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The questions of the organization of engineering support of residential and non-residential construction objects at the stages from planning to commissioning are studied. So far, these issues have been addressed for each site in a collegial decision-making mode: at Staff meetings, etc., separately, as they arise. This approach demonstrates inconsistency between the actions of construction and resource-supplying organizations both during design and at the construction site, and, as a result, delays in the construction and commissioning of facilities and the growth of social tension. The article presents new tools for increasing the level of interaction between the participants of engineering support processes based on the preliminary, advance interconnection of actions of all participants of the engineering support process and all involved objects. Such tools are especially relevant in large-scale renovation programs of large cities’ housing stock; the article shows the developments applied in the Moscow Housing Renovation Program framework.

The article is dedicated to the multi agent approach for urban public transport dispatch system to organize priority transfer management, based on interaction with traffic lights control system. The article deals with the issue of urban passenger transport priority transfer of signalized intersections. Priority transfer management algorithm developed by the authors based on interaction of traffic lights control and public transport dispatch systems. The authors considered the role of agents in interaction process of traffic lights control and public transport dispatch systems for implementation of the buses priority passage. Criterion for decision making taking into account savings and loses of time of all participants of traffic at the intersection. The results of simulation modelling of priority public transport passage a signalized intersection are presented

The article deals with the direction of solving complex problems of interaction between the elements of the transport and logistics system of the Moscow region as a complex structure of management methods in multi-criteria systems and technologies for monitoring the quality of processes. The control method should optimize the system parameters, and control technologies (radio frequency cargo identification) implement feedback in the system.

From the very beginning, the emulation of biological principles has been the primary avenue for the development of energy-efficient artificial intelligence systems. Reservoir computing, which has a solid biological basis, is particularly appealing due to its simplicity and efficiency. So-called memristors, resistive switching elements with complex dynamics, have proved beneficial for replicating both principal parts of a reservoir computing system. However, these parts require distinct behaviors found in differing memristive structures. The development of a homogeneous memristive reservoir computing system will significantly facilitate and reduce the fabrication process cost. The following work employs the co-existence of volatile and non-volatile regimes in parylene-MoO x crossbar memristors controlled by compliance current for this aim. The stable operation of the memristors under study is confirmed by low cycle-to-cycle and device-to-device variations of the switching voltages. For the transition between the volatile and non-volatile regimes, factors such as compliance current and reading voltage along with possible intrinsic origins are discussed. The results provide a foundation for the future hardware development of a homogeneous parylene-based reservoir computing system, considering high MNIST dataset classification accuracy (∼96%).

This paper provides a numerical analysis of the behavior of the ion boundary layer formed during proton exchange. Thermal dissociation of benzoic acid melt molecules leads to the formation of benzoate ions and hydrogen ions. The latter can be absorbed by a lithium niobate wafer, with subsequent diffusion of lithium ions in the acid. The mathematical model proposed in the article implies that it is possible to use continuous media approximation to describe this phenomenon. The statement of the current problem includes both diffusion and electrostatic mechanisms of mass transfer and the recombination mechanism of ion interaction. The numerical scheme, based on a two-field method, has shown a steady-state formation, characterized by exponential-like profiles of concentration in the presence of an induced nonuniform electric field. It is important to note that the net charge of the system remains zero. The results of numerical simulation have demonstrated the formation of a boundary layer of benzoate ions. At the same time, nonuniformities that appear in the layer do not create instability that breaks mechanical equilibrium, and they do not lead to a high-scale concentration convection.

A mathematical model is developed to describe the process of high-temperature silicification of a carbon porous material. The cause of pores blockage is the condensation of gaseous silicon at the inner walls of tubules. Phenomenological temperature dependences for the coefficients of condensation and evaporation are proposed, which determine the intensity of the siliconizing process. It is assumed that the diffusion of the silicon vapour is the main mechanism of the material open pores filling. Numerical modelling is carried out by the finite difference method using an explicit scheme in the case of a constant value of the mobile concentration at the input and for different variants of boundary conditions on temperature. These temperature distributions on boundaries make it possible to describe siliconized regions and to delimit from them the sample parts that remain 'dry' during the silicification process. The dynamics of main physical quantities change, i.e. porosity of the composite material and concentration of the immobile component in the volume, are analysed. The results of numerical simulation for the time of full silicification and the weight gain of the product are in qualitative and quantitative agreement with all known experimental data. This article is part of the theme issue 'New trends in pattern formation and nonlinear dynamics of extended systems'.

Existing methods of neurorehabilitation include invasive or non-invasive stimulators that are usually simple digital generators with manually set parameters like pulse width, period, burst duration, and frequency of stimulation series. An obvious lack of adaptation capability of stimulators, as well as poor biocompatibility and high power consumption of prosthetic devices, highlights the need for medical usage of neuromorphic systems including memristive devices. The latter are electrical devices providing a wide range of complex synaptic functionality within a single element. In this study, we propose the memristive schematic capable of self-learning according to bio-plausible spike-timing-dependant plasticity to organize the electrical activity of the walking pattern generated by the central pattern generator., Competing Interests: AS and DM were employed by the company B-Rain LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Masaev, Suleimanova, Prudnikov, Serenko, Emelyanov, Demin, Lavrov, Talanov and Erokhin.)

Diamanes are unique 2D carbon materials that can be obtained by the adsorption of light atoms or molecular groups onto the surfaces of bilayer graphene. Modification of the parent bilayers, such as through twisting of the layers and the substitution of one of the layers with BN, leads to drastic changes in the structure and properties of diamane-like materials. Here, we present the results of the DFT modelling of new stable diamane-like films based on twisted Moiré G/BN bilayers. The set of angles at which this structure becomes commensurate was found. We used two commensurate structures with twisted angles of θ = 10.9° and θ = 25.3° with the smallest period as the base for the formation of the diamane-like material. Previous theoretical investigations did not take into account the incommensurability of graphene and boron nitride monolayers when considering diamane-like films. The double-sided hydrogenation or fluorination of Moiré G/BN bilayers and the following interlayer covalent bonding led to the opening of a gap up to 3.1 eV, which was lower than the corresponding values of h-BN and c-BN. The considered G/BN diamane-like films offer great potential in the future for a variety of engineering applications.

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B) x (LiNbO 3 ) 100-x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements.

Ultra-thin diamond membranes, diamanes, are one of the most intriguing quasi-2D films, combining unique mechanical, electronic and optical properties. At present, diamanes have been obtained from bi- or few-layer graphene in AA- and AB-stacking by full hydrogenation or fluorination. Here, we study the thermal conductivity of diamanes obtained from bi-layer graphene with twist angle θ between layers forming a Moiré pattern. The combination of DFT calculations and machine learning interatomic potentials makes it possible to perform calculations of the lattice thermal conductivity of such diamanes with twist angles θ of 13.2∘, 21.8∘ and 27.8∘ using the solution of the phonon Boltzmann transport equation. Obtained results show that Moiré diamanes exhibit a wide variety of thermal properties depending on the twist angle, namely a sharp decrease in thermal conductivity from high for "untwisted" diamanes to ultra-low values when the twist angle tends to 30∘, especially for hydrogenated Moiré diamanes. This effect is associated with high anharmonicity and scattering of phonons related to a strong symmetry breaking of the atomic structure of Moiré diamanes compared with untwisted ones.

We proposed novel carbon nanostructures based on a twisted few-layered graphene with one side passivated by hydrogen or fluorine: Moiré diamones on graphene. The presence of a dangling bond at the bottom layer of diamones leads to the appearance of spin density localization, which can be tuned by the variation of the twist angle with the following formation of Moiré diamones. The spin-polarized nature of electronic density distribution was obtained and discussed in detail on the basis of ab initio calculations. Such a feature makes Moiré diamones a promising key element in the field of controllable spintronic devices.

Currently, there is growing interest in wearable and biocompatible smart computing and information processing systems that are safe for the human body. Memristive devices are promising for solving such problems due to a number of their attractive properties, such as low power consumption, scalability, and the multilevel nature of resistive switching (plasticity). The multilevel plasticity allows memristors to emulate synapses in hardware neuromorphic computing systems (NCSs). The aim of this work was to study Cu/poly- p -xylylene(PPX)/Au memristive elements fabricated in the crossbar geometry. In developing the technology for manufacturing such samples, we took into account their characteristics, in particular stable and multilevel resistive switching (at least 10 different states) and low operating voltage (<2 V), suitable for NCSs. Experiments on cycle to cycle (C2C) switching of a single memristor and device to device (D2D) switching of several memristors have shown high reproducibility of resistive switching (RS) voltages. Based on the obtained memristors, a formal hardware neuromorphic network was created that can be trained to classify simple patterns., (© 2022 IOP Publishing Ltd.)

Carbon nanotubes have a helical structure wherein the chirality determines whether they are metallic or semiconducting. Using in situ transmission electron microscopy, we applied heating and mechanical strain to alter the local chirality and thereby control the electronic properties of individual single-wall carbon nanotubes. A transition trend toward a larger chiral angle region was observed and explained in terms of orientation-dependent dislocation formation energy. A controlled metal-to-semiconductor transition was realized to create nanotube transistors with a semiconducting nanotube channel covalently bonded between a metallic nanotube source and drain. Additionally, quantum transport at room temperature was demonstrated for the fabricated nanotube transistors with a channel length as short as 2.8 nanometers.

Oxidation is a unique process that significantly changes the structure and properties of a material. Doping of h-BN by oxygen is a hot topic in material science leading to the possibility of synthesis of novel 2D structures with customized electronic properties. It is still unclear how the atomic structure changes in the presence of external atoms during the oxidation of h-BN. We predict novel two-dimensional (2D) arrangements of boron oxynitride using the evolutionary algorithm of crystal structure prediction USPEX. All considered structures demonstrate semiconducting properties with a reduced bandgap compared with h-BN. Both molecular dynamics and phonon calculations show the dynamical stability of the new 2D B 5 N 3 O 2 phase, and our calculations demonstrate that it can form a bulk layered structure with an interlayer distance larger than that of pure h-BN. The optical characterization shows a redshift of the absorption spectrum compared with pure h-BN. Incorporation of oxygen into the structure of 2D BN during synthesis or oxidation can dramatically change the covalent network of h-BN while preserving its two-dimensionality and flatness, following the presence of local dipole moments which could improve the piezoelectric properties.

This work is aimed to study experimental and theoretical approaches for searching effective local training rules for unsupervised pattern recognition by high-performance memristor-based Spiking Neural Networks (SNNs). First, the possibility of weight change using Spike-Timing-Dependent Plasticity (STDP) is demonstrated with a pair of hardware analog neurons connected through a (CoFeB) x (LiNbO 3 ) 1-x nanocomposite memristor. Next, the learning convergence to a solution of binary clusterization task is analyzed in a wide range of memristive STDP parameters for a single-layer fully connected feedforward SNN. The memristive STDP behavior supplying convergence in this simple task is shown also to provide it in the handwritten digit recognition domain by the more complex SNN architecture with a Winner-Take-All competition between neurons. To investigate basic conditions necessary for training convergence, an original probabilistic generative model of a rate-based single-layer network with independent or competing neurons is built and thoroughly analyzed. The main result is a statement of "correlation growth-anticorrelation decay" principle which prompts near-optimal policy to configure model parameters. This principle is in line with requiring the binary clusterization convergence which can be defined as the necessary condition for optimal learning and used as the simple benchmark for tuning parameters of various neural network realizations with population-rate information coding. At last, a heuristic algorithm is described to experimentally find out the convergence conditions in a memristive SNN, including robustness to a device variability. Due to the generality of the proposed approach, it can be applied to a wide range of memristors and neurons of software- or hardware-based rate-coding single-layer SNNs when searching for local rules that ensure their unsupervised learning convergence in a pattern recognition task domain., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

A new approach to creating a new and locally nanostructured graphene-based material is reported. We studied the electric and structural properties of partially fluorinated graphene (FG) films obtained from an FG-suspension and nanostructured by high-energy Xe ions. Local shock heating in ion tracks is suggested to be the main force driving the changes. It was found that ion irradiation leads to the formation of locally thermally expanded FG and its cracking into nanoparticles with small (∼1.5-3 nm) graphene quantum dots (GQD), embedded in them. The bandgap of GQD was estimated as 1 -1.5 eV. A further developed approach was applied to correct the functional properties of printed FG-based crossbar memristors. Dielectric FG films with small quantum dots may offer prospects in graphene-based electronics due to their stability and promising properties.

Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than traditional hardware with the Von-Neumann architecture. However, the memristive weight adjustment in the formal neuromorphic networks by the standard back-propagation techniques suffers from poor device-to-device reproducibility. One of the most promising approaches to overcome this problem is to use local learning rules for spiking neuromorphic architectures which potentially could be adaptive to the variability issue mentioned above. Different kinds of local rules for learning spiking systems are mostly realized on a bio-inspired spike-time-dependent plasticity (STDP) mechanism, which is an improved type of classical Hebbian learning. Whereas the STDP-like mechanism has already been shown to emerge naturally in memristive devices, the demonstration of its self-adaptive learning property, potentially overcoming the variability problem, is more challenging and has yet to be reported. Here we experimentally demonstrate an STDP-based learning protocol that ensures self-adaptation of the memristor resistive states, after only a very few spikes, and makes the plasticity sensitive only to the input signal configuration, but neither to the initial state of the devices nor their device-to-device variability. Then, it is shown that the self-adaptive learning of a spiking neuron with memristive weights on rate-coded patterns could also be realized with hardware-based STDP rules. The experiments have been carried out with nanocomposite-based (Co 40 Fe 40 B 20 ) х (LiNbO 3-y ) 100-х memristive structures, but their results are believed to be applicable to a wide range of memristive devices. All the experimental data were supported and extended by numerical simulations. There is a hope that the obtained results pave the way for building up reliable spiking neuromorphic systems composed of partially unreliable analog memristive elements, with a more complex architecture and the capability of unsupervised learning.

In this paper, the resistive switching and neuromorphic behaviour of memristive devices based on parylene, a polymer both low-cost and safe for the human body, is comprehensively studied. The Metal/Parylene/ITO sandwich structures were prepared by means of the standard gas phase surface polymerization method with different top active metal electrodes (Ag, Al, Cu or Ti of ~500 nm thickness). These organic memristive devices exhibit excellent performance: low switching voltage (down to 1 V), large OFF/ON resistance ratio (up to 10 4 ), retention (≥10 4  s) and high multilevel resistance switching (at least 16 stable resistive states in the case of Cu electrodes). We have experimentally shown that parylene-based memristive elements can be trained by a biologically inspired spike-timing-dependent plasticity (STDP) mechanism. The obtained results have been used to implement a simple neuromorphic network model of classical conditioning. The described advantages allow considering parylene-based organic memristors as prospective devices for hardware realization of spiking artificial neuron networks capable of supervised and unsupervised learning and suitable for biomedical applications.

The memristive elements constructed using polymers - polyaniline (PANI) and polyethyleneoxide (PEO) - could be assembled on planar thin films or on 3D fibrous materials. Planar conductive PANI-based materials were made using the Langmuir-Schaefer (LS) method, and the 3D materials - using the electrospinning method which is a scalable technique. We have analyzed the influence of PANI molar mass, natures of solvent and subphase on the crystalline structure, thickness and conductivity of planar LS films, and the influence of PANI molar mass and the PANI-PEO ratio on the morphological and structural characteristics of 3D fibrous materials.

For assurance of food quality and safety, it is necessary to control routes of food pollutants, including toxic trace elements in all stages of handling, ranging from the production of agricultural products, including the processing, storage, transportation, and ending with cooking and consumption. For this control the nuclear activation analytical methods (NAAMs) and X-ray fluorescence analysis (XRFA) are studied and developed for the detection of chemical elements in different environments. NAAMs involve two stages: the activation of nuclear isotopes with thermal neutrons or fast charged particles and gamma spectrometry. XRFA technique is based on detecting characteristic fluorescent X-rays of elements. Interest in these methods is due to the relative simplicity of the analysis, the ability to provide the required sensitivity and accuracy of detection of chemical elements. The main objective of the research work is to demonstrate the potential of these techniques for controlling the quality and safety of food products and for research of trace elements' biokinetics with achieving the required accuracy (no worse than 15%) and sensitivity (below the permissible concentrations of harmful substances) and to prepare them for the practical application. Both methods complement each other in terms of expanding the list of detected chemical elements and choosing the best option in the analysis depending on the type of the test sample and readiness of technical means. In addition to the analysis of NAAMs and XRFA capabilities two different but partly connected research options were performed using NAAMs: 1) control of food quality and safety; 2) research of biokinetics of nanoparticles (NPs), incoming with food and water into laboratory animals with special attention to overcoming biological barriers. One of the important result is the demonstration of overcoming the blood/brain barrier by silver NPs when they are ingested into the animals with food or water, and possibility of their accumulation in brains with extremely low excretion (≈6% per month)., (Copyright© GEOTAR-Media Publishing Group.)

The latest achievements in 2-dimensional (2D) material research have shown the perspective use of sandwich structures in nanodevices. We demonstrate the following generation of bilayer materials for electronics and optoelectronics. The atomic structures, the stability and electronic properties of Moiré graphene (G)/h-BN bilayers with folded nanoholes have been investigated theoretically by ab-initio DFT method. These perforated bilayers with folded hole edges may present electronic properties different from the properties of both graphene and monolayer nanomesh structures. The closing of the edges is realized by C-B(N) bonds that form after folding the borders of the holes. Stable ≪round≫ and ≪triangle≫ holes organization are studied and compared with similar hole forms in single layer graphene. The electronic band structures of the considered G/BN nanomeshes reveal semiconducting or metallic characteristics depending on the sizes and edge terminations of the created holes. This investigation of the new types of G/BN nanostructures with folded edges might provide a directional guide for the future of this emerging area.

The stability and the electronic properties of new AB-stacking and moiré bilayer graphene superlattices with closed edge nanoholes are studied using DFT calculations. The closing of the edges is made of C-C bonds that form after folding the borders of the holes. Superlattices with periodic hexagonal symmetry are considered in more detail. The electronic band structure of the nanomeshes has metallic to semiconductor characteristics, depending not only on the size of the holes and the distance between them, but also on the hole shape. Bilayered graphene is considered as a platform for the engineering of nanostructures with folded edge holes.

Band gap control by an external field is useful in various optical, infrared and THz applications. However, widely tunable band gaps are still not practical due to a variety of reasons. Using the orthogonal tight-binding method for π-electrons, we have investigated the effect of the external electric field on a subclass of monolayer chevron-type graphene nanoribbons that can be referred to as jagged graphene nanoribbons. A classification of these ribbons was proposed and band gaps for applied fields up to the SiO2 breakdown strength (1 V nm(-1)) were calculated. According to the tight-binding model, band gap opening (or closing) takes place for some types of jagged graphene nanoribbons in the external electric field that lies on the plane of the structure and perpendicular to its longitudinal axis. Tunability of the band gap up to 0.6 eV is attainable for narrow ribbons. In the case of jagged ribbons with armchair edges larger jags forming a chevron pattern of the ribbon enhance the controllability of the band gap. For jagged ribbons with zigzag and armchair edges regions of linear and quadratic dependence of the band gap on the external electric field can be found that are useful in devices with controllable modulation of the band gap.

Silver nanoparticles (NPs), widely used in the manufacture of various types of consumer products and for medical applications, belong to novel types of materials that pose potential risks to human health. The potential negative effects of the influence of these NPs on reproduction are insufficiently researched. A quantitative assessment of the transfer of metallic silver nanoparticles through the placenta and breast milk was carried out during an in vivo experiment. We used 34.9 ± 14.8 nm in size silver NPs that were stabilized by low-molecularweight polyvinylpyrrolidone and labeled with the (110m)Ag radioactive isotope using thermal neutron irradiation in a nuclear reactor. [(110m)Ag]-labeled NPs preparations were administered intragastrically via a gavage needle to pregnant (20(th) day of gestation) or lactating (14-16th day of lactation) female rats at a dose of 1.69-2.21 mg/kg of body weight upon conversion into silver. The accumulation of NPs in rat fetuses and infant rats consuming their mother's breast milk was evaluated using a low-background semiconductor gamma-ray spectrometer 24 and 48 hours following labeling, respectively. In all cases, we observed a penetration of the [(110m)Ag]-labeled NPs through the placenta and ther entry into the mother's milk in amounts exceeding by 100-1,000 times the sensitivity of the utilized analytical method. The average level of accumulation of NPs in fetuses was 0.085-0.147% of the administered dose, which was comparable to the accumulation of the label in the liver, blood, and muscle carcass of adult animals and exceeded the penetration of NPs across the hematoencephalic barrier into the brain of females by a factor of 10-100. In lactating females, the total accumulation of [(110m)Ag]-labeled NPs into the milk exceeded 1.94 ± 0.29% of the administered dose over a 48 h period of lactation; not less than 25% of this amount was absorbed into the gastrointestinal tract of infant rats. Thus, this was the first time experimental evidence of the transfer of NPs from mother to offspring through the placenta and breast milk was obtained.

The Smolensk Region (Russian Federation) was used to assess how ultrasound diagnosis is made in the therapeutical and prophylactic institutions. Requirements for the diagnosis are worded in terms of regional, organizational, and other features. At the same time the paper gives main criteria for assessing the obsolescence of equipment; data on the level of organization of service; a share of brands of equipment; and characteristics of manufacturers in the ultrasound equipment market of the Smolensk Region.

The authors conducted complex clinical and X-ray examination of 80 patients suffering from the dumping syndrome of various degree. They were sent to the Central Scientific Research Institute of Expert Medical Assessment of Working Capacity for establishing the degree of severity of morphological and functional disorders affecting their capacity for work. The method of examination of such patients, including X-ray and other clinical methods, is described. The main clinical and roentgenosemeiotic signs characteristic of the mild, moderately severe, and severe forms of the dumping syndrome are pointed out. Clinical cases are described in illustration of the diagnostic possibilities and the expert approach to the results of the examination of such patients.

The paper is concerned with a clinico-roentgeno-functional++ study into sequelae of fighting injury to the osseous and soft tissue structures of the neck with the aid of testometric stereoroentgenogrammetry, a method of x-ray topical diagnosis used in fragmentation effects and gunshot injuries, as well as a method of color reading of limb roentgenograms to identify pathomorphological alterations and neurotrophic disorders of the osseous tissue. The pathofunctional alterations related to derangement of the radix structures can be verified by means of using the current methods of functional research: echoosteometry, rheovasography, etc.