Dear Aman Srivastava,

Integrating issues in the cognitive aspect are, on the one hand, the negative effects of the ever faster global warming process and the necessary actions that humanity must take, implement and / or constantly increase the scale of their implementation. These activities relate to the implementation of the goals of sustainable development, protection of biodiversity of natural ecosystems, protection of the biosphere and climate of the planet and other elements of the pro-environmental transformation of the classic economy to a sustainable, green circular economy. On the other hand, integrating issues in the analytical aspect include the use of integrated research and analytical techniques in the field of Big Data Analytics and the use of other analytical techniques for acquiring, archiving and processing large data sets and information using various ICT and Industry 4.0 information technologies. In addition, another important issue that should be taken into account in the field of integration of research, analysis and description of issues related to climate change, the effects of these processes, the impact of the development of civilization on the biosphere and climate of the planet Earth, necessary pro-environmental activities, etc. is the analysis of the correlation between the conducted research on climate change and shaping environmental policy in individual countries and globally. In the context of shaping environmental policy, the key issue is to answer the question: Are the measures taken to achieve the goals of sustainable development, protection of the planet's nature and climate and other elements of the pro-environmental transformation of the classic economy to a sustainable, green circular economy sufficient to effectively slow down the process of global warming and significantly reduce the risk of negative effects and the probability of a global climate catastrophe, which, according to long-term forecasts of mathematical models of climate change, may occur in several dozen years? This question may be a fundamental question integrating research and analysis currently carried out in various fields of knowledge and science, they are scattered and only now are they being integrated.

Best regards, Have a nice day,

Dariusz Prokopowicz

Hi Dr Aman Srivastava . I think all precautions should be taken into consideration to reduce the green house gases in all sectors in our life especially industry .

Aman Srivastava I am sure you are aware that the the key diagnostic indicators are RAI,ERT,TI,FFR,IT and CAV.Significant projects are EU FP7 ADVANCE and the forthcoming AR6. Integrated assessment models (IAMs) are widely used for climate policy and climate change analysis.You need to establish a correlation between diagnostic indicators.

Good luck.

Make a table in which the rows are the dates of observations, and the salt shakers are the factors and the results of their influence. The table cells contain the values of factors and the values of the results of their influence. Such a table can be processed in the Eidos system.

*The Eidos-X++ system differs from other artificial intelligence systems in the following parameters:

*- it was developed in a universal setting, independent of the subject area. Therefore, it is universal and can be applied in many subject areas (http://lc.kubagro.ru/aidos/index.htm);

*- it is in full open free access (http://lc.kubagro.ru/aidos/_Aidos-X.htm) and has all the relevant source texts (http://lc.kubagro.ru/__AidosALL.txt);

*- it is one of the first domestic systems of artificial intelligence of the personal level, i.e. it does not take special training in the field of technologies of artificial intelligence from the user (there is an act of introduction of system "Eidos" in 1987) (http://lc.kubagro.ru/aidos/aidos02/PR-4.htm);

*- it provides stable identification in a comparable form of strength and direction of cause-effect relationships in incomplete noisy interdependent (nonlinear) data of very large dimension of numerical and non-numerical nature, measured in different types of scales (nominal, ordinal and numerical) and in different units of measurement (i.e. does not impose strict requirements to the data that cannot be performed, and processes the data that can);

*- it contains a large number of local (supplied with the installation) and cloud educational and scientific applications (currently 31 and 290 (http://aidos.byethost5.com/Source_data_applications/WebAppls.htm), respectively) (http://lc.kubagro.ru/aidos/Presentation_Aidos-online.pdf);

*- it supports on-line environment of knowledge accumulation and is widely used all over the world (http://aidos.byethost5.com/map5.php);

*- it provides multilingual interface support in 51 languages. The language databases are included in the installation and can be replenished automatically;

*- the most time-consuming, computationally, are the operations of the synthesis models and implements recognition using graphic processing unit (GPU) where some tasks can only support up to several thousand times; the solution of these tasks is intelligent processing of big data, big information and big knowledge;

*- it provides transformation of the initial empirical data into information, and its knowledge and solution using this knowledge of classification problems, decision support and research of the subject area by studying its system-cognitive model, generating a very large number of tabular and graphical output forms (development of cognitive graphics), many of which have no analogues in other systems (examples of forms can be found in: http://lc.kubagro.ru/aidos/aidos18_LLS/aidos18_LLS.pdf);

*- it well imitates the human style of thinking: gives the results of the analysis, understandable to experts according to their experience, intuition and professional competence.

*- instead of making almost impossible demands on the source data (such as the normality of distribution, absolute accuracy and complete repetitions of all combinations of factor values and their complete independence and additivity), the  automated system-cognitive analysis (ASC-analysis) offers to process this data without any preliminary processing and thereby transform it into information, and then transform this information into knowledge by applying it to achieve goals (i.e. for the management) and solving problems of classification, decision support, and meaningful empirical research of the domain being modeled.

*What is the strength of the approach implemented in Eidos system? The strength is implementing an approach whose effectiveness does not depend on what we think about the subject area or whether we think at all. It generates models directly based on empirical data, rather than based on our understanding of the mechanisms for implementing patterns in this data. This is why Eidos models are effective, even if our understanding of the subject area is incorrect or totally absent.

*And this as well is the weakness of this approach implemented in Eidos system. Models of the Eidos system are phenomenological models, i.e. they do not reflect the mechanisms of determination, but only the fact and nature of determination.

I think these could help you;