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We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

Future developments of computer systems will be handicapped not by the limitations of hardware, but by our lack of understanding of the human reasoning processes. The development of three‐dimensional chips, cryogenic superconducting, or optical systems — and in due course, biological computers — presages the emergence of generations of super information processors whose power will dwarf the present generation of devices as they, in turn, have dwarfed the capacity of the computers of the pre‐transistor age. The effective application of such powerful future computers will be limited by the lack of an adequate theoretical basis for the processing of information. Gordon Scarrott has championed the need for a ‘science of information’ which should investigate the ‘natural properties of information such as function, structure, dynamic behaviour and statistical features…’ Such an effort should ‘… lead to a conceptual framework to guide systems design.’

The complexity of a general system is identified with its temperature and, analogously with Boltzmann's probability density in thermodynamics, this temperature is related to the informational entropy of the system. The concept of informational entropy of deterministic functions provides a straightforward modelling of Brillouin's negentropy (negative entropy), therefore a system can be characterized by its complexity and its dual complexity. States composition laws for complexities expressed in terms of Shannonian entropy with or without probability, and then the approach is extended to quantum entropy of non‐probabilistic data. Outlines some suggestions for future investigation.

Presents an example of the analysis of civilizing processes with the method of exact sciences to determine what the absolute limit of human civilization development is and what it depends on. Gives some practical instances such as the political and social interpretation of the mathematical results arising from this new kind of analysis.

Consideration is given to the problem of optimally choosing of a fixed number of experiments in sequential form from a class of available experiments. The applications of ø entropy measure in the sequential design of experiments is studied by defining ø terminal entropy. Finally, the process is established when a sufficient experiment exists. An illustrative example, which demonstrates the usefulness of the results obtained, is included.

This chapter explores the ways in which cybernetics influenced the works of F. A. Hayek from the late 1940s onward. It shows that the concept of negative feedback, borrowed from cybernetics, was central to Hayek’s attempt to explain the principle of the emergence of human purposive behavior. Next, the chapter discusses Hayek’s later uses of cybernetic ideas in his works on the spontaneous formation of social orders. Finally, Hayek’s view on the appropriate scope of the use of cybernetics is considered.

The purpose of this paper is to review previous research studies on mathematical models for entropy generation in the magnetohydrodynamics (MHD) flow of nanofluids. In addition, the influence of various parameters on the velocity profiles, temperature profiles and entropy generation was studied. Furthermore, the numerical methods used to solve the model equations were summarized. The underlying purpose was to understand the research gap and develop a research agenda.

This paper reviews 141 journal articles published between 2010 and 2022 on topics related to mathematical models used to assess the impacts of various parameters on the entropy generation, heat transfer and velocity of the MHD flow of nanofluids.

This review clarifies the application of entropy generation mathematical models, identifies areas for future research and provides necessary information for future research in the development of efficient thermodynamic systems. It is hoped that this review paper can provide a basis for further research on the irreversibility of nanofluids flowing through different channels in the development of efficient thermodynamic systems.

Entropy generation analysis and minimization constitute effective approaches for improving the performance of thermodynamic systems. A comprehensive review of the effects of various parameters on entropy generation was performed in this study.

Norbert Wiener’s cybernetic paradigm represents one of the seminal ideas of the twentieth century. Nevertheless, its full potential has yet to be realized. For instance, cybernetics is relatively little used as an analytical tool in the social sciences. One reason, it is argued here, is that Wiener’s framework lacks a crucial element – a functional definition of information. Although so‐called Shannon information has made many valuable contributions and has many important uses, it is blind to the functional properties of information. Here a radically different approach to information theory is described. After briefly critiquing the literature in information theory, a new kind of cybernetic information will be proposed called “control information.” Control information is not a “thing” but an attribute of the relationships between things. It is defined as: the capacity (know‐how) to control the acquisition, disposition and utilization of matter/energy in purposive (cybernetic) processes. This concept is briefly elucidated, and a formalization proposed in terms of a common unit of measurement, namely the quantity of “available energy” that can be controlled by a given unit of information in a given context. However, other metrics are also feasible, from money to allocations of human labor. Some illustrations are briefly provided and some of the implications are discussed.

Independent component analysis (ICA) is a widely-used blind source separation technique. ICA has been applied to many applications. ICA is usually utilized as a black box, without understanding its internal details. Therefore, in this paper, the basics of ICA are provided to show how it works to serve as a comprehensive source for researchers who are interested in this field. This paper starts by introducing the definition and underlying principles of ICA. Additionally, different numerical examples in a step-by-step approach are demonstrated to explain the preprocessing steps of ICA and the mixing and unmixing processes in ICA. Moreover, different ICA algorithms, challenges, and applications are presented.