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The decentralized finance (DeFi) ecosystem continues to expand across multiple blockchains, offering new opportunities but also introducing major challenges. One key issue is that of fragmented liquidity - assets and trading activity spread thinly across disconnected platforms. Another threat comes from weaknesses in cross-chain interoperability - bridges that promise to connect blockchains but with security flaws that leave funds at risk. To discuss these problems and potential solutions, I spoke with Boris Povar, a co-founder, and CEO of the project. Eywa Povar brings a strong technical background to tackle these complex topics. With a degree in mathematics and programming, he has worked in crypto since 2017 after a successful career running an IT outsourcing firm. As a portfolio investor and fund manager in the DeFi space, he has direct experience grappling with risks around fragmented capital and insecure bridges. Since 2020, he has focused his efforts on developing a novel architecture for trusted and efficient cross-chain communication. Povar provides insight into the scale and consequences of fragmented liquidity and flaws in today's bridges. The interview sheds light on the roadblocks hindering DeFi's progress as well as innovative solutions in development. Povar lends both technical expertise and real-world perspective to outline a vision for the next stage in DeFi evolution. What are the problems of fragmented liquidity and insecure cross-chain communication? The problem of fragmented liquidity can be formulated as follows: Liquidity located in one blockchain is isolated and not utilized in other blockchains simultaneously. Protocols addressing this issue enable your funds to operate across multiple blockchains/protocols concurrently. For instance, Liquid Staking protocols enable participation in the Ether consensus, earning income, while still utilizing funds in DeFi. Blast operates on a similar principle, where funds in their blockchain function within major DeFi protocols in Ethereum, generating income. However, examining this problem from the perspective of bridges reveals inefficiencies. Traditional lock-mint-burn-unlock bridges are ineffective with liquidity, as they connect assets and blockchains in pairs, requiring liquidity to be added to each link. Liquidity locked in such bridges remains dormant and is utilized ineffectively; L2 bridges serve as examples. Meanwhile, the TVL of bridges is rapidly growing and has reached the total TVL of DEXes (approximately $17 billion), necessitating more efficient utilization. Thus, it can be argued that bridges exacerbate the problem of liquidity fragmentation. The issue of insecure cross-chain communication is exemplified by the Multichain protocol. Projects like Curve and Fantom relied on it for cross-chain communications and liquidity transfer. However, Multichain malfunctioned at one point, resulting in losses for these projects. Their infrastructure was entirely dependent on Multichain, and finding and integrating alternative options took considerable time. Multichain, like many cross-chain protocols, had centralized elements, making its operation heavily reliant on teams. This scenario underscores the risks associated with relying on any single bridge as critical ecosystem infrastructure, which may lead to unforeseen "black swan" events. How big of an issue are these problems currently in DeFi and crypto overall? What are the consequences? High slippages in cross-chain swaps and complex UI/UX pose challenges for the majority of users. Projects are compelled to divide their liquidity across blockchains, incurring high maintenance costs. Furthermore, most bridges exhibit centralized traits. To date, bridge hacks have resulted in losses exceeding $2 billion. At EYWA, we endeavor to consolidate the liquidity of major blockchains, enhancing efficiency without compromising security and decentralization. What initially motivated you to tackle the problems of fragmented liquidity and insecure cross-chain bridges in the crypto industry? I've been actively addressing my own challenges. With involvement in asset management and DeFi capital placement since 2020, and in the industry since 2017, I possess an in-depth understanding of the problems and risks associated with cross-chain fund transfers. Drawing on my technical background, I've meticulously analyzed bridge hacks, resulting in the loss of over $2 billion. Consequently, I grasp what's necessary to ensure the security of cross-chain communications. Moreover, I recognize the imperative of holding liquidity long-term for bridge efficiency. I'm well aware of the inefficiencies in DeFi liquidity utilization, contributing to the dominance of CEXes over DEXes. The first wave of cross-chain bridges, while experimental, merely offered a pricier service without enhancing security or decentralization. I often phrase it as: "It’s still the same CEX, just in a new package." Hence, since late 2020, I've been dedicated to developing a genuinely decentralized cross-chain protocol capable of rivaling CEXes without compromising decentralization and security for development speed and convenience. How exactly does aggregated bridge and consensus architecture provide security for transfers? Most cross-chain bridges currently employ their own messaging protocols, which often fall short in terms of decentralization and entail various risks associated with teams, regulations, and management. It's fair to say that virtually all bridges on the market require trust, leaving the entire ecosystem vulnerable to potential "black swan" events. In response to these challenges, the Eywa team, in collaboration with the Curve team, is developing a trustless bridge that relies on a consensus of the most reliable messaging protocols available. We leverage Axelar, L0, Wormhole, Chainlink CCIP, and other messaging protocols to create a highly secure solution for messaging, custody, and liquidity transfer. By combining Eywa Token Bridge smart contracts with data validation consensus, we establish an Eywa Consensus Bridge that ensures security independently of any team and enables users to retain control over bridge liquidity. When transmitting information, it will be sent in parallel to several messaging protocols based on the user's choice, allowing users to specify preferred bridges and required quorums. The bridge's smart contract in the destination network aggregates transmission results and compares them until a quorum is reached. Users can customize the level of security needed, determining the number of checks. If any protocol returns incorrect data, the transmission will be halted. Imagine initiating a cross-chain transfer of a substantial sum. Initially, your funds are secured in the bridge's smart contract. Subsequently, a cross-chain transaction is initiated, transmitted, and verified concurrently by multiple data transfer protocols. In the destination network, a specialized smart contract receives and compares responses. If consistent information is received from all protocols, the final transaction executes your transfer. However, if any bridge is compromised, resulting in mismatched data, the transaction will be aborted, allowing you to reclaim your funds in the source network. The probability of all transfer protocols being compromised simultaneously approaches zero. How does utilizing Curve’s existing deep liquidity pools enable low-slippage cross-chain trading on CrossCurve? The more liquidity available, the lower the slippages, which is an evident concept. However, to elucidate how utilizes Curve pools, I must delve into its architecture. As mentioned earlier, the issue with bridge liquidity fragmentation lies in the fact that locked liquidity within bridges remains inactive in DeFi. CrossCurve To address this, we utilize Curve's LP tokens as the foundational liquidity. Leveraging Curve's ability to exchange LP tokens within the pool as standard tokens, we generate s-tokens - synthetic collateralized derivatives - by consolidating them on one public blockchain, known as Hubchain (currently Fantom). Subsequently, we establish stable liquidity pools on Curve by connecting s-tokens of similar nature. These pools offer minimal slippage. A typical exchange transaction traverses three blockchains, involving an imbalance operation to add liquidity to the Curve pool in the source network, followed by minting the corresponding s-token to the Hubchain, where it is exchanged for an s-token backed by the Curve LP token from the destination network. The s-token is then burned, releasing the LP token in the destination network, and the final step involves an imbalance withdrawal of liquidity from the Curve pool, transferring it to the user's wallet - adding liquidity, exchanging, and removing liquidity. The greater the liquidity in the pools we utilize, the lower the losses incurred. Additionally, this architecture enables more efficient capital utilization as the liquidity from all blockchains is consolidated in one location simultaneously, rather than in pairs. Consequently, CrossCurve requires substantially less liquidity compared to its competitors to achieve low slippage. What are the main benefits to projects and developers of listing their tokens on CrossCurve? Web3 projects can list their tokens on any chain against crvUSD/Stables/WBTC/WETH/Curve LPs and trade them simultaneously across all chains for any asset. They can also receive yield provided by Eywa, Curve, and Convex on provided liquidity, attracting new users from different blockchains. Fast scaling for the utility of your token in other networks, while also reducing the costs associated with maintaining cross-chain liquidity by keeping a single pool. How does CrossCurve simplify the user experience for cross-chain DeFi operations? We utilize cross-chain ZAP transactions, condensing a large number of transactions for the user. For instance, a typical exchange involves a series of transactions across three blockchains, yet the user only needs to initiate one transaction. How will the CrossCurve yield flywheel attract liquidity providers and projects to the platform? Since all CrossCurve pools are native Curve pools, we have access to various tools for managing incentives. These include direct incentives, veCRV bribes, Convex rewards, and other vote aggregators like StakeDAO, Lobby, and Yearn. Additionally, we can create our own votemarket, accumulate Curve and Convex votes, and participate in CRV Wars. Our plan involves using bonding mechanisms to attract the protocol’s own liquidity to the main pools. The first phase of our flywheel launch will focus on direct incentives for pools, veCRV bribes, and attracting our own liquidity through bonding. Subsequently, we will introduce a DAO and our own vote market. The issuance of the EYWA token will be exclusively available to long-term holders and members of the CrossCurve DAO, thereby strengthening the token's value. Curve liquidity providers will receive enhanced yields on their LPs, and we will provide them with a seamless method to contribute to CrossCurve pools. Projects will have the opportunity to offer incentives on their pools. However, to receive additional incentives from the CrossCurve DAO and partake in their distribution, they must become members.It's worth mentioning that we will assist Curve in distributing their stablecoin crvUSD, as it is their priority. In return, they will support us with incentives by voting for our main pools. How will CrossCurve boost adoption of less liquid layer 1s and layer 2s through liquidity bridges? We are currently constructing a cross-chain liquidity core by combining the largest blockchains by TVL. Subsequently, we will integrate emerging blockchains into our system and link them to this core, simplifying the process of liquidity migration between networks for liquidity providers. What role will the CrossCurve DAO play in solving liquidity fragmentation and boosting chain interoperability? CrossCurve will function as a transport hub for liquidity migration between different chains, offering additional income opportunities for liquidity providers. They will receive a base yield for providing liquidity on the original network, along with a yield for contributing their LPs to CrossCurve pools. Additionally, we intend to serve as a launchpad for Web3 projects seeking to establish multi-chain liquidity. This will enable them to utilize a single liquidity pool that serves all blockchains. How will votemarkets and bribing systems incentivize long term alignment between liquidity providers and the protocol? Long-term liquidity providers will become members of the CrossCurve DAO, engaging in the distribution of issuance among CrossCurve pools. They will also receive platform commission income and incentives from projects seeking augmented rewards. Where do you see the main competition in cross-chain DEX space, and how is CrossCurve positioned against them? My primary competitors include projects such as Stargate, Squid, Synapse, and Hashflow. We position ourselves as a solution primarily catering to liquidity providers and DeFi power users who necessitate seamless movement of substantial liquidity across blockchains. Additionally, we address the challenges faced by Web3 founders, enabling them to reduce the expenses associated with establishing and maintaining multi-chain liquidity while broadening their access to users across various blockchains, thereby diminishing their reliance on specific blockchain ecosystems. Our key advantages include the capacity to facilitate large-volume exchanges with minimal losses, akin to Curve's renowned capability, efficient utilization of liquidity, a seamless user experience, robust security measures safeguarding funds within our bridge, lucrative farming opportunities for liquidity providers, and an attractive proposition for projects seeking to establish their own liquidity pools. What chains and assets will CrossCurve focus on supporting in the initial launch stages? CrossCurve will focus on supporting the Ethereum, Arbitrum, Optimism, Polygon, Avalanche, BNB Chain, and Fantom chains along with the USDT, USDC, DAI, WBTC, ETH, crvUSD, 3crypto Curve liquidity pools, crvUSD-USDT Curve liquidity pools, and 3crv Curve liquidity pools assets. Do you intend to partner up with other major players, DAOs and bridges? If yes, in what capacity? To construct the Consensus Bridge, we are collaborating with Curve and will engage with prominent players in the cross-chain market — L0, Wormhole, Axelar, and Chainlink — as integrators of their solutions. For liquidity building, we have partnered with Curve and other entities within the Curve ecosystem, including Convex, StakeDAO, Yearn, and Lobby, to incentivize their users. We are also collaborating with yield aggregators like Beefy. We are currently in negotiations to integrate all new blockchains that Curve will support. Among those on the list are Mantle, Linea, Scroll, zksync, BNB Chain, and others. Additionally, we plan to identify a new Hubchain, a strategic partner blockchain. To enhance scalability and on-chain performance, we will collaborate with bridge aggregators such as Li.fi, Rubic, and Rango, as well as with wallets like Trust Wallet. In the future, we aim to extend support for liquidity aggregation from other DEXes, such as Balancer and Uniswap.

Fragmented No More: CEO of EYWA Explains Fixing Spread-Out Liquidity and Bridge Security

খণ্ডিত আর নয়: EYWA-এর CEO ব্যাখ্যা করেছেন স্প্রেড-আউট লিকুইডিটি এবং সেতু নিরাপত্তা

Schluss mit der Fragmentierung: CEO von EYWA erklärt die Behebung verteilter Liquidität und Überbrückungssicherheit

No más fragmentados: el director ejecutivo de EYWA explica cómo solucionar la liquidez dispersa y la seguridad del puente

Fini la fragmentation : le PDG d'EYWA explique la solution à la question de la liquidité étalée et de la sécurité du pont

अब खंडित नहीं: ईवाईडब्ल्यूए के सीईओ ने स्प्रेड-आउट तरलता और ब्रिज सुरक्षा को ठीक करने के बारे में बताया

断片化はもう終わり: EYWA の CEO がスプレッドアウト流動性の修正とブリッジセキュリティについて説明

더 이상 단편화되지 않음: EYWA CEO, 분산형 유동성 및 브리지 보안 수정에 대해 설명

Fragmented No More: CEO da EYWA explica como consertar a liquidez espalhada e a segurança da ponte

Больше нет фрагментации: генеральный директор EYWA объясняет, как исправить разброс ликвидности и безопасность моста

Artık Parçalanmış Değil: EYWA CEO'su Yayılmış Likidite ve Köprü Güvenliğinin Düzeltilmesini Açıklıyor

Không còn bị phân mảnh nữa: Giám đốc điều hành của EYWA giải thích cách khắc phục tính thanh khoản dàn trải và bảo mật cầu nối

不再支离破碎：EYWA 首席执行官解释解决流动性分散和桥梁安全问题

Spreadsheets have been a critical tool for managing information for individuals and organizations. However, manual spreadsheet tasks can be time-consuming and error-prone. This guide will show you how Python and Openpyxl, a powerful Python library for working with Excel files, can revolutionize how you manage your spreadsheets. We'll also explore the benefits of using for spreadsheet tasks and how Openpyxl can help you automate many tedious and error-prone spreadsheet tasks. Python In this article, we will learn how to perform simple data automation operations, such as Accessing Cell Values, Conditional Formatting, and working with Formulas and Calculations. You will be able to streamline your routine tasks with spreadsheets. Getting Started Let’s start with setting up your environment. Installing Python You can get your operating system's most recent version of Python from the . After that, simply adhere to the installation instructions to complete the process. official Python website Installing Openpyxl After that, add Openpyxl to your toolbox. Open your terminal or command prompt and use the following command to install Openpyxl using pip, Python's package manager. pip install openpyxl If your installation is successful, you will see the following output. Now you can use Openpyxl to automate your spreadsheet operations. Basic Spreadsheet Operations Imagine you have an Excel sheet filled with valuable data, such as employee information. For this tutorial, we will use the Excel sheet provided by . You can download it from the given link, and it will contain the following dataset. We will use this Excel sheet to show how to do various spreadsheet operations with Openpyxl. The Spreadsheet Guru Loading and Accessing Data Let’s start with accessing the Excel sheet we downloaded earlier to show how to load an Excel sheet on your local machine. Here is the folder structure of my project. Both the employee_data file and the file are in the same directory. working.py To load the Excel file with Openpyxl, execute the following Python code. import openpyxl

wb = openpyxl.load_workbook('employee_data.xlsx') It will not provide any output. But the code will be executed without any errors. Accessing Cell Values One of the easiest ways to prove that the above code opens the file is to access and display a cell value. Let's print the full name of the employee in cell B2. To access a certain cell value, you must first access the sheet it belongs to. In this case, the name of the sheet is Data. import openpyxl

#Load the Excel file
wb = openpyxl.load_workbook('employee_data.xlsx')

#Access the sheet
sheet = wb['Data']

#Access the cell value
first_name = sheet['B2'].value

#Print the cell value
print(f"The first name is: {first_name}") You will get the following output. This is a simple example of accessing cell values in an Excel sheet. Now, let’s do something more complex. Suppose you want to display all employees' full names and annual salaries stored in columns B and J, respectively. Python can help you achieve this task with the following code: import openpyxl

#Load the Excel file
wb = openpyxl.load_workbook('employee_data.xlsx')

#Access the sheet
sheet = wb['Data']

for row in sheet.iter_rows(min_row=2, values_only=True):
    full_name = f"{row[1]}"
    annual_salary = row[9]
    print(f"Employee: {full_name}, Annual Salary: {annual_salary}") Once you execute this code, you will get the following output. Let’s try another example. Filtering is one of the everyday operations you perform with Excel data. Let's filter all employees whose names start with 'E'. import openpyxl

wb = openpyxl.load_workbook('employee_data.xlsx')

sheet = wb['Data']

for row in sheet.iter_rows(min_row=2, values_only=True):
    full_name = f"{row[1]}"
    annual_salary = row[9]
   
    # Filter employees whose name starts with 'E'
    if full_name.startswith("E"):
        print(f"Employee: {full_name}") This code will give the following output. Modifying Cell Values Modifying cell values is a fundamental operation you will frequently perform in Excel. Here's an example to illustrate this. You must change an employee's phone number or address using a program. Let's say everyone previously based in Beijing is now relocating to Miami, and you want to modify the data accordingly and save it to a new Excel file. How can you make this change for multiple employees in one go? import openpyxl

wb = openpyxl.load_workbook('employee_data.xlsx')

sheet = wb['Data']

# Change cell values from Beijing to Miami
for row in sheet.iter_rows(min_row=2, min_col=13, max_col=13):
    for cell in row:
        if cell.value == 'Beijing':
            cell.value = 'Miami'

 # Save the modified workbook
wb.save('employee_data_updated.xlsx') In this code snippet, we created a new Excel file called employee_data_updated, where all the employees who lived in Beijing moved to Miami. Advanced Operations In this section, we will investigate more challenging operations with Openpyxl. Conditional Formatting Excel's conditional formatting function is very effective. Suppose you want to highlight in green all employees earning more than $50,000 annually. Follow these steps to set up the conditional formatting rules: import openpyxl
from openpyxl.styles import PatternFill

wb = openpyxl.load_workbook('employee_data.xlsx')

sheet = wb['Data']

# Define the green fill style
green_fill = PatternFill(start_color='00FF00', end_color='00FF00', fill_type='solid')

# Iterate through rows starting from the second row (assuming the headers are in the first row)
for row_number, row in enumerate(sheet.iter_rows(min_row=2, values_only=True), start=2):
    annual_salary = row[9]  
   
    # Check if the Annual Salary is greater than $50,000
    if annual_salary > 50000:
        # If the condition is met, apply a green fill to the Annual Salary cell
        cell = sheet.cell(row=row_number, column=10)  
        cell.fill = green_fill

wb.save('employee_data_updated.xlsx') You can easily understand the code by reading the comments. Once you run the code above, you will see the following change in your new Excel sheet file. However, if you want to make salaries greater than $50,000 bold and italic, you can use the following code: import openpyxl
from openpyxl.styles import Font

wb = openpyxl.load_workbook('employee_data.xlsx')

sheet = wb['Data']

# Define the bold and italic font styles combined
bold_italic_font = Font(bold=True, italic=True)

for row_number, row in enumerate(sheet.iter_rows(min_row=2, values_only=True), start=2):
    annual_salary = row[9]

    if annual_salary > 50000:
        # If the condition is met, apply the bold and italic font style to the Annual Salary cell
        cell = sheet.cell(row=row_number, column=10)
        cell.font = bold_italic_font

wb.save('employee_data_updated.xlsx') Here is the output of it. Formulas and Calculations Excel is well known for its formula capabilities. With Openpyxl, you can automate the application of these formulas. Let’s find the sum, average, and median of all salaries. You can find all salaries between the J2 cell and the J1001 cell. Although we can calculate these values using Python, we will use the Excel formula “=SUM(J2:J1001)” to show you how to automate Excel formulas. import openpyxl

wb = openpyxl.load_workbook('employee_data.xlsx')

sheet = wb['Data']

# Insert labels and formulas to find the sum, average, and median
sheet['O2'].value = "Sum"
sheet['P2'].value = "=SUM(J2:J1001)"

sheet['O3'].value = "Average"
sheet['P3'].value = "=AVERAGE(J2:J1001)"

sheet['O4'].value = "Median"
sheet['P4'].value = "=MEDIAN(J2:J1001)"

wb.save('employee_data_updated.xlsx') The updated Excel file displays equations on P2, P3, and P4 cells. Batch Processing of Multiple Files Consider a folder containing several Excel files, each requiring the same data processing steps. You can automate this batch processing with Python. import os
import openpyxl

# Define the directory containing Excel files
directory = 'excel_files/'

# Iterate through files in the directory
for filename in os.listdir(directory):
    if filename.endswith('.xlsx'):
        file_path = os.path.join(directory, filename)
       
        # Load and process each file
        wb = openpyxl.load_workbook(file_path)
        # Perform operations…
       
        wb.save(file_path) The processing of numerous Excel files is automated by this script, which helps you save time and effort. You need to update the directory paths according to your requirements and write the operations you need to perform. Combining Data from Multiple Sheets Another routine Excel task you will encounter is combining data from various sheets or workbooks. Python can make this process easier. For example, if you have sales figures for multiple quarters in separate workbooks, you can combine them using Python. import openpyxl

# Load the target workbook where data will be consolidated
target_wb = openpyxl.load_workbook('combined_sales.xlsx')
target_sheet = target_wb['CombinedData']

# List of source workbooks
source_files = ['sales_data_q1.xlsx', 'sales_data_q2.xlsx']

for source_file in source_files:
    source_wb = openpyxl.load_workbook(source_file)
    source_sheet = source_wb.active  # Assuming data is in the first sheet

    # Append rows from source to target, skipping the header
    for row in source_sheet.iter_rows(min_row=2, values_only=True):
        target_sheet.append(row)

# Save the combined data
target_wb.save('combined_sales.xlsx') With the help of this code, you can merge data from many sheets into one. You need to update the file names according to your needs. Best Practices Let’s see some of the best practices you can follow when automating Spreadsheet Operations with Python and Openpyxl. Optimizing Code Efficiency Efficiency is crucial, especially when working with enormous datasets. Use effective methods, eliminate unnecessary operations, and consider implementing multithreading or multiprocessing for parallel processing when optimizing your code. Version Control and Collaboration Using version control tools like allows you to keep track of changes, collaborate effectively, and keep a revision history for your automation scripts. Git Documentation and Comments With comments and docstrings, thoroughly document your code. It will help others to quickly understand your automation scripts and help you maintain your docs as they get bigger. Testing and Validation You must thoroughly test your automation scripts in a controlled environment before deploying them in production. Make sure they perform as expected and are capable of handling a variety of situations. You can learn about other third-party packages for working with spreadsheets using Python in this . article Conclusion Using Python and openpyxl to automate spreadsheets has many benefits. Python and Openpyxl offer the required tools and flexibility, whether creating reports, conducting data analysis, or speeding up data validation. You can gain fresh insights into data management and save time, money, and effort by becoming an expert in spreadsheet automation. We expect you to learn how to use openpyxl and Python to create innovative spreadsheets and to use automation as a fundamental skill in your daily work.

How to Automate Spreadsheets With Python and Openpyxl

: This article aims to clarify that the code provided is not a universal solution for all aspects of mathematical modeling. Instead, it serves as an educational tool, highlighting a specific step in the mathematical modeling process and employing Python for illustrative purposes, with the primary goal of enhancing awareness in this field. Disclaimer It is also important to remember that there are specific measures you must take when submitting/proposing a mathematical model project, such as one related to the field of biomathematics. These steps include, but are not limited to: defining the issue, selecting a model approach (two models will be discussed in this article), formulating equations, parameter estimation—often based on historical data, implementing numerical simulations by resolving differential equations analytically or using computational tools like and MATLAB, validations and sensitivity analysis, scenario testing, result interpretation, and so on. Python As I said earlier, this only highlights a specific step in the mathematical modeling process and employing Python for illustrative purposes. Content Overview Introduction The Purpose of Simulation The SIR Model What It Does / SIR Python Code Simulation Example What You Can Learn Understand Disease Spread Explore Parameter Sensitivity Evaluate Interventions The SEIR Model What It Does / SEIR Python Code Simulation Example What You Can Learn Model Latent Periods Evaluate Early Interventions Study Complex Outbreaks Conclusion 1. Introduction Understanding how infectious diseases spread is essential for maintaining the public's health because they have existed throughout human history for centuries. A potent tool for simulating and understanding the dynamics of disease spread is mathematical models. The (Susceptible-Infectious-Recovered) and (Susceptible-Exposed-Infectious-Recovered) models will be discussed in this article, along with how to simulate them using Python. SIR SEIR What Simulations Are For For public health professionals and epidemiologists, simulation is like a crystal ball. It enables us to foresee the potential spread of diseases under various circumstances and treatment options. These models aid in decision-making, effective resource allocation, and comprehension of the potential effects of various techniques. Let's examine two basic models namely; SIR Model, and SEIR Model. 2. The SIR Model What It Does / SIR Python Code Simulation Example The Susceptible, Infectious, and Recovered (SIR) model separates a population into these three categories. Based on variables like transmission rate ( ) and recovery rate ( ), it simulates how these compartments alter with time. β γ Before we exemplify a simulation using Python, it’s necessary to make “model assumptions” when working on mathematical models. In our assumption we’ll create model using LaTeX formular or code: \begin{align*}
\frac{dS}{dt} &= -\beta \cdot S \cdot I \\
\frac{dI}{dt} &= \beta \cdot S \cdot I - \gamma \cdot I \\
\frac{dR}{dt} &= \gamma \cdot I
\end{align*} Note: You can latexify this formular using Python. An example can be found on . https://github.com/google/latexify_py/blob/main/examples/examples.ipynb This article will not bother to write that Python code to convert LaTeX to a proper mathematical notation/equation, but I have used online editor such as so that you see the formula/equation assumptions glaringly below: https://latexeditor.lagrida.com/ : where S represents susceptible individuals, I represents infected individuals, R represents recovered individuals. The parameters β and γ govern the rates of transmission and recovery, respectively. The negative sign (i.e −β) indicates that the number of susceptible individuals (S) decreases over time. The dot notation indicates “multiplication.” In summary, these equations describe the dynamics of the SIR model, where the number of susceptible people decreases as they contract the disease (i.e. dS/dt), the number of infectious people rises as a result of new infections and falls as they recover (i.e. dI/dt), and the number of recovered people rises as the disease is treated (i.e. dR/dt). Because the change in each compartment depends on the multiplication of pertinent components, dots (.) is used to denote multiplication. Since all assumptions are set, we can then run a simulation using Python for  SIR Model and then visualize the dynamics: import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt

# SIR model equations
def SIR_model(y, t, beta, gamma):
    S, I, R = y
    dSdt = -beta * S * I
    dIdt = beta * S * I - gamma * I
    dRdt = gamma * I
    return [dSdt, dIdt, dRdt]


"""
Initial conditions (such as S0, I0, and R0) are not to be random but I hardcoded them with specific values. These choices are typically made based on the characteristics of the disease being modeled and the context of the simulation. Initial condition are set such that S0 = 99%, which indicates the proportion of susceptible individuals when the simulation starts. I0 is set to 1%, which indicates proportion of infected individuals to be 1% when the simulation starts. R0 is set to 0% which is expected that there are are no recovered individuals when the simulations start.
"""
S0 = 0.99  
I0 = 0.01  
R0 = 0.00  
y0 = [S0, I0, R0]

# Parameters
# β (beta) is transmission rate and I chose 30%. γ (gamma) is set to 1%
beta = 0.3  
gamma = 0.1  

# Time vector
t = np.linspace(0, 200, 200)  # Simulate for 200 days

# Solve the SIR model equations using odeint()
solution = odeint(SIR_model, y0, t, args=(beta, gamma))

# Extract results
S, I, R = solution.T

# Plot the results
plt.figure(figsize=(10, 6))
plt.plot(t, S, label='Susceptible')
plt.plot(t, I, label='Infected')
plt.plot(t, R, label='Recovered')
plt.xlabel('Time (days)')
plt.ylabel('Proportion of Population')
plt.title('SIR Model Simulation')
plt.legend()
plt.grid(True)
plt.show() For differences between and , I will like to point you to and for you to make sense of it further. scipy.integrate.ode scipy.integrate.odeint odeint ode What You Can Discover Running the SIR model in Python allows you to: : Observe the epidemic curve and visualize how a disease spreads through a community. Running Python SIR Model above, you can see the outcome/result in the graph below: Understand Disease Spread Try testing various beta and gamma values to observe how they affect the length and peak of the outbreak. Explore Parameter Sensitivity: By changing the parameters, you can simulate the consequences of interventions like Evaluate Interventions: social isolation or immunization. 3. The SEIR Model What It Does / SEIR Python Code Simulation Example By adding a "Exposed" compartment, the SEIR model expands upon the SIR model. It takes into account the period of incubation during which people have been exposed but are not yet contagious. How to emulate it in Python is shown here. import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt

# SEIR model equations
def SEIR_model(y, t, beta, sigma, gamma):
    S, E, I, R = y
    dSdt = -beta * S * I
    dEdt = beta * S * I - sigma * E
    dIdt = sigma * E - gamma * I
    dRdt = gamma * I
    return [dSdt, dEdt, dIdt, dRdt]

# Initial conditions
S0 = 0.99  
E0 = 0.01  
I0 = 0.00  
R0 = 0.00  
y0 = [S0, E0, I0, R0]

# Parameters
beta = 0.3    
sigma = 0.1   
gamma = 0.05 

# Time vector
t = np.linspace(0, 200, 200)  

# Solve the SEIR model equations
solution = odeint(SEIR_model, y0, t, args=(beta, sigma, gamma))

# Extract results
S, E, I, R = solution.T

# Plot the results
plt.figure(figsize=(10, 6))
plt.plot(t, S, label='Susceptible')
plt.plot(t, E, label='Exposed')
plt.plot(t, I, label='Infected')
plt.plot(t, R, label='Recovered')
plt.xlabel('Time (days)')
plt.ylabel('Proportion of Population')
plt.title('SEIR Model Simulation')
plt.legend()
plt.grid(True)
plt.show() The only difference in this case is the introduction of to explore different scenarios and understand the dynamics of infectious diseases with an “exposed” period. It demonstrates how it accounts for an incubation period before individuals become infectious. latent period rate (σ) What You Can Learn When you use Python to run the SEIR model, you can: Model Latent Periods Recognize the differences between the behaviors of immediately infectious illnesses and diseases with incubation periods. Evaluate Early Interventions Analyze the effects of isolation and early detection strategies. Study Complex Outbreaks For illnesses like COVID-19 where exposed people are a major factor in transmission, use SEIR. 4. Conclusion Python's simplicity and robust libraries, like SciPy, make it the perfect language for modeling diseases. And by performing these simulations with it, you're learning more about the dynamics of infectious diseases. This can equip you with verdicts and prowess that can help make decisions that are well-informed and improve your own ability to evaluate epidemics in the real world. There is much more to come after the SIR and SEIR models. There are other complex models such as SEIRS (Susceptible-Exposed-Infectious-Removed-Susceptible) Model, Spatial Models, Network Models, etc. Investigating intricate models, as well as real world data, such as geospatial data, epidemiological data, behavioural data, etc, and carefully examining the effects of interventions, such as vaccination strategies,  treatment availability, social distancing measures, you will develop your modeling abilities even further. Your knowledge of these modeling tools can significantly impact protecting public health in a future where understanding infectious disease transmission is vital. This is already too long, but I hope I have been able to show you how to simulate SIR and SEIR Models in mathematical model, using Python.

Simulating Infectious Disease Spread with Python: SIR and SEIR Models

পাইথনের সাহায্যে সংক্রামক রোগ ছড়ানোর অনুকরণ: SIR এবং SEIR মডেল

Simulation der Ausbreitung von Infektionskrankheiten mit Python: SIR- und SEIR-Modelle

Simulación de la propagación de enfermedades infecciosas con Python: modelos SIR y SEIR

Simuler la propagation de maladies infectieuses avec Python : modèles SIR et SEIR

पायथन के साथ फैलने वाले संक्रामक रोग का अनुकरण: एसआईआर और एसईआईआर मॉडल

Python を使用した感染症の蔓延のシミュレーション: SIR モデルと SEIR モデル

Python을 사용한 전염병 확산 시뮬레이션: SIR 및 SEIR 모델

Simulando a propagação de doenças infecciosas com Python: modelos SIR e SEIR

Моделирование распространения инфекционных заболеваний с помощью Python: модели SIR и SEIR

Python ile Bulaşıcı Hastalık Yayılımının Simülasyonu: SIR ve SEIR Modelleri

Mô phỏng sự lây lan của bệnh truyền nhiễm bằng Python: Mô hình SIR và SEIR

使用 Python 模拟传染病传播：SIR 和 SEIR 模型

Companies are generating an ocean full of data, with marketing teams swimming in customer insights and finance departments navigating through intricate transactions. And let’s not even talk about how enormous spreadsheet files generated by cybersecurity teams are. Unfortunately, spreadsheet platforms like Microsoft Excel and Google Sheets can’t handle big data spreadsheet files with millions or billions of rows. As of this writing, the . Whereas, Google Sheets has a . maximum row limit of Excel is 1,048,576 maximum cell limit of 10 million One of the best ways to smartly analyze big data spreadsheets without causing your browser to crash or your computer to run out of memory is using Gigasheet. Recently, I interviewed , the Co-founder of , a big data spreadsheet platform that allows its users to upload and analyze gigantic-sized spreadsheets packed with millions or billions or rows. Jason Hines Gigasheet What is your company in 2–5 words? The big data spreadsheet. Why is now the time for your company to exist? Spreadsheet platforms like Excel, Airtable, Smartsheet, and Google Sheets are not built for handling big data. These tools are incredibly powerful and offer tons of useful capabilities, but because of the limited capabilities that restrict users from working with huge datasets. That’s where Gigasheet is a game-changer. We have built this platform for users looking for something more powerful than Excel and Google Sheets. We want users to get insights from big data without having to write code or set up databases. You shouldn’t need a dedicated Data Science team or a Computer Science degree to get answers from really big datasets. And that’s what our sole focus is. Since spreadsheets are second-nature to most professionals, we didn’t want to reinvent the wheel with a new product with the same functionality as a sheet. Instead, we decided to simplify spreadsheets for huge datasets. With Gigasheet, we offer data science capabilities in a lite platform with features like data enrichment, grouping, filtering, and much more. In a line, we combine the power of big data with the convenience of spreadsheets to work seamlessly with large datasets. What do you love about your team, and why are you the ones to solve this problem? I worked with cybersecurity analysts and revenue teams for a long time in my career. I realized that the volume of the data they handled was so large and complicated that they had to go through too much hassle to export and import it into a database. Besides, not everyone had database skills. So, they had to wait for other teams to support them in the research process, creating lengthy dependencies. That was the core problem statement we wanted to solve. My first-hand experience of working with these analysts and observing their problems in real-time helped me build Gigasheet as a powerful solution for working with large datasets, minus the hassle. If you weren’t building your startup, what would you be doing? Wow, that’s a good question. To be honest, since I committed to building Gigasheet, I haven’t even considered doing anything else. If I wasn’t doing this, I guess I’d probably be building another company or helping someone else do it. At the moment, how do you measure success? What are your metrics? I believe the goal of a venture-backed startup like ours is to fund our growth. So, we’re measuring success based on the platform’s utility for our end-users and our ability to go forward with our product roadmap as planned. In a few sentences, what do you offer to whom? We offer a big data spreadsheet platform with several use cases and advanced capabilities. We serve users from multiple industries and roles, including eCommerce, cybersecurity, marketing, sales, analysts, and more. What’s most exciting about your traction to date? Just the sheer number of users and data we’re processing is kind of mindblowing. We recently processed our 5 trillionth data point and have more than 30,000 users on Gigasheet today. It’s wild to think about it, but we’re processing, on average, about 200,000 cells of data every second. Where do you think your growth will be next year? We've recently launched APIs to help users connect other systems to Gigasheet. This makes Gigasheet a powerful solution for enterprises, particularly heavy-duty applications like Snowflake or Salesforce. You can automate everything possible on the platform. So, whether you want to upload your data on a simpler interface, schedule import/export, or clean and inspect it, it's all possible with our enterprise API. We're also launching shared workspaces for teams. Multiple users from the same company use Gigasheet for collaboration on big files. That motivated us to expand beyond the single-user interface and add the shared workspace functionality to help teams work together seamlessly. Another cool thing on our radar is introducing more enrichments with our predefined partners. So, users can enrich massive data at scale without any coding skills. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was an accountant in Canada. We never spoke to them, but it seems like they were successful. Each month we’ve been adding more customers, which is great, but our primary focus remains on building out more features in our product for 2023. What’s your biggest threat? As a startup, there are always 1,000 ways to die. The biggest threats are probably existential factors that are outside of our control - stability of financial markets and things like economic factors. As Paul Graham has said, “Startups rarely die in mid keystroke.” So, we keep building.

Beyond Excel and Google Sheets: Innovations in Handling Massive Spreadsheets Amidst the Big Data Rev

Hello! I wrote a blog on finding the a couple of months ago. Since then, I have found a better, faster, and easier method, which works in . last non-empty rows most cases Find the Last Row & Column You can copy and paste one of the following functions as per your needs. is meant for a bound script, is very easy to implement, and works on the currently active worksheet. FindLastRowNColBoundScript(), should be used with an independent script. The given code is very simple. You can modify this function and pass variables such as ID and Worksheet names as parameters to make this function more dynamic. FindLastRowNColIndependentScript() /**
 * Function should be used for bound script.
 * Function use getDataRange().getValues() method to 
 * find lastRow and LastColumn of that Spreadhseet.
 */
function findLastRowNColBoundScript() {
  const sheet = SpreadsheetApp.getActiveSheet();

  const data = sheet.getDataRange().getValues();
  const lastRow = data.length;
  const lastCol = data[0].length;

  console.log(lastRow);
  console.log(lastCol);

  return [lastRow, lastCol];
}

/**
 * Function should be used for independent script.
 * Function use getDataRange().getValues() method to 
 * find lastRow and LastColumn of that Spreadhseet
 */
function findLastRowNColIndependentScript() {
  const ss = SpreadsheetApp.openById("<Your Spreadsheet's ID>");
  const sheet = ss.getSheetByName("<Name of the worksheet>");

  const data = sheet.getDataRange().getValues();
  const lastRow = data.length;
  const lastCol = data[0].length;

  console.log(lastRow);
  console.log(lastCol);

  return [lastRow, lastCol];
}


/**
 * This script is for finding last non empty rows and columns in a spreadsheet. 
 * This works in most cases but not in some cases such as columns with checkboxes
 * It is created in such a way that it only works with bound script.
 *
 * Created by: Nibesh Khadka.
 * linkedin: https://www.linkedin.com/in/nibesh-khadka/
 * website: https://nibeshkhadka.com
 */ This strategy of using () to find the last row and columns works in most cases but few such as columns filled with checkboxes on their entire rows. DataRange Thank You for Your Time My name is Nibesh Khadka, and as a freelance automation expert, I specialize in automating Google products with Apps Script. If you need my services, let me . know Don’t forget to like and share this blog. Also published . here

How to Get the Last Non-empty Row of a Column of a Spreadsheet Accurately

Hey there, lovely ladies and fruity fellows! It’s your favorite Tutti Frutti Women talking to you, and we have a serious topic to discuss with you all. April is , and we need to spread the word about this sneaky disease that affects so many people every year. Esophageal Cancer Awareness Month Did you know that esophageal cancer is one of the fastest growing types of cancer in the world? It’s true! According to the American Cancer Society, there were over 19,000 new cases of esophageal cancer in the United States alone in 2020, and over 15,000 deaths from the disease. That’s a lot of people who need our help and support! occurs when abnormal cells develop in the lining of the esophagus, which is the tube that connects your throat to your stomach. There are two main types of esophageal cancer: squamous cell carcinoma, which occurs in the upper part of the esophagus, and adenocarcinoma, which usually develops in the lower part of the esophagus. Esophageal cancer for esophageal cancer include smoking, heavy alcohol consumption, being overweight, and having acid reflux. But don’t worry, there are plenty of ways to reduce your risk too! Eating a healthy diet full of fruits and veggies, staying active, and avoiding tobacco and excessive alcohol can all help keep your risk of esophageal cancer at bay. Risk factors Unfortunately, esophageal cancer often goes undetected until it reaches an advanced stage, which makes it more difficult to treat. That’s why it’s so important to you may be experiencing, like difficulty swallowing, chest pain, or unexpected weight loss. If you notice any of these symptoms, be sure to talk to your doctor right away. can make all the difference when it comes to beating this disease. pay attention to any symptoms Early detection options for esophageal cancer vary depending on the stage of the disease and other individual factors, but they may include surgery, chemotherapy, and radiation. Your doctor will work with you to determine the best course of action based on your individual case. Treatment But here’s the thing, ladies and gents — about esophageal cancer doesn’t have to be all doom and gloom. raising awareness That’s where comes in! We’re all about spreading the love and raising awareness in a fun and delicious way. Our NFT collection celebrates cancer warriors and survivors, and a portion of the proceeds goes toward cancer research and providing weekly health grants to members. Tutti Frutti Women By participating in our community and minting our NFTs, you can show your support for (esophageal) cancer awareness and help us make a difference in the fight against this disease. We’re a global community of warriors, survivors, and amazing people all united in the fight against cancer. So come join us, ladies and gents, and let’s spread the word about (esophageal) cancer in a way that’s just as sweet and vibrant as our tutti frutti colors! Remember, cancer awareness doesn’t have to be all doom and gloom. Let’s make it fun and creative! As part of our cancer awareness campaign, we invite you to create a visual that promotes awareness for esophageal, head and neck, or testicular cancer and tweet it using #TFWtalkscancer and tag @tfw_nft. Get your creative juices flowing and show us what you’ve got! Let’s work together to spread awareness and support cancer patients and survivors around the world! The best visual will win a #TFWnft as a token of our appreciation for raising awareness and making a difference. Sources: American Cancer Society. Key Statistics for Esophageal Cancer. Retrieved from https://www.cancer.org/cancer/esophagus-cancer/about/key-statistics.html Mayo Clinic. Esophageal Cancer. Retrieved from https://www.mayoclinic.org/diseases-conditions/esophageal-cancer/symptoms-causes/syc-20356084

Spreading the Word About Esophageal Cancer in a Delicious Way

Spread and rest operators are two of the most useful pieces of shorthand in JavaScript. Spread operators allow us to expand an array or object into its individual elements, while rest operators allow us to condense multiple elements into a single array or object. Spread operators are written using three consecutive dots (...), and they provide us with an easy way to break up an array or an object into its individual elements. For example, let’s say we have the following array: let array = [1, 2, 3, 4]; If we wanted to add a fifth element to our array, we could use the Spread Operator like this: let newArray = [...array, 5]; The Spread Operator will take our array, expand it into its individual elements (1, 2, 3, 4), and then add the fifth element (5). The result is a new array that is one element longer than the original: newArray = [1, 2, 3, 4, 5]; The rest operator is essentially the opposite of the spread operator. It collects multiple elements into an array. This is useful if you don’t know how many arguments a function may receive, and you want to capture all of them as an array. The spread and rest operators are useful for quickly and easily manipulating collections and arguments. They make code much cleaner and easier to read, as well as reduce the number of lines of code needed. They’re extremely versatile tools, and every JavaScript programmer should know how to use them.

 JavaScript Spread and Rest Operators: A Guide

The Javascript spread operator permits an array or string, or other iterable, to be expanded in places with zero or more arguments (for function calls) or elements. The syntax for this is three periods (…) In this article, I will explain the use cases of the spread operator in JavaScript, so let’s jump right to it. Use Cases Adding the elements of an existing array into a new array let newarr = ["Red", "Yellow"];
let otherarr = ["Blue", ...newarr, "Green"] 
console.log(otherarr); // ["Blue", "Red", "Yellow", "Green"]; So let’s explain what is happening here, is an array containing two strings and , then the second array otherarr contains two strings and the . Usually, when we add this array without the three periods operator it will add the new array into the other array. But as we have the spread operator it will add the element of the array into the other array. newarr Red Yellow newarr You can pass elements of an array as arguments to a function function addThreeNumbers(x,y,z){
   return x+y+z;
}
let args = [1,2,3];
addThreeNumbers(...args); // 6 Another use case is that you can pass an element of an array as a function. in the above example, we have a function with three parameters which return the sum of these three numbers. Now we have an array with three values 1,2,3. In a normal state, we can pass three arguments to the function but with the help of the spread operator, we just pass the array with the three-dot operator (…), which will spread out the array to each element of the function. addThreeNumbers x,y,z Note: If we have a fourth element in the array it going to ignore the fourth element in the array, like the example below. function addThreeNumbers(x,y,z){
   return x+y+z;
}
let args = [1,2,3,4];
addThreeNumbers(...args); // 6 . Copy arrays let arr1 = [1,2,3];
let arr2 = [...arr1] If we have arr1 we can use the spread operator to copy the array into the second array. Concatenate array let arr1 = [1,2,3];
let arr2 = [5,6,7];
arr1.concat(arr2); // [1,2,3,5,6,7]
arr1 = [...arr1 ...arr2]; // [1,2,3,5,6,7] Another use case is that we can concatenate arrays together, normally how we do this, is we use the method to concatenate arrays. With the spread operator, we can concatenate arrays too. concat Reference Also published here. Lead image generated with stable diffusion.

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