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Discusses the efficiency of a cybernetic approach to non‐oscillatory luminescence processes, generated by perturbed biosystems, and applies it to oscillatory luminescence processes. Constructs multiplicative stochastic models of oscillatory bio‐ and chemiluminescence processes, generated by some perturbed/stimulated biosystems (a temperature‐stimulated soybean root system, light‐stimulated microporocytes of larch, antiviral drug‐treated vero cells infected by Herpes simplex virus). Determines a correlation structure for these models by analysing their transfer functions. Uses the memory function approach to compare and contrast the oscillatory processes with their non‐oscillatory analogs. Formulates a hypothesis about the dependence between the persistence and the oscillatory behaviour of biosystems and proposes stochastic perturbation measures founded on those multiplicative models.

A new approach to living organisms with irreversibly perturbed homeostatis, based on the integrated moving average IMA (0,1,1) and autoregressive‐integrated moving average ARIMA (1,2,1) linear stochastic models of non‐stationary photon emission processes, is proposed. The approach consists of introducing a stochastic formulation of the transfer function and memory functional into a general description of non‐equilibrium states of perturbed organisms. A memory function‐based quantitative measure of perturbed biohomeostasis has also been proposed and discussed.

A kinetic model for the biphasic modulation of phagocytosis, fulfilling the requirements of biochemistry of ligand‐binding reactions, was constructed on the basis of a biocybernetic notion of the feedback loop‐containing (autocalytic) process. Using boundary conditions for parameters of possible kinetic models, a single model was selected, in which the biphasic modulatory effect exerted on a phagocytic activity of human neutrophils by the peptide preparation Immax A¹ was described as a result of mutual counteraction of two antagonistic compounds (stimulator and inhibitor of phagocytosis) competing for bacterial chemotactic peptide receptors on neutrophils. This model, in which the integrated luminescence‐based normalised measure of inhibition of phagocytosis stands for the reaction rate, was found to have a form of a 2:3 rational function of the peptide preparation concentration. A corresponding stoichiometric scheme, describing the binding both of the inhibitor and of the stimulator to neutrophils, was constructed on the assumption that inhibition was not total when connected with the generation of three‐component complexes, stimulator‐neutrophil‐inhibitor.

A control of the first‐line human defence system, using mechanisms analogous to specific and non‐specific (parametric) triggering of phagocytosis, is proposed on the basis of an autocatalytic model of biphasic modulation of phagocyte luminescence by experimental peptide medicines. Properties of the autocatalytic model are described and its catastrophe and bifurcation sets determined. The mechanism analogous to parametric triggering is shown to result from a fold catastrophe produced by the changes in the autocatalytic interaction parameter characterising a given peptide preparation. Usefulness of the model was shown by its application to two distinct peptide preparations having different immunomodulatory properties.

Shows how the essential ecotoxicological question of constructing a quantitative model of the interactions between environmental stress factors, and their synergistic effects resulting in a biosystem's perturbation, even in its pathology, has been formulated from a systemic viewpoint. The proposed model has been developed on the basis of the multicorrelational structure of heavy‐metal concentrations in the blood of pregnant women with a high environmental exposure to those metals. The proposed model uses an original notion of the interaction which allows one to find and explain the observed connection between the phenomenon of spontaneous abortion as an environmentally induced foetal pathology and some heavy metals, particularly cadmium and lead.

A method for approximation of the Shannon entropy of Gaussian photon‐counting processes with infinite history was constructed on the memory function of these processes, described by autoregressive‐integrated moving average (ARIMA) models. Most frequently, photon‐counting processes are stationary or nonstationary multidimensional Gaussian discrete‐time stochastic ones which justify the use of the ARIMA models. Starting from the memory function, a memory time‐equivalent finite autoregressive representation of a given process with infinite history, i.e. a stationary finite‐order Gaussian Markov chain, was determined, then corresponding autocorrelation matrices were calculated from the truncated memory function using the Yule‐Walker equations, and an autocorrelation‐based formula for approximation of the entropy of the process through the entropy of its stationary Markovian representation was given. An ARMA(1,1) process together with its stationary (MA(1)) or nonstationary (IMA(0,1,1)) boundary cases were considered to demonstrate opposite changes in the entropy as the memory time increases at a fixed variance of the process: the entropy was found to decrease for stationary processes and increase for nonstationary ones. It was also found on experimental examples (perturbed human neutrophils and yeast cells) that those changes can be reversed by opposite changes in the process variance. The method allows us to determine, at any desired accuracy, the Shannon entropy of time‐discrete stochastic processes, and reveals new aspects of the relationship between the process' stationarity, memory, entropy and heteroskedasticity.

Machine Intelligence and the Human Window Writing in Applied Artificial Intelligence (Vol. 5 No. 1, 1991, pp. 1–10), Donald Michie of the Turing Institute, Glasgow, UK, considers Machine Intelligence and the Human Window. He says that: