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Since October 2007, we have been conducting rigorous scientific research on the unexplained “power” of a pyramidal structure (PS). From our research results so far, we could classify the pyramid effects by the PS into the following two types. (i) The pyramid effects in which the PS converted the test subject’s unexplained energy to affect biosensors when the test subject entered the PS and meditated. (ii) The pyramid effects in which the potential power of the PS affect ed biosensors if the test subject ha d not been inside the PS for at least 20 days and the test subject’s unexplained energy was excluded. In this paper, we report new results regarding (ii). As a result of dividing a year according to the four seasons of winter, spring, summer, and autumn and analyzing the pyramid effect of each period, the following points were found. 1) There was a pyramid effect without seasonal variation. The pyramid effect on the lower and upper layers was different throughout the year for the biosensors placed at the PS apex in two layers, regardless of the season. 2) There was a pyramid effect with seasonal variation. The value of the psi index, which indicates the magnitude of the pyramid effect, changed as the seasons changed, while different pyramid effects were maintained on the lower and upper layers. Regarding the change in the pyramid effect depending on the season, the psi index in summer was larger than that in winter in both the lower and upper layers. From these results, we found that there are two types of potential power at the PS apex: seasonal potential power and non-seasonal potential power.

From the latter half of the 1930s, attention has been given to the so-called “pyramid power”, the effects of a pyramid that cannot be scientifically explained. And to date, many books on pyramid power have been published [1 - 18]. However, regarding the scientific research of pyramid power, there have been only a few papers published in academic journals [19,20] other than the research papers we have published. We have been studying the unexplained “power” of the pyramidal structure (PS) since October 2007. As a result of rigorous scientific experiments and analysis, the existence of an unexplained “power” of the PS, an unexplained “function” of the PS was demonstrated with statistically very high significance. The results of our research on the PS have been published as eight original papers [21 - 28], three research summaries [29 - 31], and one chapter in a book [

We verified the pyramid effects by analyzing the concentration of gas emitted from biosensors. The method of using cucumber section as biosensors was developed by the International Research Institute (IRI). In addition to detecting the pyramid effects, this method also detected a healer’s non-contact effect on the biosensors and a wave like bio-field around the healer [33 - 35].

From our research results so far, the pyramid effects by the PS can be classified into the following two types. (i) Pyramid effects in which the PS converted the test subject’s unexplained energy to affect the biosensors when the test subject entered the PS and meditated, Hemi-Sync® [^{−3}, Welch’s t-test, two-tails [^{−7} [

The purpose of this paper is to demonstrate the coexistence of two types of pyramid effects, the pyramid effect with and without seasonal variation, for the biosensors placed in two layers at the PS apex.

We created the PS shown in

In order to clarify the presence of the “pyramid power” in the PS, we verified the pyramid effect on the biosensors (cucumber fruit sections). In general, injuries to living bodies such as plants are known to cause biological protection and repair reactions [37 - 40]. Regarding these reactions, we noticed that there was a variable gas concentration from a gas generation reaction on the cut section (an injury) of the cucumbers. Then we measured the concentration of gas released from the biosensors, cucumber fruit section Cucumis sativus “white spine type”, to detect the pyramid effects, that is, the pyramid power of the PS. In order to prepare uniform biosensors, we used the simultaneous calibration technique (SCAT) (

In _{E} indicated an experimental sample and G_{C}, a control sample. The G_{E} and G_{C} of a pair had the same section, but the directions of the axes were different. The direction of the upper surface

of the cucumber section placed on the Petri dish was defined as the direction from the lower surface in contact with the Petri dish to the upper surface. At this time, the section on the upper surface of the G_{E} was in the same direction as the growth axis of the cucumber, and the section on the upper surface of the G_{C} was in the opposite direction to the growth axis of the cucumber. Here, the growth axis of cucumber was the direction from the vine side to the flower side of the cucumber fruit. The gas concentration depends on the direction of the section, and we have demonstrated that G_{E} < G_{C} [_{E1} and G_{E2} were placed at the PS apex in two layers, and G_{C1}, G_{C2}, G_{E3}, G_{E4}, G_{C3} and G_{C4} were placed on the calibration control point 8 m away from the PS in two layers. The difference in height between the two layers was 20 mm. The larger subscript number was the upper layer, and the lower layer was Layer 1, and the upper layer was Layer 2. The center point of the bottom of the Petri dish of G_{E1} placed at the PS apex coincided with the extension of the center of the four aluminum pipes near the PS apex (

We expressed the pyramid effects on the biosensors by the psi index Ψ [27,28,32]. Ψ was 100 times

the natural logarithm of the ratio calculated for the gas concentrations of each pair. The relationship between the J value we used previously and Ψ was Ψ = 100 J [

Ψ 1 = 100 ln ( G E1 / G C1 ) , Ψ 2 = 100 ln ( G E2 / G C2 ) , Ψ 3 = 100 ln ( G E3 / G C3 ) , Ψ 4 = 100 ln ( G E4 / G C4 ) . (1)

In Equations (1), G_{E1} - G_{E4} and G_{C1} - G_{C4} were gas concentrations (ppm) measured from the biosensors. Ψ_{1} - Ψ_{4} were the psi indices before calibration.

Ψ 1 ( E-CAL ) = Ψ 1 − ( Ψ 3 + Ψ 4 ) / 2 , Ψ 2 ( E-CAL ) = Ψ 2 − ( Ψ 3 + Ψ 4 ) / 2 , Ψ 3 ( C-CAL ) = Ψ 3 − ( Ψ 3 + Ψ 4 ) / 2 , Ψ 4 ( C-CAL ) = Ψ 4 − ( Ψ 3 + Ψ 4 ) / 2 . (2)

Ψ_{1(}_{E-CAL)} and Ψ_{2(E-CAL)} in Equations (2) are the results of calibration for environmental factors such as temperature, humidity, atmospheric pressure, geomagnetism, etc., and they are also calibrated for the cutting direction. The absolute values of Ψ_{1(}_{E-CAL)} and Ψ_{2(E-CAL)}, which are calibrated psi indices, represent the magnitude of the pyramid effects. The average of the pyramid effects on the biosensors placed in two layers at the PS apex is calculated by Equation (3).

Ψ ( E-CAL ) = ( Ψ 1 ( E-CAL ) + Ψ 2 ( E-CAL ) ) / 2 . (3)

When determining the pyramid effect on the lower and upper layers of the biosensors placed at the PS apex in two layers, it is necessary to calibrate the difference between the upper and lower layers when stacked in two layers. Therefore, the pyramid effects on the lower (Layer 1) and upper (Layer 2) biosensors are calculated by Equation (4).

Ψ 1 ( E-CAL ) Layer1 = Ψ 1 ( E-CAL ) − Ψ 3 ( C-CAL ) = Ψ 1 − Ψ 3 , Ψ 2 ( E-CAL ) Layer2 = Ψ 2 ( E-CAL ) − Ψ 4 ( C-CAL ) = Ψ 2 − Ψ 4 . (4)

Ψ_{(}_{E-CAL)LayerAve}, which is the average value of Ψ_{1(E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2}, matches Ψ_{(E-CAL)}.

Ψ ( E-CAL ) LayerAve = ( Ψ 1 ( E-CAL ) Layer1 + Ψ 2 ( E-CAL ) Layer2 ) / 2 = ( Ψ 1 ( E-CAL ) + Ψ 2 ( E-CAL ) ) / 2 = Ψ ( E-CAL ) . (5)

_{(}_{E-CAL)LayerAve}. The total number of data was n = 468, which were results of experiments conducted between July 2010 and September 2017 [27,28]. The horizontal axis is the number of days (1 - 366) counted from January 1st of each year. _{(}_{E-CAL)LayerAve}. From this graph, the average value of the pyramid effects on the biosensors placed in two layers at the PS apex was zero within the margin of error. The error bar shows the 99% confidence interval. We have already demonstrated that the pyramid effects on the biosensors placed in two layers at the PS apex differed between the lower and upper layers [_{1(}_{E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2}.

_{1(}_{E-CAL)Layer1} and the Ψ_{2(E-CAL)Layer2} of the biosensors placed in two layers at the PS apex. The horizontal axis is the number of days counted from January 1st, as in _{1(}_{E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2}. The Ψ_{1(}_{E-CAL)Layer1} average was −3.01, and it had 1% statistical significance on the negative side. The Ψ_{2(}_{E-CAL)Layer 2} average was 5.52, and it had 1% statistical significance on the positive side. The p-value between Ψ_{1(}_{E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2} was p = 4.0 × 10^{−7}. As a result of analyzing the lower and upper layers separately for the biosensors placed at the PS apex in two layers, the pyramid effects could be detected with statical significance. Moreover, we obtained the result that the signs were different between Ψ_{1(}_{E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2}. The error bar shows the 99% confidence interval.

In _{1(}_{E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2} in

day before the winter solstice (9/23 - 12/21). The data for period (I) are blue (n = 84), the data for period (II) are green (n = 108), the data for period (III) are red (n = 144), and the data for period (IV) are dark brown (n = 132). The solstice and equinox days vary slightly depending on the year, but in this paper, we set the winter solstice as 12/22, the spring equinox as 3/21, the summer solstice as 6/21, and the autumn equinox as 9/23. Figures 6(a)-(d) show the averages of Ψ_{1(}_{E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2} in the periods (I)-(IV). In any period of (I)-(IV), Ψ_{1(}_{E-CAL)Layer1} < Ψ_{2(E-CAL)Layer2}. We calculated the p-values between Ψ_{1(}_{E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2}, and obtained the following values: (I) p = 7.6 × 10^{−2}, (II) p = 1.4 × 10^{−3}, (III) p = 2.7 × 10^{−2}, and (IV) p = 3.6 × 10^{−3}. The p-values of (II) and (IV) had 1% statistical significance, and (III) had 5% statistical significance. No significant difference was obtained in (I), probably because the number of data was smaller than in the other periods. We expect that a significant difference can be detected between Ψ_{1(}_{E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2} as the number of data increases. The error bar shows the 99% confidence interval. We also found that the value of the psi index changed while maintaining the state of Ψ_{1(}_{E-CAL)Layer1} < Ψ_{2(E-CAL)Layer2} in each period of Figures 6(a)-(d). In particular, both Ψ_{1(}_{E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2} were negative values in (I), but they were positive values in (III). From this, we clarified that there is a pyramid effect indicating seasonal variation, and the pyramid effect is qualitatively reversed in “summer” and “winter”.

_{1(}_{E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2} during the periods (I)-(IV). In (I), Ψ_{1(}_{E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2} showed the minimum values. However, the period showing the maximum value was (II) in the case of Ψ_{1(}_{E-CAL)Layer1} and (III) in the case of Ψ_{2(E-CAL)Layer2}, and the periods were shifted. _{(}_{E-CAL)LayerAve} of Ψ_{1(E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2} in the periods (I)-(IV). The p-value of Ψ_{(}_{E-CAL)LayerAve} in (I) and (III) was 1.8 × 10^{−3}. The error bar shows the 99% confidence interval.

The result that there are two types of pyramid effects for the biosensors placed at the PS apex can be qualitatively understood from the results of the moving averages of Ψ_{1} - Ψ_{4} shown in _{1} and Ψ_{2}, Ψ_{1} < Ψ_{2} throughout the year, and both Ψ_{1} and Ψ_{2} have larger psi index values in summer than in winter. On the other hand, Ψ_{3} and Ψ_{4} are Ψ_{3} > Ψ_{4} throughout the year, but the values of the psi index in winter and summer are not so different. From this, when considering the lower and upper psi indices, Ψ_{1(}_{E-CAL)Layer1} = Ψ_{1} − Ψ_{3} and Ψ_{2(E-CAL)Layer2} = Ψ_{2} − Ψ_{4}, the relationship of Ψ_{1(E-CAL)Layer1} < Ψ_{2(E-CAL)Layer2} can be obtained throughout the year. It can also be understood that both Ψ_{1(}_{E-CAL)Layer1} and Ψ_{2(E-CAL)Layer2} have a larger psi index in summer than in winter. In addition, the characteristics of the pyramid effects on the biosensors due to the two types of potential power of the PS can be theoretically explained by the model we proposed in our previous papers [27,28].

In this paper, we analyzed the pyramid effects on biosensors by the potential power of the PS when the effect of the test subject on the PS was excluded. As a result of analyzing the pyramid effects of each period by dividing the year into four seasons of winter, spring, summer, and autumn, the following points were found. 1) The existence of the pyramid effect without seasonal variation: For the biosensors placed in two layers with a height difference of 20 mm on the PS apex, the pyramid effects on the lower and upper layers differed throughout the year regardless of the season, and the lower layer psi index was smaller than the upper layer psi index. It became clear that Ψ_{1(}_{E-CAL)Layer1} < Ψ_{2(E-CAL)Layer2}. 2) The existence of the pyramid effect with seasonal variation: We clarified that the value of the psi index changed as the seasons changed, while maintaining the condition that Ψ_{1(}_{E-CAL)Layer1} < Ψ_{2(E-CAL)Layer2} where the pyramid effects on the lower and upper layers were different. As for seasonal changes, the psi index in summer was larger than that in winter in both the lower and upper layers. From these results, we found that at the PS apex, there were two types of potential power: seasonally variable potential power and the non-seasonally variable potential power.

Prior to our reports, there were few reliable academic studies and statistically significant data on so-called “pyramid power”. Since 2007, we have been conducting rigorous scientific experiments and analyses using biosensors to elucidate the unexplained “power” of the PS. As a result, we demonstrated the existence of “pyramid power”, which was often recognized as having no scientific basis, with high statistical accuracy. While research on pyramid power is still often regarded as heretical in academia, our extremely high statistical accuracy experimental results are the world’s first scientific research results in this field. In the future, these results will surely be widely recognized and will become the basis for a new research field. Furthermore, we expect that pyramid power will have a wide range of applications.

The authors declare no conflicts of interest regarding the publication of this paper.