force process

FORCE PROCESS

By Henryk Szubinski

singularity of force

= velocity 3 / simultaneous 4

= 4 root Volume

= 3 differenciality of Volume Dv process extansionality

= gravity leveling by process uncertainty of 3 diff 4 D4

=process of sigmats 3 . sigmats 4

= process response simulations back to gravity leveling as seperated from vector value 1 retracer 3 S = 3 .4x

=12 x

process now breaks up into 2 vector value spreads into 4 vectors

=this type of vector will define the process compatability with vector value 3

=3/4 of the process by the remaining 1 /4 value

=4 root volume by INVERTED VALUE locators = 1 /4 x ( INV 4 F) to define the values of the force that would otherwise

= a singular 1/4 x 4

so that the force is a basis usage of a singularity of a volume 1 /F to try to make a differencial moove on a value process as uncertain as the value previous to division as a 4 to the x value = singularity

the type of reference made to a value velocity 3 / simultaneous 4

= can be the singular 1 or it can be a value that is reduced to a singularity by the process of the force that is to be defined as

Vel 3 /( 4 sim to the x)  = 1 /F ´

kinds of usage

a velocity can be simulated by making reference relations of the reduced values of the negative value decimals that result for the process of divisions as the value of reductions will define the reasons of why and how a velocity can be computatively mimicked by the process of the singularity formed by the values of the force between the process vectors as follows:

The Dewey Decimal Classification (DDC, also called the Dewey Decimal System) is a proprietary system of library classification developed by Melvil Dewey in 1876; it has been greatly modified and expanded through 22 major revisions, the most recent in 2003.^[1] This system organizes books on library shelves in a specific and repeatable order that makes it easy to find any book and return it to its proper place. The system is used in 200,000 libraries in at least 135 countries.^[2][3]

A designation such as Dewey 16 refers to the 16th edition of the DDC.

Uncertainty Reduction Theory was introduced in 1975 in the paper Some Exploration in Initial Interaction and Beyond: Toward a Developmental Theory of Interpersonal Communication. This theory, a collaborative effort of Charles R. Berger and Richard J. Calabrese, was proposed to predict and explain relational development (or lack thereof) between strangers.

The scope of the theory is narrowed down to rest on the premise that strangers, upon meeting, go through certain steps and checkpoints in order to reduce uncertainty about each other and form an idea of whether one likes or dislikes the other. To study this phenomenon, the interaction is viewed as going through several stages. Berger and Calabrese also introduce axioms and theorems regarding initial interaction behaviors.

Parallel evolution is the development of a similar trait in different not closely related species (that is in species of a different clade), but descending from the same ancestor

Evolution at an amino acid position. In each case, the left-hand species changes from incorporating alanine (A) at a specific position within a protein in a hypothetical common ancestor deduced from comparison of sequences of several species, and now incorporates serine (S) in its present-day form. The right-hand species may undergo divergent, parallel, or convergent evolution at this amino acid position relative to that of the first species.

Principal curves and manifolds

Principal curves and manifolds give the natural geometric framework for nonlinear dimensionality reduction and extend the geometric interpretation of PCA by explicitly constructing an embedded manifold, and by encoding using standard geometric projection onto the manifold. How to define the “simplicity” of the manifold is problem-dependent, however, it is commonly measured by the intrinsic dimensionality and/or the smoothness of the manifold.^[1]

[edit]Kernel Principal Component Analysis

Perhaps the most widely used algorithm for manifold learning is kernel PCA^[2]. It is a combination of Principal component analysis and the kernel trick. PCA begins by computing the covariance matrix of the  matrix 

It then projects the data onto the first k eigenvectors of that matrix. By comparison, KPCA begins by computing the covariance matrix of the data after being transformed into a higher-dimensional space,

It then projects the transformed data onto the first k eigenvectors of that matrix, just like PCA. It uses the kernel trick to factor away much of the computation, such that the entire process can be performed without actually computing . Of course Φ must be chosen such that it has a known corresponding kernel. Unfortunately, it is not trivial to find a good kernel for a given problem, so KPCA does not yield good results with some problems. For example, it is known to perform poorly with the swiss roll manifold.

KPCA has an internal model, so it can be used to map points onto its embedding that were not available at training time.

ANTI MATTER WAVEFORM OPPOSED

anti matter type

WAVEFORM OPPOSITIONS

By Henryk Szubinski

imagine getting back everything that was ever lost by increasing the size and distance between them.

This is the promise of anti matter for a case of a fallen body = to the sequenciality of its non falleen states.

THE VOLUME ALTERATIONS  MIMICK BY ANTI MATTER BY DISPLACEMENT AT HIGHER VELOCITIES SIMILAR TO CONTRACTION &/OR EXPANSIONS

data on the process of a 1 value singularity based on the functions of the data on 3 level vectors = to variance by usage on the values of the full type immersive volume =to the basis of electron values to which

G / e-

= process values of anti matter

S / e +

WHERE G a very large number defined as Grahams

so that the process values of the full formats of the data usage of the oppositions as effective in the displacements of oppsed vector values = Force (e+ S) 4

on the basis of the value to which the variance of curvature by waveforms and their introduced formats

values of waveform = 1/4 +  e + waveform

waveform opposed = similarity of 1/3 -e (waveform)

where both vectors are a 1/2 value formats of the data on the functionings of the process to which the 4 R = 3S

(e-S) +( G.e ) =

Force (e+ S) 4

so that :  1 + G.e =4

values of waveform  1 + G.e = (1/4 +  e + waveform)4

waveform opposed 1 + G.e = similarity of 1/3 -e (waveform)4

theese 2 equations are then:

common divisive  waveform / opposition +1 = 1/4 (1/3)+1

waveform / opposition = 2 .1/4 (1/3)

0.749925 = waveform / opposition

WHERE

3S = 4 R

4 R  (WAVE FORM / OPPOSITION)  = 3S 0.7499

2.2497 S = 4R  ( pfasor / opposition )

1.7503 S =R pfased opposition

the difference between the phasor and its format comparatives of the wave in sinusodiality as being accellerated to a position ahead of or behind of the 2 values of resistance and displacement so that the fase equalisations based on a resistance wave and a displacement wave will show what values of pfaseing are developed into a opposition where the basics of solidity would command that the process stops so that the differencial of this block = the pfasor responsible for the displacement through a solid field opposed by the displacement and the amount of the vector that has passed trough a material singularity as powerfull as its density multiple of a increased level of density upto the point of allowing a electron in positive guise to pass through the solid format as the type reliance of matterr density to the first contact with the similarity of electrons in orbit around the material atomic cores

Resistance ( wave ) e – Proton + Neutron= density + e (displacement ) opposed

as a basic elemental or chemical formulation the values of H2O can be exchanged in place of the eleectron values or their proton and neutron atomic values

H2O + wave (e)- p +n ———–heat————>density as H2O (s) + e – atomic bond (responsible for elongation  by displacement)+opposition as H2O (p)

1.75 is then the value of vector alterational velocity in compatability with the oppositions of plasma state H2O bond length and density

as  10 values

1) 1.75 e Vol  =10 ( velocity. displacement.plasma.elongations. oppositions.heat.cold.density.proton.neutron)

basically H2O in flow by friction to such large values that the absorbtional spectral is core related

meaning that the 10 value is related to beta decay or the process by which proton and neutron values are equally shared in a state of friction of H2O

In β^⁻ decay, the weak interaction converts a neutron (n) into a proton (p) while emitting an electron (e^⁻) and an electron antineutrino (ν_e):

n

→

p

+

e⁻

+

νe

At the fundamental level (as depicted in the Feynman diagram below), this is due to the conversion of a down quark to an up quark by emission of a W^⁻ boson; the W^⁻ boson subsequently decays into an electron and an electron antineutrino.

the friction of the H2O must be using beta decay

• Dry friction resists relative lateral motion of two solid surfaces in contact. Dry friction is also subdivided into static friction between non-moving surfaces, and kinetic friction (sometimes called sliding friction or dynamic friction) between moving surfaces.

• Lubricated friction^[1] or fluid friction^[2][3] resists relative lateral motion of two solid surfaces separated by a layer of gas or liquid.

• Fluid friction is also used to describe the friction between layers within a fluid that are moving relative to each other.^[4][5]

• Skin friction is a component of drag, the force resisting the motion of a solid body through a fluid.

• Internal friction is the force resisting motion between the elements making up a solid material while it undergoes deformation.^[5]

An example of applying Absorption spectroscopy is the first direct detection and chemical analysis of the atmosphere of a planet outside our solar system in 2001. Sodium fliters the alien star light of HD 209458as the hot Jupiter planet passes in front. The process and absorption spectrum are illustrated above. Image Credit: A. Feild, STScI and NASAwebsite.

10 ( H2O ) anti matter = electron voltage 1.75

because resistance is being induced meaning heat is leaving the system , the values of heat are based on the heat leaving the H2O (f) meaning that the freeze is causing the reaction:

H2O (f) e-p+n————-heat =—-cold —————>H2O (s) +e + (x+ Bond S) H2O (anti matter)

it is this heat /cold value that is responsible for the fluid  fase  cause of the H2O (s) state with the input of electron proton neutron cooling that is the resultance of the lectron being remnant in the freeze state and its expansion by the opposed values which would in normal situations cause contraction by cold so that the lectron positive anti matter can be located.Actually the displacement increase can be the effect of the process in contractions where the volume releases a type of volume sustainement dynamics of the process

displacement +1 / displacement -1

or

10 p.n pairs (1 anti neutrino displacement +1 / anti neutrino displacement -1) = 1.75 e+ Vol

= a mean value curve that will define the increase of displacement as a  function of x values not the increase or decrease of volume

so that the volume quantality = displacement x mean value vector

The heat death is a suggested final thermodynamic state of the universe, in which it has “run down” to a state of no thermodynamic free energyto sustain motion or life. In physical terms, it has reached maximum entropy. The hypothesis of a universal heat death stems from the 1850s ideas of William Thomson, 1st Baron Kelvin who extrapolated the theory of heat views of mechanical energy loss in nature, as embodied in the first two laws of thermodynamics, to universal operation.

Phasors

Main article: Phasor (electronics)

A phasor is a constant complex number, usually expressed in exponential form, representing the complex amplitude (magnitude and phase) of a sinusoidal function of time. Phasors are used by electrical engineers to simplify computations involving sinusoids, where they can often reduce a differential equation problem to an algebraic one.

The impedance of a circuit element can be defined as the ratio of the phasor voltage across the element to the phasor current through the element, as determined by the relative amplitudes and phases of the voltage and current. This is identical to the definition from Ohm’s law given above, recognising that the factors of  cancel.

[edit]Device examples

The phase angles in the equations for the impedance of inductors and capacitors indicate that the voltage across a capacitor lags the current through it by a phase of π / 2, while the voltage across an inductor leads the current through it by π / 2. The identical voltage and current amplitudes tell us that the magnitude of the impedance is equal to one.

The impedance of an ideal resistor is purely real and is referred to as a resistive impedance:

Ideal inductors and capacitors have a purely imaginary reactive impedance:

Note the following identities for the imaginary unit and its reciprocal:

Thus we can rewrite the inductor and capacitor impedance equations in polar form:

The magnitude tells us the change in voltage amplitude for a given current amplitude through our impedance, while the exponential factors give the phase relationship.

vertical dialations

vertical dialations

By Henryk Szubinski

WHAT IS A EQUATABILITY OF MASS GRAVITY HEIGHT BY CUBIC VOLUMES AS A SINGULAR DISPLACEMENT VALUE

LOCATING A MULTIPLE VECTOR SPACE WHICH WILL ALTER WARPING OF THE PROCESS LIFT DIFFERENCE BETWEEN MASS AND GRAVITY SHARED IN A 1 VALUE VECTOR SPACE =THE HEIGHT OF A OBJECTIVE HIGHER VELOCITY LINK TO A SINGULAR DISPLACEMENT AS THE TYPE OF FUSED GRAVITY INVOLVED WITH BLACK HOLES AND THE GRAVITY EFFECT.

2 =h.m

based on

216 m cubed = S

as well as :

sum Diff =648 /h.g.m Dm

1296 /648 = waveform of the differencial divisive g 6 cubed

will define the altered divisives of gravity by mass values in their height responsive multiples so that a falling body with mass m will alter its fallen trajectory by a String which defines the absolute gravity attractions by super mass relations to height values:

Basically a m will not alter its vector value verticie in a instance of fallen vector value in a black hole:so that reversing the divisive

648 /1296

=0.5 g squ m h

the relations are then

h.m = 0.5 g squ h.m

1 =0.5 g squared

In mathematics, a dilation is a function f from a metric space into itself that satisfies the identity

for all points (x,y) where d(x,y) is the distance from x to y and r is some positive real number.

In Euclidean space, such a dilation is a similarity of the space. Dilations change the size but not the shape of an object or figure.

Every dilation of a Euclidean space that is not a congruence has a unique fixed point that is called the center of dilation. Some congruences have fixed points and others do not.

continuiims of the retractability of continuous responses to the actual zone = the definitions against the process of a non parameter as the isolations of the data source = to the continuous reversals of the vector force into the type 1

LIGHT SABRE of a plasma value:

warp drives and anti gravity.

the approaches to light speed of cylinder vertical relativity of the dialated loss of the h 4  vector by velocity S3 in the relations of what displacement the m1 does and the amount of displacement the m 2 does

S3 m1 S3 =h.h /4

S9 squ = h squ /4

differencially then:

S9 (h squ )/4 / h squ /4 <——D4

S9 /4 —–>D4

S9 /4 =h squ

reasons:

basics of the alterations by gravity levels:

x / h

and its responsive :

h /x

xh+ h squ /x.h

=h squ

= S9/4

where S9/4 = m1.m2 .m3

will define the mass 4 value with its continuiim conservations of mass by displacement

m6 cubed (4) = S9

m24 cubed = S 9

216 m cubed = S

as the S = 1

m alters into a vector p value

In mathematics and statistics, a probability vector or stochastic vector is a vector with non-negative entries that add up to one.

The positions (indices) of a probability vector represent the possible outcomes of a discrete random variable, and the vector gives us the probability mass function of that random variable, which is the standard way of characterizing a discrete probability distribution.

Here are some examples of probability vectors:

Writing out the vector components of a vector p as

the vector components must sum to one:

One also has the requirement that each individual component must have a probability between zero and one:

for all i. These two requirements show that stochastic vectors have a geometric interpretation: A stochastic vector is a point on the “far face” of a standard orthogonal simplex. That is, a stochastic vector uniquely identifies a point on the face opposite of the orthogonal corner of the standard simplex.

as the basic invertions of what a mass will do by displacements that define the vector value of related weight influences on the amount of displacement made as a basic singularity that excludes the velocity into ONLT mass and displacement values:

defining the gravity formats of the mass objects and the resultance of the  neutralisations of their value relations to mass gravity waveforms STRINGS

data on the process of the divisive levels of a displace dialations cylinder = the 1/2 way point of the objective velocity of light to the process values of the run completed by the prime mass object = m3

as being in the dialations zone of the accellerated format mass in a specific value height= to the process of 1/2 of the cylinder having gone through the process of the tube dialations = to the loss of the light speed value of the cylinder in relations to the force of the process by which the responsive state of the data singularity of the relations of the m 1 and the m3 as the value process of mass objects mooving away to or towards each other in a dialations vector value = 1/4 m 3

=the h as the value = S1

as the data on the x  level of h 1,h2,h3,h3+1 = S1

where the subtractions of

h1/h2 ( m 1 m2) 1/2 = S

basic definitions of displacement as being the influence of m fallen state values and the gravity relations of such influence on motion symmetry by

216 m cubed = S

or what is defined as large sequences of mass to gravity to height as the process in its 216 value relations in multiples which can be located by a differencial usage of any value

Diff = 216 /x .g Dg

Diff 216 / h.g Dg

Diff 216 /h.m Dm

sum Diff =648 /h.g.m Dm

In multilinear algebra and tensor analysis, covariance and contravariance describe how the quantitative description of certain geometric or physical entities changes when passing from onecoordinate system to another. The components of a geometrical vector and of a dual vector can be measured with respect to a given basis.

• For a vector (such as a direction vector or velocity vector) to be coordinate system invariant, the components of the vector must contra-vary with a change of basis to compensate. That is, the components must vary in the opposite “direction” (the inverse transformation) as the change of basis. Vectors (as opposed to dual vectors) are said to be contravariant. Examples of contravariantvectors include the position of an object relative to an observer, or any derivative of position with respect to time, including velocity, acceleration, and jerk. In Einstein notation, contravariant components have upper indices as in

.

• For a dual vector, (such as a gradient) to be coordinate system invariant, the components of the vector must co-vary with a change of basis to maintain the same meaning. That is, the components must vary by the same transformation as the change of basis. Dual vectors (as opposed to vectors) are said to be covariant. Examples of covariant vectors generally appear when taking a gradient of a function (effectively dividing by a vector). In Einstein notation, covariant components have lower indices as in

.

In physics, vectors often have units of distance or distance times some other unit (such as the velocity), whereas covectors have units the inverse of distance or the inverse of distance times some other unit. The distinction between covariant and contravariant vectors is particularly important for computations with tensors, which often have mixed variance. This means that they have both covariant and contravariant components, or both vectors and dual vectors. The valence or type of a tensor is number of variant and covariant terms. The duality between covariance and contravariance intervenes whenever a vector or tensor quantity is represented by its components, although modern differential geometry uses more sophisticated index-free methods to represent tensors.

The terms covariant and contravariant were introduced by J.J. Sylvester in 1853 in order to study algebraic invariant theory. In this context, for instance, a system of simultaneous equations is contravariant in the variables. The use of both terms in the modern context of multilinear algebra is a specific example of corresponding notions in category theory.