ChristoAnd.com

Author: Chris

  • 16 Similar Haitian Creole and Spanish Words

    Every Haitian Creole and Spanish word on this list have the same definitions, although they may not be spelt the exact same way, they at least have similar pronunciations from one another. This is usually because the French-based Creole and Spanish share similar words from previous descended languages.

    Something I did to make this post unique was that I made sure that the Creole and Spanish equivalent of the word did NOT comparably resemble the word in English. (Ex. dola, dólar, dollar, or nimewo, número, number) — That list is for another time.

    All numbered words contain footnote comments located below!

    Haitian CreoleSpanishEnglish
    di 1decir 1to say
    lavelavarto wash
    lakay 2la casa 2house
    mal, malad 3mal, malo 3bad, ill
    fasilfácileasy 4
    menmanohand
    platplanoflat
    manton anla mentónchin
    kouri 5correr 5to run
    vinivenirto come
    travaytrabajarto work
    konpranncomprenderto understand
    lileerto read
    premyeprimerfirst
    si 6si 6if
    dòmidormirto sleep

    1 ‘di’ and ‘decir’ are very easily to click towards for speakers of both languages.
    “¿Cómo dices esto?” and “Koman ou di sa?” both translate to: How do you say this?

    2 ‘lakay’ and ‘la casa’ both are almost pronounced alike.

    3 ‘mal’ in both Creole and Spanish mean bad, or wrong. Meanwhile, ‘malad’ and ‘malo’/’mala‘ mean to sick or ill.

    4 ‘facile’ is also a word in English derived from the same Latin roots. But I rarely ever hear the word in English, so i’ll just use the more popular word of the two which is unanimously: ‘easy’

    5 The pronunciation difference for ‘kouri’ and ‘correr’ may be a bit of a stretch due to the distinctive trill of Spanish’s double R (“rrrrr”) and Haitian Creole’s particularly soft R (phonetic symbol of … ɣ or /ɰ/). But I still found the distinction close enough to add it to this list. I recommended this video: https://www.youtube.com/watch?v=eacLGstVSBY from Learn Haitian Creole w/ Fè to learn more.

    6 Crazy to think that two languages have a word that is spelt exactly the same and means exactly the same thing? ‘If’ in Spanish can also translate to ‘a ser’ or ‘cuando’ in some cases.

  • Haitian Creole place prepositions

    KreyòlEnglish
    An/NanOn, over
    AnbaUnder
    AntravèThrough
    A koteBeside
    DèyèBehind
    Kote/Bò koteNext (to)
    PamiThrough/In the middle
    An dedan/DedanInside
    AnfasIn front
    DevanAhead
    DeyòOutside
    LwenFar
    PreClose

  • Haitian Creole, Interrogative Words

    EnglishKrèyol Ayisyen
    cankapab / ka
    howkijan, kouman, kòman
    whatki sa / ki
    whenki lè / lè
    whypoukisa
    wherekote / ki kote / ki bò
    whetherte mèt
    whether (or not)wè pa wè*
    whichkilès
    who/whomki moun, ki yès
    whoseki gen, pou ki moun
    *wè pa wè is used more as a figure of speech

    Special thanks from sweetcoconuts.blogspot.com.

  • Types of Stresses to Consider

    cylindrical, thin-walled vessel

    circumferencial, or hoop stress

    \begin{aligned} \sigma_{1} =\sigma_{hoop} = \frac{Pr}{t} \end{aligned}

    P = internal pressure
    r = the radius of the cylinder, (the the outer edge–including thickness)
    t = thickness of wall, (r/t ≥ 10)

    longitidunal hoop stress

    \begin{aligned} \sigma_{2} = \frac{Pr}{2t} = \frac{\sigma_{hoop}}{2}\end{aligned}

    P = internal pressure
    r = the radius of the cylinder, (the the outer edge–including thickness)
    t = thickness of wall, (r/t ≥ 10)

    circular, thin-walled vessel

    circumferencial, or hoop stress

    \begin{aligned} \sigma_{\circ-1} =\sigma_{\circ-hoop} = \frac{Pr}{2t} \end{aligned}

    P = internal pressure
    r = the radius of the cylinder, (the the outer edge–including thickness)
    t = thickness of wall, (r/t ≥ 10)

    longitidunal hoop stress

    \begin{aligned} \sigma_{\circ-2} = \frac{Pr}{4t} = \frac{\sigma_{\circ -hoop}}{2}\end{aligned}

    P = internal pressure
    r = the radius of the cylinder, (the the outer edge–including thickness)
    t = thickness of wall, (r/t ≥ 10)

    σnormal

    \begin{aligned} \sigma_{N} = \frac{P}{A}\end{aligned}

    P = axial applied load, or normal force
    A = cross sectional area

    τat a point

    \begin{aligned} \tau = \frac{VQ}{It}\end{aligned}

    V = shear force at the cross-sectional cut, or at point
    Q = ȳ’A’, or first moment of area (consider above point, or below)
    I = Moment of Inertia of cross-sectional cut
    t = thickness at the focal point/point of interest

    torsional stress

    \begin{aligned} \tau = \frac{Tc}{J} = \frac{Tr}{J}\end{aligned}

    T = torque applied (axially)
    c = distance from center of shaft to point
    r = disance from center of shaft to outer edge of shaft (radius)
    J = polar moment of inertia of shaft (is cross-section solid, or hollow?)

    torsion equation

    \begin{aligned} \frac{\tau_{max}}{r} = \frac{T}{J} = \frac{G\theta}{L}\end{aligned}

    τmax = maximum shear
    T = torque applied (axially)
    r = disance from center of shaft to outer edge of shaft (radius)

    G = shear modulus
    J = polar moment of inertia of shaft (is cross-section solid, or hollow?)
    θ = angle of twist
    L = length of beam

    angle of twist

    \begin{aligned} \theta = \frac{TL}{JG}\end{aligned}

    T = torque applied (axially)
    c = distance from center of shaft to point
    r = disance from center of shaft to outer edge of shaft (radius)
    J = polar moment of inertia of shaft (is cross-section solid, or hollow?)

  • Shape Factor

    The shape factor is a function of the cross section geometry and is a measure of how efficiently the material is used to resist bending.

    k=\frac{M_P}{M_Y}

    The moment at yield, 𝑀𝑌, is given by

    M_Y=\frac{P_{Y}L}{4}

    The plastic moment, 𝑀𝑃, is given by

    M_P=\frac{P_{P}L}{4}

  • Mean and Variance of Geometric Distribution

    \begin{aligned}&

 
\textcolor{orange}{k}=\textmd{no. of trials}\\&

 
\textcolor{blue}{p}=\textmd{probability}


\end{aligned}
    Probability Mass Function
    \begin{aligned} P(X=\textcolor{orange}{k})=\textcolor{blue}{p}(1-\textcolor{blue}{p})^{\textcolor{orange}{k}-1} \end{aligned}
    Cumulative Distribution Function
    \begin{aligned} &P(X\leq\textcolor{orange}{k})=1-(1-\textcolor{blue}{p})^{\textcolor{orange}{k}} \\ \\ &

P(X \geq \textcolor{orange}{k})=(1-\textcolor{blue}{p})^{\textcolor{orange}{k}-1}

\\ \\&

P(X > \textcolor{orange}{k})=1-P(X\leq\textcolor{orange}{k})=(1-\textcolor{blue}{p})^{\textcolor{orange}{k}}

\end{aligned}
    Mean
    \begin{aligned} &\mu =E(X)=\frac{1}{\textcolor{blue}{p}}
\end{aligned}
    Variance
    \begin{aligned} \sigma^2=V(X)=\frac{(1-\textcolor{blue}{p})}{\textcolor{blue}{p}^2}
\end{aligned}
    Standard Deviation
    \begin{aligned} \sigma=\sqrt{\sigma^2}
\end{aligned}

  • Hypothesis Testing

    Because we are interested in the average (as denoted by the use of x bar in the problem); we use the following z-score formula:

    Z=\frac{\bar{X}-\mu}{\sigma/\sqrt{n}}

    When using MS Excel—
    Use NORM.DIST
    input…

    X = …
    mean = 0
    stndrd_dev = 1
    cumulative = TRUE

    output = 0.00135

  • Bayes’ Theorem

    \begin{aligned} P(A|B) =\frac{P(B|A)\times P(A)}{P(B)}=\frac{P(A∩B)}{P(B)}\end{aligned}
    \begin{aligned} P(B|A) =\frac{P(A|B)\times P(B)}{P(A)}=\frac{P(B∩A)}{P(A)}\end{aligned}
    \begin{aligned} P(A∩B)=P(B∩A)\end{aligned}

  • Reading and Understanding Normal Distributions on z-score

    Statistical models are used to identify, analyze, and quantify the potential risks by using the probability theory. This will enable engineers to understand the risks induced in a specific project, and subsequently prepares those same engineers to take effective measures to mitigate the risks as much as possible.

    Note: We are going to use two different Normal Distribution formulas. The Standard Normal Distribution and the (Unstandard) Normal Distribution.

  • Poisson Random Distribution formula

    x = 0, 1, 2, 3, …
    λ = mean number of occurrences in the interval
    e = Euler’s constant (2.71828…)

    \begin{aligned} P(X={x})=\frac{\lambda^xe^{-\lambda}}{x!}\end{aligned}