Analysis of the diversity of population and convergence of genetic algorithms based on Negentropy

With its wide use in different fields, the problem of the convergence of simple genetic algorithms (GAs) has been concerned. In the past, the research on the convergence of GAs was based on Holland's model theorem. The diversity of the evolutionary population and the convergence of GAs are studied by using the concept of negentropy based on the discussion of the characteristic of GA. Some test functions are used to test the convergence of GAs, and good results have been obtained. It is shown that the global optimization may be obtained by selecting appropriate parameters of simple GAs if the evolution time is enough.

Entropy and Negentropy Principles in the I-Theory

Applying the I-Theory, this paper gives a new outlook about the concept of Entropy and Negentropy. Using S∞ particle as 100% repelling energy and A1 particle as the starting point of attraction, we are able to define Entropy and Negentropy on the quantum level. As the I-Theory explains that repulsion force is driven by Weak Force and attraction is driven by Strong Force, we also analyze Entropy and Negentropy in terms of the Fundamental Forces.

Incipient Gear Fault Detection Using Adaptive Impulsive Wavelet Filter Based on Spectral Negentropy

Adaptive wavelet filtering is a very important fault feature extraction method in the domain of condition monitoring;however, owing to the time-consuming computation and difficulty of choosing criteria used to represent incipient faults, the engineering applications are limited to some extent. To detect incipient gear faults at a fast speed, a new criterion is proposed to optimize the parameters of the modified impulsive wavelet for constructing an optimal wavelet filter to detect impulsive gear faults. First, a new criterion based on spectral negentropy is proposed. Then, a novel search strategy is applied to optimize the parameters of the impulsive wavelet based on the new criterion. Finally,envelope spectral analysis is applied to determine the incipient fault characteristic frequency. Both the simulation and experimental validation demonstrated the superiority of the proposed approach.

Informational Structure of the Living Systems: From Philosophy to Informational Modeling

In this paper,there are discussed the informational functions of the living structures,analyzing the properties of the simplest eukaryotic cell as an example of a structural unit of the living unicellular and multicellular systems.The initiation of this analysis starts from an older example of an imaginary mechanism,particularly that described by the Maxwell’s demon experiment,which along the history of the information development concepts accompanied the philosophic vision on the structuration of matter and of the living entities,showing that these are actually the result of the intervention of information on the matter available substrate.Particularly,it is shown that the deoxyribonucleic acid(DNA)structure is appropriate to store a large quantity of structural information,allowing the transfer of this information by transcription and translation mechanisms to proteins,which act as(re)structuration/transmission informational agents,or the generation of a new cellular daughter structure by a replication process.On the basis of the theory of information in communication channels,applicable also in biological systems,it was discussed the followed line for the evaluation of the quantity of structural information in various cells,demonstrating the evolution of organism complexity by the increase of the structural information quantity from unicellular(bacterium)to human cell.Applying a natural strategy of entropy lowering mainly by heat elimination,folding protein structuration and compartmentalization on the evolutionary scale,the living structures act as dynamic entities assuring their self-organizational structure by a permanent change of matter,energy and information with the environment in an efficient way,following a negative entropic process by internal structuration,similarly with Maxwell’s demon work.It is shown that to assure such a communication with external and internal intracellular structure,it was necessary the development of an own info-operational system of communication and decision,in which the operational“Yes/No”decisional binary(Bit)unit is essential.These revolutionary results show that the cell unit complies with the similar informational functions like the multicellular structure of the human body,organized in seven-type informational components,allowing the informational modeling of the activity of the living biologic structures and the opening of a shortcutting way to mimic the biologic functions in artificial cells.

Thermodynamic Formulation of Living Systems and Their Evolution

The purpose of this review article is to present some of the recent contributions that show the use of thermo-dynamics to describe biological systems and their evolution, illustrating the agreement that this theory pre-sents with the field of evolution. Organic systems are described as thermodynamic systems where entropy is produced by the irreversible processes, considering as an established fact that this entropy is eliminated through their frontiers to preserve life. The necessary and sufficient conditions to describe the evolution of life in the negentropy principle are established. Underlining the fact that the necessary condition requires formulation, which is founded on the principle of minimum entropy production for open systems operating near equilibrium. Other formulations are mentioned, particularly the information theory, the energy inten-siveness hypothesis and the theory of open systems far from equilibrium. Finally suggesting the possibility of considering the lineal formulation as a viable alternative;that is, given the internal constrictions under which a biological system operates, it is possible that the validity of its application is broader than it has been suggested.

An Analysis of SARS-CoV-2 on the Molecular and Subatomic Levels through Applied I-Theory

The novel SARS-CoV-2 virus is spreading very effectively and is highly contagious. The SARS-CoV-2 virus is not acting like other viruses and many various symptoms in the human body have been observed. The analysis of the SARS-CoV-2 structure in light of the functioning of the CRISPR-Cas9, Cas12a and CasX mechanism suggests that the virus is man-made through genetic modification. Vaccination currently appears to be a feasible and promising means of pandemic containment. The first vaccines have been approved even though the mechanism of action of the virus is not yet fully understood. Two of the prominent vaccines are mRNA vaccines, which use genetic engineering. It cannot be ruled out that the mRNA vaccines would negatively affect human cells as well. This paper provides an analysis of the SARS-CoV-2 virus mechanism, based on an emerging unified quantum theory called the I-Theory, which gives a deeper understanding of the concepts of entropy and negentropy in the field of health. The analyses are developed on the molecular and subatomic levels in order to demonstrate how the entropy-negentropy balance is disrupted and the effects upon infected cells. It is shown that the base and the ionized oxygen atom of the RNA phosphate group have been replaced by a hydroxyl radical. On a subatomic level, this modification results in one electron (a fermion) missing from the phosphate base. It is assumed that due to the missing fermions and resulting higher levels of entropy, SARS-CoV-2 has a very high level of repulsion force, destroying the cytoplasm. The entropic effect leads to a sort of ballooning and weakening of infected cells, and in the case of low immunity or diseased cells, creates damage to the organs and various long-term effects.

Rapid Algorithm for Independent Component Analysis

A class of rapid algorithms for independent component analysis (ICA) is presented. This method utilizes multi-step past information with respect to an existing fixed-point style for increasing the non-Gaussianity. This can be viewed as the addition of a variable-size momentum term. The use of past information comes from the idea of surrogate optimization. There is little additional cost for either software design or runtime execution when past information is included. The speed of the algorithm is evaluated on both simulated and real-world data. The real-world data includes color images and electroencephalograms (EEGs), which are an important source of data on human-computer interactions. From these experiments, it is found that the method we present here, the RapidICA, performs quickly, especially for the demixing of super-Gaussian signals.

Thermodynamic Formulation of Living Systems and Their Evolution II

In the previous paper [1], the application of the general thermodynamic theory was considered to biological systems. The nature of living matter has been presented from the mesoscopic to the macroscopic point of view, for different time scales (ontogenetic and phylogenetics). Herein, we continue with this application, and present three characteristics of life in the form of statements or postulates. The first characteristic describes the probability of survival against aging. In particular, the behaviour of life is shown as an independent mode of aging. The second characteristic refers to the adaptation of the species according to the environment. The relationship between the phenomenon of organic homeostasis and the origin of the clinical parameters that define health is highlighted. And finally, the third characteristic applies the principle of negentropy to describe evolution. A representative model is given as an example of each postulate.

The Four Most Basic Elements In Machine Cognition

The widespread use of ChatGPT has normalized the dialogue Turing test.To meet this challenge,China's major national development strategy suggests that for a new generation of artificial intelligence,it is first necessary to answer the big questions raised by Turing in 1950 from the perspective of cognitive physics:Can machines think?How do machines think?How do machines cognize?Whether it is carbon-based human cognition or silicon-based machine cognition,it is an interaction between complex constructs composed of the four most basic elements:matter,energy,structure,and time.Both humans and machines depend on negative entropy for living,and time is the cornerstone of cognition.Structure and time are parasitic on matter and energy in physical space,forming hard-structured ware.The soft-structured ware in cognitive space is mind,which is parasitic on the hard-structured ware or other existing soft-structured ware,and constitutes a rich hierarchy of multi-scale feelings,concepts,information,and knowledge.Extending"abstraction"from the symbolic school of artificial intelligence,"association"from the connectionist school,and"interaction"from the behaviorist school,the core of cognition is established on the shoulders of such scientific giants such as Schrodinger,Turing and Wiener.Soft and hard-structured ware interact.Cognitive machine can comprise heterogeneous hard-structured ware,such as field programmable gate arrays(FPGAs),data processing units(DPUs),central processing units(CPUs),graphics processing units(GPUs),tensor processing units(TPUs),and memory.It can also be implanted with the"Baby Cognitive Nucleus"which is hard-structured ware genetically inherited and naturally evolved to form the embodied machine.Then the hard-structured ware is parasitized by rich,multi-scale soft-structured ware.By regulating matter and energy through soft-structured ware,machines produce orderly events,form coordinated and orderly thinking activities.The heterogeneous sensors configured by the machine and the speed of thinking will no longer be trapped by the extreme values of biochemical parameters of carbon-based organisms but will be able to perceive through multi-channel cross-modal means,carry out intense thinking,and maintain cognitive continuity with memory.To generate computational and memory intelligence in cognitive space which can bootstrap,self-reuse and self-replicate,imagination and creativity are improved through memory-constrained computing.The new generation of artificial intelligence will leap beyond mechanized mathematics to automation in thinking and self-driven growth of cognition,and the thinking in the cognitive space and the behavior in the physical space verify each other,from the dialogue Turing test to the embodied Turing test.Humans have entered the intelligent era of human-machine co-creation with cognitive machines iteratively inventing,discovering,and creating alongside scientists,engineers,and skilled craftsmen,each wise in its way,improving thinking ability,and amplifying human energy.

Noisy component extraction with reference

Blind source extraction （BSE） is particularly at- tractive to solve blind signal mixture problems where only a few source signals are desired. Many existing BSE methods do not take into account the existence of noise and can only work well in noise-free environments. In practice, the desired signal is often contaminated by additional noise. Therefore, we try to tackle the problem of noisy component extraction. The reference signal carries enough prior information to dis- tinguish the desired signal from signal mixtures. According to the useful properties of Gaussian moments, we incorporate the reference signal into a negentropy objective function so as to guide the extraction process and develop an improved BSE method. Extensive computer simulations demonstrate its validity in the process of revealing the underlying desired signal.

Pulsed Eddy Current Signal Denoising Based on Singular Value Decomposition

The noise as an undesired phenomenon often appears in the pulsed eddy current testing(PECT)signal, and it is difficult to recognize the character of the testing signal. One of the most common noises presented in the PECT signal is the Gaussian noise, since it is caused by the testing environment. A new denoising approach based on singular value decomposition(SVD) is proposed in this paper to reduce the Gaussian noise of PECT signal. The approach first discusses the relationship between signal to noise ratio(SNR) and negentropy of PECT signal. Then the Hankel matrix of PECT signal is constructed for noise reduction, and the matrix is divided into noise subspace and signal subspace by a singular valve threshold. Based on the theory of negentropy, the optimal matrix dimension and threshold are chosen to improve the performance of denoising. The denoised signal Hankel matrix is reconstructed by the singular values of signal subspace, and the denoised signal is finally extracted from this matrix. Experiment is performed to verify the feasibility of the proposed approach, and the results indicate that the proposed approach can reduce the Gaussian noise of PECT signal more effectively compared with other existing approaches.