Cyclic Components Research Articles

Research Spotlights

This issue features three Research Spotlight articles. The first of these is entitled “Variance and Covariance of Distributions on Graphs" and is coauthored by Karel Devriendt, Samuel Martin-Gutierrez, and Renaud Lambiotte. Given a distribution on the graph (that is, a function $p$ from the set of nodes to $[0,1]$ such that the sum of all values is one) the key ingredient in the authors' definition of the variance and covariance is a notion of distance between the nodes of a graph. Although their definitions of (co)variance are valid for different choices of distance, the authors focus on a metric called the effective resistance. The effective resistance resembles geodesic distance in that it reflects the length of the paths between a pair of nodes but differs in that it takes into account all paths, and their lengths, between a node pair. Conveniently, the resulting variance and covariance values can be calculated via evaluation of a quadratic product and matrix trace. In support of the newly introduced measures, the balance of the paper is devoted to the application of the new definitions in practice and to the explanation of the conceptual correspondence between their (co)variance measures and some known graph characteristics. Citing the benefits of the framework induced by their (co)variance measures, the authors leave the reader with suggestions for future application in fields such as neuroscience, economics, and social networks. Competitive tournaments are an integral part of daily life, from schoolyard games to professional sports, to politics and biology. Authors Alexander Strang, Karen C. Abbott, and Peter J. Thomas tackle the difficult problem of quantifying competition in tournament modeling. Their article, “The Network HHD: Quantifying Cyclic Competition in Trait-Performance Models of Tournaments,” outlines how to adapt the discrete Helmholtz--Hodge decomposition (HHD), first introduced in the literature as a method for ranking objects from incomplete and imbalanced data, to study competitive tournaments characterized by intransitivity, the presence of which provides a challenge in ranking. Specifically, an intransitive tournament is one in which there is no clear global ranking of all competitors, and it corresponds to a cycle in the model. The authors show that the HHD arises in the context of representing a generic tournament as a weighted sum of so-called perfectly transitive and perfectly cyclic components. Since a trait-performance model assumes that “the probability that one competitor beats another is a function of their traits” the goal becomes that of identifying which traits and performance functions influence the weights in the HHD. This is addressed with the theoretical results in section 4. Schematics and graphs complement the discussion. Using the code made available by the authors, the interested reader may wish to try an analysis of a tournament for themselves. The third article, “Bilinear Optimal Control of an Advection-Reaction-Diffusion System," authored by Roland Glowinski, Yongcun Song, Xiaoming Yuan, and Hangrui Yue, offers both theoretical and computational insights to solving the mathematical problem specified in the title. Readers may appreciate the space devoted to the motivation and introduction to the problem as well as to the description of the associated difficulties both in deriving existence results and in numerical computation of solutions. Following a few preliminaries, the authors derive a proof of existence of optimal controls for the problem, labeled in the introduction as BCP, without the special case assumptions used elsewhere in the literature. The balance of the article is devoted to issues associated with the numerical implementation. Although their proposed nested conjugate gradient method is straightforward to present on paper, several obstacles arise in the implementation, and the authors tackle each of these in turn. For computational efficiency, for example, an inexact line search is proposed to replace the computationally intractable exact line search; preconditioned CG is employed for two internal subproblems that arise. The choice of the time and space discretizations is explained as well. There are many technical details to consume, but a key point for the readers is well summarized by the authors just before the detailed experiments, namely, “despite its apparent complexity, the nested CG method” given here “is easy to implement" with aspects that are amenable to parallelizability. Misha E. Kilmer Section Editor Misha.Kilmer@tufts.edu

Concatenated Codes Based on the Plotkin Construction and Their Soft-Input Decoding

Reed-Muller (RM) codes have recently regained some interest in the context of low latency communications and due to their relation to polar codes. RM codes can be constructed based on the Plotkin construction. In this work, we consider concatenated codes based on the Plotkin construction, where extended Bose-Chaudhuri-Hocquenghem (BCH) codes are used as component codes. This leads to improved code parameters compared to RM codes. Moreover, this construction is more flexible concerning the attainable code rates. Additionally, new soft-input decoding algorithms are proposed that exploit the recursive structure of the concatenation and the cyclic structure of the component codes. First, we consider the decoding of the cyclic component codes and propose a low complexity hybrid ordered statistics decoding algorithm. Next, this algorithm is applied to list decoding of the Plotkin construction. The proposed list decoding approach achieves near-maximum-likelihood performance for codes with medium lengths. The performance is comparable to state-of-the-art decoders, whereas the complexity is reduced.

Impulsive components separation using minimum-determinant KL-divergence NMF of bi-variable map for bearing diagnosis

In vibration-based monitoring techniques, the periodic impulse response is a typical manifestation of the localized failure of rolling element bearings (REBs). Cyclic spectral help reveal the cyclostationarity of such signals by bridging the carrier frequency and the cyclic frequency, which are represented in bi-variable maps. By integrating the components within the optimal band over the carrier axis, the Enhanced Envelope Spectrum (EES) or Squared Envelope Spectrum (SES) can be obtained and provide attractive analysis tools for the bi-variable map. In this paper, as a complement of integration analysis of the bi-variable map, Non-negative Matrix Factorization is adopted to separate the cyclic components in bi-frequency map. In addition to using KL-divergence as the fidelity term, the minimum determinant regularization term is crafted to enforce the sparseness, uniqueness, and identifiability of the solution. With numerical simulation and bearing fault experiments, the result shows that the number of cyclic components is estimated correctly and that the periodic impulsive components are separated and enhanced from the bi-variable map, especially in the cases that cyclic interferences mask the fault feature.

How Light Reactions of Photosynthesis in C4 Plants Are Optimized and Protected under High Light Conditions.

Most C4 plants that naturally occur in tropical or subtropical climates, in high light environments, had to evolve a series of adaptations of photosynthesis that allowed them to grow under these conditions. In this review, we summarize mechanisms that ensure the balancing of energy distribution, counteract photoinhibition, and allow the dissipation of excess light energy. They secure effective electron transport in light reactions of photosynthesis, which will lead to the production of NADPH and ATP. Furthermore, a higher content of the cyclic electron transport components and an increase in ATP production are observed, which is necessary for the metabolism of C4 for effective assimilation of CO2. Most of the data are provided by studies of the genus Flaveria, where species belonging to different metabolic subtypes and intermediate forms between C3 and C4 are present. All described mechanisms that function in mesophyll and bundle sheath chloroplasts, into which photosynthetic reactions are divided, may differ in metabolic subtypes as a result of the different organization of thylakoid membranes, as well as the different demand for ATP and NADPH. This indicates that C4 plants have plasticity in the utilization of pathways in which efficient use and dissipation of excitation energy are realized.

Molecular arrangement and fringe identification and analysis from molecular dynamics (MAFIA-MD): A tool for analyzing the molecular structures formed during reactive molecular dynamics simulation of hydrocarbons

Reactive molecular dynamics (RMD) simulations are becoming popular, with the recent developments in high-performance and efficient computing architecture, for investigating fundamental physico-chemical behaviors of reacting species. Due to the complexity of hydrocarbon systems, characterization of molecules formed during RMD simulations of hydrocarbons can be very challenging for simulations involving a large number of molecules. The novel post-processing utility “MAFIA-MD” – an acronym for “Molecular Arrangement and Fringe Identification and Analysis from Molecular Dynamics” – presented in this manuscript can analyze and perform molecular characterization of a large number of RMD trajectory files (.XYZ files) simultaneously. The utility will be useful for analysis and chemical characterization of trajectories without the large bond information files. A graphical user interface (GUI) is developed for easy operation. The utility analyzes the existing cyclic structures in the domain and generates statistics of alicyclic and aromatic hydrocarbons based on the number of carbon atoms. Alongside the statistical analysis, the program also separates the constituent molecules and extracts their chemical information in terms of Simplified Molecular-Input Line-Entry System (SMILES) and Spatial Data File (.SDF)”. A methodology for calculating fringe spacing is also implemented in the code for validation of RMD simulation with High-Resolution Transmission Electron Microscopy (HRTEM) images. Program summaryProgram title:MAFIA-MDCPC Library link to program files:https://doi.org/10.17632/s7dsk553fh.1Developer's repository link:https://github.com/kmmukut/MAFIA-MD.gitLicensing provisions: MITProgramming language:Python 3.7Supplementary material: Bash script for splitting long continuous trajectory files with multiple timestep into trajectory files for individual timestep and some example input trajectory files for testing.Nature of problem: Reactive molecular dynamics (RMD) simulations of hydrocarbons allow chemical reactions between different hydrocarbon molecules through bond breaking and new bond formation. The information about the chemical changes is stored in the bond information files and trajectory of individual atoms are stored in trajectory files. While the bond files can become very large for large simulations, trajectory files remain quite small. MAFIA-MD will be useful for practitioners interested in analyzing the chemical structures of emerging molecular clusters from small individual trajectory files without requiring the large, memory-intensive bond information files. Identification of chemical characteristics, particularly aromatic and alicyclic ring structures and molecular fringes, are important for gaining insights from RMD simulations of hydrocarbons, especially in exploration of soot formation during combustion. The capability of isolating the cyclic and non-cyclic molecules from trajectory files is the novel part of MAFIA-MD.Solution method: The coordinates of individual atoms are taken as the input for the program. The coordinates are then analyzed to create an adjacency matrix [1] based on the bond distance between carbon atoms. This adjacency matrix is then converted into a directed graph and the cyclic structures are extracted from the graph by implementing an efficient depth-first-search algorithm [2] developed by Johnson [3]. Apart from the ability to isolate cyclic and non-cyclic components by chemical structural analysis, a methodology to calculate fringe spacing is also implemented by calculating the distances between cyclic structures. The utility uses a Python interface for easy management of the input and output parameters. The detailed solution methodology has been discussed in the manuscript.Additional comments including restrictions and unusual features: In the current version, the utility can analyze only carbon and hydrogen atoms. The future versions will implement the addition of other common atoms found in aromatic molecules, e.g. nitrogen, oxygen, sulfur, etc. MAFIA-MD can be run on Linux, macOS, and Windows operating systems.

Cyclic symplectic component decomposition with application in planetary gearbox fault diagnosis

Due to the structural complexity of planetary gearbox, it is difficult to extract the fault information in the vibration signal of planetary gearbox, which seriously affects the accuracy of fault diagnosis. Although the existing fault diagnosis methods, such as local characteristic-scale decomposition (LCD) and ensemble empirical mode decomposition (EEMD), are widely used in the field of fault diagnosis, they are difficult to decompose complex signals effectively. Therefore, a new signal processing method, called cyclic symplectic component decomposition (CSCD) based on the Toeplitz matrix and the cyclic matrix, is proposed in this paper. In CSCD, to obtain the closed-loop solutions, each row is the right cyclic shift of the previous row, so as to avoid information leakage. Meanwhile, the combination of sample covariance and symplectic geometry similarity transformation is constructed to further protect the status information and weaken the influence of noise. In addition, the reconstruction of the same mode is completed by using the extreme difference similarity (EDS), which can realize the adaptive decomposition of the signal. The research results of simulation signal and actual planetary gearbox fault signal show that the proposed CSCD method has superior decomposition performance.

About the Role of Digitalization of Agriculture in Reducing the Impact of Climate on the Technological Development of Crop Production

The article presents the generalized results of century-long observations of changes in the yield of grain crops of Udmurtia against the background of climate variation and the development of production technologies. The trends in the development of crop production are revealed and the influence of meteorological indicators on the efficiency of agriculture is proved. The correlations of grain yield with the temperature regime and moisture supply in certain periods of plant growth are considered. It is established that the climatic conditions of plant growth from April to June have the greatest role in the formation of the crop. There is a positive relationship between the yield and the average annual temperature of the previous year. Based on the analysis of time series of meteorological indicators for temperatures and precipitation in the summer period, trend equations are obtained, which prove the regularities of the growth of the average daily temperature and average monthly precipitation over a multi-year interval. A rather significant direct influence on plant growth of the average temperature of April, which determines the intensity of soil processes, has been revealed. At the same time, the existence of cyclic components in changes in temperature and precipitation is confirmed, and the frequency of these cycles is calculated. The tendency of reducing the role of natural factors in the formation of the crop with the use of innovative technologies and digitalization of the industry is proved. The possibilities of applying modern scientific achievements and technical solutions to reduce the role of climatic factors in the production of crop products are shown.

КРАТНОМАСШТАБНЫЙ ВЕЙВЛЕТ-АНАЛИЗ ПРОФИЛОГРАММЫ

The functional properties of the surface are significantly affected by its microgeometry, therefore it is necessary to optimize the parameters of the microgeometry of the surface of metal structures. The analysis and evaluation of the microgeometry of the surface allows an assessment of the fatigue strength of metal structures. In order to study the dependence of the durability of thin-sheet structures on the degree of corrosion destruction, the microgeometry of the surface was analyzed using a profilometer 130 in accordance with GOST 25142-82. Low-carbon cold-rolled steel 08kp was used as the test material. The surface roughness of the samples was Ra = 0.22 microns. The roughness parameters were calculated according to GOST 2789-73. A profilogram representing a discrete series of values of peaks and troughs of the relief of the metal plate surface was studied. To assess the microgeometry of the surface, a wavelet analysis was used, which allows processing signals that are nonstationary in time or inhomogeneous in space. The signal in the form of successive approximations has a trend, cyclic components and local features (fluctuations) around the components of the signal. The locality property of wavelets gives advantages over the trigonometric Fourier transform: the sines and cosines are defined on the entire numerical axis, and the wavelets have a compact carrier. For multiscale analysis, a fast cascade algorithm of calculations has been developed by analogy with the fast Fourier transform. In the study, the signal was decomposed to the 9th level. The reconstructed detailing coefficients represent high-frequency and low-frequency fluctuations. The decomposition of the signal to the 9th level made it possible to analyze the surface roughness of the 08kp steel using a multiple-scale wavelet analysis.

Precision synthesis of linear oligorotaxanes and polyrotaxanes achieving well-defined positions and numbers of cyclic components on the axle.

Interest in macromolecules has increased because of their functional properties, which can be tuned using precise organic synthetic methods. For example, desired functions have been imparted by controlling the nanoscale structures of such macromolecules. In particular, compounds with interlocked structures, including rotaxanes, have attracted attention because of their unique supramolecular structures. In such supramolecular structures, the mobility and freedom of the macrocycles are restricted by an axle and dependent on those of other macrocycles, which imparts unique functions to these threaded structures. Recently, methods for the ultrafine engineering and synthesis, as well as functions, of "defined" rotaxane structures that are not statistically dispersed on the axle (i.e., control over the number and position of cyclic molecules) have been reported. Various synthetic strategies allow access to such well-defined linear oligo- and polyrotaxanes, including [1]rotaxanes and [n]rotaxanes (mostly n > 3). These state-of-the-art synthetic methods have resulted in unique functions of these oligo-and polyrotaxane materials. Herein, we review the effective synthetic protocols and functions of precisely constructed one-dimensional oligomers and polymers bearing defined threaded structures, and discuss the latest reports and trends.

STR: Seasonal-Trend Decomposition Using Regression

We propose a new method for decomposing seasonal data: a seasonal-trend decomposition using regression (STR). Unlike other decomposition methods, STR allows for multiple seasonal and cyclic components, covariates, seasonal patterns that may have noninteger periods, and seasonality with complex topology. It can be used for time series with any regular time index, including hourly, daily, weekly, monthly, or quarterly data. It is competitive with existing methods when they exist and tackles many more decomposition problems than other methods allow. STR is based on a regularized optimization and so is somewhat related to ridge regression. Because it is based on a statistical model, we can easily compute confidence intervals for components, something that is not possible with most existing decomposition methods (such as seasonal-trend decomposition using Loess, X-12-ARIMA, SEATS-TRAMO, etc.). Our model is implemented in the R package stR, so it can be applied by anyone to their own data.

Experimental assessment on the dynamic mechanical response of rocks under cyclic coupled compression-shear loading

Rock engineering structures are quite susceptible to cyclic coupled compression-shear loading. Accurately characterizing the dynamic compression-shear behaviors of rocks is thus crucial for the construction safety and disaster prevention of rock engineering. In this study, a convenient and reliable testing method is proposed to characterize the fatigue mechanical response of rocks under cyclic coupled compression-shear loading: an inclined specimen under cyclic uniaxial loading. With the specimen axis inclining the loading direction, a cyclic shear component is introduced into rocks when subjected to cyclic uniaxial compression. Our method has certain distinct merits in the specimen preparation and experimental operation since no undesired loading devices are required. To demonstrate this method, 21 cyclic loading tests are conducted on inclined sandstone specimens with different tilting angles (i.e., 0°, 3°, 6° and 10°) and under different loading frequencies (i.e., 0.5, 1, 2, and 3 Hz). Based on experimental results, the influence of the tilting angle and loading frequency on the dynamic properties of rocks is revealed and a fatigue life prediction function is proposed. With increasing tilting angle of rock specimens or decreasing loading frequency, rock specimens are featured by an increase of nominal hysteresis energy density, leading to higher damage variable and lower fatigue life. Quite different from the axial splitting failure of right cuboid specimens along the loading direction, all inclined rock specimens (i.e., 3°, 6° and 10°) exhibit a prominent shear-dominated failure accompanied by localized tensile damage, and the shear component becomes more remarkable as the tilting angle increases. In addition, with a higher tilting angle or under lower loading frequency, the cyclically failed specimen is characterized by a higher degree of fragmentation.

빈도영역 분석법을 통한 주택가격과 거래량의 공통 사이클 분석

The movements of housing prices and trading volume have long-term cycles. Thus, it can be presumed that there would be a close relationship between the cyclic components embedded in these two variables. However, it has been challenging to find studies that analyzed the cyclical characteristics and the relationship between the common cycles embedded in these two variables. This study, in contrast to previous studies that used methodologies in the time domain analysis to confirm the correlation and lead-lag relationship between housing prices and trading volume, analyzed, from different perspectives, the cyclical characteristics embedded in these two variables, using spectral analysis in the frequency domain. When forecasting cycles in housing prices and trading volume, for comparison, we added the results of the VAR(p) model analysis in the time domain to the results of the spectral analysis in the frequency domain. We could confirm that there were common cycles between the movements of housing prices and trading volume. Further, we confirmed that the movement of trading volume slightly precedes that of housing prices. According to our results, when the housing price has an upward trend, the housing market was expected to face a slow speed of an increase in housing prices while a decrease in trading volume for the time being after 2019. However, after 2020, both the trading volume and housing prices were expected to witness a relatively rapid increase. Ultimately, we improved the rationality of the forecasting results of future cycles by confirming the results from the time and frequency domain analyses.

Parametric simulator of cyclic and non-cyclic impulsive vibration signals for diagnostic research applications

In recent years cyclostationary analysis of vibration signals is considered to be one of the most potent approaches for diagnostics of machines with rotating components. However, it is a subject of an extensive research towards extending its robustness due to its significant inefficiency in the presence of non-cyclic impulsive components in measured data. This problem is especially visible in datasets measured on machines such as ore crushers, where the high-energy impacts are a natural phenomenon. Unfortunately, due to practical inaccessibility, real-life datasets necessary to properly study this problem are extremely difficult to obtain. To address this issue, the authors propose an easy to use simulator of impulsive components. It covers both cyclic components that can describe various types of fault signatures, and non-cyclic ones that can represent impacts occurring naturally due to the nature of machine operation. Simulated signals have been compared with real ones to ensure a high similarity degree, which in turn guarantees a relatively high level of realism.

Fault-related impulsive component detection for vibration-based diagnostics in the presence of random impulsive noise

In this paper, the authors propose the methodology for vibration-based diagnostics towards local damage detection in rolling element bearings in the presence of non-Gaussian noise. In real-life cases, the main problem making the analysis difficult is the non-Gaussianity of the high-energy noise present in the operational environment. Because of this fact, popular impulsiveness-related detection techniques cannot be used. In the presented article, a real-life data measured in an industrial scenario will be presented and a proposition of an approach to cyclic component extraction will be discussed. The proposed approach takes advantage of the Cyclic Spectral Coherence (CSC) map as multidimensional data representation. It can be very useful for indicating cyclic modulated components in the otherwise non-cyclic signal content. However, due to the limitations of statistics used in CSC map calculation impacting the quality of CSC map in the presence of non-cyclic impulsive behavior in the signal, Nonnegative Matrix Factorization idea is used as a method for component separation. The presented method allows for obtaining carrier-related and modulation-related features of the component of interest. The main advantage of the presented method is pairing very useful multidimensional data representation (CSC) with very potent decomposition technique (NMF), that allows to reconstruct separated components in the final stage of the analysis. As a consequence, the time series of the component can be reconstructed based on the carrier feature.

New possibilities of the PCA-Seq method in the analysis of time series (on the example of solar activity)

When analyzing a 1D time series, it is traditional to represent it as the sum of the trend, cyclical components and noise. The trend is seen as an external influence. However, the impact can be not only additive, but also multiplicative. In this case, not only the level changes, but also the amplitude of the cyclic components. In the PCA-Seq method, a generalization of SSA, it is possible to pre-standardize fragments of a time series to solve this problem. The algorithm is applied to the Anderson series – a sign alternating version of the well-known Wolf series, reflecting the 22-year Hale cycle. The existence of this cycle is not disputed at high solar activity, but there are doubts about the constancy of its period at this time, as well as its existence during the epoch of low solar activity. The processing of the series by the PCA-Seq method revealed clear oscillations fluctuations of almost constant amplitude with an average period of 21.9 years, and it was found that the correlation of these oscillations with the time axis for 300 years does not differ significantly from zero. This confirms the hypothesis of the existence of 22-year oscillations in solar activity even at its minima, like the Maunder minimum.

Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression

Many voltammetry methods have been developed to monitor brain extracellular dopamine levels. Fewer approaches have been successful in detecting serotonin in vivo. No voltammetric techniques are currently available to monitor both neurotransmitters simultaneously across timescales, even though they play integrated roles in modulating behavior. We provide proof-of-concept for rapid pulse voltammetry coupled with partial least squares regression (RPV-PLSR), an approach adapted from multi-electrode systems (i.e., electronic tongues) used to identify multiple components in complex environments. We exploited small differences in analyte redox profiles to select pulse steps for RPV waveforms. Using an intentionally designed pulse strategy combined with custom instrumentation and analysis software, we monitored basal and stimulated levels of dopamine and serotonin. In addition to faradaic currents, capacitive currents were important factors in analyte identification arguing against background subtraction. Compared to fast-scan cyclic voltammetry-principal components regression (FSCV-PCR), RPV-PLSR better differentiated and quantified basal and stimulated dopamine and serotonin associated with striatal recording electrode position, optical stimulation frequency, and serotonin reuptake inhibition. The RPV-PLSR approach can be generalized to other electrochemically active neurotransmitters and provides a feedback pipeline for future optimization of multi-analyte, fit-for-purpose waveforms and machine learning approaches to data analysis.Graphical abstract

Investments and growth: an analysis of the effects of public and private investment in the economies of Cameroon and Ivory Coast

The objective of this article is to highlight the extent of the crowding out and spillover effects of public investments in Ivory coast and Cameroon on the one hand, then the contributions of the latter and of private investments to the growth of these two economies on the other hand. To do that the decomposition of Hodrick-Prescott by WDI (2019) data, to show the presence of crowding in the short term, in both savings through the cyclic component, with higher amplitude at Cameroon. The trend component makes it possible to identify the spillover effects of public investment on private investment. Moreover, to take account of these com behaviors different investments dynamics short and long terms, the error correction model was mobilized to show that are impacts directs individuals public investment and of private investment on economic growth are positive, with greater awareness in Cameroon than in Ivory coast. This is probably due to the consequences of the effect of crowding out public investment on private investment, stronger in Cameroon that e n Ivory Coast.

Analysis of volatility characteristics of five jujube varieties in Xinjiang Province, China, by HS-SPME-GC/MS and E-nose.

In this study, headspace solid‐phase microextraction coupled with gas chromatography‐mass spectrometry (HS‐SPME‐GC/MS) was used to identify individual volatile compounds in five jujube varieties, and E‐nose was used to identify their flavor. The results showed that a total of 45 volatile compounds were detected by GC‐MS in the five varieties, and the proportion of acids was the highest (38.29%–54.95%), followed by that of aldehydes (22.94%–47.93%) and esters (6.33%–26.61%). Moreover, different varieties had obviously different volatile components. E‐nose analysis showed that the R7 and R9 sensors were more sensitive to the aroma of jujube than other sensors. The strong response of R7 sensor was attributed to terpenes (or structurally similar substances) in jujube fruit, such as 1‐penten‐3‐one, 2‐octenal, (E)‐2‐heptanaldehyde, and (E)‐2‐hexenal and that of R9 sensor was attributed to the cyclic volatile components such as benzaldehyde, benzoic acid, and methyl benzoate. The multivariate data analysis (PCA, OPLS‐DA, and HCA) of the results of GC/MS and E‐nose showed that the five varieties could be divided into three groups: (1) Ziziphus jujuba Mill. cv. Huizao (HZ) and Z. jujuba cv. Junzao (JZ). Acids were the main volatile components for this group (accounting for 47.44% and 54.95%, respectively); (2) Z. jujuba cv. Hamidazao (HMDZ). This group had the most abundant volatile components (41), and the concentrations were also the highest (1285.43 µg/kg); (3) Winter jujube 1 (Z. jujuba cv. Dongzao, WJ1) and Winter jujube 2 (Z. jujuba cv. Dongzao, WJ2). The proportion of acids (38.38% and 38.29%) and aldehydes (40.35% and 38.19%) were similar in the two varieties. Therefore, the combination of headspace solid‐phase microextraction coupled with gas chromatography‐mass spectrometry and E‐nose could quickly and accurately identify the volatile components in jujube varieties from macro‐ and microperspectives. This study can provide guidance for the evaluation and distinguishing of jujube varieties and jujube cultivation and processing.

Financial technologies for bank financial stability management in economical turbulence conditions

The development of the global economy is characterized by an increase in turbulence and the restoration of a cyclic crisis component of development. An important driving force for the development of the economy is the financial stability of the bank, which manifests itself at all levels – from the micro level to the global economic system. The market of banking services is the most dynamic segment of the financial market, where the number of subjects and spectrum of products, the level of service and technologies is constantly changing.
 The priority instrument for managing the financial stability of the bank in the conditions of turbulence of the economy is a financial engineering, which is a system of synergistic functioning, pre-developed and implemented combinations and processes (namely the interaction of financial innovations, financial technologies and financial instruments) in financial management of the bank due to the release of innovation resource reserve. to ensure the financial stability of the bank.
 Financial innovations include financial instruments and financial technologies. Financial technologies are technologically supported by financial innovations, which can lead to new business models, programs, processes or products that have a significant impact on financial markets and institutions that provide financial services.
 It is proposed to manage the financial stability of the bank in the conditions of turbulence of the economy of Ukraine to introduce a process approach that is focused on improving the motivation of banking institutions and a clear distribution of their responsibilities; achievement of internal transparency for guidance, obtaining information in real time due to the development and strengthening of the system of current and prospective control; improving the quality and predictability of the results of work; providing trust of stakeholders to banking institutions and confidence in the financial stability of its functioning; minimization of expenses and reducing the terms of organization of technological cycles of banking operations.
 In order to ensure the financial stability of the bank on the basis of financial technologies, the state of turbulence of the economy should be taken into account, react promptly to its changes that require enhanced control.

Effects of hysteretic models on the seismic evaluation of a collapsed irregular building from bidirectional near-fault ground motions on a shake table

A 7-story reinforced concrete building with a soft bottom story and plan-asymmetry in one horizontal direction was collapsed on a shake table by generation of bi-directional near-fault ground motions. The numerical model of the 7-story building is introduced and verified through a comparison with the experimental results. The hysteretic models of the verified numerical model are calibrated according to static cyclic component tests. To assume the condition that the static cyclic component tests are not available, the hysteretic models of some critical structural components are intentionally altered. Three additional numerical models of the building, which serve as the distorted numerical models, are obtained from the combinations of the altered hysteretic models. Incremental dynamic analyses (IDA) are applied to the four numerical models of the building (i.e., one verified model and three distorted models). Comparing the IDA results of the four numerical models helps to elucidate the effect of the hysteretic models on the full range of the seismic responses (i.e., from elastic response to total collapse) of the building.