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Traditional estimation of distribution algorithms (EDAs) often use Gaussian densities to optimize continuous functions, such as the estimation of Gaussian network algorithms (EGNAs) which use Gaussian Bayesian networks (GBNs). However, this assumes a parametric density function, and, in GBNs, linear dependencies between variables. Furthermore, the EGNA baseline learns a GBN at each iteration based on the best individuals in the last iteration, which may lead to local optimum convergence or large variance between solutions across multiple independent runs of the algorithm. In this work, we propose a semiparametric EDA in which the restriction of assuming Gaussianity in the variables is relaxed using semiparametric Bayesian networks (SPBNs), in which nodes estimated by kernels coexist with nodes that assume Gaussianity, and the algorithm itself is able to determine where to use each type of node. Additionally, our approach takes into account information from several past iterations to learn the SPBN from which the new solutions are sampled in each iteration. The empirical results show that semiparametric EDAs are a useful tool for continuous scenarios compared to different kinds of EDAs and other optimization techniques in continuous environments.

Recognition-encoded melamine oligomers (REMO) are synthetic polymers that feature an alternating 1,3,5-triazine-piperazine backbone and side-chains equipped with either a phenol or phosphine oxide recognition unit. An automated method for the solid-phase synthesis (SPS) of REMO of any specified sequence has been developed starting from dichlorotriazine monomer building blocks. Complementary homo-oligomers with either six phenols or six phosphine oxides were synthesized and shown to form a stable duplex in nonpolar solvents by NMR denaturation experiments. The duplex was covalently trapped by equipping the ends of the oligomers with an azide and an alkyne group and using a copper-catalyzed alkyne–azide cycloaddition (CuAAC) reaction. The SPS methodology was adapted to synthesize mixed sequence libraries by using a mixture of two different dichlorotriazine building blocks in each coupling cycle of an oligomer synthesis. The resulting libraries contain statistical mixtures of all possible sequences. The self-assembly properties of these libraries were screened by using the CuAAC reaction to trap any duplexes present. In mixed sequence libraries of 6-mers, the trapping experiments showed that only sequence-complementary oligomers formed duplexes at micromolar concentrations in dichloromethane. The automated synthesis approach developed here provides access to large libraries of mixed sequence synthetic polymers, and the covalent trapping experiment provides a convenient tool for screening functional properties of mixtures. The results suggest high-fidelity sequence-selective duplex formation in mixtures of 6-mer sequences of the REMO architecture.

The array of rate constants (323 K) for the addition of peroxyl radicals of different structures to the π-bonds of unsaturated compounds was obtained. These data were analyzed using a combination of correlation and quantum chemical methods. The prospects of using Denisov’s parabolic model are shown in comparison with classical linear correlations. It is noted that QSPR models using quantum chemical and structural descriptors can also be used to estimate the reactivity of reacting particles in this elementary act. The obtained kinetic information can be the basis for elucidating the detailed mechanism of oxidation of biologically significant molecules.

Bringing GNSS navigation further indoors by adding new users to a collaborative network can help realize the concept of ubiquitous navigation. Increasing the number of receiver nodes to improve signal-to-noise [...]

The trigger and data acquisition (TDQA) system of a toroidal large hadron collider (LHC) apparatus (ATLAS) experiment at the LHC at CERN is composed of a large number of distributed hardware and software components, which provide the data-taking functionality of the overall system. During data-taking, huge amounts of operational data are created in order to constantly monitor the system. The persistent back end for the ATLAS information system of TDAQ (P-BEAST) is a system based on a custom-built time-series database. It archives any operational monitoring data published online, resulting in about 18 terabyte (TB) of highly compacted and compressed raw data per year. P-BEAST provides command line and programming interfaces for data insertion and retrieval, including integration with the Grafana platform. Since P-BEAST was developed, several promising database technologies for efficiently working with time-series data have been made available. A study to evaluate the possible use of these recent database technologies in the P-BEAST system was performed. First, the most promising technologies were selected. Then, their performance was evaluated. The evaluation strategy was based on both synthetic read and write tests and on realistic read patterns (e.g., providing data to a set of Grafana dashboards currently used to monitor ATLAS). All the tests were executed using a subset of ATLAS operational monitoring data, archived during the LHC Run II. The details of the testing procedure and of the testing results, including a comparison with the current P-BEAST service, are presented.

Model-based fault diagnosis for dynamical systems is a sophisticated task due to model inaccuracies, measurement noise and many possible fault scenarios. By presenting faults in terms of a dictionary, the latter obstacle is recently addressed using well-known techniques for recovering sparse information (e.g. lasso). However, current state-of-the-art methods still require accurate models and measurements for adequate diagnosis. In our contribution we address the problem of data-driven fault diagnosis in the sense that the model of the linear time-invariant (LTI) system is unknown in addition to the fault. Moreover, our aim is to diagnose (concurrent) faults while only having input/output data and the fault dictionary. This implies the user simply plugs in the data and specifies the set of possible faults in order to know the active faults together with an estimate of the dynamic model. The problem is formulated within a blind system identification context resulting in computationally efficient solutions based on convex optimization.

Quantum architecture search (QAS) involves optimizing both the quantum parametric circuit configuration but also its parameters for a variational quantum algorithm. Thus, the problem is known to be multi-level as the performance of a given architecture is unknown until its parameters are tuned using classical routines. Moreover, the task becomes even more complicated since well-known trainability issues, e.g., barren plateaus (BPs), can occur. In this paper, we aim to achieve two improvements in QAS: (1) to reduce the number of measurements by an online surrogate model of the evaluation process that aggressively discards architectures of poor performance; (2) to avoid training the circuits when BPs are present. To detect the presence of the BPs, we employed a recently developed metric, information content, which only requires measuring the energy values of a small set of parameters to estimate the magnitude of cost function’s gradient. The main idea of this proposal is to leverage a recently developed metric which can be used to detect the onset of vanishing gradients to ensure the overall search avoids such unfavorable regions. We experimentally validate our proposal for the variational quantum eigensolver and showcase that our algorithm is able to find solutions that have been previously proposed in the literature for the Hamiltonians; but also to outperform the state of the art when initializing the method from the set of architectures proposed in the literature. The results suggest that the proposed methodology could be used in environments where it is desired to improve the trainability of known architectures while maintaining good performance.

Decreasing an overall time of curing for UHPC products is a perspective task that can be solved by combining different heat treatment technologies. The paper presents the results of studying the UHPC microstructure after combined steam and non-steam heat treatment. Concrete specimens after demolding were steam cured for 16 hours, than – in oven camber. Heat treatment in oven chamber for 8 hours under temperature 250 ̊С leads to micro-defects self-healing, due to the new crystalline phase formation. The combined heat treatment allows unleashing of additional potential of UHPC mixes, which could take years in normal conditions The considered curing method with further treatment in oven can not only accelerate the process, but also unleash the additional part of UHPC pozzolanic potential for different mix compositions.

The article presents experimental data obtained during the use of powdered calcium stearate and its suspension as bulk water repellents for fine-grained concrete. The use of powdered calcium stearate in an amount of 0.5% of the mass of cement led to an increase in air intake by 1.7 times, mobility by 8.5%, and a decrease in density by 2.5% compared to similar indicators of a concrete mixture without a water repellent. Powdered calcium stearate had no significant effect on compressive strength and flexural strength. With the introduction of CS in the form of a suspension and a powder in an amount of 0.5%, the capillary water absorption was reduced by 3 times, the water absorption by weight after the first day was 19% less than that of the samples without the additive. During the first day, water absorption for samples with a suspension of CS in an amount of 0.5% was 8.8%, for samples with CS powder - 7.6%. The highest water absorption is typical for samples without a hydrophobization and was equal to 9.3%, the lowest for samples with an CS suspension in the amount of 1.5% –4.4%. The introduction of a suspension of calcium stearate in an amount of 0.5% by weight of cement led to an increase in air entrainment by 2.1 times, a decrease in density by 4%, and a decrease in mobility by 5% compared to the samples without additive.

Trajectory reconstruction using a tightly coupled solution of GPS, two-dimensional laser scanner, and inertial sensors estimates the user delta position between consecutive update epochs. Potential applications for this technique include [...]

THANKS TO THE DEVELOPMENT OF MICROMACHINED SENSORS, the cost of some fairly sophisticated three-axis inertial navigation systems has dropped to a level where they can be seriously considered for use [...]

We have applied a combination of blind deconvolution and deep learning to the processing of Shack–Hartmann images. By using the intensity information contained in spot positions, and the fine structure of the separate images created by the lenslets, we have increased the sensitivity and resolution of the sensor over the limit defined by standard processing of spot displacements only. We also have demonstrated the applicability of the method to wavefront sensing using extended objects as a reference.

Decomposition of methane is the most efficient method for obtaining pure hydrogen. As catalysts for decomposition of methane, in this work, Ni-foam and Ni–Fe composites obtained by the electrochemical method were used for the first time. Thin iron films were electrochemically grown by potential cycling on the Ni-foam surface. The obtained catalysts were tested for decomposition of methane in the temperature range of 650–850 °C and characterized using XRD, RAMAN, SEM, TGA/DTA, H2-TPR and BET analysis of specific surface area and pore size. The effect of cycles (75, 150, and 250) of iron deposition on Ni-foam on its activity in methane decomposition was studied. It has been determined that the highest catalytic activity is observed for the composite, where the iron deposition cycle on nickel foam is 150. The Ni-Fe150 catalyst showed an initial methane conversion of 91% at a temperature of 850 °C, which increased from 60 min to 96.7% and from 180 min to 98.6%, and was stable for 540 min, while the hydrogen yield was 76%. It has been stated that graphite-like carbon is formed on all catalysts, and the largest amount (32%) is formed on Ni-Fe150. The data obtained in the work indicate that the increase in the activity of Ni-Fe150 in the decomposition of methane is associated with the formation of a Ni–Fe alloy and an increase in the reducibility of iron cations in the composition of the Ni–Fe alloy. In addition, the formation of graphite-like carbon with a high defectiveness on the surface of the Ni-Fe150 catalyst promotes the decomposition of methane in areas not covered with carbon.

Unmanned aerial vehicles initially designed to satisfy the needs of military organizations have significantly expanded their functional purpose during the past decades and are now used by many government institutions and bodies addressing a wide range of organizational, strategic and prognostic issues. Studying the equipment capability of government institutions participating in road traffic safety facilities resulted in a conclusion that unmanned aerial vehicles are intensively used when addressing organizational, administrative, operative, analytical, preventive and tactical-technical issues. Payload elements of modern aerial vehicles enable dealing with planning, construction and operation of motor ways, situational and prediction modeling of traffic flows in order to ensure population mobility, monitoring of climatic conditions and environmental situation, and help addressing the issues of crime prevention, promoting detection and investigation of road safety offenses. Unmanned aerial vehicles are an element of an intellectual transport system comprising modern informational, communication and telematic technologies. These aerial vehicles provide for automated search, recording and reception of information for automated processing, which allows for efficient monitoring of road transport system and groups of vehicles in the conditions of certain spatial and time parameters in order to ensure transport mobility of population and road safety in general.

Abstract: The popularity and diversity of artificial neural networks for various applications are ever increasing. The development of neural networks in the form of software models and hardware systems emphasizes their relevance and range of applicability, from a ten-minute Python code, an AlphaZero neural network, and intelligent image and speech recognition algorithms to IBM and Qualcomm neuromorphic chips and D-Wave quantum computing systems. The superconductor implementation of neural networks, along with the obvious advantages of superconductor technology in terms of energy efficiency and operating speed, makes it possible to combine a neural network and a superconducting quantum processor in one computing unit. In this case, the quantum core of a complex system can be used to learn a neural network by a global optimization method. It is noteworthy that the world’s leading IT companies clearly demonstrate the market’s focus on superconducting elements. The relevance of this direction is analyzed against a historical retrospective.

In modern medicine, biotechnological products and systems are used and widely studied, in which various elements and parts interact with biological fluids, soft and hard muscle tissues. This mainly concerns products used in cardiovascular surgery, orthopaedic surgery, dentistry and reconstructive surgery, as well as in ophthalmology. The article studies scientific approaches to the creation of biocompatible implant materials, considers their types, defines the basic requirements for biocompatible materials.

The authors' determinations of the U–Pb isotopic age of zircon (LA–ICP–MS method) in the main types of igneous rocks emphasize the presence of both Neogene and older (up to Late Paleozoic) intrusions at the deposit. The earliest are rocks of the “trondhjemite” massif, which were emplaced in the Late Paleozoic. This age of the rocks of the trondhjemite massif, confirmed by the U–Pb isotopic method (302 ± 4 Ma) for zircon from rocks of its main intrusive phase (tonalites–granodiorites), is consistent with manifestation of Late Paleozoic tungsten-bearing magmatism and associated W mineralization in the North Caucasus (in the Peredevoi Range) and allows a possible Late Paleozoic age of large bodies of altered skarns with W mineralization at the Tyrnyauz deposit. At the next, Neogene stage, the intrusion of leucocratic granites took place with the formation of bodies of igneous breccias, the emplacement of an accompanying aplite dike formation, and the development of intense molybdenum (with subordinate W) mineralization in large vein–veinlet, stockwork systems. The U–Pb isotopic age of zircon in leucocratic granites is 2.67 ± 0.04 Ma. Later, a large massif of biotite (Eldzhurt) granites and associated formation of aplite and alaskite dikes were formed. Emplacement of subvolcanic rhyolite bodies followed. The U–Pb isotopic age of zircon in the Eldzhurt granites is 2.10 ± 0.08 Ma, and in rhyolites, 2.05 ± 0.04 Ma. Intrusion of these rocks was accompanied by the formation of Mo–W–Bi–Te–Au mineralization.

Abstract: The thermochemical treatment of tungsten trioxide (WO3) nanostructured films in hydrogen sulfide to obtain tungsten disulfide (WS2) layers in the WS2/WO3hybrid structure is studied. The temperature and treatment time influence the structural-phase state, morphology, optical properties of a WS2/WO3/FTO photocathode (on fluorinated tin oxide (FTO) substrates), and its photoelectrocatalytic activity in the hydrogen evolution reaction in acidic solution is established. The sulfidation of WO3nanoneedle films leads to the formation of a WS2/WO3nanocrystalline hybrid structure under optimal conditions, which provides the separation of photogenerated carriers (electrons and holes) at the interphase boundaries (heterojunctions) necessary for the efficient photoactivated hydrogen evolution reaction according to Z scheme. The calculations of thermodynamic properties of the WS2/WO3hybrid nanocatalyst show that synergistic effect of nanophases is possible in it to increase the catalytic activity of hydrogen evolution both on the basal planes of WS2nanoclusters and on the surface of metal oxide nanoclusters.

Carbon-supported Mg–Al oxide (hydrotalcite-derived) hybrid catalysts for aqueous ethanol conversion into 1-butanol were prepared and characterized by X-ray diffraction, scanning electron microscopy, low-temperature nitrogen ad(de)sorption, Fourier transform infrared and X-ray photoelectron spectroscopies, and temperature-programmed desorption of NH3, CO2, and H2O. Enhanced catalytic activity and stability of operation over time on stream of the hybrid catalysts versus Mg–Al oxides were achieved using both rectified and aqueous ethanol as feed. Redistribution of the active sites of the Mg–Al oxide phase over a highly dispersed support and increasing their availability for the reagents were suggested to be the causes of the enhanced activity of the hybrid catalysts.

Aberrant activation of FGFR has been linked to the pathogenesis of many tumor types. Selective inhibition of FGFR has emerged as a promising approach for cancer treatment. Herein, we describe the discovery of compound 38(INCB054828, pemigatinib), a highly potent and selective inhibitor of FGFR1, FGFR2, and FGFR3 with excellent physiochemical properties and pharmacokinetic profiles. Pemigatinib has received accelerated approval from the U.S. Food and Drug Administration for the treatment of adults with previously treated, unresectable locally advanced or metastatic cholangiocarcinoma with a FGFR2 fusion or other rearrangement. Additional clinical trials are ongoing to evaluate pemigatinib in patients with FGFR alterations.

An investigation of hydrotalcite-derived ternary Mg-Al-Ce oxides as catalysts for vapour phase condensation of ethanol to 1-butanol in a flow reactor under atmospheric pressure was carried out. The Mg-Al-Ce oxide systems with Mg/(Al + Ce) ratio from 1 to 4 were synthesized and characterized by XRD, SEM, NMR, and XPS. The study of acid-base characteristics of the systems with different Mg/(Al+Ce) ratio by NH3/CO2quasi-equilibrium thermal desorption techniques shows that the ratio of the catalyst oxide components (Mg, Al, Ce) can provide acid/base capacity ratio close to 3 for the effectivity of the target process. The highest selectivity 68% is reached over Mg-Al-Ce oxide catalyst with the ratio of Mg/(Al+Ce) = 2.

The Corsica–Sardinia lithospheric block is traditionally viewed as a region of notoriously low seismicity. Earthquake catalogs report only three moderate earthquakes in the range of magnitudes 5.0–5.1. The scarcity of documented seismicity in the region does not allow for the reliable assessment of the seismic hazard. In this paper, we make an attempt to evaluate the seismic potential of the Corsica–Sardinia region employing the morphostructural zoning (MSZ) that allows for the identification of locations of potential earthquakes. The MSZ delineates the hierarchical system of morphostructural blocks, the network of lineaments bounding the blocks, and the loci of nodes formed around intersections of lineaments. Nodes are treated as earthquake controlling structures. We have used the existing criteria of seismicity defined earlier for seismogenic (M5+) nodes in Iberia and the Western Alps with the goal to identify seismogenic nodes capable of generating earthquakes M5+ in the Corsica–Sardinia block. We test the criteria of seismicity for these regions relying on modern models of the Mediterranean geodynamics that view the eastern margin of Iberia and Provence adjacent to the Western Alps as locations of the Corsica–Sardinia block before drifting. Due to a common tectonic evolution the structure of the Corsica–Sardinia block is roughly similar in the basic features to the structural setting of Iberia and southern France. Out of the 81 nodes delineated by MSZ (deliberately ignoring the available earthquake catalogue) we identify 24 nodes capable of earthquakes M5+ using the criteria of seismicity defined for Iberia. Nodes hosting three documented M5+ earthquakes are assigned to seismogenic ones and most of the nodes accommodating smaller events with magnitudes 4.0–4.9 are also classified as seismogenic by pattern recognition. Applying the criteria of seismicity defined for the Western Alps, we have properly identified only two nodes hosting the documented M5+ earthquakes and missed practically all the nodes with smaller events. The performed analysis reveals that the criteria of seismicity defined for Iberia are more pertinent for the identification of seismogenic nodes in Corsica–Sardinia, because they recognize as seismogenic the nodes marked by documented earthquakes. Our study suggests that the seismic potential of the Corsica–Sardinia block can be specified as low-to-moderate. The defined locations of seismogenic nodes provide key information for knowledgeable long-term seismic hazard assessment of the region.

This paper describes how positioning with signals of opportunity (SOOP) encounters two major technical challenges in urban mobile environments, namely, multipath and clock errors. The presence of signal fading, multipath [...]

The results of the operation of a wavefront correction system based on a deformable bimorph mirror of the PEARL subpetawatt laser facility are presented. An improvement in the quality of focusing of laser radiation, which led to an increase in the Strehl ratio from 0.3 to 0.6, is demonstrated. The features of the compensation for phase distortions of the wavefront in the case of a low pulse repetition rate, as well as the correct allowance for the noise of the CCD camera when calculating the Strehl ratio are investigated.

In this research the questions of microsilica usage and ways of its activation for modification of structure of cement stone and cement on its basis. The actual questions corresponding any type of filler, particularly microsilica are: how and how much should be put, which mechanisms are involving to process of structuring, how much is received result of structure modification and others. Therefore the role of microdispersed fillers in modification process of cement rock and cement on its basis must be viewed in connection with other dimensional inclusions on different large-scale levels. Reducing of concrete consumption by its substitution by microsilica, has a positive influence to physico-chemical characteristics of concrete.

In this Letter, weWe report on an algorithm and its implementation to reconstruct the wavefront as a continuous function from a bitmap image of the Hartmann–Shack pattern. The approach works with arbitrary raster geometry and does not require explicit spot definition and phase unwrapping. The system matrix, defining the coefficients of wavefront decomposition in the system of basis functions, is obtained as a result of a series of convolutions and thresholding operations on the reference and sample images.

We present an efficient phase retrieval approach for imaging systems with high numerical aperture based on the vectorial model of the point spread function. The algorithm is in the class of alternating minimization methods and can be adjusted for applications with either known or unknown amplitude of the field in the pupil. The algorithm outperforms existing solutions for high-numerical-aperture phase retrieval: (1) the generalization of the method of Hanser et al., based on extension of the scalar diffraction theory by representing the out-of-focus diversity applied to the image by a spherical cap, and (2) the method of Braat et al., which assumes through the use of extended Nijboer–Zernike expansion the phase to be smooth. The former is limited in terms of accuracy due to model deviations, while the latter is of high computational complexity and excludes phase retrieval problems where the phase is discontinuous or sparse. Extensive numerical results demonstrate the efficiency, robustness, and practicability of the proposed algorithm in various practically relevant simulations.