Using the ultrasound for therapeutic heat in PE

Is it better to use PGE-1 to close the gap between BPFSL and BPEL or could I also just use manual exercises? Like jelqing and pumping 5x5x3 routine ?

I don’t think I have access to PGE-1 and it could be dangerous to experiment with. It seems to be the most time efficient solution though. It’s handsfree and you don’t notice it during sleep.

What would you suggest if one couldn’t get PGE-1 ?

I think lengthening the penis isn’t the bottleneck here, it’s actually closing that BPEL gap.

Igigi was asked what was the girth protocol he used. Not that he is recommending anyone to follow the route.
He have made lot of research to find the smallest dose possible to amplify the nocturnal erections, not directly inducing the erection by the substance.

There is just not enough practitioners gone through the concept , but yes, we can suspect that closing the gap is the main issue.

Therefor exercises are needed to produce the needed stimulus for inducing cavernous tissue to grow volume.
For the latter stage of the period no matter waht and maybe to some extend all the way the campaign.
It can be seen as a better BPEL development.

Actually manual exercises work fine when closing the gap. Actually I was gaining even girth simultaneously when having jelqing in the repertoire during length campaign. Just concentrate on best the possible expansion during the exercises.

Reading through this thread has been very interesting and I’m very close to buying a US pro myself to give this routine a try :) . I had seen in one update that ultrasound heating while clamping was unable to obtain the desired temperature results, presumably from the increased heat dissipation due to all of the blood present. Has anyone tried doing bundled stretches using ultrasound for girth gains? It could be a way of targeting girth in much of the same way this protocol targets length.

You are right and we are working on it. The proper on-line spreadsheet is the way to go as you suggested. Programmed Excel based basic graph gathering and presenting the outcomes is one way to make it happen.

For predicating projections and simulations further developed data analysis software will be used.
When the platform has been decided we will be asking to bring data in format best suiting the cause.

Ideas are welcomed !

And here comes the first part of Krypa’s announcement.

Here we have an Excel template that should simplify our documentation.
The template is intentionally kept simple and most of the cells are locked. Finally, the yellow cells are to be filled in, the blue cells then result from the formulas. It should be self-explanatory.
The formulas are created with a German Excel version. I hope this does not cause any trouble.

Furthermore I have inserted two diagrams:
- pre-BPFSL and post-BPFSL incl. Logarithmic regression (we will see if we do well with this one).
- Strain rate

Please also enter the boundary conditions, such as load and duration during stress relaxation and ultrasound application, as well as the characteristics of your US device. This data then relates to the predictions mentioned by Krypa.
The goal is, once enough data is available, to make a prediction based on statistical calculation. For this, I will somewhat abuse the DoE (Design of Experiment) method. This approach works if we do not all proceed completely identically. Since we are working with different frequencies, powers and training duration, hopefully this will work.
For this we have to create a model, which I want to do together with you guys, more about this will follow later!

Please take a look at the template and give me feedback on what needs to be improved. I am looking forward to your answers!

As mentioned by Krypa, we need to create a platform for data analysis. The goal is to bring this into the Excel template. I have two suggestions on what we can do to evaluate a period.

1.) Evaluate regression curve BPFSL

In the Excel spreadsheet attached above, the calculation of the regression curve is already included. Once we have collected some data sets, we can try to find similarities. Excel calculates the coefficient of determination R^2 and the corresponding formula.
For logarithmic regression, the formula looks like this: y = c + a* ln(x).
I could imagine that the following equation could fit y = BPFSL_Start + a * ln(x)
With multiple logs, there is now the possibility to determine the quantity “a”. So maybe a simple prediction about the course of a period would be possible.

2.) Evaluation of existing data or DoE (Design of Experiment)

I must say right at the beginning that I am not an absolute professional for DoE, I have worked with it a few times.
If you want to optimize different input variables for a perfect result, you theoretically need a full factorial experiment. This means that every combination is tested and the result is evaluated.
To do this, you need to know how many values an input variable can take. If we consider the US frequency as an example in our case, there are 2 values circulating here in the forum (1 MHz and 3 MHz).
For example, if you have 5 input variables, each of which can take 2 values, you would have to run 2^5 (=32) trials to fully factorize all 5 input variables.
If these 5 input variables can take 3 values, then it would already be 3^5 trials (=243) trials.

And now DoE comes into play! DoE means that an experimental design is performed statically. A special software proposes an experimental program that tests only certain combinations. The other missing combinations are calculated with statistical methods. The test effort is drastically reduced. This is interesting for companies to reduce test efforts.

But what is our benefit?
Well, there are two ways to deal with this.
The first would be a statistical design of experiments and everyone would get an “order” to perform their period in a certain way. I don’t think we can make that work here.

The second, and probably more feasible, would be to analyze existing data using exactly this method.
To make this clear, I have created a FAKE model with fictitious numbers. ==> see FAKE_model_collected_data.png
This model is calculated using regression where the number of cycles, frequency, power, strain duration, US duration are input variables and BPFSL is the output variable ==> FAKE_model_Regression.png
The model allows us to use the predicted response graph to calculate the expected BPFSL after the period has been finished. The result in this FAKE model for the input variables 15 cycles, 27 min strain duration, 20 min US duration, at 1 MHz and 1.6 cm^2 is 0.97 cm +/- 0.89 cm (FAKE_model_Prediction_01.png).
Now we can adjust the input variables and determine the new expected output value based on the regression. The output in this FAKE model at 18 cycles, 33 min strain duration, 25 min US duration, 1 MHz and 2.5 W/cm^2 is now 1.86 cm +/- 0.81 cm.
The more measured values we have, the smaller the confidence interval becomes. I want to emphasize that these are no real numbers, it is FAKE data to explain the approach.

Then we should convert the whole thing into reality. This is not a short-term task, but a long way until we have reliable data. The whole thing only works if we have variances between periods (duration, power, frequencies.). It is important that the training does not change during a period, otherwise the evaluation of the period is useless and distorts the data. If someone of you documents several periods, he can do each period differently, no problem, but each period must be gone through without change.

I would like to encourage discussion here in this round about what all we should consider. Just to give an example: This FAKE model does not take into account the sweet spot, it is assumed that everyone knows it and is in the range. If we think that needs to go in, then we should discuss it here. However, we must not forget that the need for data increases with each new input variable. Therefore, I would like to keep the model compact.
Furthermore, we should consider whether we want to optimize the input variables for BPFSL or for Strain. So there are still many unresolved issues.

Let the discussions begin :)

As mentioned by Krypa, we need to create a platform for data analysis. The goal is to bring this into the Excel template. I have two suggestions on what we can do to evaluate a period.

1.) Evaluate regression curve BPFSL

In the Excel spreadsheet attached above, the calculation of the regression curve is already included. Once we have collected some data sets, we can try to find similarities. Excel calculates the coefficient of determination R^2 and the corresponding formula.
For logarithmic regression, the formula looks like this: y = c + a* ln(x).
I could imagine that the following equation could fit y = BPFSL_Start + a * ln(x)
With multiple logs, there is now the possibility to determine the quantity “a”. So maybe a simple prediction about the course of a period would be possible.

2.) Evaluation of existing data or DoE (Design of Experiment)

I must say right at the beginning that I am not an absolute professional for DoE, I have worked with it a few times.
If you want to optimize different input variables for a perfect result, you theoretically need a full factorial experiment. This means that every combination is tested and the result is evaluated.
To do this, you need to know how many values an input variable can take. If we consider the US frequency as an example in our case, there are 2 values circulating here in the forum (1 MHz and 3 MHz).
For example, if you have 5 input variables, each of which can take 2 values, you would have to run 2^5 (=32) trials to fully factorize all 5 input variables.
If these 5 input variables can take 3 values, then it would already be 3^5 trials (=243) trials.

And now DoE comes into play! DoE means that an experimental design is performed statically. A special software proposes an experimental program that tests only certain combinations. The other missing combinations are calculated with statistical methods. The test effort is drastically reduced. This is interesting for companies to reduce test efforts.

But what is our benefit?
Well, there are two ways to deal with this.
The first would be a statistical design of experiments and everyone would get an “order” to perform their period in a certain way. I don’t think we can make that work here.

The second, and probably more feasible, would be to analyze existing data using exactly this method.
To make this clear, I have created a FAKE model with fictitious numbers. ==> see FAKE_model_collected_data.png
This model is calculated using regression where the number of cycles, frequency, power, strain duration, US duration are input variables and BPFSL is the output variable ==> FAKE_model_Regression.png
The model allows us to use the predicted response graph to calculate the expected BPFSL after the period has been finished. The result in this FAKE model for the input variables 15 cycles, 27 min strain duration, 20 min US duration, at 1 MHz and 1.6 cm^2 is 0.97 cm +/- 0.89 cm (FAKE_model_Prediction_01.png).
Now we can adjust the input variables and determine the new expected output value based on the regression. The output in this FAKE model at 18 cycles, 33 min strain duration, 25 min US duration, 1 MHz and 2.5 W/cm^2 is now 1.86 cm +/- 0.81 cm.
The more measured values we have, the smaller the confidence interval becomes. I want to emphasize that these are no real numbers, it is FAKE data to explain the approach.

Then we should convert the whole thing into reality. This is not a short-term task, but a long way until we have reliable data. The whole thing only works if we have variances between periods (duration, power, frequencies.). It is important that the training does not change during a period, otherwise the evaluation of the period is useless and distorts the data. If someone of you documents several periods, he can do each period differently, no problem, but each period must be gone through without change.

I would like to encourage discussion here in this round about what all we should consider. Just to give an example: This FAKE model does not take into account the sweet spot, it is assumed that everyone knows it and is in the range. If we think that needs to go in, then we should discuss it here. However, we must not forget that the need for data increases with each new input variable. Therefore, I would like to keep the model compact.
Furthermore, we should consider whether we want to optimize the input variables for BPFSL or for Strain. So there are still many unresolved issues.

Let the discussions begin :)

Hey igigi,

Thank you for testing the spreadsheet. There seems to be an issue with the date as you wrote per PM. It can be that US-version of excel (and in this case I don’t mean ultrasound) requires the American format mm/dd/yy.

Did you leave away the gaps of the off days on purpose? For a real evaluation you should keep them, otherwise the formula of the regression will be wrong.
I just filled it based on the data you posted in your log. The equation for the regression is now correct in this spreadsheet.
Could you pls check the date, I entered? Is it working for you?

Thanks
Rocco

Understood.
The new template is still waiting for approval, but as soon as I wake up I will give it a try and I will include the days off in between.

Thank you!!

You don’t need to edit the data, I already did it with the newly uploaded file. Just open it and check it please.

With your initial file, just try the American date (mm/dd/yy) please.