A lobster-inspired multi-robot control strategy for monitoring non-stationary concentration fields

We propose a new lobster-inspired chemotaxis decentralized control strategy for monitoring a non-stationary concentration field using a team of nonholonomic mobile robots. The task of the team is to locate and trace the movement of the point (or points) with the highest field value (i.e. source), provided that the robots are not aware of the dynamics of the field and can only periodically sample the field at their locations. As an example of the concentration field we consider a population of biological species modeled by a self-organizing multi-agent system with agents acting as individuals of the population in accordance with some flocking rules. The proposed strategy combines the lobsters’ plume localization behavior and flocking mechanisms to efficiently solve the problem even with a small group of robots. Simulations and experimental works on physical unicycle robots are performed to validate the efectiveness of the approach for the cases of non-stationary fields.

Numerical simulation of deflagration in hydrogen-air gas mixes

Verification of validity of different manifolds of chemical reactions and coefficients in Arrhenius formulae was made for numerical simulation of deflagration appearing in hydrogen-air gas mixes. Kinetic model of branching chain reaction was tested for initial stage of detonation of this kind of mixes. One dimensional numerical simulations of deflagration initiation where provided for small closed heat isolated region. The next problem was solved numerically:in small closed volume, initially filled by hydrogen-air mix with atmospheric meanings of gas dynamics parameters at moment t=0 temperature rising till meaning, at which reaction of deflagration should begin. Numerical experiment consist of calculation of thermodynamics parameters of gas mix in small isolated volume. Meanings of molar concentration of components of gas mix where calculated by implicit numerical method of Gir for numerical decision. Calculation where provided till zero concentration of hydrogen or not appearing of deflagration at all. Characteristic feature of hydrogen-air gas mix deflagration is appearance of sudden explosion after long period of induction. In this induction period grows of radicals H, O and OH appears. Mass of radicals, nevertheless stay small, and one radical component transverse to the others. This explosion mechanism is branching chain reaction introduced by N.N.Semenov. In agreement with branching chain reaction theory during process of branching chain reaction radicals H, O, OH many times initiates reaction with other components of the mix. Nevertheless mass of radical components preserve small during the reaction, them almost fully disappeared in every time of the process. That’s why method of “quasi - stationary concentration” is treated to components O, OH (velocity of changing of this components concentration is equal to zero). For concentration of component H one simplified differential equation is treated. Speed of changing H essentially grater then speed of changing “slow” components H2, O2, H2O, that’s why equation for H should be solved separately. Algorithm was developed for numerical simulation of hydrogen-air mixes on the basis of theory branching chain reactions. Calculations provided demonstrate applicability of developed algorithm for numerical simulations of initial stage of deflagration of hydrogen-air mixes.

Assessment of exposure determinants and exposure levels by using stationary concentration measurements and a probabilistic near-field/far-field exposure model

Background: The Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation requires the establishment of Conditions of Use (CoU) for all exposure scenarios to ensure good communication of safe working practices. Setting CoU requires the risk assessment of all relevant Contributing Scenarios (CSs) in the exposure scenario. A new CS has to be created whenever an Operational Condition (OC) is changed, resulting in an excessive number of exposure assessments. An efficient solution is to quantify OC concentrations and to identify reasonable worst-case scenarios with probabilistic exposure modeling. Methods: Here, we appoint CoU for powder pouring during the industrial manufacturing of a paint batch by quantifying OC exposure levels and exposure determinants. The quantification was performed by using stationary measurements and a probabilistic Near-Field/Far-Field (NF/FF) exposure model. Work shift and OC concentration levels were quantified for pouring TiO2 from big bags and small bags, pouring Micro Mica from small bags, and cleaning. The impact of exposure determinants on NF concentration level was quantified by (1) assessing exposure determinants correlation with the NF exposure level and (2) by performing simulations with different OCs. Results: Emission rate, air mixing between NF and FF and local ventilation were the most relevant exposure determinants affecting NF concentrations. Potentially risky OCs were identified by performing Reasonable Worst Case (RWC) simulations and by comparing the exposure 95th percentile distribution with 10% of the occupational exposure limit value (OELV). The CS was shown safe except in RWC scenario (ventilation rate from 0.4 to 1.6 1/h, 100 m3 room, no local ventilation, and NF ventilation of 1.6 m3/min). Conclusions: The CoU assessment was considered to comply with European Chemicals Agency (ECHA) legislation and EN 689 exposure assessment strategy for testing compliance with OEL values. One RWC scenario would require measurements since the exposure level was 12.5% of the OELV.

Relaxation times of ligand-receptor complex formation control T cell activation

One of the most important functions of immune T cells is to recognize the presence of the pathogen-derived ligands and to quickly respond to them while at the same time not to respond to its own ligands. This is known as an absolute discrimination, and it is one of the most challenging phenomena to explain. The effectiveness of pathogen detection by T cell receptor (TCR) is limited by the chemical similarity of foreign and self-peptides and very low concentrations of foreign ligands. We propose a new mechanism of the absolute discrimination by T cells. It is suggested that the decision to activate or not to activate the immune response is controlled by the time to reach the stationary concentration of the TCR-ligand activated complex, which transfers the signal to downstream cellular biochemical networks. Our theoretical method models T-cell receptor phosphorylation events as a sequence of stochastic transitions between discrete biochemical states, and this allows us to explicitly describe the dynamical properties of the system. It is found that the proposed criterion on the relaxation times is able to explain available experimental observations. In addition, our theoretical approach explicitly analyzes the relationships between speed, sensitivity and specificity of the T cell functioning, which are the main characteristics of the process. Thus, it clarifies the molecular picture of the T cell activation processes in immune response.

Ensuring the Reliability of Pneumatic Classification Process for Granular Material in a Rhomb-Shaped Apparatus

This article is devoted to a detailed description of the developed physical model of the pneumatic classification process for detecting the rotating suspended layer and ensuring the frequency of loading and unloading of a pneumatic classifier. The related mathematical model is developed for estimating the non-stationary concentration of fine particles in a gas-dispersed flow with respect to time and height of the working space of the apparatus. The research is aimed at developing a pneumatic classification method for granular materials using a rhomb-shaped apparatus and ensuring the reliability of the operating process based on the influence of the flow on the granular material concentrations. The obtained experimental results allow evaluating the rational geometrical parameters of the working space in pneumatic granulators, as well as verifying the proposed mathematical model based on the implementation of the quasi-linear regression procedure. It is shown that the rhomb-shaped pneumatic classifier provides effective separation of granular material, reaching up to 95% of the target fraction. As a result, the proposed methodology can be implemented for optimizing geometrical profiles of pneumatic classifiers in terms of defining the required technological parameters of the pneumatic classification process.