Artificial Earth Economics General Intelligence AEEGI

Artificial Earth Economics General Intelligence (AEEGI) is a natural progression of Artificial General Intelligence (AGI) that caters to the Economics of the Earth caring. It is crucial to optimize the entire spectrum of generating, transporting, storing, and consuming earth resources for the betterment of humanity, the environment, industry, and the scientific community. Most research efforts focus on a specific sector, leading to a disconnect between multiple disciplines and hindering effective problem-solving while ignoring the economic impact on the whole ecosystem, including people. AEEGI proposes creating a new coin specific to earth economics and integrating the positive initiative and outcomes from each sector to create an optimal solution that simultaneously addresses multiple objectives that generate the required coins (cash) to achieve them; we call this coin the Hubnomics Earth Wise Coin (EarthYzcoin). The generation of EarthYzcoin and integration across all industry sectors and its value chain are more complex than solving each industry sector or specific value chain challenges separately, but achieving a sustainable and efficient industry-environment-economic system is necessary. Because the industry, as per the current economic system, would have to invest more to become more environmentally friendly. Therefore, with EarthYzcoin, the required investment would be generated by EarthYzcoin and given to industries and universities to research and develop cleaner processes and technologies. At the same time, as per the definition of Yzcoin, the fund that Yzcoin generates is not taken from anyone; instead, it is purely generated as an equivalent value to what the environmental project deliverables outcome. However, EarthYzcoin is an upfront generation of value provided to institutions that would work on researching and developing cleaner solutions to industries and life. The role of AEEGI is to ensure continuous learning from every new outcome to keep optimizing

Hubnomics: Chapter 1 - The World Biggest Challenges Solution

Hubnomics is a book revealing the 3rd-millennium novel economic system. Why shall we think of the biggest world challenges or risks? What are these challenges? And how to solve them

Hubnomics: Chapter 3 - The Economy and the Beauty of Time with YZCOIN Behavior Dimension

It was a beautiful sunny London morning. Well, you would be thinking about sunny London, as such weather might fall under the third probability sector of the Gaussian bell curve. Let's say I was lucky that day. What would you suggest to me to do that morning, a Monday morning? Hoping not to remind me that I should go to work. I will do something else: a morning run in the Regent Park. This Monday appears to be especially lucky. But my wife, Eve, is calling me now, asking me to take our daughter to the new school bus stop. I love to be with my daughter in the morning; she has many funny stories. And I wish that life, the market, and the economy would not change her spirit of fun as days pass, so what I can do for her and all world children is keep that positive eye on our world. Not only a positive look but a positive reality for their future. The mornings are the best time for everything. The best time to do sport. The best time to work. The best time to study. The best time to write. The best time to be with my family. The best time to call my parents. The best time to do a new interview. The best time to … But how many mornings do we have in the year for going to work, and how many times do we have for all the other good things to do in the mornings? Of course, we have more mornings at work than mornings for doing other things. Can economists solve this? Someone would ask, "Why do you think that economists can solve everything?" the same person may try to answer, "Is it because economists can structure a good incentive system that drives people's behavior to change?". I think it is an acceptable answer. I would give an example from a class activity of a live experiment with a simulator. We did it in the Operation Management course at London Business School. The professor has set the live exercise with specific incentive and punishment schemes. The structure of the incentive and the punishment managed to change the behavior of each member accordingly. The behavior here means the decision taken by each student based on the incentive and punishment schemes

Artificial Energy General Intelligence AEGI

Artificial Energy General Intelligence (AEGI) is a natural progression of Artificial General Intelligence (AGI) that caters to the energy industry. It is crucial to optimize the entire value chain involved in generating, transporting, and storing energy for the betterment of humanity, the environment, industry, and the scientific community. Most research efforts focus on a specific area of the value chain, leading to a disconnect between multiple disciplines and hindering effective problem-solving. AEGI proposes integrating the learning from each discipline in the energy sector to create an optimal solution that simultaneously addresses multiple objectives. This integration is more complex than solving each discipline's challenges separately, but achieving a sustainable and efficient energy system is necessary

Cloud Service Marketing Strategy Framework for Higher Value Customer-Segments Deployment

The cloud service has emerged as one of the 21st-century novel Storage as a Service (SaaS) solutions that eased access to digital storage as needed, with zero maintenance and management efforts on the end users. However, multiple options are currently available with different end-user segments and needs. The service providers would need strategic focus on what segment and feature to target that would be more profitable and attractive to the end users. We analyze the family end-users, compared to corporate and e-commerce, and identified the customer-segment-focused marketing strategy. Although each segment's need differs, the cloud service's capabilities share potential higher value generation to multiple segments. We started by reviewing available strategic marketing analysis techniques to select the best that fits the available data. Finally, we outlined the marketing program and implementation control to deliver growing value to the service provider and end-users

AI Fluid Flow AIFF for Understanding Porous Media Behavior from Micro to Reservoir Scale Aided by Machine Learning

Developing an optimal water flooding scheme involves considering various petrophysical reservoir properties. These properties, such as permeability, capillary pressure, relative permeability, and wettability, make predicting water flooding outcomes complex. Our research aims to understand oil extraction processes at the laboratory level to design and implement injection schemes at the reservoir scale. We used Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) alongside artificial intelligence semantic segmentation image analyses to observe and analyze fluid behavior in intricate pore throat networks. We explored the in-situ 3D fluid flow profile with NMR-MRI images calibrated with multiple CCA, SCAL, 3D micro-models, and 3D printed reference calibrators. We conclude an Artificial Intelligence (AI) aided flood monitoring process that characterizes and understands complex carbonate sweep efficiency

Auto Artificial General Intelligence

This project is initiated to build the required algorithms that enable Autonomous Artificial General Intelligence that would make the machine understand the objectives and challenges from the data and recommend optimized solution

The Discovery of Calcite Intrinsic Wettability by the First-Ever Optical Illumination Inside Dark Fluid using IRIDW Apparatus

Wettability is one of the critical physical-chemical properties controlling multiphase flow in porous media. Therefore, it is vital to identify the wettability for each rock type when building 3D geological models for predicting the fluid flow behavior using a numerical simulator. Wettability-unique relative permeability curves are part of each flow simulator's rock type for proper simulation predictions. The reference approach for wettability determination is contact angle measurement. The literature recoded the wettability of contact angle measurement inside transparent fluid like water and decane. However, we need to visualize and measure the water-rock contact angle inside dark fluid like the hydrocarbon. We propose visualizing and measuring the wettability contact angle for rock-water inside dark hydrocarbon fluid. We use the Illumination through Rock Inside Dark fluid for Wettability measurement (IRIDW) apparatus