policies through stocks, flows, and feedback loops, providing a dynamic and intuitive understanding of how the economy evolves over time. For added realism, you can combine elements of computational general equilibrium (CGE) modeling to simulate market interactions and sectoral responses, creating a hybrid model that balances simplicity with robust economic behavior.

Kanchan Bharti

The .pkml file contains most of the information you need (compartments, flows, drug properties, and simulation settings). You may also need any experimental data or custom reactions not included in the file. In Python, you can parse the .pkml (XML format) using xml.etree.ElementTree or lxml and map the parameters to a PBPK ODE model for simulation.

You can! This is a very common way to automate experiments, sensitivity analysis, or run batch simulations.

The core approach is to call the DSSAT command-line executable from within a Python or MATLAB script, rather than opening the GUI.

@@ Core Principles

Instead of clicking the "Run" button in the DSSAT interface, you will use a function in Python/MATLAB to directly execute the DSSAT core program (e.g., DSCSM047.EXE for version 4.7).

The general process is as follows:

Preparation (Script): Your script (Python/MATLAB) automatically modifies the input files (e.g., FileX, fertilizer file, weather file) to set up the scenario you want to simulate.

Run (Script): The script calls the DSSAT executable in "batch mode".

Collect (Script): DSSAT runs in the background (no interface) and produces output files (e.g. Summary.OUT, PlantGro.OUT).

Analyze (Script): Your script reads these output files to extract results and analyze.

@@ How to do it in Python (Recommended)

Python is more powerful for this, especially with the subprocess library for running commands and pandas for processing output files.

1. Prepare the file

Executable: Locate your main executable. This is usually C:\DSSAT47\DSCSM047.EXE (the file name may vary depending on the version, e.g. DSCSM048.EXE for v4.8).

Batch Control File: DSSAT uses a file to know which tests to run, usually DSSBatch.v47 (or .v48, etc.). This file is located in the DSSAT root directory (e.g. C:\DSSAT47\).

Test File (FileX): You must ensure your FileX (.SNX, .WHX, .SCX, etc.) is set up correctly.

2. Python Script

Let's say you already have your test files (FileX) and just want to run them.

Python

import subprocess

import os

# --- 1. Configure path ---

dssat_dir = r"C:\DSSAT47"

# Model executable file name (varies with your version)

dssat_exe = "DSCSM047.EXE"

# Batch file name (varies with your version)

batch_file_name = "DSSBatch.v47"

# --- 2. (Optional) Modify Batch file ---

# The batch file (DSSBatch.v47) should contain the tests you want to run.

# You can use Python to override this file, for example:

# Sample DSSBatch.v47 file content:

# $BATCH(SN)

# ! FILEX TRTNO RP SQ OP CO

# SNX001 1 1 1 1 1

# SNX001 2 1 1 1 1

# Python code to create that file:

batch_content = """$BATCH(SN)

! FILEX TRTNO RP SQ OP CO

SNX001 1 1 1 1 1

SNX001 2 1 1 1 1

""

try:

with open(os.path.join(dssat_dir, batch_file_name), "w") as f:

f.write(batch_content)

except Exception as e:

print(f"Error writing batch file: {e}")

# --- 3. Run the simulation ---

# 'B' is an important flag to tell DSSAT to run in Batch mode

command = [os.path.join(dssat_dir, dssat_exe), "B", batch_file_name]

print(f"Running command: {' '.join(command)}")

# IMPORTANT: Run DSSAT with working directory (cwd) as the root directory of DSSAT

# DSSAT needs this to find weather, soil, etc. files

try:

# subprocess.run() will wait until DSSAT finishes running

result = subprocess.run(

command,

cwd=dssat_dir, # Set working directory

capture_output=True, # Get console output (if any)

text=True,

check=True # Report an error if DSSAT returns an error code

)

print("DSSAT ran successfully!")

# print(result.stdout) # Print output (if needed)

except subprocess.CalledProcessError as e:

print(f"DSSAT ran failed. Error:")

print(e.stderr)

except FileNotFoundError:

print(f"Error: File {dssat_exe} not found at {dssat_dir}")

# --- 4. Read the result ---

# Now you can use pandas to read Summary.OUT

import pandas as pd

summary_path = os.path.join(dssat_dir, "Summary.OUT")

if os.path.exists(summary_path):

# You may need to process the Summary.OUT file to read it properly

# Here is a simple example (may need tweaking)

print(f"Reading results from {summary_path}")

# (Reading the .OUT file can be tricky, usually use pandas' read_fwf)

else:

print(f"The Summary.OUT file was not found. Check if the simulation failed.")

@@ How to do it in MATLAB

Same principle, but you use MATLAB's system() function.

Matlab

% --- 1. Configure the path ---

dssatDir = 'C:\DSSAT47';

% Executable file name (varies by version)

dssatExe = 'DSCSM047.EXE';

% Batch file name (varies by version)

batchFile = 'DSSBatch.v47';

% --- 2. (Optional) Modify Batch File ---

% You can use MATLAB's fopen/fprintf to create/modify DSSBatch.v47 file

% (Similar to Python example)

% --- 3. Run simulation ---

% Save current directory

currentDir = pwd;

% Move to DSSAT directory (IMPORTANT)

cd(dssatDir);

% 'B' is flag for Batch mode

commandToRun = sprintf('"%s" B %s', fullfile(dssatDir, dssatExe), batchFile);

fprintf('Running command: %s\n', commandToRun);

% Call executable

[status, cmdout] = system(commandToRun);

% Return to the original directory

cd(currentDir);

if status == 0

disp('DSSAT run successfully!');

else

disp('DSSAT run failed. Error:');

disp(cmdout);

end

% --- 4. Read the result ---

summaryFile = fullfile(dssatDir, 'Summary.OUT');

if exist(summaryFile, 'file')

disp('Reading results from Summary.OUT...');

% Use readtable, textscan, or dlmread to read the file

% Example:

% T = readtable(summaryFile, 'FileType', 'text', ...); % (Options required)

else

disp('Summary.OUT file not found.');

end

@@ Important points to note

DSSAT version: The executable file name (DSCSM047.EXE) and batch file (DSSBatch.v47) vary by version (e.g. v4.7, v4.8). Check your DSSAT directory.

Working Directory: This is the most common error. DSSAT is very "sensitive" to the location it is run from. You must set the working directory (CWD) to the root directory of DSSAT (e.g. C:\DSSAT47\) before calling the .EXE file. Both examples above have handled this (cwd parameter in Python, cd command in MATLAB).

+Permissions: Make sure your script has write permission to the DSSAT directory (to create the DSSBatch.v47 file) and run the .EXE file. Sometimes you need to run the script as Administrator.

+Input Modification: The real power of this is to use the script to automatically modify the FileX file (e.g. change N amount, planting date) before calling subprocess.run(). This is how you do sensitivity analysis.

@@ Advanced Solution (Python)

If you use Python, there is a library called pydssat that does all the heavy lifting for you. It provides functions to:

- Read and modify FileX easily.

- Automatically find and run DSSAT.

- Read output files (like Summary.OUT, PlantGro.OUT) directly into pandas.DataFrame.

- You can install it with: pip install pydssat

This is the recommended way if you plan to work a lot with Python and DSSAT.

Dear Tolga Sarıca , It's a great question. AI will not completely replace data analysts; it will transform their role. While AI can automate data cleaning, visualization, and even statistical modeling in tools like SPSS, R, Python, and Jamovi, it cannot replace human judgment in interpreting data or making ethical and strategic decisions. Data analysts understand business context, data limitations, and the story behind numbers-something algorithms alone can’t grasp. In the future, analysts will work alongside AI, using it to speed up routine tasks and focus on insights, problem-solving, and communication. So instead of ending data analysis as a profession, AI is redefining it from data processing to data intelligence.

i solve it using cauchy logarithm integral.

its complex to solve in 255 characters. so you can read a work about with the apa:

AVILA CACERES, I. A. (2023). LOS NUMEROS PRIMOS, UNA HISTORIA QUE NO TERMINA.

traslate it and figure out how use integrals to solve optimizing. the work is imcomplete for security reasons. try to understand it the best you can. good day

To calculate monthly safety stock using SKU contribution ratios, CCF factors, and monthly stock reduction percentages, multiply the SKU’s demand variability or forecast error by its contribution ratio to weight its importance in the inventory pool, adjust this by the CCF factor to capture supply chain uncertainty or criticality, and then factor in the monthly stock reduction percentage to scale safety stock downward or upward based on planned inventory lean-down; the final formula essentially becomes: Safety Stock = (Forecast Error × Contribution Ratio × CCF) × (1 – Reduction %).

Thank you, Digonta Chandra Roy, for your response. I will try with the same. and let you know

To streamline and automate data extraction from research papers, consider a hybrid approach combining NLP pipelines, structured extraction tools, and quality assurance. Start by using PDF-to-text parsers (e.g., Grobid or ScienceParse) to convert articles into machine-readable formats. Then, apply named-entity recognition (NER) and regular expression patterns to extract key variables, tables, and figures relevant to your model. For repeated queries with large language models, use prompt templates and few-shot learning to improve consistency, and validate outputs against a curated dataset to reduce errors. Finally, integrating these steps into an automated workflow with version control (e.g., using Python scripts or Apache Airflow) allows scalable, reproducible extraction with minimal manual intervention. This approach balances speed with reliability while enabling efficient machine learning model building.

I actually want research on this topic .

Using Python libraries is easy. First you have to install the required libraries; this will vary according to your operating system, but you can find everything on the net. Second, you have to load the required library at the beginning of your code file. Then you can use it in the proper code syntax. I wish I understood your question correctly.

Min–Max normalization → scales each spectrogram to [0, 1]

Machine Learning: A Probabilistic Perspective (2nd edition) by Murphy

Please do not spam this space with irrelevant responses. I have asked about transportation related open-source projects that people are working on. Please read the question again.

Rushan Ziatdinov , Some excellent free resources to teach the Julia programming language include the official Julia documentation (https://docs.julialang.org/) and the curated list of open books on the Julia Learning page (https://julialang.org/learning/books/) for foundational reference and advanced study. Julia Data Science (https://juliadatascience.io) is a comprehensive open-access book that integrates teaching on DataFrames, visualization, and reproducible research workflows. Quantitative Economics with Julia (https://julia.quantecon.org/) is a full online textbook that covers numerical methods and economic modeling, suitable for quantitative or scientific computing courses. The MIT 18.S191 Introduction to Computational Thinking course has complete Pluto notebook-based modules and a dedicated YouTube playlist (https://www.youtube.com/playlist?list=PLP8iPy9hna6Q2Kr16aWPOKE0dz9OnsnIJ) making it ideal for classroom or self-guided learners. JuliaCon’s official YouTube channel (https://www.youtube.com/c/TheJuliaLanguage) hosts lectures, tutorials, and workshops from world experts, useful for both introductory classes and specialized topics. The Exercism Julia track (https://exercism.org/tracks/julia) offers free, mentored coding exercises with automatic feedback and is a great resource for homework or skill-building practice.

Hi Milad Najafzadeh

I used PANTHEON for connectivity analyses. It is a Python toolbox that can be freely downloaded from GitHub. If you have any further questions regarding this, please feel free to contact me.

Khadersab Adamsab , Data growth techniques such as randaugment and autoauaugment improve model performance by artificially increasing the variety of training data. They apply changes such as rotation, crop, flipping, or color changes, which helps the model to normalize in a better way for ignorant data. By highlighting the model in different versions of the same image, the growth reduces overfiting and prevents it from remembering examples of training. Randaugment simplifies the process by applying a certain number of random changes with controlled magnitude. On the other hand, the autoagment learns optimal growth policies using reinforcement learning, stitching them into a dataset. Overall, these methods improve the strength, accuracy and generalization of deep learning models in various tasks.

I’ve encountered the same problems as you. Have you managed to solve them already? If so, would you mind sharing your solutions with me?

Dear Madam..

First, thanks for your introductory video.

Thanks and Regards

Dr.Khader

if(x< a) return approximation1; if(x>b) return approximation2; return integral(a,b); // a&b must be optimized to reach the best accuracy-to-speed ratio.

h t t p : / / d o i . o r g / 1 0 . 1 5 1 7 4 / a u . 2 0 1 9 . 2 1 2 4

The convergence of machine learning and control theory is reshaping how we approach system dynamics in complex, uncertain environments—particularly for discrete-time systems where traditional model-based methods face limitations. Classical control design depends on precise mathematical models, which can be difficult to derive for nonlinear systems or scenarios with incomplete data. Machine learning, however, introduces a powerful alternative: data-driven system identification and adaptive control that learns directly from observations, bypassing the need for explicit analytical models.

At the core of this shift is the ability to approximate discrete-time state-space representations from input-output data, enabling control strategies that evolve with system behavior. Reinforcement learning (RL) further enhances this adaptability, allowing controllers to optimize performance through real-time interaction effectively "learning by doing" rather than relying on pre-defined dynamics. Python’s rich ecosystem scikit-learn for regression and classification, TensorFlow/PyTorch for deep learning-based modeling, and Stable-Baselines3 for RL implementations provides the computational tools to translate theory into practice efficiently.

Yet, this integration is not without challenges. While machine learning offers flexibility, it must reconcile with control theory’s rigor ensuring stability, interpretability, and robustness in safety-critical applications. Hybrid approaches, combining data-driven learning with model-based constraints, are emerging as a promising middle ground. For instance, neural networks can approximate system dynamics while Lyapunov-based methods guarantee stability, or RL policies can be trained within physically plausible bounds.

The implications are profound: adaptive controllers that generalize across operating conditions, reduced dependency on idealized models, and faster deployment in scenarios where first-principles modeling is impractical. As this field matures, the collaboration between control theorists and machine learning practitioners will be pivotal balancing innovation with reliability to advance next-generation control systems. The future lies not in replacing classical methods, but in augmenting them with learning-driven adaptability, all while maintaining the mathematical foundations that ensure safe and predictable behavior.

Key Takeaways:

As an industrial engineer at heart, I found this tutorial particularly compelling — especially in how reinforcement learning (RL) can help identify optimal buy and sell points in financial markets. After your video, I am thinking about the uncertainty or potential for failure. That is why a well-structured and efficiently implemented environment can significantly mitigate risks. And I am excited about using OpenAI Gym for better strategy testing and adaptation over time, even for entrepreneurship in many fields.

Excellent initiative and a great step toward bridging reinforcement learning and practical financial applications!

DSTools is a valuable Python library that streamlines many stages of the data science workflow, from data exploration to model evaluation and synthetic data generation. Built on top of widely used libraries like Pandas and Scikit-learn, it offers practical, battle-tested functions derived from decades of real-world experience across multiple industries. This toolkit helps data scientists and researchers write cleaner, more efficient, and reproducible code, addressing common challenges in large-scale data projects.

It’s especially useful for those who want to bridge the gap between theoretical concepts and practical implementation, making it easier to handle tasks such as feature preprocessing, EDA, and hyperparameter optimization with Optuna integration

Dear Khadersab Adamsab ,

Data augmentation, including methods like RandAugment and AutoAugment, improves model performance by enhancing generalization and reducing overfitting. This is achieved through the following mechanisms:

Data augmentation is an essential component of deep learning. It not only improves generalization, but it is also a core component of many self- and semi-supervised learning algorithms. However, while data augmentation is ubiquitous for training deep neural networks on natural images (i.e., images of human-scale scenes captured by ordinary digital cameras), when it comes to training such models on medical images its proper usage is not as common and clearly understood

Regards,

Shafagat

Mohammadamir Ghasemishabankareh

Your research topic is both valuable and timely. Brain tumor detection and tracking using AI and image processing addresses a high-impact medical challenge where early and accurate diagnosis significantly influences patient outcomes. A good PhD proposal in this area should aim to contribute both clinically and technically.

From a value standpoint, developing robust AI models for segmentation (e.g., identifying tumor boundaries in MRI scans), classification (distinguishing tumor types such as glioma vs. meningioma), and tracking (monitoring tumor progression or regression over time) are all high-impact tasks. Incorporating multi-modal data (e.g., combining T1, T2, FLAIR MRI sequences) increases accuracy and relevance. One valuable direction is explainable AI (XAI), which helps clinicians trust and adopt AI tools by offering interpretable outputs.

For recent and still-evolving areas, consider integrating deep learning architectures such as Vision Transformers (ViTs) or hybrid CNN-RNN models for spatial-temporal analysis of tumor growth. Also, semi-supervised learning or federated learning methods can address the scarcity and sensitivity of labeled medical datasets. Lastly, aligning your research with clinical workflows (e.g., tumor grading, surgical planning) will increase translational impact and relevance for real-world application.

Make sure your proposal includes a clear research question, defined datasets (e.g., BraTS dataset), performance benchmarks, and a plan for validation using clinical expert feedback or prospective studies. Collaboration with a medical institution or hospital will significantly enhance the credibility and applicability of your work.

Henrik G.S. Arvidsson,

Your reflection resonates deeply. I, too, have found that the critical fault line in our age is not the boundary of what we can build, but the clarity of why we build it, and for whom. The question is no longer one of engineering capacity — it's one of ethical direction, epistemic humility, and moral imagination.

In my own work straddling AI ethics, systems design, and philosophical inquiry, I’ve often seen how progress detached from empathy leaves a subtle but profound trail of exclusion. Intelligence without care can produce sleek systems that optimize efficiency while quietly eroding agency, dignity, and voice. And often, those most affected are the very people least included in their creation.

I agree — we don’t just need clever minds; we need minds willing to be vulnerable, critical, and accountable. Feminist principles as design values — equity, care, voice, agency — are not mere philosophical gestures, but the very scaffolding of a livable future. In fact, I believe we are entering an era where moral architecture must precede technical architecture.

Let’s connect. I’d welcome an honest conversation — not about showcasing what we’ve done, but about asking together what still needs to be done, and how we might do it differently. Some systems fail silently because no one had the courage to ask: Whose future are we building, and who is missing from the table?

Warm regards,

Kwan Hong TAN

The following Python libraries you'll need, but the exact nature of the optimization of process parameters involved in data is also significant.

Jaswinder Singh & Ramakoti Reddy Thamma

Hello, do your database need a large number of small molecule compounds' pharmacological properties and application protocols?

We (MedChemExpress, MCE) have many small molecule drugs and disease-inducing drugs in mainstream fields such as cancer and immunity. For example, the following product information is introduced. If you are willing to cooperate further, please contact us.

ECG Database on Physionet

Physionet link: https://physionet.org

Various ECG database: https://physionet.org/about/database

Files format for ECG databse on Physionet

a) *.atr: Reference Annotation

b) *.dat: Data file (Signal)

c) *.hea: Header file (contains basic info of ECG signal)

MATLAB not compatible for above files format

To read in Matlab

Using WFDB SWIG Toolbox (not easy way): https://physionet.org/content/wfdb-swig-matlab/1.0.0/

Using PhysioNet ATM (easy way) to get ECG signal in MATLAB compatible format (*.mat) : https://archive.physionet,org/cgi-bin/atm/ATM

Proposed Discrete Wavelet Transform(DWT) based Method (Without Deep CNN)

uses undecimated DWT and Symlet4 wavelet for decomposition

Thank you Vincent Bailey Ladd

OK, we should probably differentiate two things here:

a) You have already published your paper somewhere else -> do what Peter Breuer said.

b) It is not published yet -> publish first with a real publisher and then upload here assuming RG doesn't add it automatically.

If you only upload a document here without associating it to a serious publisher, you could as well just burn it. Serious researchers will not cite that.

Hamming tapers the signal at around 0.08 of the signal, which might not be enough. Therefore, trying with a Hanning function might be better.

Additionally, after windowing and before rfftn, the amount of padding is crucial for reducing side effects, the standard padding is size calculation is 2n-1.

What problem you are facing? Do you have the dataset?

@Khadersab Adamsab Thanks for your input to this great mission.

MCR-ALS is a chemometric technique used to decompose complex multivariate data (like spectroscopic data from chemical mixtures) into a set of "pure" component profiles (e.g., spectra) and their corresponding concentration profiles over time or space. The "alternating least squares" part refers to an iterative optimization algorithm that alternates between estimating the pure component profiles and the concentration profiles until convergence.

Here's a breakdown of how to approach MCR-ALS in MATLAB, Python, and R, along with considerations:

Core Concept of MCR-ALS

Imagine you have a series of measurements (e.g., UV-Vis spectra) taken from a chemical reaction over time. Each spectrum is a mixture of the individual components (reactants, intermediates, products). MCR-ALS aims to:

It does this by decomposing the data matrix D (where rows are measurements, columns are variables like wavelength) into the product of a concentration matrix C and a pure component spectra matrix ST:

D=CST

During the iterative process, various constraints can be applied to C and S (e.g., non-negativity, unimodality, closure, known profiles) to ensure physically meaningful solutions and improve convergence.

How to do MCR-ALS in MATLAB, Python, or R

While the core algorithm is the same, the implementation and available libraries vary.

1. MATLAB

MATLAB has a strong history in chemometrics, and it's quite common to find MCR-ALS implementations here.

2. Python

Python has rapidly become a powerful environment for scientific computing and chemometrics, with several libraries supporting MCR-ALS.

3. R

R is widely used in statistics and increasingly in chemometrics.

Key Considerations for MCR-ALS

Dear Rahul Jain ,

Natural language processing is a large and multidisciplinary field as it contains infinitely many sentences. NLP began in the 1950s as the intersection of artificial intelligence and linguistics. Also there is much ambiguity in natural language. There are many words which have several meanings, such as can, bear, fly, orange, and sentences have meanings different in different contexts. This makes creation of programs that understands a natural language, a challenging task

Regards,

Shafagat

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on enabling computers to understand, interpret, and generate human language. At its core, NLP involves breaking down language into its fundamental components and analyzing their relationships. This process relies heavily on understanding syntax, which deals with the structural rules of a language.

Syntax in NLP: The Rules of Language

Syntax refers to the rules governing the structure of sentences and phrases in a language. It's about how words combine to form grammatically correct and meaningful expressions. In NLP, understanding syntax is crucial for tasks like:

Let's delve into some key components of syntax in NLP:

1. Regular Expressions (Regex)

Regular Expressions are powerful tools for pattern matching within text. While not a full-fledged syntactic parser, regex is a fundamental building block for many NLP tasks, especially in the initial stages of text processing.

Regex defines search patterns using a sequence of characters. It allows you to:

Example: To find all email addresses, you might use a regex like \b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b.

2. Morphology

Morphology is the study of the internal structure of words and how they are formed. It analyzes morphemes, the smallest meaningful units in a language. Understanding morphology helps NLP systems to:

3. Parse Trees (Syntactic Parsing)

Parse trees, also known as syntax trees or derivation trees, are hierarchical data structures that visually represent the grammatical structure of a sentence. They show how words are grouped into phrases and how those phrases combine to form a complete sentence according to a particular grammar.

Python Parsing Explained

Python offers powerful libraries for performing NLP tasks, including parsing. The most popular ones are NLTK and spaCy.

1. NLTK (Natural Language Toolkit)

NLTK is a comprehensive library for NLP research and education. It provides a wide range of tools and resources for tasks like tokenization, stemming, lemmatization, Part-of-Speech (POS) tagging, and parsing.

Python

import nltk # Define a simple grammar grammar = nltk.CFG.fromstring(""" S -> NP VP NP -> Det N | N VP -> V NP Det -> 'the' | 'a' N -> 'cat' | 'dog' | 'boy' | 'ball' V -> 'chased' | 'ate' """) # Create a parser parser = nltk.ChartParser(grammar) sentence = "the cat chased a dog".split() # Parse the sentence for tree in parser.parse(sentence): tree.pretty_print()

This would output a tree-like structure representing the sentence's constituents.

2. spaCy

spaCy is a more modern and production-ready NLP library known for its speed and efficiency. It offers pre-trained models for various languages and provides a streamlined API for common NLP tasks, including tokenization, POS tagging, named entity recognition (NER), and dependency parsing.

Python

import spacy # Load a pre-trained English model nlp = spacy.load("en_core_web_sm") text = "The quick brown fox jumps over the lazy dog." doc = nlp(text) # Iterate over tokens and print dependency information for token in doc: print(f"{token.text} -- {token.lemma_} -- {token.pos_} -- {token.dep_} -- {token.head.text}") # Visualize the dependency tree (requires displacy) # from spacy import displacy # displacy.serve(doc, style="dep")

The -- the -- DET -- det -- fox quick -- quick -- ADJ -- amod -- fox brown -- brown -- ADJ -- amod -- fox fox -- fox -- NOUN -- nsubj -- jumps jumps -- jump -- VERB -- ROOT -- jumps over -- over -- ADP -- prep -- jumps the -- the -- DET -- det -- dog lazy -- lazy -- ADJ -- amod -- dog dog -- dog -- NOUN -- pobj -- over . -- . -- PUNCT -- punct -- jumps

This output shows each word, its lemma (base form), its Part-of-Speech tag, its dependency relation, and its head word in the dependency tree.

Conclusion

Understanding syntax is fundamental to NLP. Regular expressions provide basic pattern matching, morphology delves into word structure, and parse trees offer a hierarchical representation of sentence structure. Python libraries like NLTK and spaCy provide the tools to implement these concepts, enabling computers to process and understand human language with increasing sophistication. As NLP continues to evolve, a strong grasp of these fundamental syntactic concepts will remain essential for building effective language processing applications.

There is "Y-Value: Color Mapping" feature in "Color" drop-down menu in Origin.

Use EggNog mapper

To publish on arXiv in the cs.LG (Machine Learning) or cs.AI (Artificial Intelligence) categories, you need an endorsement from an established arXiv author. Here’s how to get one:

Steps for Endorsement:

Note:

Chris: Good point. Thanks.

Hello Ali,

There are some open source tools for that.

One of them is this: https://github.com/ebranlard/matlab2python that is based on SMOP (https://github.com/victorlei/smop/) and some other libraries that you can find here: https://stackoverflow.com/questions/9845292/a-tool-to-convert-matlab-code-to-python/17535694#17535694

But MATLAB provides two-way integration with Python. The MATLAB Engine API for Python (https://www.mathworks.com/help/matlab/matlab-engine-for-python.html) allows you to call MATLAB functions from Python. You can also build Python packages from MATLAB programs by using MATLAB Compiler SDK (https://www.mathworks.com/help/compiler_sdk/python_packages.html)

My Python functions called stepAIC and stepBIC perform stepwise regression to find the optimum set of predictors to minimize the AIC or BIC, and optionally eliminate non-significant predictors. The stepAIC and stepBIC functions are available at the following github link:

Thank you.

Dear Rahul Jain ,

Machine Learning (ML) is one of the most sought-after skills in the tech industry today. Whether you are a beginner looking to get started or an experienced professional aiming to advance, havMachine Learning (ML) is one of the most sought-after skills in the tech industry today. Whether you are a beginner looking to get started or an experienced professional aiming to advance, having a structured roadmap is crucial.

Regards,

Shafagat

Thank you Bipul Kumar and Badarvada Yadidya

It took a little tweaking but did exactly what I needed it to do.

Adnan: I love your response. Very insightful and thought provoking.

"Pygen" refers to tools that leverage artificial intelligence, particularly large language models (LLMs), to automate the creation of Python packages from simple prompts. There are a couple of distinct uses of the name Pygen, so I will discuss both.

Here's a breakdown of what Pygen entails:

The evolution of humor over centuries is a fascinating reflection of shifting societal values, technological advancements, and cultural norms. Here's a breakdown of some key changes:

In essence, humor has evolved alongside society, reflecting our changing values, beliefs, and technologies.

A logistics student should work on projects like inventory optimization, last-mile delivery solutions, warehouse automation, green logistics, demand forecasting, blockchain in supply chains, reverse logistics, route optimization, cold chain management, and AI-driven supply chain analytics.

If you’re looking to run molecular dynamics (MD) simulations for large protein structures, like those generated by AlphaFold, Here are some options to consider:

1.CHARMM-GUI

This is a user-friendly, web-based platform that helps you set up and run MD simulations. It works well with large systems and integrates seamlessly with popular MD engines like CHARMM, NAMD, GROMACS, and AMBER.

Check it out here: [CHARMM-GUI](https://www.charmm-gui.org)

2.GROMACS Online

If you’re a fan of GROMACS, this cloud-based service is perfect for running simulations on large protein structures. It even offers pre-configured workflows to make your life easier.

Visit their site: [GROMACS](https://www.gromacs.org)

3. **AMBER MD Web Portal**:

AMBER’s web interface is another excellent choice for running MD simulations on large systems. It also provides handy tools for preparing and analyzing your structures.

Explore it here: [AMBER](https://ambermd.org)

4.HADDOCK

While HADDOCK is mainly known for docking, it also supports MD simulations for large protein complexes. It’s a versatile tool worth checking out.

Take a look: [HADDOCK](https://wenmr.science.uu.nl/haddock2.4)

5.MDWeb

This web-based platform uses GROMACS for MD simulations and is designed to handle large structures. It also offers automated workflows to streamline the process.

Learn more: [MDWeb](https://mmb.irbbarcelona.org/MDWeb)

6.Google Colab with OpenMM

For those who are comfortable with a bit more advanced setup, Google Colab can be a powerful and free option. Using OpenMM, you can run scalable MD simulations for large systems.

These tools should give you options to work with, CHARMM-GUI is great choice Hope it helps :]

Follow@

I recommend using a dedicated virtual environment for nupaac, this will avoid conflicts with packages on your host machine. Since `radioactivedecay` is a dependency of `nupaac`, you can use the virtual env to explore the capabilities of both packages.

There are plenty of different use-cases, have a look at the tutorial file that I linked in the initial post for `nupaac` (https://codeberg.org/Cs137/NuPaAc/src/branch/main/tutorial.md), and for `radioactivedecay` at it's documentation: https://radioactivedecay.github.io/

The latter provides also information about the accuracy and methodology applied to perform decay calculations.

I use a program called GSAS II (B.H. Toby and R.B. Von Dreele, J. Appl. Cryst. 46, 544-549 (2013)) for Rietveld analysis of materials using XRD data which is python based. The plotting functions are useful for group presentations but for external presentations or publications my boss prefers plots from Origin. I have used HighScore in the past and find it brilliant for phase ID but again I would use Origin to produce a plot for dissemination.