Systems Of Different Complexity Research Articles

A Critical View on the Quantification of Model Catalyst Activity

The conversion of reactants, reaction rate referred to catalyst mass, and turnover frequency (TOF) are values typically employed to compare the activity of different catalysts. However, experimental parameters have to be chosen carefully when systems of different complexity are compared. In order to characterize UHV-based model systems, we use a highly sensitive sniffer setup which allows us to investigate the catalytic activity by combining three different measurement modes: temperature-programmed desorption, continuous flow, and pulsed-reactivity experiments. In this article, we explore the caveats of quantifying catalytic activity in UHV on the well-studied and highly defined reference system of CO oxidation on Pt(111), which we later compare to the same reaction on Pt19 clusters deposited on Fe3O4(001). We demonstrate that we can apply fast heating ramps for TOF quantification, thus inducing as little sintering as possible in the metastable clusters. By changing the reactant ratio, we find transient reactivity effects that influence the TOF, which should be kept in mind when comparing catalysts. In addition, the TOF also depends on the surface coverage that itself is a function of temperature and pressure. At a constant reactant ratio, in the absence of transient effects, however, the TOF scales linearly with total pressure over the entire measured temperature range from 200 to 700 K since the reaction rate is dependent on both reactant partial pressures with temperature-dependent reaction order. When comparing the maximum TOF at this particular reactant ratio, we find a 1.6 times higher maximum TOF for Pt19/Fe3O4(001) than for Pt(111). In addition, pulsed-reactivity measurements help identify purely reaction-limited regimes and allow for a more detailed investigation of limiting reactants over the whole temperature range.

How hydrophobicity shapes the architecture of protein assemblies

The interactions that give rise to protein self-assembly are basically electrical and hydrophobic in origin. The electrical interactions are approached in this study as the interaction between electrostatic dipoles originated by the asymmetric distribution of their charged amino acids. However, hydrophobicity is not easily derivable from basic physicochemical principles. Its treatment is carried out here considering a hydrophobic force field originated by “hydrophobic charges”. These charges are indices obtained experimentally from the free energies of transferring amino acids from polar to hydrophobic media. Hydrophobic dipole moments are used here in a manner analogous to electric dipole moments, and an empirical expression of interaction energy between hydrophobic dipoles is derived. This methodology is used with two examples of self-assembly systems of different complexity. It was found that the hydrophobic dipole moments of proteins tend to interact in such a way that they align parallel to each other in a completely analogous way to how phospholipids are oriented in biological membranes to form the well-known double layer. In this biological membrane model (BM model), proteins tend to interact in a similar way, although in this case this alignment is modulated by the tendency of the corresponding electrostatic dipoles to counter-align.Graphical abstractHelical conformation of influenza virus PDBid: 6Z5L. Two monomers are shown in cyan and green. The corresponding dipole moment vectors are shown in red (electric dipoles) and blue (hydrophobic dipoles). From the inset figure, it can be seen that the growth of the helix is due to electrical attraction of the monomers, overcoming a hydrophobic repulsion (see text)

Taxonomy to Unify Fault Tolerance Regimes for Automotive Systems: Defining Fail-Operational, Fail-Degraded, and Fail-Safe

This paper presents a taxonomy that allows defining the fault tolerance regimes fail-operational, fail-degraded, and fail-safe in the context of automotive systems. Fault tolerance regimes such as these are widely used in recent publications related to automated driving, yet without definitions. This largely holds true for automotive safety standards, too. We show that fault tolerance regimes defined in scientific publications related to the automotive domain are partially ambiguous as well as taxonomically unrelated. The presented taxonomy is based on terminology stemming from ISO 26262 as well as from systems engineering. It uses four criteria to distinguish fault tolerance regimes. In addition to fail-operational, fail-degraded, and fail-safe, the core terminology consists of operational and fail-unsafe. These terms are supported by definitions of available performance, nominal performance, functionality, and a concise definition of the safe state. For verification, we show by means of two examples from the automotive domain that the taxonomy can be applied to hierarchical systems of different complexity.

Electronic Flow Emulator for the Test of Ultrasound Doppler Sensors

Doppler ultrasound techniques are currently employed in several industrial, consumer, and biomedical applications. They are implemented in electronic systems of different complexity, from simple, low-cost embedded boards to high-end echographs. The development, implementation, and periodic verification of such systems involve complex tests carried out through flow rigs and phantoms. Unfortunately, these hydraulic circuits are affected by several issues, such as the huge dimensions and the lack of the accurate reference for the velocity distribution developed by the fluid. In this article, we present an innovative flow emulator board (FEB). The FEB is compact and does not need moving fluids or pumps. Moreover, it produces a signal that mimics a programmable velocity profile with known characteristics. The FEB can replace the flow rigs in most of the tests where pulsed wave Doppler (PWD) methods and PWD electronics’ systems are involved. The presented experiments show that the proposed FEB synthetizes arbitrary flow velocity profiles with programmable clutter, white noise, propagation attenuation, and sample volume extension, and show how FEB is employed for testing an industrial sensor and a research echograph.

Theories and cognitive models of postnonclassical learning in natural science education

Introduction. The cognitive models formed in post – non-classical science-the theory of systems, the theory of self-organization and management, which were developed in natural science, are now widely used in social and humanitarian research. However, they have not yet been properly reflected in the content of natural science education of future employees of these fields – graduates of social, humanitarian and pedagogical departments of higher education institutions. The purpose of this study: theoretical and methodological justification, development and testing of a set of methodological tools for adapting cognitive models of post-non-classical science in the content of natural science education of students of social and humanitarian fields of higher education. Methodology and methods. The methodological basis of the research is based on the ideas of the system-synergetic approach, works on the philosophy and methodology of education, fundamental positions and basic principles of modern didactics. The theoretical basis is the works of domestic and foreign scientists on the development of the content of education, educational technologies and the organization of the educational process at the University. Used – modeling and forecasting, analysis and synthesis, observation, questioning, testing, experimental work. Results and scientific novelty. The theoretical and methodological justification of the need to adapt cognitive models of post-non-classical science in the content of natural science education of the Humanities is given, and the principles are defined. The complex has a linear-concentric structure and includes a basic training course, a system of elective training courses and interdisciplinary modules, technologies and teaching methods that support it. During testing of the complex was performed: ability to operate basic concepts of post-nonclassical science and apply them to describe the behavior of social and natural systems of different complexity, the volume of natural Sciences students ' knowledge, proficiency with the operation of their transfer to other subject areas.

Charge assisted assembly of zwitterionic pyridone hydrates

Two pyridone derivatives, bearing the pyridinium moiety (1), or dimethylpyridinium moiety (2), have been synthesized and their crystal structures have been determined. The compounds crystalize in hydrated zwitterionic forms with either two (1·2H2O) or four (2·4H2O) water molecules. The zwitterionic networks contain different types of water clusters, generated into channels, incorporating them into the network by sandwiching. The type of channel depends on the crystal lattice and the nature of non-covalent interactions established between zwitterions as well as the number of water molecules incorporated into the architecture. 1 affords tubes filled in with water channels formed by water tetramers, contrary to 2, which affords a layered network altering the zwitterionic layer and the layer formed by water tetramers and hexamers. A detailed study of intermolecular interactions of both crystal structures and a quantification of interaction energies has been performed using PIXEL lattice energy calculations, giving an insight to a quantitative evaluation of interactions through Coulombic, disperse, repulsion and polarization energies. The strongest pairwise, in both structures, is found to be a dipole–dipole interaction between oppositely charged heterocyclic rings. The differences in the crystal packings of these hydrates have been elucidated by the fingerplot analysis. The comparative studies between experimental and calculated (DFT) data of molecules 1·H2O and 2·4H2O for systems of different complexity are performed. Furthermore, correlations of experimental and calculated bond lengths and the simulation of compound solvation with the CPCM model are done.

Investigation of the physical properties of polymers by molecular dynamics and using LAMMPS (Review)

The presence of polymers in all areas of human life has led to the need to create new polymers that meet new needs. There is a problem of limited resources in modern economics and ecology, so computer modeling methods become an alternative to experimental research of polymers.The LAMMPS software package allows to model physical systems of different complexity of the structure and the number of its constituent particles. He implements the molecular dynamics method for modeling the evolution of the system over time by integrating particle motion equations according to Newton's second law. The calculation of the physical properties of materials is based on the Lennard-Jones potential, which determines the nature of the interaction of particles of the system.Computer modeling of polymers has to begin with an accurate formulation of a research objective as the settlement model depends on the studied processes change of structure of a crystal lattice. Depending on the scale of the modelled system allocate two classes of polymeric models: atomistic and coarse-grained. The atomistic model allows to consider structure of material at the level of monomers, to study vibration and interaction of an unstructured polymeric system. They are limited in time and demand the powerful equipment. The coarse-grained model allows to investigate the generalized polymeric models for forecasting of structure of a polymeric system in the wide range of criteria and parameters. Properties of the analyzed system define in the accurate sequence: initialization of particle’s parameters, determination of lattice structure, adjustment of interaction parameters of particles, boundary conditions and variables, calculation modeling. After calculation the obtained data are analyzed to determination of the studied physical properties of polymers.Computer modeling is an effective way to investigate the physical properties of polymers, giving many opportunities for the development of materials science and new technologies.

Modular Modeling of Genetic Circuits in SBML Level 3.

The System Biology Markup Language (SBML) Level 2 has been used extensively to make models for biological systems of different complexity. However, the lack of modularity was a serious hurdle for its application to Synthetic Biology where genetic circuits are preferably modeled by putting together the models of their components. SBML Level 3 with the Hierarchical Composition Package overcame this limit. Here, we describe how to realize a modular model for a eukaryotic AND gate in SBML Level 3. Circuit modules, such as transcription units and pools of molecules, are modeled separately and connected, to close the circuit, via Python scripts that utilize the libSBML API. Circuit simulations with COPASI confirm the validity of this modeling approach.

Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems

In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle this problem, a multilayer neural network-based approximate model is set up which will work in parallel to the plant and the control scheme. The network parameters are updated using the dynamic backpropagation (BP) algorithm.

Setup and testing of smart controllers for small-scale district heating networks: An integrated framework

Nowadays, the growing availability of renewable energy resources is an opportunity to reduce carbon emissions but also a challenge, as advanced control technologies are required. The new generation of smart district heating and cooling networks, for instance, pledges efficient energy distribution, flexibility and low-carbon energy integration. However, in the literature it is hard to find comprehensive frameworks for the integrated setup and testing of smart control strategies. This paper defines and demonstrates a framework that involves all steps of the controller development for small-scale district heating networks: from conceptualization to prototype testing. The innovative controller prototype, which relies on Model Predictive Control and aims to minimize operating costs and/or energy, is demonstrated in three original case studies, one in a simulation environment and two in real systems of different complexity in operational conditions. Compared to the control approaches previously adopted, based on predefined rules and operator experience, the smart solution achieves 6% reduction in cost and up to 34% reduction in energy consumption while meeting user requirements. The fast replicability of the proposed integrated methodology can foster the transition toward the next generation of smart heating networks.

Interfacial Phenomena on the Phase Boundary Between Hydrocarbon Systems and Acids

A study was carried out on aspects of phenomena on the interfacial boundary of acids and hydrocarbon systems of different complexity. These phenomena were evaluated relative to the interfacial tension measured by the pendant drop method. We discovered peculiarities of the interaction of hydrochloric and sulfamic acids with individual hydrocarbons as well as complex hydrocarbon systems. The complex dependence of the interfacial tension on the composition and properties of the hydrocarbon phase, type and concentration of the acid, and the chemical composition of asphaltenes was elucidated.

Evaluation of the effects of weak oscillating magnetic fields applied during freezing on systems of different complexity

Abstract The effects of weak oscillating magnetic fields (OMFs ≤7 mT at 50 Hz) on freezing were studied in three systems of different complexity. To do so, ferric chloride solutions, lactate dehydrogenase (LDH), and minced hake muscle experimentally infected with Anisakis L3 were frozen with and without OMF application. OMFs did not affect freezing kinetics of either ferric chloride solutions or minced hake muscle. LDH activity, Anisakis mortality, and water-holding capacity of the hake muscle after thawing were not affected by OMF either. Further studies are needed to evaluate the effectiveness of stronger OMFs in a wider frequency range.

Assessing the State of Mechanical Systems of Different Complexity

Abstract—The use of information theory to assess the state of mechanical systems of different complexity is considered.

Silver Nanoparticles as Nanoantibiotics: A Comparative Analysis of their Toxicity on Biological Systems of Different Complexity

Currently, silver nanoparticles (AgNPs) are extensively studied for biomedical applications, but although nanomaterials provide many benefits, recently their comparative toxicity have barely been explored. In the current work, AgNPs toxicity on biological systems of different levels of complexity was assessed in a comprehensive and comparatively way. The organisms included viruses, bacteria, microalgae, fungi, animal and human cells (including cancer cell lines). We found that growth of biological systems of different taxonomical groups –in vitro, at a cellular level- is inhibited at concentrations of AgNPs within the same order of magnitude (101 μg/ml). Thus, the AgNPs toxicity does not depend on the complexity of the organisms. The fact that cells and virus are inhibited with a concentration of AgNPs within the same order of magnitude could be explained considering that silver affects fundamental structures for cells and virus alike.

Peptides at the Interface: Self-Assembly of Amphiphilic Designer Peptides and Their Membrane Interaction Propensity.

Self-assembling amphiphilic designer peptides have been successfully applied as nanomaterials in biomedical applications. Understanding molecular interactions at the peptide–membrane interface is crucial, since interactions at this site often determine (in)compatibility. The present study aims to elucidate how model membrane systems of different complexity (in particular single-component phospholipid bilayers and lipoproteins) respond to the presence of amphiphilic designer peptides. We focused on two short anionic peptides, V4WD2 and A6YD, which are structurally similar but showed a different self-assembly behavior. A6YD self-assembled into high aspect ratio nanofibers at low peptide concentrations, as evidenced by synchrotron small-angle X-ray scattering and electron microscopy. These supramolecular assemblies coexisted with membranes without remarkable interference. In contrast, V4WD2 formed only loosely associated assemblies over a large concentration regime, and the peptide promoted concentration-dependent disorder on the membrane arrangement. Perturbation effects were observed on both membrane systems although most likely induced by different modes of action. These results suggest that membrane activity critically depends on the peptide’s inherent ability to form highly cohesive supramolecular structures.

Neuromodulation and Synaptic Plasticity for the Control of Fast Periodic Movement: Energy Efficiency in Coupled Compliant Joints via PCA.

There are multiple indications that the nervous system of animals tunes muscle output to exploit natural dynamics of the elastic locomotor system and the environment. This is an advantageous strategy especially in fast periodic movements, since the elastic elements store energy and increase energy efficiency and movement speed. Experimental evidence suggests that coordination among joints involves proprioceptive input and neuromodulatory influence originating in the brain stem. However, the neural strategies underlying the coordination of fast periodic movements remain poorly understood. Based on robotics control theory, we suggest that the nervous system implements a mechanism to accomplish coordination between joints by a linear coordinate transformation from the multi-dimensional space representing proprioceptive input at the joint level into a one-dimensional controller space. In this one-dimensional subspace, the movements of a whole limb can be driven by a single oscillating unit as simple as a reflex interneuron. The output of the oscillating unit is transformed back to joint space via the same transformation. The transformation weights correspond to the dominant principal component of the movement. In this study, we propose a biologically plausible neural network to exemplify that the central nervous system (CNS) may encode our controller design. Using theoretical considerations and computer simulations, we demonstrate that spike-timing-dependent plasticity (STDP) for the input mapping and serotonergic neuromodulation for the output mapping can extract the dominant principal component of sensory signals. Our simulations show that our network can reliably control mechanical systems of different complexity and increase the energy efficiency of ongoing cyclic movements. The proposed network is simple and consistent with previous biologic experiments. Thus, our controller could serve as a candidate to describe the neural control of fast, energy-efficient, periodic movements involving multiple coupled joints.

Dynamic Buffer Capacity in Acid-Base Systems.

The generalized concept of ‘dynamic’ buffer capacity βV is related to electrolytic systems of different complexity where acid–base equilibria are involved. The resulting formulas are presented in a uniform and consistent form. The detailed calculations are related to two Britton–Robinson buffers, taken as examples.

Genetically Encoded Fluorescent Redox Sensors

Redox processes play a key role in cells of all.organisms. These processes imply directed flows of electrons via so-called redox pairs: substances that exist in both reduced and oxidized states simultaneously within the cell. Examples of redox pairs are NAD+/NADH, NADP+/NADPH, GSSG/2GSH. Until recently, studies of redox processes in the living cells were challenged by the lack of suitable methods. Genetically encoded fluorescent biosensors provide a new way to study biological processes including redox ones. Biosensors allow real-time detection of messengers, metabolites and enzymatic activities in living systems of different complexity from cultured cells to transgenic animals. In this review, we describe the main types of known redox biosensors with examples of their use.

Applications of proteochemometrics - from species extrapolation to cell line sensitivity modelling

Proteochemometrics (PCM) is a predictive bioactivity modelling method to simultaneously model the bioactivity of multiple ligands against multiple targets. Therefore, PCM permits to explore the selectivity and promiscuity of ligands on biomolecular systems of different complexity, such proteins or even cell-line models. In practice, each ligand-target interaction is encoded by the concatenation of ligand and target descriptors. These descriptors are then used to train a single machine learning model. This simultaneous inclusion of both chemical and target information enables the extra- and interpolation to predict the bioactivity of compounds on targets, which can be not present in the training set. In this thesis, a methodological advance in the field is firstly introduced, namely how Bayesian inference (Gaussian Processes) can be successfully applied in the context of PCM for (i) the prediction of compounds bioactivity along with the error estimation of the prediction; (ii) the determination of the applicability domain of a PCM model; and (iii) the inclusion of experimental uncertainty of the bioactivity measurements. Additionally, the influence of noise in bioactivity models is benchmarked across a panel of 12 machine learning algorithms, showing that the noise in the input data has a marked and different influence on the predictive power of the considered algorithms. Subsequently, two R packages are presented. The first one, Chemically Aware Model Builder (camb), constitues an open source platform for the generation of predictive bioactivity models. The functionalities of camb include : (i) normalized chemical structure representation, (ii) calculation of 905 one- and two-dimensional physicochemical descriptors, and of 14 fingerprints for small molecules, (iii) 8 types of amino acid descriptors, (iv) 13 whole protein sequence descriptors, and (iv) training, validation and visualization of predictive models. The second package, conformal, permits the calculation of confidence intervals for individual predictions in the case of regression, and P values for classification settings. The usefulness of PCM to concomitantly optimize compounds selectivity and potency is subsequently illustrated in the context of two application scenarios, which are: (a) modelling isoform-selective cyclooxygenase inhibition; and (b) large-scale cancer cell-line drug sensitivity prediction, where the predictive signal of several cell-line profiling data is benchmarked (among others): basal gene expression, gene copy-number variation, exome sequencing, and protein abundance data. Overall, the application of PCM in these two case scenarios let us conclude that PCM is a suitable technique to model the activity of ligands exhibiting uncorrelated bioactivity profiles across a panel of targets, which can range from protein binding sites (a), to cancer cell-lines (b).

Evaluation System for Autonomous Control Methods in Coupled Planning and Control Systems

Autonomous control is an approach to cope with increasing complexity in production and logistics. Autonomous control methods are characterised by decentralised coordination of logistic objects in a heterarchical organisation structure. Autonomous objects themselves are capable of processing information in order to make and execute decisions on their own. Up to now, research in this field mainly focuses on the effects of autonomous control methods disregarding their integration in existing planning systems. Thereby, it is currently not possible to give a substantiated recommendation, which combination of planning and autonomous control methods achieves a sufficient degree of logistic objective achievement in production systems of different complexity. Existing evaluation systems in the field of autonomous control methods remain on a mainly qualitative level disregarding the planning system. Therefore, this paper presents a quantitative, three-dimensional evaluation system. First, it operationalises the degree of complexity in production systems depending on shop floor conditions and disturbances. Second, the paper operationalises the degree of autonomy of production systems. Thereby, it considers the type and intensity of coupling between the planning and control level. Third, a vector of performance indicators is defined to measure the logistic objective achievement and the degree of plan fulfilment. The result is an evaluation system which allows a complete quantitative evaluation of autonomous control in production systems. Furthermore, the focus of consideration is expanded from the mere control level to the planning and control level. The influence of the strength of coupling between planning and control methods on the autonomy is also taken into account and operationalised. Finally, the evaluation system is exemplarily applied to a published simulation study.