#Gas Discharge Visualisation

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Biophysical Energy Transfer Mechanisms in Living Systems:

The Basis of Life Processes.

KONSTANTIN KOROTKOV

, Ph.D. 1, BERNEY WILLIAMS, Ph.D. 2,and LEONARD A. WISNESKI, M.D., F.A.C.P. 3

1 – St. Petersburg Technical University ITMO, Russia; 2 – Holos University Graduate Seminary, Fairview, Missouri; 3-George Washington University Medical Center.

Abstract The main reservoir of free energy in biological processes is electron-excited states of complex molecular systems. Communities of delocalized excited π-electrons in protein macromolecules are the basis of this energy reservoir. Specific structural-protein complexes within the mass of the skin provide channels of heightened electron conductivity, measured at acupuncture points on the surface.

Stimulated impulse emissions from the skin are also developed mainly by transport of delocalized π-electrons. Stimulated by high voltage impulses, optical emissions, with amplification in gaseous discharge, are registered by optical sensors (Gas Discharge Visualization – GDV). This quantum model supports an argument that GDV techniques provide indirect judgment about the level of energy resources at the molecular level of functioning in structural-protein complexes. Several years of GDV research have provided clinical correlations with well-accepted physiological parameters.

Gas Discharge Visualization methods for investigating human functional states, by assessing electro-optical parameters of the skin, are based on the registration of physical processes emerging from electron components of tissue conductivity.

[thequirkdetective]

Investigation 3 (29/5/2020): Electrification – Denki Kaminari

For this investigation, I will be examining the quirk that has become rather notorious in the BNHA community – Denki Kaminari’s ‘Electrification’. This is mainly due to the quirk’s overuse temporarily inhibiting the user’s cognitive ability. The quirk itself allows Kaminari to create a voltage between different areas of his body or the ground, allowing him to electrically shock opponents via contact, proximity or through some conductive medium. Therefore, to explain this quirk we first need to understand electricity.

‘Electricity’ is simply the movement of charge. Most commonly this charge is the delocalised electrons in wires, but it can also be soluble ions, or any moveable charged particle. When a potential difference (most commonly called ‘voltage’) is induced between two points it creates forces that act upon the charges and, if the charges are delocalised, move them. If the charge is negative, then the charge will be forced away from the positive terminal and towards the negative, and vice versa.

To create a potential difference, one must separate differing charges. A common demonstration of this is rubbing a balloon on a jumper, where the action physically takes electrons from the jumper and adds them to the balloon. This creates a positive charge in the jumper, and a negative in the balloon. A wire with flowing current has a potential difference across it too. Potential differences can be visualised as slopes, where the charges roll from the top to the bottom. In the case of the metal wire, the negative end is the elevated end of the slope, and the negative electrons ‘roll’ down to the positive end. However, if there were flowing positive charges, those would ‘roll’ the other way.

Knowing this, we now need to understand how electricity is generated. The first port of call is power plants, where either burning fuel, or decaying radioactive isotopes boil water which drives a turbine. The turbine can also be driven by wind, flowing water, or even the movement of waves. The turbine is attached to a magnet, which spins inside a coil of wire, inducing a potential difference across the coil. The physics of this is rather complex for a blog post, so I won’t explain it here. The issue with this for Kaminari is that it requires both a magnet and wires to exist and be actively rotated within his body. However, not all electricity requires moving parts.

Alkaline batteries use a chemical reaction between two chemicals involving the loss of electrons from one chemical and the gain of electrons by the other. The chemicals are each connected to one terminal, and when they are connected with a wire, the electrons flow between the two, and the reaction occurs. The downside of this method for use by the quirk is twofold: 1, the process is irreversible, so only a certain amount of power can ever be generated by the quirk, and 2, the chemicals used are highly corrosive to organic tissue.

Rechargeable batteries operate on the same principle, but with a stronger and opposing potential difference the reaction reverses; the electrons are dragged from the negative ions back to the positive. These batteries do suffer the same drawback as disposable ones, however, regularly employing cadmium compounds in their reactions.

In principle, any redox reaction could be employed to generate voltage, reversible or not. Redox reaction is the umbrella term for any reaction involving the movement of electrons, from the words reduction, meaning the gaining of electrons, and oxidation, the loss of electrons. If Kaminari’s cells contained two reactants of a redox reaction, their connection through a conductive medium could create a voltage. Then, if the reaction were irreversible, the spent chemicals could be disposed of, and replaced via ingestion. If the reaction were reversible then it could be possible for Kaminari to literally recharge via a large DC current, such as the output of an AC to DC power converter. Sadly, sticking his fingers into a power outlet will not suffice, since all electricity supplies in Japan and most of the world are AC. The products can also be disposed of and renewed in the same manner of excretion and ingestion. Interestingly though, due to redox reactions being balanced, any electricity created in this way will ground back to Kaminari.

One possible candidate for the redox reaction is respiration. This reaction is the one that supplies cells with energy (or more specifically ATP) and allows them to carry out their functions. It’s an exothermic redox reaction, and so can be used to generate a potential difference, albeit with a few changes in biology. Firstly, in cells the electrons are carried by molecules called electron shuttles. These ‘pick up’ electrons from the glucose and deposit them into the oxygen, forming CO2 and H2O. If the electrons shuttles were replaced by a conductive medium, then they would flow through that medium, creating electricity. Since the reaction is exothermic, it will give off energy rather than require it, and so will cause a potential difference. The question now, is how much potential difference?

In season 1 of the anime, Denki uses a large discharge of electricity to stun and incapacitate surrounding villains[1], one of the largest uses of his quirk. The area of effect covers a sphere roughly 10m in diameter. The dielectric breakdown of air (how much electricity it takes to ionize it) is roughly 3 million volts per metre, so the voltage of this move is around 30 million volts, or a third of an average lightning bolt. Now, to work out the voltage given by the breakdown of one mole of glucose, we turn to the Nernst Equation, which gives the approximate voltage of any electrochemical cell. The equation is as follows: where is the voltage given, is the standard electrode potential () at room temperature, 1atm pressure, and 1mol/dm3 concentration, is the gas law constant; 8.314J/K/mol, is the temperature in Kelvin (assumed to be 298.15K), is the number of moles of electrons transferred in the reaction (every molecule of glucose transfers 12 electrons), and is Faraday’s constant, of 9.649×104C/mol. Unfortunately, it is impossible to calculate the value of   in this reaction, as no one seems to have published the reduction potential of glucose. However, we can estimate that a maximum voltage from one mole of glucose could be no more than around 10 volts. We could be out by a factor of 10 either side, but since we’re working with numbers in the millions this shouldn’t matter too much. Now, under this assumption Kaminari needs to react 3 million moles of glucose to generate the voltage seen. This is ~54050 tonnes of glucose, meaning Denki needs to eat, convert, and somehow simultaneously metabolise 55000 tonnes of sugar. Sadly, we have again reached the impasse of the human body’s limited use as an energy source, and must therefore conclude that the method of generating the energy the quirk harnesses are fantastical. However, once the energy is generated, it obeys the laws of physics perfectly.

This means that the quirk somehow creates 3o million volts of electricity, which somehow needs to find its way out of Denki’s body. As previously stated, the energy created here is equal to a third of the energy in a lightning bolt, and all of it passes through Denki. When someone is struck by lightning, they can be affected in many ways, including most commonly a large heart attack, but also include loss of consciousness, dizziness, confusion, memory loss, temporary paralysis, hearing loss, cataracts, and physical injuries from the force and temperature like burns, broken, fractured or dislocated bones, neck injuries, and lung damage. To work out how many of these apply to Kaminari, we have to find out what happens when he lets loose a full 30-million-volt blast. The air around him becomes immediately ionised – it heats up and expands, creating a large shockwave and loud bang in the same way lightning causes thunder. A lot of the charge is immediately grounded if Kaminari is standing on something earthed, but it spreads through the ground and shocks anyone standing nearby. The shockwave of ionised air spreads outwards, heated by the electricity. The force of the blast won’t be enough to knock someone over unless they are very close, but it may cause superficial burns to skin and clothing, as well as almost certainly bursting the eardrums of anyone in the near vicinity, causing pain, dizziness, and of course loss of hearing. Even those whose eardrums survive will likely gain some kind of hearing loss, the severity of which rises the closer they are to the epicentre. Kaminari suffers the maximum possible severity of all these effects since he is the epicentre. His hearing would be severely damaged, and prolonged, high voltage quirk use could cause permanent hearing loss. The force of the shockwave will also cause blunt trauma to different areas of his body, and the millions of volts that flow through his body will almost certainly cause a heart attack, and some kind of memory loss or brain damage (this is the only symptom shown in the anime). Interestingly enough, some people who have been struck by lightning had it flow around them, simply blowing their clothes off and leaving few signs of injury, though both a specially designed hero costume and the show’s age rating could help prevent this.

In conclusion, Denki Kaminari’s Electrification quirk somehow generates voltage in Kaminari’s body, up to around 30 million volts. A discharge such as this one creates a shockwave of ionized air, burning anyone immediately next to Kaminari, and causing hearing loss to anyone in the vicinity. The electricity mostly spreads through the ground, shocking anyone nearby and causing pain, convulsions, and possible heart attacks and mild brain damage. Kaminari is hit with the brunt of the shockwave, and he most probably gains broken or fractured bones, burst eardrums, memory loss, loss of consciousness, and more seriously, immediate cardiac arrest.

[1] Season 1 episode 11: “Game Over”

If you liked this investigation and want to have a say in the next one, then make sure to send a recommendation for which quirk I should investigate!

[biointernet]

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[juniperpublisherscasestudies]

Juniper Publishers-Open Access Journal of Case Studies

Retroperitoneal Duodenal Foreign Body Perforation - A Novel Laparoscopic Approach

Authored by Sunder Balasubramaniam

Abstract

Gastrointestinal perforation from foreign bodies can lead to life threatening sepsis, and pose a significant challenge given the need to drain the septic source as well as safely extract the offending object. A 71-year-old Chinese lady presented with abdominal pain without peritonitis or fever. A computed tomographic (CT) scan of the abdomen revealed two contiguous retroperitoneal abscesses with a 4cm fishbone. One of the cavities was surrounding the right external iliac artery and had a visible connection from the third portion of the duodenum to the superior abscess cavity. 

She underwent radiology-guided drainage of the collections, followed by a gastrografin swallow which did not demonstrate a leak. Retrieval of the fishbone was first attempted endoscopically by placing a 5 mm laparoscopic port into the cavity to insufflate it with gas, followed by introduction of a flexible choledochoscope. Unfortunately, the bone could not be visualised, and the procedure was converted to open retroperitoneal approach via a 4 cm incision. The bone was successfully retrieved, and a repeat CT done post-operatively showed near resolution of the abscess and complete removal of the fishbone. The patient was fit for discharge on the 4th post-operative day.

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To read more…Fulltext please click on: https://juniperpublishers.com/jojcs/JOJCS.MS.ID.555762.php

[inerginc]

Utilities house enormous datasets that defy traditional analysis, for which machine-learning could be of great benefit. When machine-learning is applied to IoT data, utility companies are able to realise the next generation power grid that can eventually handle billions of endpoints on utility networks autonomously. Pacific Gas and Electric’s (PG&E) emerging technologies leader Tom Martin and Paul Doherty, corporate relations, discuss how machine learning and data science is being leveraged for asset maintenance and the integration of distributed energy resources (DER).

MSEI: What does machine-learning mean to PG&E? What is your definition of machine-learning?

TM: Machine-learning at PG&E is the ability to use analytics to drive optimisation in our operations. We view the grid as becoming increasingly complex, as is PG&E’s grid. We have millions of smart meters; hundreds of thousands of rooftop solar installations that will very soon have a controllable output; and electric vehicles (EVs) that can charge or discharge, based on different market signals. As the grid becomes more complex, we are trying to understand how our operators and operating engineers can use data gathered from these devices, to be predictive and prescriptive, in ways that we can address the complexity and volume of decisions associated with managing tremendous volumes of distributed resources – on a scale that humans wouldn’t be able to. PG&E has more private solar than any other utility in the US, with more than 300,000 private solar customers that are connected to the grid. Furthermore, we connect about 4,000 to 6,000 new solar customers to the grid monthly, which equates to about one every seven minutes. Similarly, one in five electric vehicles in the US is registered in PG&E’s service area. There are more than 200,000 EVs in the state of California and over 85,000 are in PG&E’s service area.

MSEI: How are you using machine learning and big data for asset maintenance/asset management?

TM: Right now, we are beginning the journey for better leveraging big data. One of the projects that we have underway is called ‘STAR’ (System Tool for Asset Risk). The objective behind project STAR is to determine how we can better prioritise asset replacement and asset maintenance using the rich data sources we have; and to create a dynamic risk scoring model, which pulls in more data sources than were previously available and previously digestible. The risk model allows our planners and our asset maintenance team to use this data to build a risk score, which will determine which assets need to be replaced in order of highest priority.

MSEI: Which assets are you looking to apply the STAR risk model to?

TM: There’s a wide variety – everything from identifying which power poles are most likely to need replacement, through to our giant transformers and substation equipment. These are the two ends of the spectrum and includes everything in between. We’re starting to focus on a limited number of asset groups, but the idea is that we have this wealth of data and there’s a huge opportunity to leverage that data to optimise decision making.

MSEI: What is needed to create a good machine-learning system? Is the risk model built on software that uses machine-learning algorithms? How is this made up?

TM: We are in the process of answering that exact question. In its simplest form, the answer is we need a platform that can integrate a wide variety of data sources: not just utility-owned data (eg asset location, asset type, smart meter data), but also external data sources such as weather patterns, customer-sited solar input and so forth. The goal here is to build a platform that allows data science and operational tools/dashboards to all share access to the same data. Historically, if a new data analytics application was going to be built, there would be a unique connection built to tie the isolated data source to the new application. Then, if a second application came along that wanted to use some or all of those data sources for a different purpose, you would have to rebuild that backend, resulting in additional effort. Finally, if the core data source was changed (ie data was migrated to a new and improved system), all of those individual pipes that brought the data to the multiple different applications would have to be rebuilt. The idea moving forward is: By building an analytics platform you can connect once to that dataset and build your applications on top of the platform without having to rebuild the back-end because the data connection is already there. Moreover, if the data source changes, you only have one pipe you have to rebuild in order to get the data back into the platform, but you don’t have to rebuild every application that is utilising that data set.

MSEI: How are you using machine-learning technology to create potential “what if” scenarios that would trigger self-healing functions should something go wrong?

TM: We have implemented self-healing technology called FLISR (Fault Location, Isolation and Service Restoration). FLISR technology is able to restore service following an electrical fault, resulting in a significant reliability improvement compared with the traditional manual restoration process. There is enough data that can identify where that fault is and perform switching operations in real-time, restoring power in just a few minutes. We have been deploying this technology for the past four to five years and we have experienced some significant increases in customer reliability. We have, for the past seven years, constantly topped our customer reliability performance, which is largely due to our use of data and automation with the FLISR self-healing technology.

MSEI: Could this technology also perform diagnostics and alert PG&E to the source of faults on the grid?

TM: We are getting there and have several efforts toward this end. We have recently wrapped up testing and are working toward the full deployment of wireless communicating line sensors.

Several years ago, fault current indicators would be monitored by trouble men patrolling the line. These fault current indicators blink when there’s an outage. The trouble man would then be able to see where that fault occurred.

Now, we’re working to turn these fault current indicators into smart line sensors that enable the smart grid to communicate the location of the fault instantaneously. So, instead of someone having to go and patrol the line and look for the lights flashing, the operator in the control centre instantly knows that outage happened in the segment between line sensor five and line sensor six, for example. We can then begin to perform switching operations and also send a trouble men directly to the segment where that outage has occurred – so that is step one.

The other functionality of the line sensors is to capture really granular waveform data. These are signals coming through the conductor and the goal of this is to be able to proactively identify a waveform signature. A waveform signature could be a tree branch brushing up against a line. This is a scenario where it could’ve been a little bit windy. The analytics would then be able to identify this waveform signature so that the tree branch could be trimmed before the next big storm which might break that branch and cause an outage.

The advantage here is that this technology enables us to take a proactive approach, instead of a reactive approach, to identifying issues on the grid.

MSEI: Will you use this technology for routine asset maintenance?

TM: Absolutely, that’s the goal that we would like to achieve. Algorithms can be trained to identify the assets that are likely to fail or are most likely to fail, or need replacement. We have a reliability programme in place to ensure that we are optimising every utility asset with the use of analytics and machine-learning as well as to refine that reliability plan and reduce operations and maintenance costs.

MSEI: How has machine-learning helped PG&E to process and analyse not only volume and velocity of data but also a variety of data?

TM: We established a pilot project called GOSI (Grid Operations Situational Intelligence). The GOSI project set out to demonstrate real-time data integration and visualisation for distributed energy resources, evaluate benefits and use cases of a single-interface software platform – to provide a single software interface as a tool for distribution operators and engineers/power quality end users.

The project developed key data, system, and user experience learnings through integrating more than 20 data sources into a single visualisation tool allowing users to view complex data sources in ways that were not possible through current solutions. This project formed the foundational learnings which will allow PG&E to potentially explore other complex situational awareness tools and applications to allow users to target information to help manage changes on the grid.

MSEI: How challenging has it been to rethink existing business models and existing value chains, based on how quickly the market is changing? And how quickly technology has progressed?

TM: The challenge is communicating the breaking down of existing processes, and rebuilding these processes in a way that does not work the same but that, qualitatively, we know is better and has an opportunity to drive a lot of value for our customers. It’s a question that we are still in the infancy of figuring out – we know there is value in the data science and machine learning technology toward delivering better safety, affordability and reliability for our customers.

Exactly how we tell that story and how we identify the specifics of that totally new business case, is something that we haven’t been fully able to answer yet. We are involved in a utility programme in California called EPIC (Electric Programme Investment Charge). EPIC is an initiative put forward by the state of California to fund technology demonstration that helps advance grid innovation and helps demonstrate how California’s investor-owned utilities can create the grid of the future through small projects today.

At PG&E, we have really benefited from this innovative programme in that it allows us to have a small project here and a small project there, where we’re able to test different vendor solutions, approaches and capabilities to see what works and what doesn’t. MI.

  Image credit: 123rf.

The post PG&E leverages machine-learning and data science for asset management and DER integration appeared first on Metering.com.

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The Energy of Space with GDV Sputnik by Kirill Korotkov Via Flickr: The Energy of Space project with GDV Sputnik www.iumab.org/category/the-energy-of-space/

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The Energy of Space with GDV Sputnik by Kirill Korotkov Via Flickr: The Energy of Space project with GDV Sputnik www.iumab.org/category/the-energy-of-space/

[gdvcamera]

GDV/EPI Diagnostics 2

Medical and Psychological analysis of Korotkov images with Kirill Korotkov and Alexander Dvoryanchikov GDV/EPI Diagnostics 2 online course

inside Human Light System online course

For all models of GDVCAMERA users (Bio-Net, Bio-Well, Pro, VivAlign, etc)

Gas Discharge Visualisation (Electrophotonic Imaging)

GDV Diagnostics 2 – professional GDV secrets from the source

Kirill Korotkov –…

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GDV Diagnostics 2 online course

GDV Diagnostics 2 online course

GDV Diagnostics 2 – online course Medical and Psychological analysis of Korotkov images with Kirill Korotkov and Alexander Dvoryanchikov

inside Human Light System online course

For all models of GDVCAMERA users (Bio-Net, Bio-Well, Pro, VivAlign, etc)

GDV Diagnostics 2

GDV Diagnostics 2 – professional GDV secrets from the source

Kirill Korotkov – psychologist, working with GDV technologies since…

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GDVCAMERA

GDVCAMERA is a software and hardware (optoelectronic device) for registration energy field.  GDV (Gas Discharge Visualisation), EPC, EPI (ElectroPhotonic Imaging), GDV/EPC, Kirliangraphy, Kirlian photography, Bioelectrography, Electrophotonics, Crownscopy, etc.  (more…)

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Old GDVCAMERAs Software

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The Best GDV Course

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Best GDV Course

Best GDV Course = GDV Diagnostics + Bio-Well Course

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Bio-Well Video Course

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with Kirill Korotkov, Konstantin Korotkov, Dieter Zenke, Karen O’Dell, Dmitry Orlov, Thornton Streeter, Boris Petrovic and Krishna Madappa

18 Lectures from the best GDV specialists all over the World! (more…)

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GDV Diagnostics

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with Dr. Alexander Dvoryanchikov

GDV = Western Medicine + Traditional Chinese Medicine + Ayurveda

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