Similarity Of Dynamics Research Articles

A dynamically consistent discretization method for the Goodwin model with nonlinear Phillips curve. Comparing qualitative and quantitative dynamics

AbstractThe Goodwin model is a widely used economic growth model able to explain endogenous fluctuations in employment rate and wage share; in its initial version, the standard Phillips curve is used. In the present work, we suggest a revised Phillips curve that takes into account how the wage share influences the rate of changes of the wage itself thus obtaining a continuous-time modified Goodwin model. Since applying models to real data often requires working in a discrete-time setup, we then move from the continuous-time to the discrete-time version of the proposed model, by using a general polynomial discretization method in backward and forward-looking (hybrid discretization). By comparing the continuous-time system to its discrete-time counterpart we prove that fixed points and local dynamics do not change, as long as the time step is not too high. Moreover, numerical simulations employing Dynamic Time Warping, cross-correlation, and semblance analysis consistently affirm that enhancing the similarity of quantitative dynamics is achieved by reducing the time step.

SimCalib: Graph Neural Network Calibration Based on Similarity between Nodes

Graph neural networks (GNNs) have exhibited impressive performance in modeling graph data as exemplified in various applications. Recently, the GNN calibration problem has attracted increasing attention, especially in cost-sensitive scenarios. Previous work has gained empirical insights on the issue, and devised effective approaches for it, but theoretical supports still fall short. In this work, we shed light on the relationship between GNN calibration and nodewise similarity via theoretical analysis. A novel calibration framework, named SimCalib, is accordingly proposed to consider similarity between nodes at global and local levels. At the global level, the Mahalanobis distance between the current node and class prototypes is integrated to implicitly consider similarity between the current node and all nodes in the same class. At the local level, the similarity of node representation movement dynamics, quantified by nodewise homophily and relative degree, is considered. Informed about the application of nodewise movement patterns in analyzing nodewise behavior on the over-smoothing problem, we empirically present a possible relationship between over-smoothing and GNN calibration problem. Experimentally, we discover a correlation between nodewise similarity and model calibration improvement, in alignment with our theoretical results. Additionally, we conduct extensive experiments investigating different design factors and demonstrate the effectiveness of our proposed SimCalib framework for GNN calibration by achieving state-of-the-art performance on 14 out of 16 benchmarks.

EVALUATION OF PROVINCES IN TÜRKİYE WITH HEALTH INDICATORS BY DENSITY-BASED SPATIAL CLUSTERING ANALYSIS

This study aims to evaluate the health resource distribution of provinces in Turkey using DBSCAN cluster analysis method. The optimum values of DBSCAN parameters (epsilon and minPts) were tested by simulation and the clustering silhouette value was taken as the basis for selecting the appropriate parameter set. The results of the descriptive statistical analysis of the dataset show a high coefficient of variation, indicating inequalities in the distribution of health resources. By dividing provinces into two clusters, the study reveals the similarity of local dynamics in the inequality of resource distribution. The findings provide important insights for relevant stakeholders to address the disparities between provinces in Turkey. The fact that the study adopts a method other than the hierarchical and k-means clustering methods dominant in the literature and that the codes of the algorithm are shared in Python language broadens the horizons of the relevant researchers and increases the transparency and reproducibility of the study.

Fractal Geometry in High-Frequency Trading: Modeling Market Microstructure and Price Dynamics

This theoretical article delves into the intricate world of high-frequency trading (HFT) without empirical testing of real-world data, focusing on the incorporation of fractal geometry principles to enhance our understanding of market microstructure and price dynamics. In the introduction, we outline the significance of this research in the context of modern financial markets and lay out the objectives of our theoretical analysis. The article then takes an in-depth dive into fractal geometry fundamentals, illuminating its core concepts and its relevance within financial markets. Subsequently, the article explores the landscape of high-frequency trading, offering an overview of this dynamic domain and how fractal geometry can be incorporated into trading models. The section on modeling market microstructure presents theoretical approaches to understanding order flow dynamics, including novel derivations and equations. It then transitions into fractal-based approaches for analyzing the complexities of market microstructure, providing both an original perspective and numbered equations. Moreover, this article investigates the theoretical modeling of price dynamics, underscoring the pivotal role of fractal geometry in enriching these models. The discussion revolves around the fundamental autoregressive models and multifractal models, and it elucidates how fractal geometry principles, such as the Hurst exponent, come into play. We explore the self- similarity of price dynamics, fractal dimensions, and how these aspects can be integrated into high-frequency trading strategies. Overall, this article offers a comprehensive theoretical exploration of fractal geometry's implications in the realm of high-frequency trading, providing valuable insights for both researchers and practitioners seeking to fathom the complexities of market microstructure and price dynamics. The incorporation of fractal principles into financial models fosters a deeper understanding of self-similarity and complexity within financial markets, even in the absence of empirical data.

A 2-dimensional model framework for blood glucose prediction based on iterative learning control architecture.

The accurate, timely, and personalized prediction for future blood glucose (BG) levels is undoubtedly needed for further advancement of diabetes management technologies. Human inherent circadian rhythm and regular lifestyle resulting in similarity of daily glycemic dynamics play a positive role in the prediction of blood glucose. Inspired by the iterative learning control (ILC) method in the field of automatic control, a 2-dimensional (2-D) model framework is constructed to predict the future blood glucose levels by taking both the short-range information within a day (intra-day) and long-range information between days (inter-day) into account. In this framework, the radial basis function neural network was applied to capture nonlinear relationships in glycemic metabolism, that is, short-range temporal dependence and long-range contemporaneous dependence on previous days. We build models for each patient, and the models were tested on the in silico datasets at various prediction horizons (PHs). The learning model developed in the 2-D framework successfully increases the accuracy and reduces the delay of predictions. This modeling framework provides a new point of view for BG level prediction and contributes to the development of personalized glucose management, such as hypoglycemia warning and glycemic control.

Unusual similarity of DNA solvation dynamics in high-salinity crowding with divalent cations of varying concentrations.

Double-stranded DNA bears the highest linear negative charge density (2e- per base-pair) among all biopolymers, leading to strong interactions with cations and dipolar water, resulting in the formation of a dense 'condensation layer' around DNA. Interactions involving proteins and ligands binding to DNA are primarily governed by strong electrostatic forces. Increased salt concentrations impede such electrostatic interactions - a situation that prevails in oceanic species due to their cytoplasm being enriched with salts. Nevertheless, how these interactions' dynamics are affected in crowded hypersaline environments remains largely unexplored. Here, we employ steady-state and time-resolved fluorescence Stokes shifts (TRFSS) of a DNA-bound ligand (DAPI) to investigate the static and dynamic solvation properties of DNA in the presence of two divalent cations, magnesium (Mg2+), and calcium (Ca2+) at varying high to very-high concentrations of 0.15 M, 1 M and 2 M. We compare the results to those obtained in physiological concentrations (0.15 M) of monovalent Na+ ions. Combining data from fluorescence femtosecond optical gating (FOG) and time-correlated single photon counting (TCSPC) techniques, dynamic fluorescence Stokes shifts in DNA are analysed over a broad range of time-scales, from 100 fs to 10 ns. We find that while divalent cation crowding strongly influences the DNA stability and ligand binding affinity to DNA, the dynamics of DNA solvation remain remarkably similar across a broad range of five decades in time, even in a high-salinity crowded environment with divalent cations, as compared to the physiological concentration of the Na+ ion. Steady-state and time-resolved data of the DNA-groove-bound ligand are seemingly unaffected by ion-crowding in hypersaline solution, possibly due to ions being mostly displaced by the DNA-bound ligand. Furthermore, the dynamic coupling of cations with nearby water may possibly contribute to a net-neutral effect on the overall collective solvation dynamics in DNA, owing to the strong anti-correlation of their electrostatic interaction energy fluctuations. Such dynamic scenarios may persist within the cellular environment of marine life and other biological cells that experience hypersaline conditions.

Clustering of Linear Time-Invariant Systems: A Bayesian Nonparametric Method

In this paper, we consider the clustering of linear time-invariant (LTI) systems according to the similarity of dynamics. There are two main difficulties for clustering of LTI systems: one is to determine the labels of LTI systems and the number of clusters, and another one is to build models of LTI systems. To address these two difficulties simultaneously, a Bayesian nonparametric method based on Dirichlet process mixture models (DPMM) and kernel-based regularization method is proposed. To be specific, the Dirichlet process mixtures of Bayesian linear regression models (DPM-BLRM) is proposed to determine the label of each LTI system according to the probability that the system belongs to each cluster, and adapt the number of clusters to the complexity of data. In addition, DPM-BLRM incorporates the prior knowledge of LTI systems to deal with the possible large variance of model parameters estimation when the collected data is short or has low signal-to-noise ratio, where the carrier of prior knowledge is kernel matrix. Then Markov chain Monte Carlo (MCMC) method is used for Bayesian inference for the DPM-BLRM. The simulation results illustrate the efficiency of the proposed method.

Similarity of catchment dynamics based on the interaction between streamflow and forcing time series: Use of a transfer entropy signature

The transfer of the hydrological information between catchments is founded on the definition of hydrological similarity, which is in turn strictly connected to the features to be regionalised. In order to characterise the catchment behaviour in the streamflow generation processes, the similarity should reflect also the interaction between meteorological forcings and river streamflow time series. While previous hydrological research has identified basins with similar meteorological forcings (i.e. similarity of climate) or with similar streamflow time-series (i.e. similarity of runoff response), the present work proposes, for the first time, to quantify the interaction between the entire time-series of different forcing data and streamflow observations, to be considered as a novel hydrological signature and used as a catchment similarity metric.In particular, the present study proposes the use of a multi-variate entropy-based measure, the so-called transfer entropy, a time-asymmetric quantity which analyses the interaction between different signals. The concept of transfer entropy is applied for identifying the dominant hydrological processes occurring in a catchment, measuring the transfer of information from different meteorological forcings over the catchment to the corresponding observed time series of daily streamflow at the basin outlet. The resulting transfer entropy values are then used as signatures to characterise the main catchment dynamics, and a classification of the basins region is obtained assuming that similar values of transfer entropy correspond to hydrologically similar basins.The methodology is tested on a densely-gauged set of more than 200 catchments across Austria and the outcomes of the approach are evaluated against a set of morpho-climatic catchment attributes and typical streamflow signatures.Despite the limitations, the method is able to distinguish the predominant or partial role of snow melt and evapotranspiration across the dataset, helping to assess differences in catchment response time and to highlight the role of very high orographic precipitation in catchments with a dominant snow regime. The study demonstrates the potential of transfer entropy as complementary to consolidated streamflow signatures for assessing hydrological similarity and for quantifying the connection between different catchment processes.

(Invited) Probing Charge Carrier Dynamics in Porphyrin-Based Bulk Heterojunction Thin Films with Time-Resolved Terahertz Spectroscopy

Organic photovoltaic (OPV) solar cells have attracted much attention in recent years because of the low production cost and ease of device fabrication. However, the power conversion efficiency is still low compared to the silicon and perovskite solar cells so that further research is necessary to improve their performance. In particular, understanding of the detailed mechanisms of charge carrier generation, recombination and carrier transport process is very important for developing design principles of new compounds for OPVs. Until now, polymer-based compounds have been widely used for p-type semiconductors. However, it is not easy to obtain information on how the microscopic structure of bulk heterojunction (BHJ) blend of donor and acceptor materials affect the charge carrier dynamics because one cannot easily control the molecular weight and conformational distributions of polymer compounds. On the other hand, small molecule-based semiconductors have several unique advantages such well-defined chemical structures, ease of purification and small batch-to-batch variations.Tetrabenzoporphyrin (BP) has been frequently used as a small-molecule organic semiconductor. Recently, Yamada and coworkers synthesized diketopyrrolopyrrole-linked tetrabenzoporphyrin (DPP-BP) conjugates that have linear alkyl groups on the DPP moieties whose chain length is defined as n, namely, Cn-DPP-BP (n=4, 6, 8, or 10). For DPP-BP based solar cells, they demonstrated that alkyl chain length affects the power conversion efficiency. Therefore, it is very interesting to see how the local BHJ structures of DPP-BP based thin films affect the initial step of the charge carrier dynamics because such studies are difficult to perform for polymer-based systems. We can take advantage of small molecule-based systems to study the relationship between the local structure and the charge carrier dynamics.In this work, we used time-resolved terahertz (THz) spectroscopy to investigate the charge carrier dynamics of DPP-BP BHJ thin films blended with [6,6]-phenyl-C61-butyric acid methyl ester (PC61BM). Time-resolved THz spectroscopy is a unique method to quantify charge carrier mobility with very fast time resolution (less than picosecond). This spectroscopy reflects the charge carrier dynamics in the local spatial region because of the high-frequency nature of the THz electric field. We chose C4-DPP-BP:PC61BM and C10-DPP-BP:PC61BM thin films for the THz measurements. In the previous study, it was found that C4-DPP-BP molecule prefers to take a face-on geometry on the substrate. A grain size of the aggregates was around 100 nm or smaller which were measured by atomic force microscopy. In such a structure, one can expect that charge transport takes place very efficiently to the electrode. Actually, power conversion efficiency is 5.2 % which is highest among a series of DPP-BP chromophores. On the other hand, C10-DPP-BP adopts an edge-on geometry on the substrate and forms micrometer-sized aggregates. In this structure, molecular stacking is oriented perpendicular to the electrode so that power conversion efficiency decreases to 0.19%.In contrast to the other measurements, the THz spectroscopy has sensitivity to in-plane motion of the charge carriers with respect to the substrate, we expect to observe higher in-plane mobility of the charge carriers in the C10-DPP-BP:PC61BM BHJ films compared to C4-DPP-BP based films. In contrast to our intuitive expectation, we found that the product of the carrier mobility and the yield of the photo-generated charge carriers is similar to each other. We consider that the similarity of the charge carrier dynamics results from the high-frequency nature of the local mobility of the polarons because most of the polarons are confined in less than a 10-nm length scale. Furthermore, the lowest limit of the carrier mobility obtained from our results is about 1.0 cm2V-1s-1 when the quantum yield of the charge carrier generation is assumed to be unity. This value is much higher than those obtained by space-charge-limited current method. We expect that charge separation occurs very efficiently when the local mobility of the charge carrier is high.In this contribution, we will present importance of the local mobility of the charge carriers and difference of charge transport between short and long length scales in DPP-BP based solar cells.

An Active Flow Control Approach for Spatially Growing Mixing Layer

Abstract A control approach is proposed in this study that controls the growth of a spatially growing mixing layer in an open loop. Due to the similarity of the mixing layer dynamics in the temporal and spatial domains, the control is exercised in the temporal domain that leads to optimization of the layer in the spatial domain. The proportional-integral-derivative (PID) control in the temporal domain uses Navier–Stokes equations as the plant model. Apart from being prohibitively expensive, control in the spatial domain presents convective time delay problems. These barriers are circumvented by solving the control problem in the temporal domain very rapidly, based on a given set point in the linear regime. Once the set point in the temporal domain is reached, the corresponding initial conditions in the temporal domain are mapped to inflow boundary conditions in the spatial domain. With these mapped inflow boundary conditions in the spatial domain, the spatial development of the mixing layer is followed, which confirms the efficacy of control in the spatial domain. The present control methodology offers a new paradigm for control of the spatially growing mixing layer.

Dynamics of tax revenues in the budget of Ukraine and their forecast during the crisis period

It is extremely important for the budget process to obtain accurate forecasts of potential tax revenues, especially in periods of disruption and crisis. The paper is devoted to the study of dynamics of tax revenues’ volumes in the budget of Ukraine and the forecast of their values during the crisis.The dynamics of tax revenues in the Consolidated Budget of Ukraine, studied by using randomized R|S-analysis, fractal and probabilistic analyses as well as entropy calculation based on the data on monthly tax revenues for the period 2011–2021, is anti-persistent, fractal-like and unpredictable based on parametric dependencies, simple and complex trends. The topological dimension of the lines of dynamics for tax revenues of all types of taxes is much higher than 1, and the Hirst index indicates either fractal similarity of dynamics or its chaos. The map of dissipation periods of tax revenues in Ukraine, determined on the basis of entropy calculation and periods of negative entropy production according to the dynamics of tax revenues, coincided with the periods of maximum reduction in their volumes. The most crisis periods in the formation of tax revenues are 2019–2020, for certain types of taxes – 2016–2020, but the dissipation of tax revenues is projected for 2021–2022.The comparison of the level of fractal similarity in dynamics of the volume of tax revenues and peculiarities of the dynamics of entropy and entropy production, allowed to substantiate the division of taxes into nine types, of which five were found in Ukraine.

A General Metric for the Similarity of Both Stochastic and Deterministic System Dynamics.

Many problems in the study of dynamical systems—including identification of effective order, detection of nonlinearity or chaos, and change detection—can be reframed in terms of assessing the similarity between dynamical systems or between a given dynamical system and a reference. We introduce a general metric of dynamical similarity that is well posed for both stochastic and deterministic systems and is informative of the aforementioned dynamical features even when only partial information about the system is available. We describe methods for estimating this metric in a range of scenarios that differ in respect to contol over the systems under study, the deterministic or stochastic nature of the underlying dynamics, and whether or not a fully informative set of variables is available. Through numerical simulation, we demonstrate the sensitivity of the proposed metric to a range of dynamical properties, its utility in mapping the dynamical properties of parameter space for a given model, and its power for detecting structural changes through time series data.

Ultraviolet Photodissociation Dynamics of the Cyclohexyl Radical: The H-Atom Product Channel.

The ultraviolet (UV) photodissociation dynamics of the jet-cooled cyclohexyl (c-C6H11) radical is studied using the high-n Rydberg atom time-of-flight (HRTOF) technique. The cyclohexyl radical is produced by the 193 nm photodissociation of chlorocyclohexane and bromocyclohexane and is examined in the photolysis wavelength region of 232-262 nm. The H-atom photofragment yield (PFY) spectrum contains a broad peak centered at 250 nm, which is in good agreement with the UV absorption spectrum of the cyclohexyl radical and assigned to the 3p Rydberg states. The translational energy distributions of the H-atom loss product channel, P(ET)'s, are bimodal, with a slow (low ET) component peaking at ∼6 to 7 kcal/mol and a fast (high ET) component peaking at ∼44-48 kcal/mol. The fraction of the average translational energy in the total excess energy, ⟨fT⟩, is in the range of 0.16-0.25 in the photolysis wavelength region of 232-262 nm. The H-atom product angular distribution of the slow component is isotropic, while that of the fast component is anisotropic with an anisotropy parameter of β ≈ 0.5-0.7. The bimodal product translational energy and angular distributions indicate two dissociation pathways to the H + C6H10 products in cyclohexyl. The high-ET anisotropic component is from a repulsive, prompt dissociation on a repulsive potential energy surface coupling with the Rydberg excited states to produce H + cyclohexene. The low-ET isotropic component is consistent with the unimolecular dissociation of hot radical on the ground electronic state after internal conversion from the Rydberg states. The similarity of the photodissociation dynamics of the cyclohexyl radical to the previously studied small linear and branched alkyls expands on the understanding of the dissociation dynamics of alkyl radicals to include larger cyclic alkyl radicals.

Prefrontal Inter-brain Synchronization Reflects Convergence and Divergence of Flow Dynamics in Collaborative Learning: A Pilot Study.

Flow is a highly motivated and affectively positive state in which a person is deeply engaged in an activity and feeling enjoyment from it. In collaborative activities, it would be optimal if all participants were in a state of flow. However, flow states fluctuate amongst individuals due to differences in the dynamics of motivation and cognition. To explore the possibility that inter-brain synchronization can provide a quantitative measure of the convergence and divergence of collective motivational dynamics, we conducted a pilot study to investigate the relationship between inter-brain synchronization and the interpersonal similarity of flow state dynamics during the collaborative learning process. In two English as a Foreign Language (EFL) classes, students were divided into groups of three-four and seated at desks facing each other while conducting a 60-min group work. In both classes, two groups with four members were randomly selected, and their medial prefrontal neural activities were measured simultaneously using wireless functional near-infrared spectroscopy (fNIRS) devices. Later the participants observed their own activities on recorded videos and retrospectively rated their subjective degree of flow state on a seven-point scale for each 2-min period. For the pairs of students whose neural activities were measured, the similarity of their flow experience dynamics was evaluated by the temporal correlation between their flow ratings. Prefrontal inter-brain synchronization of the same student pairs during group work was evaluated using wavelet transform coherence. Statistical analyses revealed that: (1) flow dynamics were significantly more similar for the student pairs within the same group compared to the pairs of students assigned across different groups; (2) prefrontal inter-brain synchronization in the relatively short time scale (9.3-13.9 s) was significantly higher for the within-group pairs than for the cross-group pairs; and (3) the prefrontal inter-brain synchronization at the same short time scale was significantly and positively correlated with the similarity of flow dynamics, even after controlling for the effects of within- vs. cross-group pair types from the two variables. These suggest that inter-brain synchronization can indeed provide a quantitative measure for converging and diverging collective motivational dynamics during collaborative learning, with higher inter-brain synchronization corresponding to a more convergent flow experience.

Similarity between the kinematic viscosity of quark-gluon plasma and liquids at the viscosity minimum

Recently, it has been found that the kinematic viscosity of liquids at the minimum, \nu_mνm, can be expressed in terms of fundamental physical constants, giving \nu_mνm on the order of 10^{-7}~{m^2/s}10−7m2/s. Here, we show that the kinematic viscosity of quark-gluon plasma (QGP) has a similar value and support this finding by experimental data and theoretical estimations. The similarity is striking, given that the dynamic viscosity and the density of QGP are about 16 orders of magnitude larger than in liquids and that the two systems have disparate interactions and fundamental theories. We discuss the implications of this result for understanding the QGP including the similarity of flow and particle dynamics at the viscosity minimum, the associated dynamical crossover and universality of shear diffusivity.

Intramolecular Photo-Oxidation as a Potential Source to Probe Biological Electron Damage: A Carboxylated Adenosine Analogue as Case Study.

A carboxylated adenosine analog (C-Ado−) has been synthesized and probed via time-resolved photoelectron spectroscopy in order to induce intra-molecular charge transfer from the carboxylic acid moiety to the nucleobase. Intra-molecular charge transfer can be exploited as starting point to probe low-energy electron (LEE) damage in DNA and its derivatives. Time-dependent density functional theory (TD-DFT) calculations at the B3LYP-6311G level of theory have been performed to verify that the highest occupied molecular orbital (HOMO) was located on carboxylic acid and that the lowest occupied molecular orbital (LUMO) was on the nucleobase. Hence, the carboxylic acid could work as electron source, whilst the nucleobase could serve the purpose of electron acceptor. The dynamics following excitation at 4.66 eV (266 nm) were probed using time-resolved photoelectron spectroscopy using probes at 1.55 eV (800 nm) and 3.10 eV (400 nm). The data show rapid decay of the excited state population and, based on the similarity of the overall dynamics to deoxy-adenosine monophosphate (dAMP–), it appears that the dominant decay mechanism is internal conversion following 1ππ* excitation of the nucleobase, rather than charge-transfer from the carboxylic acid to the nucleobase.

Characterizing the function of domain linkers in regulating the dynamics of multi-domain fusion proteins by microsecond molecular dynamics simulations and artificial intelligence.

Multi-domain proteins are not only formed through natural evolution but can also be generated by recombinant DNA technology. Because many fusion proteins can enhance the selectivity of cell targeting, these artificially produced molecules, called multi-specific biologics, are promising drug candidates, especially for immunotherapy. Moreover, the rational design of domain linkers in fusion proteins is becoming an essential step toward a quantitative understanding of the dynamics in these biopharmaceutics. We developed a computational framework to characterize the impacts of peptide linkers on the dynamics of multi-specific biologics. Specifically, we first constructed a benchmark containing six types of linkers that represent various lengths and degrees of flexibility and used them to connect two natural proteins as a test system. We then projected the microsecond dynamics of these proteins generated from Anton onto a coarse-grained conformational space. We further analyzed the similarity of dynamics among different proteins in this low-dimensional space by a neural-network-based classification model. Finally, we applied hierarchical clustering to place linkers into different subgroups based on the classification results. The clustering results suggest that the length of linkers, which is used to spatially separate different functional modules, plays the most important role in regulating the dynamics of this fusion protein. Given the same number of amino acids, linker flexibility functions as a regulator of protein dynamics. In summary, we illustrated that a new computational strategy can be used to study the dynamics of multi-domain fusion proteins by a combination of long timescale molecular dynamics simulation, coarse-grained feature extraction, and artificial intelligence.

КОНСОЛИДАЦИЯ ПЕРЕЛОМОВ БЕДРЕННОЙ КОСТИ ПРИ ПРИМЕНЕНИИ РАЗНЫХ МЕТОДИК ЛЕЧЕНИЯ С УЧЕТОМ ПРОДОЛЖИТЕЛЬНОСТИ ПЕРИОДА ОТ ТРАВМЫ ДО ОСТЕОСИНТЕЗА

Objective. To study the characteristics and healing duration of the diaphyseal femoral fractures, depending on the technologies used for osteosynthesis and the time between trauma and osteosynthesis. Methods. The study was performed on bred dogs (n=24). In the operating theatre all animals were modeled the transverse fracture of the femoral diaphysis in the middle third using a chisel. The animals were divided into 4 groups (6 per group). Animals of group 1 (one hour after the fracture) underwent transosseousosteosynthesis with a pin-rod external fixation device (PREF). In group 2, PREF was performed on the fourth day after the injury. In group 3 (one hour after the injury) blockable intramedullary osteosynthesis (BIOS) was performed. In group 4, the similar BIOS was performed on the fourth day after the fracture. To assess the results of treatment, the clinical, radiological and laboratory research methods were used. The duration of post-operative observation of the animals was 100 days. Results. It has been found out that the dynamics of osteoreparative processes in the studied groups was similar and did not depend on the time between trauma and osteosynthesis. The median time for the femoral shaft fracture healing in animals of group 1 was 46 days (Q1-Q3: 38-57), in group 2 - 73 days (Q1-Q3: 71-78) (differences between groups are significant at p=0.004); in group 3 - 49 days (Q1-Q3: 44-60), in group 4 - 72 days (Q1-Q3: 70-93) (differences between groups are significant at p=0.008). It is shown that the reason for the increase in fixation terms in dogs with the delayed osteosynthesis is the long-term persistence of the acute phase reaction caused by trauma (using the growth of C-reactive protein as an example). Conclusion. The healing terms of a femoral shaft fracture with the use of PREF technology and BIOS technology are comparable, both in the conditions of urgent and delayed osteosynthesis. What this paper adds It has been determined the similarity of the osteoreparation dynamics in the healing of diaphyseal femoral fractures: under conditions of osteosynthesis with a pin-rod external fixation device (PREF) and blocked intramedullary osteosynthesis (BIOS). For the first time it has been shown that healing terms of a diaphyseal femoral fractures when osteosynthesis performed in 4 days after injury is 1.5 fold longer in comparison with the performance of osteosynthesis within 1 hour after the injury. For the first time it has been shown that in the case of delayed osteosynthesis, the effectiveness of PREF and BIOS is comparable in terms of the duration of the consolidation of the diaphyseal femoral fractures.

Прохождение кризиса COVID-19 российской промышленностью: взгляд с макроуровня

The paper provides an express analysis of factors, general results and sectoral features of the COVID-crisis in the Russian industry. Six sectoral clusters are identified by the criteria of similarity of dynamics in production volumes, profits and investment activity. The most important sectoral factors that impacted the trends in the indicators are identified. It is shown that the obvious success observed in the first half of 2021 (namely the general growth in output and profits) is the effect of the deferred demand and the forced switch of demand from services to goods (meanwhile growth in food production, chemistry and wood and paper production continued to be based on net export growth). The factors that emerged by mid-2021 and negatively affected the development of manufacturing in the short term are indicated: the first is the exhausting deferred demand and, the second is the intensifying cost inflation. Meantime there is one supporting growth factor which is oil export recovery.

Кореляція основних показників економічного розвитку регіонів

The stability of dynamics of economic phenomena in the regional economy is associated with the general level of economic development of the country. The most important studies are based on the results of the analyses of the economy of developed countries. The article formulates conclusions on the relationship between economic differentiation and economic stability, as well as between instability and economic growth. Research of regional development should be based on a comprehensive and comprehensive definition of the management object – the region. Therefore, the authors emphasize that studying the economic development of regions is of particular relevance in modern economic conditions, when decentralization processes are taking place in Ukraine. To study the status of Ukraine’s economy at the regional level, it is proposed to implement a methodology based on the methods of synthesis and gradual specifying. To formalize the results of the practical application of the proposed research methodology, generally accepted indicators of the dynamics of certain directions of the country’s economy development were used. The use of dynamics indicators allowed to identify, on the one hand, long-term tendencies and trends of individual elements, and on the other hand, short-term changes in the processes of economic growth and development. The use of the built regression model can be used in forecasting GDP values per capita within the framework of the regional economic development policy. The authors demonstrate that an analysis of the similarity of dynamics of indicators of the economy of regions (correlation) and the dynamics of results of their economic activity, calculated according to the GRP indicator per capita, should be applied when substantiating the provisions of the regional economic development policy. It is recommended to build the policy of economic development of the regions taking into account the experience of the above-mentioned regions in the sphere of economic development. An interdependence is identified, which indicates an increase in the share of the resource economy and a decrease in the share of the science-intensity economy, the development of which contributes to the welfare of the country’s population.