Reishi Mushroom Mycelium Experiment
Reishi Mycelium Experiment 2019
Mushrooms reproduce through spores. Mass growing of spores create mycelium. Because spores exist in any parts pf the mushroom body, it is possible to handpick tissue from different parts of the mushroom and develop mycelium.
Part I: https://youtu.be/x2B_m5EGCeE
In the natural world, the chances of mushroom spores germinating and then producing a mushroom are high. Usually, scientists use sterilized conditions and work in a laboratory, isolated from airborne contamination, the probability of success is much improved. However, the test proved that Reishi mushroom mycelium could be grown from any spore sources like body of the tissue or old block or used grain spawn.
Part II: https://youtu.be/gwZ0HRtql0U
As we indicated mushroom culture can be taken from spores or from tissue that could be found in fresh mushrooms, old substrates and any reminders of mushroom production like roots. The probability of growing mushroom mycelium in taking a tissue culture (clone) from a living mushroom is higher than using other sources of spore materials. With spores, a single strain must be selected from the multitude of strains created. In both cases, the result is a network of cells called, collectively, the mushroom mycelium. The hypothesis was correct, so that Reishi mushroom mycelium was grown from spores picked in fresh mushrooms, used block and grain spawn. However, most of the grain spawn inoculated was contaminated. So that sterilized conditions are required.
Part III: https://youtu.be/YwcW43ZA-rw
UNIQUE MATHEMATICAL MODEL FOR DEVELOPMENT OF OPTIMUM PRODUCTION and SUSTAINABLE BUSINESS PROFITABILITY
Optimization modeling provides:
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Sustainability of the balance between input and output for production;
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Minimization of costs;
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Maximization of revenue;
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Optimization of input and output for growing circle;
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Role of management in production efficiency and the market channel
The given model is based on industrial-mathematical methods allowing to carry out the deep research-and-production analysis of existing results of scientific experiences and to develop new directions for response to optimum decisions.
The purposes of the industrial-mathematical analysis of optimum decisions are the following:
1) To provide general estimation of the received results and to reveal variables included into the previous production plan and value of possible criterion function;
2) To compare the existing inputs and outputs of business or production with the traditional methods, standards and to define possible effect of optimization of the new budget or production plan;
3) To disclose possibilities that reserve significant increase in efficiency, growth, and development of existing business operation to propose further administrative plan of actions;
4) To define the general economic indicators of results of materials application agenda for the object (product) efficiency maximization in the planned period;
5) To establish limits of possibilities for updating new alternative decisions as a result of changes of initial parameters of past research problem.
Benefits of utilizing this model are obvious. As an example of our optimization model, we could demonstrate complete projects in an agricultural industry when we served farmers and assisted them to create a new optimum schedule for fertigation of selected cultivar vegetables for greenhouse and open field production line up. In addition, the model provides the ideal schedule for any business or industry emphasizing the cost of materials and equipment and provide input minimization and output maximization (cost-revenue-profit). Target proposal will be scientifically made in order to answer a question: whether it is really possible to lower expenses in the real situation to increase financial yield and to improve your business or production. Finally, we will come up with a list of recommendations on what resources for improvement you need and how it is possible to make it?
PUBLISHED ARTICLES:
Novotorov, A.V., & Brikach, G.E. (2016). Economic-Mathematical Model of the Hirsch Index Calculation.
International Journal of Business Management and Commerce, Vol. 1 No. 1; August 2016.
http://www.ijbmcnet.com/index.php/current
Novotorov, A.V. (2013). New Micro Porous Irrigation Technology is a Solution for Future Water Shortages.
Life Sciences / Agricultural Sciences. September. My Net Research – Empowering Collaboration.
September, 2013, www.mynetresearch.com
Novotorov, A.V., and Brikach, G.E. (2012). The Imitating Model of the Perfect Competition. MSUT
International Scientific-Practical Conference. Nizhniy Novgorod, Russia, pp 4-8.
Novotorov, A.V., Sinda E. P. (2010). Cabbage Root Elongation. My Net Research – Empowering
Collaboration. September, 2010, www.mynetresearch.com
Novotorov, A.V., Sinda E.P. (2010). Estimating Cabbage Water Consumption with Eco-Ag Irrigation
Technology. Franklin Business & Law Review Journal, December 2010,
Novotorov, A.V., Brikach G.E. (2009). Forecasting Profit: Optimization of Production Cost at Fort Hays State
University Farm. My Net Research – Empowering Collaboration. March, 2009,
Brikach, G.E., Novotorov, A.V. and White, G.A. (2008). Forecasting on the Basis of Imitating Model of
the Perfect Competition. The Ethics & Critical Thinking Journal, December 2008,
Novotorov, A.V., Brikach, G.E. (2008). New Model of Forecasting Prices for Farmers. Insights to a Changing
World Journal, June 2008, www.franklinpublishing.net.
Shiriaev, E.N., Brikach, G.E. & Novotorov, A.V. (2008). Method of allocation of perfect competition
parameters from "expenses vs. revenue" data collected in the agro-industrial complex enterprises of
USA and Russia. Economics of Agro-Processing Companies Magazine, January 2008.
White, G.V., Novotorov, A.V., Tuhy J.P and Brikach G.E. (2006). Analysis of Modeling Commodity Prices
Forecasting for Farmers. Applied Research and Technology Conference, N. Novgorod, Russia, pp. 17- 21.