This article is excerpted from Engineering, No. 2, 2016, journal of the Chinese Academy of Engineering
Author: Li Jinghai
来源：Exploring the Logic and Landscape of the Knowledge System: Multilevel Structures, Each Multiscaled with Complexity at the Mesoscale[J]. Engineering,2016,2(3):276-285.
Editor's Note
The structure of the material world and human beings themselves and the logical relationship in it are manifested as multi-hierarchical, each level is expressed as a multi-scale structure, and the establishment of the relationship between each level and the relationship between different levels is the central task of modern science, of which the mesoscale structure of each level is the key to achieving this central task, so multi-level, multi-scale and mesoscale will be a significant feature of a complete and reasonable knowledge system.
Academician Li Jinghai of the Chinese Academy of Sciences published an article entitled "Exploring the Logic and Architecture of Knowledge Systems: Multi-level, Multi-Scale and Mesoscale Complexity" in the 2nd issue of Engineering, journal of the Chinese Academy of Engineering. The article believes that mesoscale science is a frontier direction that deserves the common attention of various disciplines. Starting from breaking through inertial thinking, on the basis of clarifying the structure and logic of the knowledge system, this paper proposes that the salient characteristics of the knowledge system are multi-level, multi-scale attributes and mesoscale complexity, and points out that the knowledge system and technical system can be integrated, and puts forward a conceptual model of scientific and technological layout. The article also points out the path to fill the missing links in the existing knowledge system, puts forward methods for refining the common principles of interscience science, and guides the formation of new scientific research models.
First, break through the inertial thinking mode
In order to meet the new scientific and technological revolution, realize the new scientific research model, and cope with global challenges, we urgently need to break through inertial thinking. In the new era of science and technology, there are more opportunities than challenges. In this new era, however, governments and the tech community need to realize that perhaps the most important issue is not the investment and feedback that is often discussed.
Modern science and technology made rapid progress in the 20th century. Human understanding of the natural world and the ability to transform nature continue to improve, science in the expansion to two extreme time and space scales at the same time, gave birth to many new technologies, especially the development of energy, materials, information and biotechnology, fundamentally changed human production and lifestyle, promote the progress of human civilization.
However, people have gradually realized that while human sustainable development is facing new challenges and needing solutions, there are still many problems in nature, engineering, human beings themselves and social sciences that cannot be solved with existing knowledge, detail-oriented reductionism and system theory that pays attention to overall behavior cannot be integrated, and the relationship between different levels and between different scales at the same level is still difficult to achieve. This severely constrains the ability of human beings to develop sustainably and poses a challenge to the natural and social sciences.
At the same time, the progress of information technology and the explosive expansion of the knowledge system are promoting the formation of new scientific research models, and the intersection and integration of disciplines have increasingly become the main ways to make new breakthroughs, and the openness and globalization of science have become the trend of contemporary science. In such an era when opportunities outweigh challenges, countries around the world have introduced various major research plans, reconstructed national innovation systems, and strived to increase investment in science and technology. Everyone is generally looking forward to a new scientific and technological revolution.
To this end, it is natural that the scientific and technological circles in various countries have called on governments to increase investment in science and technology, and governments are more than ever expecting the scientific and technological community to give more feedback to scientific and technological investment. Therefore, the relationship between government, industry, academia and research has attracted more and more attention from all walks of life, and its complexity seems to even exceed that of science itself.
Increasing investment in science and technology and promoting the combination of government, industry, education and research are of course very important, but what issues are more important than these issues that have not yet attracted attention? The author believes that there is indeed a problem of ignoring the development law of science and technology itself, and these problems may be more critical, and solving these problems may be more important to cope with global challenges, accelerate scientific and technological progress and establish a new scientific research model. Like what:
(1) Knowledge system and its missing links: Can we sort out the logical relationship and structural system between scientific knowledge on the basis of existing knowledge accumulation, so as to clarify the missing knowledge links and optimize and improve the layout of modern science and technology?
(2) Actions to promote new models of scientific research: In the face of the development trend of big data, open access and scientific globalization, how to rationally guide and promote the formation and development of new scientific research paradigms, rather than passively waiting?
Correspondingly, the understanding of knowledge architecture and the change of scientific research paradigm will also put forward a series of new requirements for the structure and management of national innovation systems, and this paper attempts to get rid of inertial thinking and explore these issues.
Second, clarify the structure and logic of the knowledge system
We should systematically and clear the structure and logic of the modern knowledge system, and make the structure and logic of scientific knowledge and applied technology and their relationship with each other the basis for research and development and education layout. By clarifying the structure and logic of the knowledge system, we can organize all disciplines and scientific research fields into a logical framework, promote the cross-integration of disciplines, and greatly improve the efficiency of scientific research and accelerate the process of scientific and technological progress.
The research objects of various disciplines and fields of natural science and technology include nature, material processing science, life science, social science, etc. There is a strict logical relationship between these objects, and the knowledge and technology produced should have a strict structure and logic, and this structure and logic should be determined by the structure and logic between the objects being studied.
However, the layout of existing disciplines and fields is not based on this inherent structure and logic, but in the case of people's understanding is very limited, affected by some accidental or human factors, classified according to the specific problems studied and gradually accumulated and evolved, objectively lacking systematic consideration of the entire knowledge system. For example, the basic disciplines include mathematics, science, chemistry, heaven, earth, and life, and further refine the formation of various sub-disciplines; the application fields include energy, materials, environment, information, etc., as well as the sub-fields formed by further specialization; and then there are interdisciplinary disciplines formed by different disciplines and fields. According to statistics, there are 8530 subject areas that can be defined. The lack of systematic logic between these disciplines and fields makes it difficult to accurately reflect the internal relationship between the various disciplines. What should be paid more attention to is that the formation of disciplines, fields and their branches, although it had its positive significance at that time, will gradually form an invisibly isolated from other disciplines, which is not conducive to the seamless intersection and integration of disciplines.
From this, we have reason to ask the following question: What is the logical relationship between the various disciplines? Is there a certain pattern between the various aspects of knowledge that have been accumulated? Is it possible to break the classification of the original disciplines and fields and outline the complete layout of science and technology according to the logical relationship between existing knowledge? Are there any missing links in the existing body of knowledge? If missing, in what ways is it possible? Do these aspects constitute a bottleneck in the development of modern science and technology? These are very important questions to ponder at the moment, perhaps more important than arguing about investing and giving back.
According to the accumulation of existing science and technology, clarifying the logical relationship between various disciplines and fields is not only conducive to the development and organization of scientific research institutions and the construction of the education system, but also promotes the intersection of disciplines and realizes the seamless integration of related disciplines to minimize duplication and promote cooperation, and at the same time will greatly promote the reconstruction of the education system and the cultivation of interdisciplinary talents. In this sense, the conditions that knowledge architecture and logic should meet are:
(1) Similarity: the structure and logic of the scientific knowledge system should be consistent with the structure and logic of the research object to form a complete architecture;
(2) Universality: maximize the generalization of commonalities, reduce duplication, and facilitate intersection and integration;
(3) Adaptability: Organically unify the hierarchy of research objects and knowledge systems and major socio-economic needs, so as to respond to global challenges more scientifically with the full support of the knowledge system.
Third, the multi-level, multi-scale attributes and mesoscale complexity of the knowledge system
We need to pay attention to the multi-level, multi-scale attributes and mesoscale complexity of the knowledge system. The structure of the material world and human beings themselves and the logical relationships in them are expressed as multi-hierarchical, and each level is manifested as a multi-scale structure, and establishing the relationship between each level and the relationship between different levels is the central task of modern science, of which the mesoscale structure of each level is the key to achieving this central task. Therefore, multi-level, multi-scale and mesoscale will be a salient feature of a complete and reasonable body of knowledge.
The scientific and technological research objects listed in Figure 1 are: the natural world, the science of material processing formed by people in the process of transforming nature, the life science formed in the process of understanding human beings themselves, and the social science of understanding interpersonal behavior.
Figure 1 Multi-level and multi-scale characteristics and mesoscale complexity of scientific and technological research objects
The English word prefix "meso" is derived from the ancient Greek word mésos, meaning "middle" or "between". When studying a problem or process, we usually think of a large group of "units" as "systems". Systems are also affected by their boundaries with the environment. The "mesoscale" here does not refer to absolute physical dimensions, but to a relative concept, referring to any scale range between the unit scale and the system scale. This mesoscale can exist at different levels, so the specific sizes can be very diverse. The "mesoscopic" scale that physics usually talks about is just an example of a mesoscale, which is a mesoscale at the atomic and molecular scales as the unit scale and the block material scale as the system scale.
Traditional methods focus on the unit scale and system scale at each level, and the key to understanding this multi-scale problem lies in the mesoscale structure, that is, the dynamic non-uniform structure exhibited on the scale between the unit and the system, or the static structure derived from such dynamic structure, which is a common challenge in all fields. It should be noted that the mesoscale process is not only related to the field in which it is located, but also to the level of the same field, as shown in Figure 1. This is the root cause of the inherent complexity of the mesoscale problem.
(1) Nature
The smallest unit in nature is the elementary particle. On top of this, there are hadrons, nuclei, atoms, different atoms further constitute molecules and macroscopic materials or minerals, and different minerals constitute rocks, which in turn form geological unit structures, and then further from geological units to form the earth and various stars, and so on, to constitute the entire universe. Thus, there are multi-level, multi-scale structures from elementary particles to the universe. Due to the limitations of knowledge, different levels constitute different disciplines, and the integration and integration between different disciplines is very difficult. On the one hand, this is a natural reflection of the attributes of the knowledge system itself, and on the other hand, this multi-layered attribute leads to the gap between the levels. The object of study as a "system" in a discipline is a "unit" in its adjacent discipline, and vice versa. Therefore, different terms and methods used for the same subject of study may vary greatly between disciplines, creating barriers between disciplines. This is a long-standing problem in the field of science and technology. Although everyone may be aware of it, it is not paid enough attention.
(2) Science of material processing
Similarly, the science of material processing that transforms nature is characterized by multi-level and multi-scale. In this regard, due to the continuous deepening of research work, the understanding of its multi-level and multi-scale characteristics is also more clear. The material conversion process involves three levels: materials, reactors and ecological environment, which correspond to different stages of material processing research and development, namely process innovation, process equipment research and development and system integration. Specific to each level, its interior can often be divided into unit scale, medial scale and system scale.
Although the content and objects of study at the three levels are very different and form different sub-disciplines, they have the following common properties:
(1) All three levels have multi-scale characteristics;
(2) For the boundary scales involved in the three levels (atoms/molecules, particles, unit equipment and ecological environment), the traditional theoretical research has been more in-depth, and gradually formed different disciplines: chemistry, chemical engineering and process systems engineering;
(3) The understanding of the mesoscale problem between the respective boundary scales in the three levels is very limited, which corresponds to the bottleneck problems in the stages of process innovation, process equipment amplification and system integration, respectively, and has become the focus of modern material science and engineering research and development, and is also the key to further breakthroughs.
(3) Life sciences
Life systems also present typical multi-level, multi-scale, and mesoscale structures. Although the problems, contents and methods of different levels of research are different, the four levels have multi-scale characteristics: the biological macromolecule level includes amino acids and nucleotides, secondary structures and proteins, nucleic acids, etc.; the cellular level includes biological macromolecules such as proteins, and many molecules (including biological macromolecules and other molecules) forming supermolecular machines or (sub)organelles and cells; the organ level is composed of cells, tissues and functional organs; the living body level is composed of organs, functional systems (such as digestive system, blood system, nervous system) and complete life body composition. For the boundary scales involved in the four levels, namely the basic unit molecules, biological macromolecules, cells, organs and living organisms, the traditional theoretical research has been relatively in-depth, and gradually formed different disciplines: molecular biology, cell biology, histology and systems biology. However, the understanding of the mesoscale problems between the respective boundary scales in the four levels is very limited, corresponding to the bottlenecks in non-coding RNA, the dynamic structure of biological macromolecules, organelle regulation, organization and functional systems, which have become the focus of modern biology and medical research and development for a long time.
(4) Social sciences
The social sciences are a major category of disciplines that involve society and the interpersonal relationships within it, and also show multi-level attributes, such as family, town, state, etc. Each level also contains multiple scales and exhibits mesascale complexity. That is, the phenomenon of groups at each level is also the most challenging problem for the corresponding sub-disciplines. I will not comment on it in detail here.
(5) The common attributes of the four types of science
The above four categories (see Figure 1) are only representative and easy-to-understand scientific and technological research. In fact, there are many contents, although non-physical existence, but also manifested as multi-level, multi-scale characteristics. Such as neural and cognitive systems, such as linguistic logic and structure, and so on. Although there are differences in these specific objects, their commonality is obvious, that is, they all contain multi-level systems, and each level is manifested as multi-scale, that is, the unit scale, the system scale and the mesoscale between the unit and the system, and the mesoscale problem is also the bottleneck in understanding the multi-scale features.
Studies in recent years have shown that the multi-level mesoscale problem has become a challenging problem to quantify and correlate each level. All mesoscale problems, despite their diversity and complexity, have in common the fact that they may be governed by common principles. These common features include non-uniformity, dynamics, and equality. Modern branches of science generally take one level as the object of study, and the integration and integration between different levels is still very difficult. At one level (or discipline), modern science pays more attention to its unit and system scales, and less to the problem of the intermediate scale between units and systems. As a result, people are often forced to adopt averaging methods to deal with mesoscale non-uniform structures. Thus also emerged the science of complexity that attempted to correlate the unit scale with the system scale. However, complexity science does not pay enough attention to the hierarchical and each level of the mesoscale problem, and does not recognize the missing scientific principles on the mesoscale, which is the fundamental reason for proposing the mesoscale science.
Fourth, realize the integration of technical system and knowledge system
Knowledge systems and technical systems can be integrated. Broadly speaking, the structure and logic of all applied technical fields involve the same category of scientific knowledge, the difference is only that the way in which this knowledge is used to solve problems is different, so it also presents the characteristics of multi-level, multi-scale and meso-scale, which can be integrated with the knowledge system.
The formation of specific technologies and application areas is based on the knowledge at all levels mentioned above, and provides common laws summarized from specific problems for the formation of knowledge systems in development. The increasing blurring of the line between science and technology is the result of this property.
According to the research content, we can roughly classify the scientific and technological fields related to social economy, such as energy, materials, information, earth and climate, life and health, agriculture, space, etc. (of course, there can be different inductions, but it does not affect our analysis of the relationship between knowledge and technology fields) to a multi-level knowledge system. In each field, in the process of development, it involves the above-mentioned multi-level and multi-scale knowledge, but the objects of application of knowledge are different.
However, due to the limitations of understanding, for a long time, the study of these common basic laws has been called basic research, and the study of applying knowledge to solve specific problems is called applied research. Now it seems that such a distinction is not conducive to the intersection of various fields and to the integration of knowledge systems. It is believed that this distinction will gradually fade as the integrity of the knowledge system improves.
Combined with the content discussed in the above two sections, considering the multi-level and multi-scale attributes of existing knowledge and nature, the relationship between the knowledge system and the application field is formed, that is, the layout of science and technology, as shown in Figure 2. Among them, "latitude" (concentric circles) is knowledge, involving elementary particles, molecular atoms, materials, engineering, geoscience, space, astronomy, the universe and other levels (the "engineering" level here is more extensive than other levels, involving various fields); "longitude" (radiation) is technology, each field spans all levels of knowledge; the central area is tools, theories, methods and general knowledge (such as mathematics, mechanics, systems science, etc.). In this way, organizing and deploying scientific research according to the structure and logic of Figure 2 is expected to be more effective with half the effort. Of course, Figure 2 is only a rough framework and needs to be further improved. In fact, different people will have different organizational plans, but the structure and logic of Figure 2 will not change much.
Figure 2 Conceptual model of the layout of science and technology
Fifth, fill in the missing links in the existing knowledge system
We need to fill in the gaps in existing knowledge systems. The mesoscale problem at all levels is a common missing link in the knowledge and technology system, and the mesoscale problem at different levels may have a common law and be governed by a unified principle, which will lead to the transformative progress of science and technology as a whole.
In the multi-level knowledge and technical system described above, at each level, we pay more attention to the units and systems (boundary scale) of this level, study how many units constitute systems, and try to associate unit behavior with system behavior. This hierarchical understanding gradually leads to the formation of various sub-disciplines. However, it has gradually become apparent that the behavior of the units at each level is relatively simple and can be described using existing knowledge; and the interaction between multiple units, to a large extent, determines that the properties of the system at this level (which is also the unit of the previous level) are very complex and cannot be solved by traditional theories and methods. Treatment of mesoscale problems is often based solely on experimental phenomena or on assumptions only: statistical mechanics assumes distribution functions, fluid mechanics assumes constitutive equations, astronomy coarse-grains countless stars and galaxies, and so on. The neglect of mesoscale processes and their principles has become a missing link in modern scientific knowledge and a serious obstacle to the further development of science and technology. For example, many problems in engineering also rely on averaging treatment, ignoring the mesoscale structure; many engineering applications such as turbulence calculation, chemical processes, meteorology, and climate also use averaging empirical parameter processing. Some disciplines even deal with mesoscopic scales, but have not yet recognized the importance of mesoscales.
Many nominal multi-scale studies are actually mainly concerned with the unit scale and the system scale, and insufficient attention is paid to the mesoscale, ignoring the important dominance principle on the mesoscale. This has improved in recent years, but attention to the mesoscale remains inadequate. It is even more difficult to achieve a seamless integration of different levels of knowledge. Moreover, more comprehensively, the boundary scale between two adjacent levels is actually influenced by the mesoscales in these two levels, so only by fully understanding these two mesoscales can we fully grasp this boundary scale. That is to say, traditional knowledge about boundary scales also needs to be updated on the basis of understanding the effect of the mesoscale.
In recent years, the concept of mesoscale science that has gradually emerged from the development of chemical engineering research has touched and attracted everyone's attention to this problem, and more importantly, it is recognized that different levels of mesoscale problems may satisfy common physical principles (coordination of control mechanisms in competition) and mathematical frameworks (multi-objective variations). Once this concept is confirmed and developed into an interdisciplinary science, the gaps in existing knowledge will be filled. This will strongly promote the progress of various disciplines and the integration between different levels of disciplines. Therefore, mesoscale science is a frontier direction that deserves the common attention of all disciplines and should belong to the central area of Figure 2.
In the chemical industry, we have made some progress: from the early specific mesoscale modeling of gas-solid fluidization systems [the so-called least energy multiscale (EMMS) model] to the proposed possible general principle of dominant mechanism coordination in competition (EMMS principle). We believe that all mesoscale problems or processes are dominated by at least two mechanisms. For the sake of discussion, we take two mechanisms as an example, that is, assume that "mechanism A = extremum 1" and "mechanism B = extremum 2" together control the behavior of the system. At this point, the states dominated by mechanism A and mechanism B coexist in a space-time alternating manner. Thus, the variational criterion of the system can be physically expressed as the coordination of the dominant mechanism in competition, and mathematically expressed as a multi-objective variational problem:
Obey the law of conservation: Fi(X)=0, i=1, 2, ..., m(m<n)
where the structural parameters X={x1, x2, ..., xn}.
With the relative enhancement of the dominant role of B (relative to A), three regions pass in turn, as shown in Figure 3 (example of gas-solid fluidization results), showing a distinctly different structure.
Figure 3 As the dominant role of mechanism B increases (as opposed to mechanism A), three regions appear in turn
(1) Mechanism A dominates: When "A = extremum 1" plays a dominant role, and "B = extremum 2" is suppressed, the steady state of the system is almost completely controlled by A, and mechanism B has almost no effect on the structure of the system.
(2) Coordination in A-B competition: With the enhancement of the dominant role of "mechanism B = extremum 2" relative to "mechanism A = extremum 1", there is often a critical point, at which time A loses its absolute dominant advantage relative to B and must coordinate with B. This causes the dominant state of mechanism A and the dominant state of mechanism B (the dominant state at this time is not necessarily the complete dominant state, but is often related to the relative dominance of the two mechanisms, as shown in the three results in the middle of Figure 3) alternately in space-time, resulting in the complexity of the dynamic change of the system at the mesoscale.
(3) B mechanism dominance: When the dominant role of B reaches another critical value, A is completely suppressed, while B is fully realized, and the system is completely controlled by mechanism B.
Although the forms of transition in the control region caused by changes in the relative dominance of the mechanism may be complex and diverse depending on the field of study and the specific system, the above "three regions" characteristics may be universal. It is precisely because the mesoscale phenomenon is not only related to the operation area, but also related to the research field and level, so it is very difficult to explore its common principle.
In order to verify the universality of the EMMS principle and expand its scope of application, we need cross-cutting and integration between different disciplines to find more evidence of the principle of "coordination in competition". By examining different levels of systems, the impact of the hierarchy can be elucidated; by changing the operating conditions, the impact of the operating area can be verified. Once progress is made and the mesophytics are established, we are expected to solve the mesoscale problems in different fields and promote revolutionary advances in theory, computing, and experimentation in different fields, as shown in Figure 4.
Figure 4 Correlation between big data, supercomputing, interscience, virtual reality and the implementation of new technological models
Since the complexity and diversity of the real world always appear on the mesoscale, theory, experiment, and calculation should all focus on the mesoscale phenomenon, as shown in Figure 4. First of all, it is necessary to establish relevant theories to express the coordination principle in the competition on the mesoscale, and to solve the multi-scale dynamic structure by multi-objective variational method. As evidence accumulates from specific problems, interscience may evolve into a interdisciplinary science. Secondly, experiments can generate multi-scale data, and the use of mesophytic methods can identify the dominant mechanism behind these data and model these data at mesoscales, which is also expected to provide a basis for the universality of metascience. Third, computing can also be based on mesophyletic multi-scale modeling, and software and hardware can be developed by achieving similarities in logic and structure, so-called "virtual reality". If the intermediate science is established, it can not only play an important role in revealing the mechanism behind the phenomenon, but also improve the predictive ability and computing speed of the model, and promote the realization of virtual reality and the formation of new scientific research models.
Sixth, refine the principle of scientific commonality
Only by studying specific mesoscale problems and paying attention to the universality of their laws can we obtain the common principle of tectonics. That is, induction from the special to the general. The essence of most of the challenges we face today stems from mesoscale complexity. Because of this diversity of complexity, it may not be realistic to derive a general theory of the mesoscale directly. Instead, we can accumulate sufficient evidence for revealing the principles of tectonics by studying specific problems.
According to the above analysis, combined with the current frontiers and difficulties in various fields, the following problems are typical mesoscale problems. The concept of applied mesosciology (see Figure 4) will accelerate the solution of these problems, and their breakthroughs will also lead to significant progress in the corresponding disciplines and fields, and in turn provide concrete examples for mesosocras, which will effectively promote the formation and development of mesoscale science.
(1) Breakthroughs in photovoltaic, photosynthetic and catalytic mechanisms: will promote the revolution of sustainable energy and material technology, and provide solutions for coping with climate change and achieving sustainable development;
(2) The understanding of the two levels of mesoscale problems of protein three-dimensional dynamic structure and intracellular dynamic processes: will promote the occurrence of the life and health science revolution;
(3) Understanding of turbulence, meteorology, climate, engineering, astronomy, and complex systems of the universe: it will greatly enhance the ability of sustainable development, and increase the ability of human beings to understand nature, transform nature, and prevent and reduce disasters;
(4) Nervous system and intelligent science: through the understanding of the multi-level and multi-scale information transmission and processing mechanism of the nervous system, promote the progress of cognitive, brain, computing, and intelligent science;
(5) Superconductivity, energy storage (heat, electricity), quantum materials, functional materials design: will bring major breakthroughs in the fields of energy, information and materials;
(6) Material design, synthesis and scale preparation: promote industrial modernization, especially the development of manufacturing;
(7) Supercomputing, intelligence, big data, virtual reality: it will greatly enhance the ability of human beings to understand and transform nature, and create a major change in the scientific research model and a revolution in the way of life and production.
In addition, breakthroughs in the two extreme levels of smaller and larger intermediate scale problems in Figure 2, such as the further deepening of quantum mechanics, and the further understanding of the structure of galaxies and supercluxies and their evolutionary laws, will also fundamentally promote people's understanding of the material world.
Solving problems in these different fields based on the concept of interscience can consciously promote the development of interdisciplinary disciplines in three ways (Figure 5).
Figure 5 Three interdisciplinary approaches: spanning sub-disciplines at different levels within the same discipline, spanning different disciplines, and spanning common problems prevalent across all disciplines (NBIC stands for nanotechnology, biotechnology, information technology, and cognitive science)
(1) Interdisciplinary approach 1: Span sub-disciplines at different levels within the same discipline. The concepts of mesophylla discussed in this paper have been used to study the mesoscale problems at different levels (sub-disciplines) in the science of processing matter, such as the interface and material structure at the material level; the dynamic non-uniform structure of gas-solid fluidization systems at the reactor level, gas-liquid systems, turbulence; and the process integration superstructure at the ecological and environmental level.
(2) Interdisciplinary Approach 2: Across different disciplines. The challenging problems listed earlier in the study involve different areas. For example, for the nervous system, dynamic changes at different levels of the mesoscale may follow the same laws as problems such as complex flows, associated electronic systems, protein structure, etc., that is, these problems may all be related to the principle of coordination in the competition of different dominant mechanisms.
(3) Interdisciplinary Approach 3: Cross common problems that are prevalent across all disciplines. Interdisciplinary cross-cutting can be further extended to study common problems in all areas, such as big data, supercomputing, and virtual reality, as shown in Figure 4.
If different disciplines and fields can jointly explore the intersection of the above three disciplines, we will see a very different scientific and technological landscape, that is, based on the principle of commonality of shared mesoscales. This argument may be a bit abrupt, but at least it is worth paying attention to and trying.
The principle of coordination in competition (EMMS, see section 5) aimed at developing tectonics adopts a different approach to thermodynamics. Thermodynamics expects to use a single variational criterion to determine the steady state of the system.
In fact, as long as two or more dominant mechanisms are involved (the A–B coordination area discussed in Figure 3 discussed in Section 5), it is difficult to derive a single variational criterion directly, because the different dominant mechanisms have opposite objectives. At this time, it is necessary to consider the coordination of different variational criteria reflecting different mechanisms (which may correspond to different dissipation processes) in the competition. This may also be the reason why it is so difficult and controversial to select a uniform single variational criterion for dissipative structures in nonlinear non-equilibrium thermodynamics.
Based on the EMMS principle (whose adaptability has been verified in many systems), we further deduce that the disputes mentioned above are due to the neglect of the principle of coordination in competition and the law that variational criteria relate to the operating area (see Section 5 and Figure 3), although more evidence is needed to verify. In other words, the minimum dissipation and maximum dissipation correspond to different dominant mechanisms, and together they control the structure of complex systems, which have been preliminarily demonstrated in turbulent, gas-solid fluidization, and reaction-diffusion systems. The two can function in the same system, but only in the A–B coordination area (see Figure 3), and in an alternating manner, i.e., at different times, at different locations, or at different moments in the same location, respectively present their respective extremum trends. For mechanism A dominant region and B mechanism dominant region, a single extremum criterion (minimum dissipation or maximum dissipation) may be applicable, because the dissipation process at this time is dominated by a single extremum.
Based on the evidence currently available, we preliminarily deduce that considering the coordination of competition between different dominant mechanisms (corresponding to different variational criteria) and attaching importance to the dependence of variational criteria on the operating area, the above disputes can be quelled. That is, a single total dissipation (entropy-produced) variational criterion may not be sufficient to describe the A-B coordination area. This critical issue has not received much attention. One of the tasks of the interscience is to solve this problem. Another question that needs to be clarified: How applicable is the EMMS principle? The National Natural Science Foundation of China has launched the Introduction Tossy Science Research Program to fund research in different fields. The aim of the program is to gather more evidence and explore the universality of the EMMS principle of coordination in competition from different aspects, such as the basic issues involved in the principle and the variation criterion.
7. Examples of energy technology research and planning
At present, energy research is mainly organized according to the type of energy, such as nuclear energy, renewable energy, and fossil energy. In the future, according to the logic of the knowledge system, on the basis of comprehensive consideration and interdisciplinarity, an energy research complex can be designed in order to reveal the key technologies and common scientific problems in the entire field, so as to form teams, build institutions, and build platforms.
Energy is one of the key elements of sustainable socio-economic development, but its use contributes to the global challenge of climate change. Of course, most governments and industrial R&D departments make energy research their primary task. The efficiency of energy research is closely related to the ability of human beings to achieve sustainable development. Rational planning and organization of R&D activities is essential to improve research efficiency and promote energy revolution. However, the existing energy research basically does not consider the logic and structure of the knowledge system, so it is difficult to achieve interdisciplinary. This is a limitation in organizational management. For example, often different laboratories study nuclear, renewable, and fossil fuels separately, and develop them in an almost isolated manner. What's more, it is often based on completely different disciplines, ignoring their commonalities and complementarities in knowledge. As we explore new scientific and technological revolutions, new scientific research models, and responses to global challenges, we should change this situation in the future.
Based on the structure and logic of the body of knowledge, Figure 6 presents a preliminary framework for the energy research complex for nuclear, renewable and fossil energy. In other words, organizing research in the field of energy needs to follow these steps:
Figure 6 The energy research complex is planned according to the structure and logic of the knowledge system
(1) Form a professional team: Identify the most challenging scientific problems of various types of energy (such as nuclear energy, renewable energy, fossil energy) at different levels, so as to organize professional research teams to tackle these problems related to energy types.
(2) Create interdisciplinary departments: Analyze the interconnection of different types of energy specific problems at the same level to determine common scientific problems, that is, organize unified research departments for different energy sources at the same level, that is, interdisciplinary departments.
(3) Creation of interdisciplinary centers: To explore common challenges at different levels, such as mesoscale problems, it is necessary to create a department that studies common problems in the field of energy, that is, interdisciplinary centers.
(4) Establish a common platform: Establish a common platform for different teams and departments, focusing on their ability and level in theory, experimentation and computing.
This multi-level planning of the energy research complex is expected to minimize repetitive research, maximize interaction between different teams and departments, and form R&D strength. Of course, the management mechanism that matches this is also the key to achieving the desired goal.
Eighth, the formation of a new scientific research model
We need to guide the formation of new research models. Clarifying the structure and logic of the knowledge system will lead to changes in theories, methods, tools and ways of thinking, coupled with the promotion of information technology and data science, and the future scientific research model will undergo fundamental changes. How to deal with this change is also another key issue to achieve leapfrog development of science and technology, which requires the attention of the scientific and technological community, the government and relevant departments.
(1) Changes caused by information technology: Due to the development of information technology and the rise of big data, new scientific research models are also being nurtured. This process is accompanied by the openness of science and the rapid development of the world, such as open access (OpenAccess, OA) and a variety of new models of scientific research, publishing, communication, and sharing, which will bring about revolutionary changes in the scientific research environment. However, how to rationally guide this change is a major challenge facing the current scientific research management. For example, OA is certainly conducive to the sharing of new knowledge by humans, but how should OA develop healthily without over-"commodification" of knowledge affecting the healthy dissemination of knowledge? What systems should be put in place to ensure that OA benefits humanity? For example, information technology is of course very important for the transformation of the way knowledge is disseminated, but how to ensure the quality and orderliness of knowledge? How to ensure timely and reliable knowledge from massive amounts of data? For example, big data is believed to lead to changes in scientific research models, but what is the hidden scientific principle behind big data? This needs to be paid full attention to by the scientific and technological community. Problems such as these are numerous and require a global regime to regulate them. Just like the gradual establishment of the patent system in the process of technological development, at present, in the process of forming a new scientific research paradigm, which new global systems are needed for our deep consideration and global action.
(2) Changes caused by breakthroughs in research methods, theories and technologies: According to the structure and logic of the above-mentioned knowledge system, as well as the missing links in the mesoscale discussed, the research methods, theories, and tools should also have corresponding changes, and these changes will also be important features of the new scientific research model, and some disciplinary restructuring is also required. For example, mesoscale structures tend to exhibit properties of space-time dynamics and high non-uniformity, and are characterized by a mixture of order and disorder, which poses a major challenge to measurement and experimental techniques due to the higher spatio-temporal resolution required. For example, the measurement of the three-dimensional dynamic structure of proteins, the group movement of electrons in materials, and the signal transmission laws at each level of the nervous system at the mesa scale will be important contents of future scientific research. Corresponding to this, research methods will also undergo fundamental changes, and research methods dominated by analysis, deduction and determinism are and will continue to gradually give up a part of the space to numerical and graphical simulations and scientific methods of uncertainty, and even virtual reality will become an important research tool and tool. Some traditional theories based on static, linear, and balanced will be replaced by dynamic, nonlinear, non-equilibrium, and theories with the median scale structure as the core; the breakthrough of the intermediate scale problem at each level will make it possible to integrate and integrate knowledge at different levels, resulting in the penetration of different disciplines, and research institutions and organizational departments should be comprehensively reorganized to adapt to the logic of the knowledge system. These will likely become trends in the future.
We should make full preparations for the corresponding changes in scientific research and thinking modes caused by the changes in these two aspects, so as to rationally prevent the resistance to change brought about by the inertia of thinking and guide the healthy and rapid development of science and technology.
IX. Conclusion
Driving the changes necessary for the times requires the joint efforts of all parties. A complete understanding of the structure and logic of the knowledge system, as well as changes in the scientific research environment, will lead to the gradual formation of new scientific and technological layouts and new scientific research models, which will be one of the characteristics of science and technology in the 21st century. The promotion of this process requires the joint efforts of all walks of life, otherwise it may lead to a rather long process due to the inertia of our thinking. The purpose of this article is to remind all walks of life around the world that promoting this process requires the joint efforts of all disciplines and the firm support of governments. The breaking and gradual integration of disciplinary boundaries, the emergence of new ideas and timely support, all require the academic community to adopt an open mind, but also the scientific and technological community, governments, and all international scientific organizations to actively promote. The attitude of these changes will determine whether a new scientific and technological revolution can be realized and whether a new scientific research model can be formed, which is crucial for open global science. "Openness" here refers not only to the acquisition of knowledge, but also to the way of thinking; "global" refers not only to space, but also to the intersection of different disciplines, as a whole! As our understanding of possible common principles increases, the natural sciences, engineering sciences, humanities, and social sciences will be unified to a certain extent along the common path of mesoscale. All parties should be fully aware of this. Only in this way can humanity respond more effectively to global challenges.
In addition, these changes will inevitably lead to changes in the innovation system, education system and scientific research management mode of various countries, and all governments should take the initiative to adapt to these changes, make necessary adjustments to the national innovation system, and optimize the size and structure of the scientific and technological team. At the global level, international scientific organizations should consider the relationship between national innovation systems, and even consider how to promote the establishment of a global innovation system in the context of "open and global science", at least cooperation between countries, so as to effectively improve the efficiency and capacity of the innovation system and ensure that science and technology can still develop rapidly in the context of limited increased scientific research investment. This may be more important than asking for input and pursuing giving back.
Finally, I would like to emphasize that we are in an era of rapid change, and the ability and flexibility to adapt to these changes is critical to accelerating scientific and technological progress and addressing global challenges. The scientific and technological community, industry, government departments and especially international organizations, which have the ability and responsibility to make a "global voice", should first promote this paradigm change. The views expressed in this article are only one-sided and can only be discussed.
Note: The content of this article is slightly adjusted, if necessary, you can view the original text.
About the author
Li Jinghai is a chemical engineering expert and an academician of the Chinese Academy of Sciences.
Mainly engaged in the research of quantitative design and amplification of two-phase systems of particulate fluids, he is committed to computer simulation of gas-solid two-phase systems and research of multi-phase complex systems, including multi-phase flow, coal combustion, computer simulation, multi-scale methods and calculations.
Adapted original text:
Jinghai Li.Exploring the Logic and Landscape of the Knowledge System: Multilevel Structures, Each Multiscaled with Complexity at the Mesoscale[J]. Engineering,2016,2(3):276-285.