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This research examined the influence of Moon’s gravitational-wave to Earth’s global warming process and the effects of time-trend and cyclic change of the distance between Moon and Earth. In the pervious research [1], we found that the Moon’s gravitational-wave could influence the process of the Ear...
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| description | This research examined the influence of Moon’s gravitational-wave to Earth’s global warming process and the effects of time-trend and cyclic change of the distance between Moon and Earth. In the pervious research [1], we found that the Moon’s gravitational-wave could influence the process of the Earth’s global warming; and, we also found that Moon’s cyclic movement around Earth needed to be further investigated, because it gave a unique pattern of distribution in the data for the empirical analysis; while both global temperature and global carbon-dioxide increase almost linearly in the time-series. In this research we added dummy binary variables that simulate the trend of time and the cyclic changes. As a result we confirmed that the influence of Moon’s gravitational-wave is significant in the process of rising global temperature on Earth. |
| doi_str_mv | 10.20535/SRIT.2308-8893.2018.3.02 |
| first_indexed | 2025-07-17T10:24:10Z |
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Yoshio Matsuki, Petro I. Bidyuk, 2018
Системні дослідження та інформаційні технології, 2018, № 3 19
УДК 519.004.942
DOI: 10.20535/SRIT.2308-8893.2018.3.02
ANALYSIS OF MOON’S GRAVITATIONAL-WAVE AND
EARTH’S GLOBAL TEMPERATURE: INFLUENCE OF TIME-
TREND AND CYCLIC CHANGE OF DISTANCE FROM MOON
YOSHIO MATSUKI, PETRO I. BIDYUK
Abstract. This research examined the influence of Moon’s gravitational-wave to
Earth’s global warming process and the effects of time-trend and cyclic change of
the distance between Moon and Earth. In the pervious research [1], we found that
the Moon’s gravitational-wave could influence the process of the Earth’s global
warming; and, we also found that Moon’s cyclic movement around Earth needed to
be further investigated, because it gave a unique pattern of distribution in the data
for the empirical analysis; while both global temperature and global carbon-dioxide
increase almost linearly in the time-series. In this research we added dummy binary
variables that simulate the trend of time and the cyclic changes. As a result we con-
firmed that the influence of Moon’s gravitational-wave is significant in the process
of rising global temperature on Earth.
Keywords: global temperature, Moon’s gravitational-wave, trend removal, cyclic
change.
INTRODUCTION
Our previous research [1] investigated the influence of Moon’s gravitational-wave
to the process of Earth’s global warming with the methodology of empirical anal-
ysis with the database of Earth’s global temperature and global carbon dioxide as
well as the distance between Moon and Earth. Then, the result of the analysis
suggested that there was a possibility such that Moon’s gravitational-wave influ-
enced Earth’s atmospheric temperature than global carbon dioxide could do.
However, the uncertainty of the analysis [1] was also large, due to the cyclic
change of the distance between Moon and Earth. In the previous research [1], we
attempted to reduce this uncertainty, by assuming pure-heteroskedasticity and the
first-order autoregressive process of Generalized Classical Regression models;
however, we didn’t know if these assumptions were appropriate in order to ex-
plain the cyclic change of the distance between Moon and Earth.
Considering the above result [1], in this research, we continued the empirical
analysis of the same database with different techniques: maximum-likelihood es-
timation, trend removal, and removal of the influence of the cyclic change of the
distance between Moon and Earth, by adding binary variables.
The gravitational-wave was a theoretical possibility when we made the pre-
vious research [1]; also, we didn’t calculate the intensity of the gravitational-
wave. Instead, we used the inverse of the squared distance between Moon and
Earth as the surrogate of the gravitational-wave, because our mathematical meth-
od uses the deviations of the values of the variables, not necessarily the intensities
of physical energy.
Yoshio Matsuki, Petro I. Bidyuk
ISSN 1681–6048 System Research & Information Technologies, 2018, № 3 20
METHOD
The descriptive statistics of the data, from 1987 till 2009, of the global tempera-
ture (increased degree Celsius since 1978) [2], the global carbon dioxide (million
tons) [3], the distance between Moon and Earth ( r : kilometers) [4], and calcu-
lated
2
1
r
((kilometers)--22)),, are shown in Table 1.
T a b l e 1 . Descriptive statistics
Variable
Global
Temperature
oC *
CO2
mil. tons**
Distance between
Moon and Earth
r, km
2
1
r
,
km22
Mean 0,29130 1,25165 · 103 3,62618 · 105 7,60509 · 10-12
Standard
deviation 0,12125 2,14245 · 102 5,98411 · 102 2,51097 · 10-14
Minimum 0,10000 8,92000 · 102 3,61583 · 105 7,56999 · 10-12
Maximum 0,43000 1,62600 · 103 3,63483 · 105 7,64865 · 10-12
Skewness -0,21063 0,14292 -0,15249 0,15787
Kurtosis 1,29401 1,82491 1,67498 1,67879
Valid number
of observations 23 23 23 23
* Increased degree Celsius since 1978.
** To convert these estimates to units of carbon dioxide (CO2), simply multiply these es-
timates by 3,667 [3].
Analysis is made on the global temperature, the global CO2 and
2
1
r
, with
the following methods:
1. Maximum Likelihood Estimation. This method is an alternative ap-
proach, beside the Least Squares Estimation of Linear Classical Regression Mod-
el. The global temperature },,{ 1 nyyY , the constant value 1 ( 1x ), the meas-
ured global CO2 )( 2x , and
2
1
r
( 3x ), are transformed into the forms
of 1n vectors, y , 1x , 2x , 3x , where n is the number of observation, 23. Then
kn matrix },,{ 321 xxxX is defined, where 3)(rank Xk and X is non-
stochastic. And, we assume that the data in Table 1 are samples from a real na-
ture, which are multivariate normally distributed i.e. ),(~ 2IXNY , where
)(YEX , )(2 YVI , I is a unit matrix whose diagonal elements
are 1, and non-diagonal elements are 0, and )(YE is a mean value of Y ( 2ii
for all i , and that 0hi for all ih ). And
2
exp)2()( 2
1
2 w
Yf
n
,
where 1'w , Y ,
)det(
1
2
1
, and in this model, I
2
1 1
,
n)( 2 , XY .
Analysis of Moon’s gravitational-wave and Earth’s global temperature: …
Системні дослідження та інформаційні технології, 2018, № 3 21
And then
2
222
2
'
exp)()2()(
nn
Yf . Now, the Maximum Likelihood
estimates of and 2 are the values that maximize
)2log(
2
log
n
L
2
2 '
2
1
)log(
2
n
. Then L is maximized by minimizing ' with respect
to . So, is identical to the coefficients of the Least Squares Estimation of Lin-
ear Classical Regression Model ([1]). Now, inserting solution value for makes
ee'' , with XbYe , which leaves the “concentrated log-likelihood func-
tion”, as
2
222* '
2
1
)log(
2
)2(log
2
),()(
eenn
bLL , to be maxi-
mized with respect to 2 . The first derivative is
422
'
2
1)2(*
eenL
.
Equating
2
*
L
to zero and solving it gives the Maximum Likelihood estimator
of 2 as
n
ee'
.
2. Trend Removal. At first, we define 11 x and tx 2 , where t is a series
of time. (Here we simply use a series of the values from 1 to 23 as the values of
t ). Then },{ 211 xxX and },{ 432 xxX , where 3x is the measured global CO2,
and 4x is the
2
1
r
. And then, we calculate the residuals *
2X from the regression of
2X on 1X , following the matrix algebra bellow:
111 ' XXQ , where '1X is a transposed matrix of the matrix 1X ;
21
1
1 ' XXQb , where 1
1
Q is an inversed matrix of the matrix 1Q ;
bXX 22
ˆ ;
22
*
2 X̂XX .
Now *
2X is the de-trended values of },{ 432 xxX . We also calculate the de-
trended values of Y (global temperature), by calculating YbY ˆ , and then
YYY ˆ* , where *Y is the de-trended values of Y .
And then, we implement the Least Squares Estimation of Linear Classical
Regression Model of the de-trended global temperature *Y over *
2X , with the
following steps:
*
2
*
2
* ' XXQ ;
**
2
1*
1 'YXQb
;
1
**ˆ bYY : expected de-trended global temperature Y ;
** ŶYe ;
Yoshio Matsuki, Petro I. Bidyuk
ISSN 1681–6048 System Research & Information Technologies, 2018, № 3 22
1*
11
'
)(
Q
kn
ee
bV .
And square-root of the diagonal elements of )( 1bV are the standard errors of
elements of the estimated coefficient-vector, 1b .
3. Removal of Seasonal (cyclic) Influence. Moon and Earth became closer
every 4 years as shown in Fig. 1. In order to remove (de-seasonalize) the influ-
ence of the cyclic pattern from the explanatory variables (the measured global
CO2 and
2
1
r
), at first, we define four binary dummy variables:
otherwise 0
20072003,1999,1995,1991, 1987, in 1
1x ;
otherwise 0
20082004,2000,1996,1992,1988, in 1
2x ;
otherwise 0
20092005,2001,1997,1993,1989, in 1
3x ;
otherwise0
20062002,1998,1994,1990,in 1
4x .
Then we set },,,{ 43211 xxxxX . Then we calculate the Least Squares Esti-
mation of Linear Classical Regression Model of the global temperature Y over
1X in order to get residuals YYY ˆ* , with the following steps:
11' XXQ ;
YXQb '1
1*
1
;
*
11
ˆ bXY : expected global temperatureY ;
YYY ˆ* .
Now the elements of *Y are the de-seasonalized values of global tem-
perature.
And then we remove the influence of the cyclic change of the distance be-
tween Moon and Earth from the measured global CO2 ( 2z ), and
2
1
r
( 3z ). For this
purpose, at first, we regress },{ 324 zzX on the dummy variables 1X , to get the
coefficient 41
1
11 )'( XXXXF and residuals FXXX 14
*
4 , which removes
the influence of the cyclic change of the distance between Moon and Earth
from 4X . Then we make the Least Squares Estimation of the de-seasonalized
variable *Y on the de-seasonalized explanatory variables *
4X , from which the
influence of cyclic change of the distance between Moon and Earth has been re-
moved, with the following steps:
Analysis of Moon’s gravitational-wave and Earth’s global temperature: …
Системні дослідження та інформаційні технології, 2018, № 3 23
*
4
*
45 ' XXQ ;
*'*
4
1
55 YXQb ;
5
*
45 * bXYe ;
1
5
55
5
'
)(
Q
kn
ee
bV .
And square-root of each diagonal element of )( 5bV is the standard error of
each element of the estimated coefficient-vector 5b .
RESULTS
1. Result of Maximum Likelihood Estimation. Table 2 and Table 3 show
the result of the Maximum Likelihood Estimation.
T a b l e 2 . Maximum Likelihood Estimation: coefficients and standard errors
Variable Coefficient Standard error*
Intercept (1) -1,17897 1,67861 · 103
CO2 5,32537 · 10-4 2,95105 · 10-2
2
1
r
1,05675 · 1011 2,19071 · 1014
*Each standard error of each coefficient is square-root of diagonal element in Table 3.
T a b l e 3 . Variances and Covariances of Maximum Likelihood Estimation
Variable Intercept CO2 2
1
r
Intercept (1) 2,81774 · 106 -16,03004 -3,67640 · 1017
CO2 -16,03004 8,70867 · 10-4 1,95292 · 1012
2
1
r
-3,67640 · 1017 1,95292 · 1012 4,79922 · 1028
2. Result of Trend Removal. At first, we made the regression of 2X on 1X .
The calculated coefficient b is shown in Table 4.
D
is
ta
nc
e,
k
m
Fig. 1. Distance between Moon and Earth [1]
Yoshio Matsuki, Petro I. Bidyuk
ISSN 1681–6048 System Research & Information Technologies, 2018, № 3 24
T a b l e 4 . Computing b: 21
1
1 XXQb
8,75075 · 102 7,60732 · 10-12
31,38142 -1,85867 · 10-16
And then we calculated the de-trended values of },{ 432 xxX , where 3x is
CO2 and 4x is 2/1 r , by calculating bXX 22
ˆ , and then 22
*
2 X̂XX ,
where *
2X is the de-trended values of 2X . We also calculated the de-trended
values of Y (global temperature), by calculating YbY ˆ , and then YYY ˆ* ,
where *Y is the de-trended values of Y . The descriptive statistics of the adjusted
(de-trended) values ( *Y and *
2X ) are shown in Table 5. The global temperatures
before and after the removal of trend are shown in Fig. 2, and the values of CO2
and 2/1 r before and after the removal of trend are shown in Fig. 3.
T a b l e 5 . Descriptive statistics of de-trended values of global temperature,
CO2 and 2/1 r
Variable
Global
Temperature
o
C
CO2, mil. tons 2/1 r , km22
Mean -1,48809 · 10-9 -7,25622 · 10-8 -9,10100 · 10-21
Standard deviation 3,07795 · 10-2 24,50602 2,50781 · 10-14
Minimum -6,15217 · 10-2 -33,79644 -3,59113 · 10-14
Maximum 4,22332 · 10-2 60,53360 4,26366 · 10-14
Skewness -0,28508 0,41789 0,14935
Kurtosis 1,89185 2,41821 1,66733
Valid number
of observations 23 23 23
Fig. 2. Global temperatures with and without trend removal ( *Y and Y )
Analysis of Moon’s gravitational-wave and Earth’s global temperature: …
Системні дослідження та інформаційні технології, 2018, № 3 25
And then we calculated the Least Squares Estimation of the de-trended glob-
al temperature *Y over *
2X . At first, we calculated *
2
*
2
* ' XXQ , and
**
2
1*
1 YXQb
. Table 6 shows the calculated 1b .
T a b l e 6 . Comprting 1b : **
2
1*
1 YXQb
Variable Values of 1b
Intercept* for 11 x 1b : 7,94412 · 10-2
Intercept* for tx 2 1b : 1,76544 · 10-2
CO2 -1,15220 · 10-5
2/1 r 1,89763 · 109
*To calculate 1b for the intercepts (each of 1x and 2x ), we calculated YXQb 1
1
1
* , and
then 21
1
1 XXQF , and then 1
*
1 Fbbb .
And then we calculated ** ŶYe and
1*
11
'
)(
Q
kn
ee
bV to calculate the
standard errors of elements of the estimated coefficient-vector 1b . Table 7 shows
the calculated values of )( 1bV , and Table 8 shows the calculated values of stan-
dard errors of 1b .
T a b l e 7 . Comprting V: )( 1bV
Variable CO2 2/1 r
CO2 1,02316 · 10-9 -1,68386 · 105
2/1 r -1,68386 · 105 2,84905 · 1019
CO2 Detrented CO2
1/r2
Detrented 1/r2
Detrented 1/r 2
1/r 2
CO2
Fig. 3. Comprting CO2 and 2/1 r before and after trend removal ( 4X and *
4X )
Yoshio Matsuki, Petro I. Bidyuk
ISSN 1681–6048 System Research & Information Technologies, 2018, № 3 26
T a b l e 8 . Standard errors of 1b
Variable Standard errors of 1b
Intercept* for 11 x 1,39138 · 10-2
Intercept* for tx 2 1,01477 · 10-3
CO2 3,19869 · 10-5
2/1 r 5,33765 · 109
*To calculate standard errors for the intercepts ( 1x and 2x ), we calculated
1
1
111 XQXN and *
111 )( YNIe , 1
1
11
1
'
)(
Q
kn
ee
bV , and then calculated the
square root of the diagonal element of )( 1bV .
3. Result of Removal of Seasonal (cyclic) Influence. At first, we
set },,,{ 43211 xxxxX . Then, we calculated the Least Squares Estimation of the
global temperature Y over 1X in order to get the de-seasonalized values of global
temperature YYY ˆ* (Fig. 4), after calculating 11' XXQ , YXQb '1
1*
1
(Table 9), and *
11
ˆ bXY .
T a b l e 9 . Comprting *
1b : YXQb 1
1*
1
Variable 1x 2x 3x 4x
Coefficient 0,27500 0,29000 0,30500 0,29600
And then, we implemented the Least Squares Estimation of 4X on the dum-
my variables 1X , to get the coefficient 41
1
11 )( XXXXF (Table 10) and re-
Fig. 4. Global temperatures with and without seasonal adjustment ( *Y and Y )
Analysis of Moon’s gravitational-wave and Earth’s global temperature: …
Системні дослідження та інформаційні технології, 2018, № 3 27
siduals FXXX 14
*
4 (Fig. 5), to de-seasonalize 4X (to remove the influence
of the cyclic change of the distance between Moon and Earth from 4X ).
( },{ 324 zzX , where 2z is the measured global CO2, and 3z is 2/1 r ). Table 11
shows descriptive statistics of de-seasonalized values of global temperature, CO2
and 2/1 r .
T a b l e 1 0 . Coefficient F
Variable CO2 2/1 r
1x 1,21717 · 103 7,60012 · 10-12
2x 1,26083 · 103 7,58701 · 10-12
3x 1,27717 · 103 7,61690 · 10-12
4x 1,25140 · 103 7,61857 · 10-12
T a b l e 1 1 . Descriptive statistics of de-seasonalized values of global tempera-
ture, CO2 and 2/1 r
Variable
De-seasonalized global
temperature, oC
De-seasonalized
CO2, mil. tons
De-seasonalized 2/1 r ,
km
Mean -3,64431 · 10-10 -5,80497 · 10-5 1,13682 · 10-20
Standard deviation 0,12072 2,13017 · 102 2,13368 · 10-14
Minimum -0,17500 -3,25833 · 102 -4,33389 · 10-14
Maximum 0,14500 3,65167 · 102 4,30634 · 10-14
Skewness -0,19145 0,15571 9,76442 · 10-2
Kurtosis 1,28736 1,79673 2,29807
Valid number
of observations 23 23 23
CO2 De-seasonanalized CO2 1/r2
De-seasonanalized 1/r2
1/r 2
CO2
Fig. 5. Values of CO2 and 2/1 r after removal of the influence of the cyclic change ( 4X and *
4X )
Yoshio Matsuki, Petro I. Bidyuk
ISSN 1681–6048 System Research & Information Technologies, 2018, № 3 28
Then we implemented the Least Squares Estimation of the de-seasonalized global
temperature *Y on the de-seasonalized explanatory variables *
4X , from which the
influence of cyclic movement of Moon has been removed. Table 12 shows the
result of the Least Squares Estimation.
T a b l e 1 2 . Result of the Least Squares Estimation of *Y on the de-seasonalized
explanatory variables *
4X
Parameter Coefficient Standard error
1st cycle -0,77339 5,16897 · 10-2
2nd cycle -0,78101 5,16897 · 10-2
3rd cycle -0,77630 5,16897 · 10-2
Intercept *
4th cycle -0,77163 5,66233 · 10-2
CO2 5,33726 · 10-4 4,29402 · 10-5
2/1 r 5,24677 · 1010 4,28696 · 1011
*To get the coefficients of intercepts for 4 periods of the cycle, at first we calculated
YXQb 1
1*
1
, and then, 2
*
11 Fbbb , where 2b is the coefficients of CO2 and 2/1 r in
Table 12. And, to get the standard errors for the intercepts, we calculated YQXN 1
11
and YNIe )( 111 , 111
1)(
Q
kn
ee
bV , and then calculated the square root of the di-
agonal element of )( 1bV .
ANALYSIS OF THE RESULTS
In this research, we investigated influence of the trend (time) and the cyclic
change of the distance between Moon and Earth. For this purpose, we set dummy
binary variables, which replaced the intercept vectors of the Classical Regression
Model, and then we calculated the coefficients of the Least Square Estimations
between these binary variables and the global temperature and the explanatory
variables (CO2 and
2
1
r
), and then, we calculated expected influences to those
variables from each of the trend (time) and the cyclic change; and then, we sub-
tracted those expected values from the original values of the variables, in order to
make the de-trended variables and the de-seasonalized variables. As the result, we
observed that the coefficient of 2/1 r is larger than the coefficient of CO2. This ob-
servation suggests that there is the influence of Moon’s gravitational-wave to
Earth’s global temperature, which we also observed in our previous research [1].
In addition, the Maximum Likelihood Estimation shows almost as same val-
ues of the coefficients as in the Least Squares Estimation of Linear Classical Re-
gression Model [1], while their standard errors of the coefficients are larger than
those of the Least Squares Estimation. These differences of the standard errors are
due to the difference of the algorithm of these two approaches: the values of the
standard errors of the Least Squares Estimation are algebraically calculated, while
the values of the Maximum Likelihood Estimation were searched numerically.
Analysis of Moon’s gravitational-wave and Earth’s global temperature: …
Системні дослідження та інформаційні технології, 2018, № 3 29
With the trend removal, the coefficient of global CO2 became negative, be-
cause this process deformed the values of the global temperature and CO2, as
Fig. 2, 3 show.
Table 13, Table 14 and Table 15 show the results of the analysis, including
the results of our previous research [1]. Among these 7 models in Table 13, 14, 15,
the Pure Heteroskedasticity model and the Cobb-Douglas model (non-linear)
show the larger coefficient of CO2 than to the coefficient of
2
1
r
. Here, the Pure
Heteroskedastic model assumes uneven distribution of the data, although the de-
viations of the values do not reflect the uneven distributions of global temperature
and CO2, which are as shown from Fig. 2–5; therefore, this model does not de-
scribe the data correctly. Also, the Cobb-Douglas model does not describe the
distributions of the global temperature and CO2, which are almost linearly distrib-
uted as Fig. 2, 3 show. On the other hand, the values of
2
1
r
are on a same curve,
therefore they are neither uniformly distributed, nor unevenly distributed; and, we
conclude that this characteristic of
2
1
r
gives the relatively large standard errors of
the coefficient of
2
1
r
.
T a b l e 1 3 . Comparison of calculated coefficients and standard errors
Variable
and coeficients
Classical
Regression [1]
Maximum
Likelihood
Estimation
Trend
removal
Removal
of seasonal
(cyclic) influence
Intercept -1,17863 -1,17897 See Table 6 See Table 12
CO2 5,33150 · 10-4 5,32537 · 10-4 -1,15220 · 10-5 5,33726 · 10-4 Coefficient
2/1 r * 1,05537 · 1011 1,05675 · 1011 1,89763 · 109 5,24677 · 1010
Intercept 2,77830 1,67861 · 103 See Table 8 See Table 12
CO2 4,27704 · 10-5 2,95105 · 10-2 3,19869 · 10-5 4,29402 · 10-5 Standard
error
2/1 r ** 3,64933 · 1011 2,19071 · 1014 5,33765 · 109 4,28696 · 1011
* : ** 1 ; 3,46 1 ;: 2070 1;: 2,81 1 ;: 8,17
T a b l e 1 4 . Coefficients and standard errors of the coefficients in Generalized
Classical Regression Model [1]
Pure Heteroskedasticity
First-Order
Autoregressive Process Variable
Coefficient Standard error Coefficient Standard error
for 1 ( 1x ) -9,72055 22,91283 0,37507 0,78957
for Carbon
dioxide ( 2x ) 0,94202 7,55710 · 10-2 1,36503 · 10-5 1,17412 · 10-4
for
2
1
r
( 3x ) 2,18557 · 10-2 * 7,55709 · 10-3 ** 6,61708 · 109 * 9,71690 · 1010 **
* : ** 2,89 : 1 1 : 14,7
Yoshio Matsuki, Petro I. Bidyuk
ISSN 1681–6048 System Research & Information Technologies, 2018, № 3 30
T a b l e 1 5 . Coefficients of Cobb-Douglas model, 3
321
2 bb xxby [1]
Coefficients Estimated coefficient Standard error
1b , coefficient of 1 0,000103 0,02761
2b , coefficient of 2x 2,126546 0,23431
3b , coefficient of 3x 0,283107 * 10,62035 **
* :** 1 : 37,5.
Note: y : global temperature; 2x : carbon-dioxide;
23
1
:
r
x .
CONCLUSION AND RECOMMENDATION
We have examined the potential influence of Moon’s gravitational-wave to
Earth’s global temperature, in comparison with global CO2, using 7 mathematical
models for the empirical analysis. As the result, the influence of Moon’s gravita-
tional-wave was found to have some relation with Earth’s temperature rise, with
the Least Squares Estimation of Classical Regression Model, the First-Order Au-
toregressive Process of Generalized Classical Regression Model, the Maximum
Likelihood Estimation, the Least Squares Estimation after the removal of trend
(time), and after the removal of seasonal (cyclic) influence; while, the assumption
of Pure Heteroskecasticity and Cobb-Douglas model (non-linear) are not appro-
priate for this analysis, in regard to the linearly distributed Earth’s global tem-
perature and global CO2 in time series.
The further study is needed to identify the meaning of the uncertain relation
between the inverse of squared distance between Moon and Earth and Earth’s
temperature rise.
REFERENCE
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2. UK Department of Energy and Climate Change (DECC). — Available at:
http://en.openei.org/datasets/dataset/b52057cc-5d38-4630-8395-b5948509f764/
resource/f42998a9-071e-4f96-be52-7d2a3e5ecef3/download/england.surface.
temp1772. 2009.xls
3. Boden T.A. Global Regional and National Fossil-Fuel CO2 Emissions / T.A. Boden,
G. Marland, R.J. Andres. — Available at: cdiac.orbl.gov/trends/emits/
tre_glob.html cdiac.ornl.gov/trends/emits/tre_glob.html (last access, 8 August
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4. Moon Distance Calculator – How Close is Moon to Earth? — Available at:
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Received 02.05.2018
From the Editorial Board: the article corresponds completely to submitted manuscript.
|
| id | journaliasakpiua-article-150065 |
| institution | System research and information technologies |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2025-07-17T10:24:10Z |
| publishDate | 2018 |
| publisher | The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" |
| record_format | ojs |
| resource_txt_mv | journaliasakpiua/8c/9952191dbfa550e990c706ba02b8768c.pdf |
| spelling | journaliasakpiua-article-1500652019-01-17T13:31:43Z Analysis of Moon’s gravitational – wave and Earth’s global temperature: influence of time – frend and cyclic change of distance from Moon Анализ гравитационной волны Луны и глобальной температуры Земли: влияние тенденций по времени и циклических изменений Аналіз гравітаційної хвилі Місяця та глобальної температури Землі: вплив тенденцій за часом та циклічних змін Matsuki, Yoshio Bidyuk, Petro I. global temperature Moon’s gravitational-wave trend removal cyclic change глобальная температура гравитационная волна Луны удаление тренда циклическое изменение глобальна температура гравітаційна хвиля Місяця видалення тренду циклічна зміна This research examined the influence of Moon’s gravitational-wave to Earth’s global warming process and the effects of time-trend and cyclic change of the distance between Moon and Earth. In the pervious research [1], we found that the Moon’s gravitational-wave could influence the process of the Earth’s global warming; and, we also found that Moon’s cyclic movement around Earth needed to be further investigated, because it gave a unique pattern of distribution in the data for the empirical analysis; while both global temperature and global carbon-dioxide increase almost linearly in the time-series. In this research we added dummy binary variables that simulate the trend of time and the cyclic changes. As a result we confirmed that the influence of Moon’s gravitational-wave is significant in the process of rising global temperature on Earth. Проверено влияние гравитационной волны на процесс глобального потепления на Земле и эффекты от тенденций по времени и циклических изменений расстояния между Луной и Землей. В исследовании [1] обнаружено, что гравитационная волна Луны могла бы повлиять на процесс глобального потепления на Земле; кроме того, сделан вывод, что циклическое движение Луны вокруг Земли необходимо исследовать более подробно, поскольку оно обеспечивает уникальную схему распределения данных для эмпирического анализа, влияя в то же время как на глобальную температуру, так и на глобальное увеличение углекислого газа линейно во временных рядах. Добавлены контрольные бинарные переменные, симулирующие тенденции по времени и циклические изменения. В результате подтверджено, что гравитационное волне Луны имеет важное значение в процессе повышения глобальной температуры на Земле. Перевірено вплив гравітаційної хвилі на процес глобального потепління на Землі та ефекти від тенденцій за часом та циклічних змін відстані між Місяцем та Землею. У дослідженні [1] виявлено, що гравітаційна хвиля Місяця могла б вплинути на процес глобального потепління на Землі; крім того, зроблено висновок, що циклічний рух Місяця навколо Землі необхідно дослідити більш детально, оскільки він забезпечує унікальну схему розподілу даних для емпіричного аналізу, впливаючи водночас як на глобальну температуру, так і на глобальне збільшення вуглекислого газу лінійно у часових рядах. Додано контрольні бінарні змінні, що симулюють тенденції за часом та циклічні зміни. У результаті підтверджено, що гравітаційна хвиля Місяця має важливе значення у процесі підвищення глобальної температури на Землі. The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2018-10-16 Article Article application/pdf https://journal.iasa.kpi.ua/article/view/150065 10.20535/SRIT.2308-8893.2018.3.02 System research and information technologies; No. 3 (2018); 19-30 Системные исследования и информационные технологии; № 3 (2018); 19-30 Системні дослідження та інформаційні технології; № 3 (2018); 19-30 2308-8893 1681-6048 en https://journal.iasa.kpi.ua/article/view/150065/149271 Copyright (c) 2021 System research and information technologies |
| spellingShingle | глобальна температура гравітаційна хвиля Місяця видалення тренду циклічна зміна Matsuki, Yoshio Bidyuk, Petro I. Аналіз гравітаційної хвилі Місяця та глобальної температури Землі: вплив тенденцій за часом та циклічних змін |
| title | Аналіз гравітаційної хвилі Місяця та глобальної температури Землі: вплив тенденцій за часом та циклічних змін |
| title_alt | Analysis of Moon’s gravitational – wave and Earth’s global temperature: influence of time – frend and cyclic change of distance from Moon Анализ гравитационной волны Луны и глобальной температуры Земли: влияние тенденций по времени и циклических изменений |
| title_full | Аналіз гравітаційної хвилі Місяця та глобальної температури Землі: вплив тенденцій за часом та циклічних змін |
| title_fullStr | Аналіз гравітаційної хвилі Місяця та глобальної температури Землі: вплив тенденцій за часом та циклічних змін |
| title_full_unstemmed | Аналіз гравітаційної хвилі Місяця та глобальної температури Землі: вплив тенденцій за часом та циклічних змін |
| title_short | Аналіз гравітаційної хвилі Місяця та глобальної температури Землі: вплив тенденцій за часом та циклічних змін |
| title_sort | аналіз гравітаційної хвилі місяця та глобальної температури землі: вплив тенденцій за часом та циклічних змін |
| topic | глобальна температура гравітаційна хвиля Місяця видалення тренду циклічна зміна |
| topic_facet | global temperature Moon’s gravitational-wave trend removal cyclic change глобальная температура гравитационная волна Луны удаление тренда циклическое изменение глобальна температура гравітаційна хвиля Місяця видалення тренду циклічна зміна |
| url | https://journal.iasa.kpi.ua/article/view/150065 |
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