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Coronavirus outbreaks in 2019 (COVID-19) have been a huge disaster in the fields of health, economics, education, and tourism in the last two years. For diagnosis, a quick interpretation of the COVID-19 chest X-ray image is required. There is also a strong need to find an efficient multiclass segmentation technique for the analysis of COVID-19 X-ray images. Most of the threshold selection techniques are entropy-based. Nevertheless, these techniques suffer from their dependencies on the spatial distribution of grey values. To tackle these issues, a novel non-entropic threshold selection method is proposed, which is the primary key contribution having found a new source of information to the biomedical image processing field. The firsthand Square Error (SE)-based objective function is suggested. The second key contribution is the new optimizer called Fast Cuckoo Search (FCS), which is useful and brings novel ideas into the subject, used to optimize the suggested objective functions for computing the optimal thresholds. To ensure a faster convergence with a quality optimal solution, we include extra exploitation together with a chance factor. The FCS is validated using the well-known classical and CEC 2014 benchmark test functions, which shows a significant improvement over its predecessors—Adaptive Cuckoo Search (ACS) and other state-of-the-art optimizers. Further, the SE minimization-based optimal multilevel thresholding method using the FCS, coined as SE-FCS, is proposed. To experiment, images are considered from the Kaggle Radiography database. We have compared its performances with Tsallis, Kapur's, and Masi entropy-based techniques using well-known segmentation metrics and achieved a performance increase of 2.95%, 5.51% and 10.50%, respectively. The proposed method shows superiority using Friedman's mean rank statistical test and ranked first. [ABSTRACT FROM AUTHOR]

Abstract The significance of neuronal discharge symmetry lies in its reflection of the stability and orderliness within the neuron's activity. This symmetry is correlated with various issues, including the biophysical properties within neurons, network structures, and synaptic transmission. Memristors are highly analogous to neuronal synapses due to their unique memory properties, allowing them to emulate biological neural synapses. In this work, a memristor is employed into the Hindmarsh‐Rose (HR) neuron as a synapse for the construction of a memristive HR neuron. Accompanied with the introducing of the absolute value and signum function, the derived neurons exhibit complex coexisting symmetric firing patterns. The phenomenon of symmetric firing can effectively simulate the depolarization and hyperpolarization processes of neurons, providing a new approach to study the diversity of the brain.

The significance of neuronal discharge symmetry lies in its reflection of the stability and orderliness within the neuron's activity. This symmetry is correlated with various issues, including the biophysical properties within neurons, network structures, and synaptic transmission. Memristors are highly analogous to neuronal synapses due to their unique memory properties, allowing them to emulate biological neural synapses. In this work, a memristor is employed into the Hindmarsh‐Rose (HR) neuron as a synapse for the construction of a memristive HR neuron. Accompanied with the introducing of the absolute value and signum function, the derived neurons exhibit complex coexisting symmetric firing patterns. The phenomenon of symmetric firing can effectively simulate the depolarization and hyperpolarization processes of neurons, providing a new approach to study the diversity of the brain. [ABSTRACT FROM AUTHOR]

Rapid processes in the area of cell biophysics, invasions, or epidemiology are distinguished by the variety of their variants; therefore, they require original methods of mathematical description for long-term prediction of the state of biophysical systems. Without building predictions of the dynamics of biophysical interaction, it is impossible to improve the technology of industrial exploitation of natural objects. Classical models based on systems of differential equations do not describe the dynamics of real processes that are observed during aggressive invasions of alien species. Known models of "predator/prey" systems assumed the cyclical dynamics of two rival species, but in reality, the oscillatory solution of the model's equations is only a mathematical hypothesis. In real biosystems, the variants for the development of scenarios after the introduction of an aggressive predator or parasite are more complicated. Dynamics during invasions becomes extreme. In laboratory experiments with ciliates, instead of asynchronous oscillations with the prey, the infused predator after a rapid outbreak completely destroyed the entire population of prey. Processes with abrupt metamorphoses are extremely relevant, for example, the development of an immune response during the presentation of antigens of new strains of coronavirus and the activation of specific T-lymphocytes of immune memory killer cells to destroy infected cells. Modeling of extreme development in our works is based on the aspects of eventfulness and variability of choice during abrupt changes in the stages of the process under study, for example, adaptation of a parasite against a breeding invader, which is an important way to combat invasions. For rapidly changing biophysical processes, we proposed to expand the models in differential equations with the components of eventfulness, delay, trigger switching, and the logic of switching stages of development. Previously, we proposed an original formalization of the event hierarchy for continuous-discrete time in a hybrid model. In the article, we will present a method for including predicates in the model, i.e., logical functions that allow us to calculate the sequence of changes in the behavior of the invasive process. Our predictive logic approach is important for computational modeling with transformations in dangerous and fast invasive processes and waves of the COVID-19 epidemic based on a comparative analysis of scenarios. Based on the predicative choice of the behavior of a hybrid automaton defined for a set of right-hand sides of systems of differential equations, we also formalize the decision-making logic in scenarios with a controlled impact on biosystems. Post-COVID immunodeficiency is the most complex legacy of the pandemic. [ABSTRACT FROM AUTHOR]

The problem of confirming the correspondence of the dynamics of biophysical processes arising from the incomplete and noisy data obtained during monitoring of the state of bioresources or statistics gathering in a series of laboratory experiments to three behavioral modes of paths of discrete dynamic systems (equilibrium, limiting cycle, or chaos) is considered. It is shown using the examples of real monitoring data and results of laboratory experiments that the data-approximation technique yields a dependence function, which excludes the observed qualitative development of the population process. Attempts to plot regression lines based on the monitoring data for dissipative paths do not yield the necessary information. Known models of biophysics with qualitative changes in the behavior are considered that use characteristics of the reproductive function that cannot be interpreted in terms of the environmental role. The Schwartz derivative depends on the third derivative of the second iteration of the function at the moment of stationary-point stability loss. Criteria for discrete dynamic systems are proposed that can be used to analyze the results of computational simulation from the point of view of biophysical adequacy of occurring nonlinear effects. It is suggested that the rate of population change in the model has a range of negative values and the population reproduction curve has at least two nontrivial stationary points. Consideration of the effect of an aggregated group, arising during invasions, in the action models facilitates an essential interpretation of the behavior of the model path. It is proposed to delimit the nonlinear effects arising in discrete iterative models. In the case of an invasive species that has penetrated into the ecosystem and caused the depletion of its vital resources, a rapid transition to collapse of the new population is possible. Our approach with two-attractor models makes it possible to predict the state of a critically low population of an aggressive intruder. [ABSTRACT FROM AUTHOR]

Formalization in the form of systems of differential equations to describe sharp changes in the development of invasion processes remains an important and urgent problem for mathematical biophysics and bioinformatics. The spread of aggressive biological organisms is accompanied by the rapid phenomena of explosive increase in the dynamics of the population of these dangerous foreign species. Hardly predictable population outbreaks of small insects may continue until the total destruction of forests on large areas. This phenomenon of imbalance affects the entire environment and biochemical processes in the trophic chain of the biosystem. After a rapid uncontrolled (by natural physical factors and a biotic environment) outbreak, degradation of ecosystems and extinction of the invasive species may occur. The fundamental problem of controlling new aggressive species is very important for practical problems of the optimal use of bioresources. The generality of some situations requires special mathematical analysis, which can be constructed by analogy with physical processes with sharp transient regimes. Parametric changes and bifurcations cannot explain the change in behavior, because these reproductive characteristics of aggressive species in a specific biophysical environment are stable during evolutionary adaptation and change only gradually. The simulation of changes in the development rate of individuals in a competitive environment is considered as a hidden factor affecting the realization of critical effects in the dynamics of the population of species with a pronounced staging of ontogenesis. A hybrid model is constructed based on the system of equations describing actual changes in the metabolism rate in ontogenesis, which is applicable to populations in different states of the invasion process. [ABSTRACT FROM AUTHOR]

Crisis processes of biosystems often develop according to scenarios of rapid collapse, which cannot be predicted by statistical methods. This paper develops a method for constructing hybrid computational models using event-hierarchy representation for biophysical processes. System events calculated by the algorithm change the order of calculation of equations according to a given set of rules. The rules of physically conditioned redefinition of the right-hand sides of differential equations are used. The predicates employ the calculations of a group of accompanying characteristics, which are an integral part of the controlled biophysical dynamics. The model is investigated by presenting a computational scenario with a set of parameters, initial values, and an algorithm for making decisions about changing the impact for discrete time. Using computational experiments, a real outcome scenario for a situation that leads to the collapse of a biophysical system at a controlled level of exposure is described. The scenario sets the logic for making control decisions to change the level of external pressure on the natural environment. It is shown as a result of modeling that the transition of the process to an oscillatory mode leads to the choice of a risky control mode. It has been established that the dynamics of many real water populations has a point of threshold reduction in the efficiency of replenishment of stocks. The model scenario uses transformations of the phase portrait of iterations, which, in the presence of disconnected boundaries of the areas of attraction of alternative attractors and a strange chaotic repeller, lead to the uncertainty effects arising due to chaotic modes in a deterministic model. The properties of the described model scenario with a chaotic regime of dynamics are confirmed by the example of the catch of oceanic species of crustaceans. [ABSTRACT FROM AUTHOR]

Apertures are used in optical systems to limit the luminous flux. In the human eye the iris serves this purpose but can be damaged requiring an implant, which to date is static, limiting the optical performance. The authors thus report the development of a prototype demonstrating the feasibility of a flexible dynamic iris implant manufactured with an automated manufacturing technique. The presented actuators are printed via aerosol jet printing allowing a high reliability in produced electrode quality. Through optimization of the actuator design for maximum contraction they demonstrate a pupil area reduction of 13.40% or 6.94% reduction of pupil diameter, respectively. Thereby the prototype demonstrates an illuminance reduction of 18.25% in the central visual axis. Furthermore, they present a first iteration of a bionic closed‐loop control mimicking the human iris reflex thus laying the foundation for a self‐controlled iris implant. [ABSTRACT FROM AUTHOR]

The advances made by microbiome research call for new vocabulary and expansion of our thinking in microbiology. For example, the life-forms presenting in both unicellular and multicellular formats invite us to rethink microbial existence, organization, growth, pathogenicity, and therapeutics in the 21st century. A view of such populations as parts of single organisms with a loose, distributed multicellular organization, introduced here as a germ-ganism, rather than communities, might open up interesting prospects for diagnostics and therapeutics innovation. This study tested and further contextualized the concept of germ-ganism using solid cultures of bacteria and fungi. Based on our findings and the literature reviewed herein, we propose that germ-ganism has synergy with a systems medicine approach by broadening host-environment interactions from cells and microorganisms to a scale of biological ecosystems. Germ-ganism also brings about the possibility of studying the multilevel impacts of novel therapeutic agents within and across networks of microbial ecosystems. The germ-ganism would lend itself, in the long term, to a veritable biocybernetics system, while in the mid-term, we anticipate it will contribute to new diagnostics and therapeutics. Biosecurity applications would be immensely affected by germ-ganism. Industrial applications of germ-ganism are of interest as a more sustainable alternative to costly solutions such as tampered strains/microorganisms. In conclusion, germ-ganism is informed by lessons from microbiome research and invites rethinking microbial existence, organization, and growth as an organism. Germ-ganism has vast ramifications for understanding pathogenicity, and clinical, biosecurity, and biotechnology applications in the current historical moment of the COVID-19 pandemic and beyond. [ABSTRACT FROM AUTHOR]

Pupil detection in a human eyeimage or video plays a key role in many applications such as eye-tracking, diabetic retinopathy screening, smart homes, iris recognition, etc. Literature reveals pupil detection faces many complications including light reflections, cataract disease, pupil constriction/dilation moments, contact lenses, eyebrows, eyelashes, hair strips, and closed eye. To cope with these challenges, research community has been struggling to devise resilient pupil localization schemes for the image/video data collected using the near-infrared (NIR) or visible spectrum (VS) illumination. This study presents a critical review of numerous pupil detection schemes taken from standard sources. This review includes pupil localization schemes based on machine learning, histogram/thresholding, Integro-differential operator (IDO), Hough transform and among others. The probable pros and cons of each scheme are highlighted. Finally, this study offers recommendations for designing a robust pupil detection system. As scope of pupil detection is very broader, therefore this review would be a great source of information for the relevant research community. [ABSTRACT FROM AUTHOR]

The traditional bionic upper limb structure design is limited by the motion pair and cannot guarantee the flexibility of the mechanical structure. The tensegrity structure has the characteristics of high deformability, strong self-adaptability, and resistance to multi-directional impact. According to the biological characteristics of the upper limbs of the human body, an anatomical study is performed on the upper limb wrist joints that achieve adduction/abduction, flexion/extension, to obtain the relationship between the movements of the related bones and muscles, and to simplify the shape and structure of the wrist. Equivalent mapping of a mechanical model based on two-bar tensile properties. Through the contraction and stretching of the spring, the movement characteristics of the human muscles are realised, and the optimised bionic upper limb wrist tensioning robot without motion pair is further obtained. Adams simulation is used to verify that the bionic tensile wrist can simulate the change movement of the human wrist. The experimental platform was built and a physical prototype was made and the prototype was tested. The results show that the bionic tensile wrist can realise the adaptive motion characteristics of the human wrist well and stably, which proves the validity and feasibility of this design method.

Objectives: In this paper a novel approach regarding image analysis in Machine Vision applications was proposed. Methods: The presented concept consists of two issues: (1) shifting some of the complex image processing and understanding algorithms from a mobile robot to distributed computer, and (2) designing the cognitive system (in a distributed computer) in such a way, that it would be common for numerous robots. The authors of this work focused on image processing, and they propose to accelerate vision understanding by using Cooperative Vision (CoV), i.e., to get video input from cooperating robots and process it in a centralized system. Results: To verify the purposefulness of such approach, a comparative study is currently being conducted, involving a classical single-camera Computer Vision (CV) mobile robot and two (or more) single-camera CV robots cooperating in CoV mode. Conclusions: The CoV system is being designed and implemented so that the algorithm would be able to utilize multiple video sources for recognition of objects on the very same scene. [ABSTRACT FROM AUTHOR]

Special situations, which are difficult to predict and to model by traditional methods, develop rapidly in population processes. A sudden collapse of commercial fish stocks, in addition to outbreaks of forest pests, are among the most economically significant nonlinear phenomena in ecosystems. According to systems analysis of statistical data on the dynamics of catches, the transitional stages of rapid degradation in completely different species of fish and aquatic invertebrates occur in a similar way. The most representative stages of the development of fish resources collapse have been distinguished. Restoration of the already critically depleted aquatic bioresources occurs at different rates. Based on the method of dynamically redefinable hybrid computational structure, we considered situations of collapse that occurred with crab off the coast of Alaska and northern cod off the coast of Canadian provinces of Newfoundland and Labrador. The resulting computational scenarios for collapse manifestation consist of three stages, all the way up to the degradation of bioresources. Bifurcations are implemented purposefully and follow the logic of the process. The modeling method is generalized for cases with stationary food resources and with oscillatory dynamics of food organisms. [ABSTRACT FROM AUTHOR]

In this study, three types of bionic texture surfaces were designed, based on the microstructure of tree frog and gecko feet, for two typical prosthetic substrates: silicone rubber and thermoplastic polyurethane elastomer (TPU). The surface energy of all bionic texture surfaces, and the relative displacement and energy dissipation of the reciprocating friction between these bionic surfaces and skin, were investigated. The results revealed that the bionic texture of the prosthetic socket surface had an obvious influence on the surface characteristics and reciprocating characteristics of friction with the skin. According to the comprehensive weight rating method, the optimal bionic textures for the silicone rubber substrate were a regular hexagonal prism and a quadrangular prism; for the TPU substrate, only the regular quadrangular prism texture was optimal. These textures can effectively increase the surface energy of the surface of the prosthetic socket, thereby increasing its adhesion to skin and reducing the dissipation of friction energy when it interacts with the stump. This bionic prosthetic socket surface design helps improve the adaptation between the prosthetic socket and the stump.

In this Focused Review, we provide an update about evolving concepts that may link chronic stress and catecholamine autotoxicity with neurodegenerative diseases such as Parkinson's disease. Richard Kvetnansky's contributions to the field of stress and catecholamine systems inspired some of the ideas presented here. We propose that coordination of catecholaminergic systems mediates adjustments maintaining health and that senescence-related disintegration of these systems leads to disorders of regulation and to neurodegenerative diseases such as Parkinson's disease. Chronically repeated episodes of stress-related catecholamine release and reuptake, with attendant increases in formation of the toxic dopamine metabolite 3,4-dihydroxyphenylacetaldehyde, might accelerate this process. [ABSTRACT FROM AUTHOR]

In this review we describe a series of major concepts introduced during the past 150 years that have contributed to our current understanding about how physiological processes required for well-being and survival are regulated. One can theorize that hierarchical networks involving input-output relationships continuously orchestrate and learn adaptive patterns of observable behaviors, cognition, memory, mood, and autonomic systems. Taken together, these networks function as “good regulators” determining levels of internal variables and act as if there were homeostatic comparators (“homeostats”). The consequences of models with vs. without homeostats remain the same in terms of allostatic load and the eventual switch from stabilizing negative feedback loops to destabilizing, pathogenic positive feedback loops. Understanding this switch seems important for comprehending senescence-related, neurodegenerative disorders that involve the autonomic nervous system. Our general proposal is that disintegration of homeostatic systems causes disorders of regulation in degenerative diseases and that medical cybernetics can inspire and rationalize new approaches to treatment and prevention. [ABSTRACT FROM AUTHOR]

The depth measurement error of bionic eyes consisting of two active cameras is derived by considering the eyes' rotation angle errors and image feature extraction errors. The effect factors of the depth measurement are obtained from the calculation formula of depth error. Based on the analysis of these effect factors, some effective guidelines for bionic eyes are proposed to reduce the measurement errors. The guidelines suggest keeping long baseline, observing the target as nearly as possible, controlling two active cameras with the same angular velocity and keeping the target on Zw axis if possible. The simulation experiments and practical experiments in bionic eyes platform validate the effectiveness of the proposed guidelines. [ABSTRACT FROM AUTHOR]

Bioreactors are used for commercial manufacturing of products or as a simplified representation of wastewater treatment plants. If it is taken into account that individual organisms from the population in the bioreactor behave differently, an additional structure such as age can be added to the population. This leads to a nonlinear, hyperbolic, integro-partial differential equation (IPDE) with non-local integral boundary conditions (BC) in the modeling. The steady-states of this system can be uniquely determined by a substitution due to the non-local BC. Using the asymptotic properties of the system, the IPDE is split into finite-dimensional dynamics and infinite dimensional internal dynamics. The internal dynamics are globally exponentially stable. Therefore, a feedforward control based on the finite dimensional dynamics is designed, augmented with a feedback control to compensate for the remaining influence of the internal dynamics on the output. Finally, the control concept is supplemented by an observer. Global exponential attractiveness of certain reference trajectories is proven. The overall control concept is validated experimentally with a bioreactor. A system parameter is identified and it is shown that by applying the control concept, the biomass can track various reference trajectories with small errors. [ABSTRACT FROM AUTHOR]

Preterm birth is a global epidemic affecting millions of mothers across different ethnicities. The cause of the condition remains unknown but has recognised health-based implications, in addition to financial and economic ones. Machine Learning methods have enabled researchers to combine datasets using uterine contraction signals with various forms of prediction machines to improve awareness of the likelihood of premature births. This work investigates the feasibility of enhancing these prediction methods using physiological signals including uterine contractions, and foetal and maternal heart rate signals, for a population of south American women in active labour. As part of this work, the use of the Linear Series Decomposition Learner (LSDL) was seen to lead to an improvement in the prediction accuracies of all models, which included supervised and unsupervised learning models. The results from the supervised learning models showed high prediction metrics upon the physiological signals being pre-processed by the LSDL for all variations of the physiological signals. The unsupervised learning models showed good metrics for the partitioning of Preterm/Term labour patients from their uterine contraction signals but produced a comparatively lower set of results for the various kinds of heart rate signals investigated., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors. Healthcare Technology Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.)

We optimize the package of the cilium-type MEMS bionic vector hydrophone introduced by Chenyang and Wendong in 2007, which has the disadvantages of a low receiving sensitivity, narrow frequency band, and fluctuating frequency response curve. Initially, a full parametric analysis of frequency response and sensitivity with different material sound transparent cap were made. Then, we propose and realize an umbrella-type packaged structure with high receiving sensitivity and wide frequency band. The theoretical analysis and simulation analysis are conducted by ANSYS software and LMS virtual. Lab acoustic software. Finally, to verify the practicability of the package, the umbrella-type packaged hydrophone calibration was carried out in the National Defense Underwater Acoustics Calibration Laboratory of China. The test results show that the performance of umbrella-type hydrophone has been greatly improved compared with the previous packaged hydrophone: exhibiting a receiving sensitivity of -178 dB (increasing by 20 dB, 0-dB reference 1 V/μPa), the frequency response ranging from 20 Hz to 2 kHz (broaden one times), the fluctuation of frequency response curve within ±2 dB, and a good dipole directivity. [ABSTRACT FROM PUBLISHER]

The healthcare system is in crisis due to challenges including escalating costs, the inconsistent provision of care, an aging population, and high burden of chronic disease related to health behaviors. Mitigating this crisis will require a major transformation of healthcare to be proactive, preventive, patient-centered, and evidence-based with a focus on improving quality-of-life. Information technology, networking, and biomedical engineering are likely to be essential in making this transformation possible with the help of advances, such as sensor technology, mobile computing, machine learning, etc. This paper has three themes: 1) motivation for a transformation of healthcare; 2) description of how information technology and engineering can support this transformation with the help of computational models; and 3) a technical overview of several research areas that illustrate the need for mathematical modeling approaches, ranging from sparse sampling to behavioral phenotyping and early detection. A key tenet of this paper concerns complementing prior work on patient-specific modeling and simulation by modeling neuropsychological, behavioral, and social phenomena. The resulting models, in combination with frequent or continuous measurements, are likely to be key components of health interventions to enhance health and wellbeing and the provision of healthcare. [ABSTRACT FROM PUBLISHER]

The article discusses cybernetics, a science of information gathering, storage, transmission and processing that combines findings in philosophy and mathematics and applicable to general laws of control in a network-centric operations (NCO) system. It focuses on the principles of cybernetics which include multidimensional air-ground volume, multifunctionality and unification of efforts. The author attempts to put these principles into effect in combat operations by commanders and staff.

The fields of molecular biology and computer science have cooperated over recent years to create a synergy between the cybernetic and biosemiotic relationship found in cellular genomics to that of information and language found in computational systems. Biological information frequently manifests its "meaning" through instruction or actual production of formal bio-function. Such information is called Prescriptive Information (PI). PI programs organize and execute a prescribed set of choices. Closer examination of this term in cellular systems has led to a dichotomy in its definition suggesting both prescribed data and prescribed algorithms are constituents of PI. This paper looks at this dichotomy as expressed in both the genetic code and in the central dogma of protein synthesis. An example of a genetic algorithm is modeled after the ribosome, and an examination of the protein synthesis process is used to differentiate PI data from PI algorithms. [ABSTRACT FROM AUTHOR]

Exactly how do the sign/symbol/token systems of endo- and exo-biosemiosis differ from those of cognitive semiosis? Do the biological messages that integrate metabolism have conceptual meaning? Semantic information has two subsets: Descriptive and Prescriptive. Prescriptive information instructs or directly produces nontrivial function. In cognitive semiosis, prescriptive information requires anticipation and “choice with intent” at bona fide decision nodes. Prescriptive information either tells us what choices to make, or it is a recordation of wise choices already made. Symbol systems allow recordation of deliberate choices and the transmission of linear digital prescriptive information. Formal symbol selection can be instantiated into physicality using physical symbol vehicles (tokens). Material symbol systems (MSS) formally assign representational meaning to physical objects. Even verbal semiosis instantiates meaning into physical sound waves using an MSS. Formal function can also be incorporated into physicality through the use of dynamically-inert (dynamically-incoherent or -decoupled) configurable switch-settings in conceptual circuits. This article examines the degree to which biosemiosis conforms to the essential formal criteria of prescriptive semiosis and cybernetic management. [ABSTRACT FROM AUTHOR]

To what degree could chaos and complexity have organized a Peptide or RNA World of crude yet necessarily integrated protometabolism? How far could such protolife evolve in the absence of a heritable linear digital symbol system that could mutate, instruct, regulate, optimize and maintain metabolic homeostasis? To address these questions, chaos, complexity, self-ordered states, and organization must all be carefully defined and distinguished. In addition their cause-and-effect relationships and mechanisms of action must be delineated. Are there any formal (non physical, abstract, conceptual, algorithmic) components to chaos, complexity, self-ordering and organization, or are they entirely physicodynamic (physical, mass/energy interaction alone)? Chaos and complexity can produce some fascinating self-ordered phenomena. But can spontaneous chaos and complexity steer events and processes toward pragmatic benefit, select function over non function, optimize algorithms, integrate circuits, produce computational halting, organize processes into formal systems, control and regulate existing systems toward greater efficiency? The question is pursued of whether there might be some yet-to-be discovered new law of biology that will elucidate the derivation of prescriptive information and control. "System" will be rigorously defined. Can a low-informational rapid succession of Prigogine's dissipative structures self-order into bona fide organization? [ABSTRACT FROM AUTHOR]

Microarray analysis of gene expression from different cells and in different environmental conditions provides a means of identifying genes that share similar expression. Many computational approaches for discovering transcription factor binding sites have been offered in the literature. However, the majority of these approaches are organism-specific, require exhaustive calculation, or do not report the results back to the user in a friendly manner such as through the use of a nucleotide likelihood matrix. We approached the problem of de novo TFBS identification by using evolutionary computation to search for common sequence motifs across multiple upstream sequences without pre-alignment (Fogel et al., 2004). [ABSTRACT FROM PUBLISHER]

This article discusses some fundamental issues that arise as robots become much larger or much smaller than everyday human scale. The article's title is respectfully derived from C.J. Pennycuick's "Newton Rules Biology: A physical approach to biological problems" (1992), from which robot and robotic system designers can learn much from the performance, successes, and failings of biological evolution. Much of the discussion will focus on scaling issues that were first recognized in biological versus technological domains. These issues are discussed mostly from the biological perspective, with the assumption that the reader will have no difficulty extending the underlying principles to technological domains in general, and to the robotics domain specifically. Discussion is also focused on a hypothetical family of geometrically similar vacuum cleaners. [ABSTRACT FROM PUBLISHER]

The available knowledge of insect flight seems to suggest that two-winged flies implement a feedback control paradigm not exploited in engineering and have remarkable performance, achieved in a computationally simple way. This sensor-rich feedback control paradigm relies on extensive, distributed measurement of the quantities of interest in space and time. The visual system of the fly represents its motion relative to the environment through coarse, but still dense, representation of the vector field involved instead of detailed imaging by fine pixels. This representation is global, covers the whole of space, and involves several overlapping patches. The lengths of the vectors in the patches depend on the insect's direction of motion. Hence, the complex underlying differential equations of motion need not be integrated numerically, as their solutions are readily available through interpolation of the detailed, global representation of the relevant vector field. Also, this redundant representation may allow fine control of high agility maneuvers, despite inertial couplings, with little computation. Indeed, a series of small adjustments should suffice for the desired vector field patterns to be achieved. From the engineering viewpoint, this opens not only new perspectives in control, but also in sensors and instrumentation. It suggests that there is no need for high-resolution imaging cameras for flight control based on optic flow. Instead, coarse-grained arrays of sensors could give good results provided they are: arranged to offer a global view of the environment; endowed with parallel processing for extraction of the global vector field of motion via several, overlapping patches of the field; characterized by sensitivities dependent on the direction of motion. The use of other sensors is likely to be needed, and this will call for multisensor data fusion. However, the sensor-rich feedback control architecture works on the complementarity of the type of information measured, not the type of instrument used. A gravity sensor or an altimeter, for example, can replace ocelli. So, the issue is not to emulate insect sensors, but to use appropriate instrumentation to extract the relevant information with the required speed and accuracy. Further, de- tailed and multidisciplinary research is needed to reverse engineer insect flight control completely. However, what we know already hints of exciting possibilities of understanding motion control in animals and humans and acquiring a new paradigm in control engineering. The latter will have a large impact not only on the vast applications of automatic control, but also on the sensor, instrumentation, and measurement communities. [ABSTRACT FROM PUBLISHER]