Dynamic Analysis Method Research Articles

A Platform Integrating Biophysical and Biochemical Stimuli to Enhance Differentiation and Maturation of Cardiomyocyte Subtypes Derived from Human Induced Pluripotent Stem Cells

To enhance the differentiation and maturation of cardiomyocytes derived from human induced pluripotent stem cells, we developed a bioreactor system that simultaneously imposes biophysical and biochemical stimuli on these committed cardiomyocytes. The cells were cultured within biohydrogels composed of the extracellular matrix extracted from goat ventricles and purchased rat-origin collagen, which were housed in the elastic PDMS culture chambers of the bioreactor. Elastic and flexible electrodes composed of PEDOT/PSS, latex, and graphene flakes were embedded in the hydrogels and chamber walls, allowing cyclic stretch and electrical pulses to be simultaneously and coordinately applied to the cultured cells. Furthermore, a dynamic analysis method employing the transverse forced oscillation theory of a cantilever was used to analyze and discriminate the subtype-specific beating behavior of the cardiomyocytes. It was found that myosin light chain 2v (MLC2v), a ventricular cell marker, was primarily upregulated in cells aggregated on the (+) electrode side, while cardiomyocytes with faint MLC2v but strong cardiac troponin T (cTNT) expression aggregated at the ground electrode (GND) side. mRNA analysis using rtPCR and the gel beating dynamics further suggested a subtype deviation on the different electrode sides. This study demonstrated the potential of our bioreactor system in enhancing cardiac differentiation and maturation, and it showed an intriguing phenomenon of cardiomyocyte subtype aggregation on different electrodes, which may be developed into a new method to enhance the maturation and separation of cardiomyocyte subtypes.

Configuration Paths for High-Quality Development of Listed Companies Based on the TOE Framework: Evidence from China’s High-Tech Enterprises

In the context of dual-carbon goals, high-quality development has become an inevitable trend for technology enterprises. This study employs a dynamic Qualitative Comparative Analysis (QCA) method, based on the technology, organization, and environment (TOE) framework theory, and a configurational analysis perspective. Using panel data from Chinese listed high- and new-technology enterprises from 2013 to 2022 as the research sample, this study explores the combinatorial paths that drive high-quality development. The research findings are as follows: (1) The antecedent factors for high-quality development exhibit “multiple concurrent” characteristics, and a single condition does not constitute a necessary condition. (2) Based on the three driving paths for high-quality development, three models can be summarized under the synergistic effects of different antecedent conditions: “multi-factor influence type”, “green-innovation-led type”, and “digital-technology-driven type”. Regardless of the path, R&D investment and enterprise scale are indispensable driving factors. (3) Under specific objective endowment conditions, continuous digital transformation by enterprises can alleviate the challenges caused by market competition deficiencies. When enterprise executives possess a high level of green cognition, they can compensate for inadequate environmental regulations. Therefore, enterprises need to enhance scale and R&D investment, based on existing endowments, and shift from a single-factor influence concept to a comprehensive selection approach.

Multibody System Dynamics Analysis Method Approach to Determine Dynamic Roll Early Warning and Control Thresholds of Tractor Semi-Trailer Vehicles

Multibody System Dynamics Analysis Method Approach to Determine Dynamic Roll Early Warning and Control Thresholds of Tractor Semi-Trailer Vehicles

Seismic Mitigation Effect and Mechanism Analysis of Split Columns in Underground Structures in Sites with Weak Interlayers

The seismic damage of underground structures has been extensively investigated, and it has been demonstrated that underground structures located at weak interlayer sites are more prone to damage. In this study, a two-story two-span rectangular frame subway station structure is analyzed. A two-dimensional soil-underground structure model is developed using the large-scale finite element analysis software ABAQUS. The equivalent linear soil-underground structure dynamic time-history analysis method is employed to examine the seismic response of underground structures at weak interlayer sites. Variations in the thickness and shear wave velocity of the weak interlayer soil are analyzed. The seismic mitigation effects of split columns and prototype columns in underground structures at weak interlayer sites are systematically compared. The findings indicate that the relative displacement and internal force of key structural components significantly increase when the weak interlayer intersects the underground structure. Furthermore, as the thickness of the interlayer increases, the displacement and internal force also escalate. When the thickness of the weak interlayer remains constant and the shear wave velocity decreases, the relative displacement and internal force of the key structural components gradually intensify. Replacing ordinary columns with split columns substantially reduces the internal force of the middle column, providing an effective seismic mitigation measure for underground structures.

The potential of trade and economic cooperation between Brazil and the Samara region based on the integration of exports and imports

Purpose: is to reveal the potential for strengthening trade and economic cooperation between the Samara region and the Brazilian States by identifying the regional needs of the latter in non-energy goods.Methods:the article applies the statistical method of structural and dynamic analysis, calculates the index of complementarity, and adapts the Balassa index and the export potential.Results: the essence of the potential of trade and economic cooperation is revealed and the connection between the coupling of commodity flows with the non-energy potential of the region is shown. The authors identified the most active the Brazilian States and import-intensive groups of goods in trade with the Russian Federation. The Brazilian States with high potential for expanding supplies of non-energy products that can become centers of attraction in foreign trade for Russian regions were identified. Estimated values of export potential volumes, which expand the possibilities of trade and economic cooperation between the Samara region and the Brazilian States, are presented, and the conditions of export supplies are determined.Сonclusions and Relevance: the scale of coupling of commodity flows with trade partners is the basis for the formation and realization of export potential of countries and their regions. Despite the sanctions, Russian-Brazilian cooperation has reached the level of strategic partnership, almost all the Brazilian States import Russian products, many of them increase imports. It has been established that the main channels of supply of Russian products to the Brazilian market are concentrated in the port Brazil States, traditionally the most open for trade cooperation with the countries of the world. Despite the fact that the processes of product diversification in mutual trade are developing slowly, the strategic partnership allows building long-term plans for further trade cooperation by expanding trade of the Samara region with the Brazilian States with a high share of dependence on imports of non-energy goods, which the Russian region specializes in producing. The complementarity index confirmed the complementarity of commodity flows between the Samara Region and a number of the Brazilian States. In the conditions of reorientation of export flows, the opportunities of the Samara region lie in the expansion of trade in food products, chemical industry, electrical and mechanical engineering products.

Quality-aware fuzzy min-max neural networks for dynamic brain network analysis and its application to schizophrenia identification

Dynamic functional connections (dFCs) refer to the observed phenomenon that functional connectivity changes over a short time, which has become a pioneering method in the diagnosis of brain diseases. However, current dynamic brain network analysis methods are insufficient to address the fuzzy information of brain networks caused by the quality issues of rs-fMRI data, and the uncertainty arising from inconsistent data quality across different windows due to missing values and noise in individual windows. These results in unreliable integration of multiple windows. To this end, this paper proposes a dynamic brain network analysis method based on quality-aware fuzzy min–max neural networks (QFMMNet). The individual window of dFCs is treated as a view, and we define three convolution filters to extract features from the brain network under the multi-view learning framework, thereby obtaining multi-view evidence for dFCs. We design the multi-view fuzzy min–max neural networks (MVFMM) based on fuzzy sets to deal with the fuzzy information of the brain network, which takes evidence as input patterns to generate hyperboxes and serves as the classification layer of each view. A quality-aware ensemble module is introduced to deal with uncertainty, which employs Dempster-Shafer Theory (D-S theory) to directly model the uncertainty and evaluate the dynamic quality-aware weighting of each view. At the decision level of model, a weighted fusion strategy is employed for multi-view fusion based on the dynamic quality-aware weighting, thereby the quality of each view is fully considered to improve the trustworthiness of the decision. We verified the effectiveness of QFMMNet through comparison with advanced algorithms on three real schizophrenia datasets, and the results show that QFMMNet significantly improved the classification performance of brain disease diagnosis tasks. The accuracy on the Huaxi, COBRE, and Xiangya datasets reached 87.14%, 86.67%, and 85.27%, respectively.

Dynamic analysis method of cable structures based on coupled integration algorithm

Dynamic analysis method of cable structures based on coupled integration algorithm

Impact Safety Evaluation of Driver Seat Frame Using Dynamic Analysis and Testing Method

Impact Safety Evaluation of Driver Seat Frame Using Dynamic Analysis and Testing Method

Working Poverty in Kazakhstan: Causes and Solutions

Objective: To consider the causes, consequences and possible solutions to the problem of working poverty in Kazakhstan. Methods: The paper uses methods of statistical data processing, dynamic and structural analysis, and comparative analysis. Results: Working poverty in Kazakhstan means that people, having jobs, cannot provide basic needs due to low wages, unstable employment and limited access to social guarantees. The main causes of working poverty include low wages in some industries with low labor productivity, a relatively high proportion of informal employment in some industries and regions, and uneven economic, infrastructural and demographic development of the country's regions. Conclusions: The analysis of statistical data on the incomes of the population in Kazakhstan shows that the problem of working poverty has a multi-layered character and requires an integrated approach to solve it. It is necessary to regularly monitor data, develop and implement measures aimed at increasing wages, combating informal employment and improving the level of education and vocational training, and stimulating economic growth in regions with high levels of poverty. This approach will significantly reduce the level of working poverty and improve the quality of life of working citizens in Kazakhstan.

Эффект синергии транспортной сопряженности цепей поставок по маршрутам международного транспортного коридора «Север — Юг»: сценарный анализ

International transport corridors (ITC), as the main element of the formation of a new geo-economics, are becoming the most important object of research and development. Their impact on the economic growth of the countries adjacent to the routes, on the efficiency of cargo owners, transport operators, carriers, and infrastructure owners has no clear theoretical or empirical estimates. The purpose of this study is to substantiate and calculate the synergy effect indicator of transport connectivity of the North – South ITC, namely, to assess the maximum volumes of cargo transported along all ITC routes, taking into account the factors limiting its throughput and carrying capacity. Research methods: the scenario forecast for the development of the ITC was obtained by sampling — scenario distribution. In calculating the indicators for assessing the economic effects of the ITC routes, the method of comparing digital data with risk factors limiting rail freight flows was used. The analysis of the distance matrix for three ITC routes was used. The forecast of the estimated indicators is made by the method of dynamic analysis of successive series of indicators of rail freight transportation, characterizing each route, their changes over time. Results: based on the following indicators: throughput and carrying capacity of the ITC railway infrastructure; sustainable development index of the countries of the region; logistics index (customs and border clearance; quality of trade and transport infrastructure); “transportation costs” — the contingency effects were calculated, reflecting the maximum volumes of transported goods, taking into account the connectivity of infrastructure and modes of transport along all routes of the North – South ITC. Practical significance: scenario calculation of contingency effects along the ITC routes can be used in projects for he development of the transport and logistics infrastructure of the ITC, since it allows assessing the effects of growth in freight throughput.

Numerical study of the asynchronous excitation effect of Boumerdes’ earthquake on a bridge behavior

This paper represents the numerical analysis of asynchronous excitation effects on a bridge response due to the Boumerdes earthquake, using the finite element method in modeling, and the Newmark method for dynamic response analysis. The influence of the time delay of excitation between the supports on the response of structure is investigated and study how these variations can affect structural behaviour during seismic events. The dynamic displacements, normal, and shear forces and bending moments are computed through detailed simulations. The obtained results show that due to the non-uniformity in the earthquake excitation, changes in qualitative and quantitative features of dynamic displacements of the bridges. This will consequently subject the bridges to increased displacements hence compromising their structural safety and integrity. In fact, the analysis also underlines that, due to non-uniform seismic forces, the distribution of normal and shear efforts, along with bending moments, gives important concentrations of stresses that were underestimated under uniform loading conditions. From these observations, one underlines the importance of non-uniform distribution of seismic loading during bridge design or evaluation. It provides useful data to the engineers and designers who will implement the improvement in the seismic resistance of bridge structures in earthquake areas.

НЕЛИНЕЙНЫЙ СЕЙСМИЧЕСКИЙ ОТКЛИК ЖЕЛЕЗОБЕТОННЫХ КАРКАСНЫХ ЗДАНИЙ

A methodology for seismic performance evaluation of a 10-story reinforced concrete frame building is presented. The comparison between the results of nonlinear dynamic analysis and simplified modal nonlinear static method is carried out. The proposed methodology can be used to correctly estimate the maximum expected moments during a maximum considered earthquake. It is noted that traditional methods of analysis strongly underestimate seismic demands in comparison with nonlinear methods, especially in the middle part of the height of structures.

Accurate Dynamic Analysis Method of Cable-Damper System Based on Dynamic Stiffness Method

To suppress large vibrations of the cable in cable-stayed bridges, it is common to install transverse dampers near the end of the cable. This paper focuses on the cable-damper system; based on the dynamic stiffness method, an accurate dynamic analysis method considering cable parameters, damper parameters, and cable forces is proposed. First, a mechanical analysis model is established which is closer to the cable with a transverse damper installed in the bridge. The model considers the cable bending stiffness, sag, inclination angle, cable force, damper stiffness, damping coefficient, and damper installation height. Then, the characteristic frequency equation of the cable-damper system is established, and a solution method that combines the initial value method and Newton–Raphson method is proposed. This method is confirmed to provide more accurate frequency analysis for the cable-damper system. Finally, using this method, the effect of the damper parameters on the dynamic characteristics of the system is investigated.

Attention-Based Malware Detection Model by Visualizing Latent Features Through Dynamic Residual Kernel Network.

In recent years, significant research has been directed towards the taxonomy of malware variants. Nevertheless, certain challenges persist, including the inadequate accuracy of sample classification within similar malware families, elevated false-negative rates, and significant processing time and resource consumption. Malware developers have effectively evaded signature-based detection methods. The predominant static analysis methodologies employ algorithms to convert the files. The analytic process is contingent upon the tool's functionality; if the tool malfunctions, the entire process is obstructed. Most dynamic analysis methods necessitate the execution of a binary file within a sandboxed environment to examine its behavior. When executed within a virtual environment, the detrimental actions of the file might be easily concealed. This research examined a novel method for depicting malware as images. Subsequently, we trained a classifier to categorize new malware files into their respective classifications utilizing established neural network methodologies for detecting malware images. Through the process of transforming the file into an image representation, we have made our analytical procedure independent of any software, and it has also become more effective. To counter such adversaries, we employ a recognized technique called involution to extract location-specific and channel-agnostic features of malware data, utilizing a deep residual block. The proposed approach achieved remarkable accuracy of 99.5%, representing an absolute improvement of 95.65% over the equal probability benchmark.

How Does the Digital Innovation Ecosystem Enable Green Regional Development? A Dynamic QCA Study in China

The impact of digital empowerment on green innovation is increasingly evident, enabling various subjects to improve the integration of innovation elements and enhance innovation efficacy across a broader temporal and spatial scope. A comprehensive examination of the mechanisms that underlie this process is required. This paper constructs the ‘elements-subjects-environments’ research framework of digital innovation ecosystems, collecting data from 30 provinces in China from 2017 to 2021 and using green total factor productivity (GTFP) to evaluate the level of green regional development. In this study, the dynamic qualitative comparative analysis (QCA) method is employed to analyze the intricate causal mechanisms and configurations of green regional development that are driven by digital innovation ecosystems from both temporal and spatial perspectives. The results show that: (1) green regional development requires the interaction of multiple elements, subjects, and the environment, and a single condition does not constitute a necessary condition; (2) there are four pathways with different configurations for high-level green development: data elements-driven enterprise application innovation, data elements-driven enterprise-user co-creation, data elements-driven multi-collaborative innovation, and digital environment-driven university basic innovation; (3) the temporal and spatial dimensions of China’s green regional development pathways are heterogeneous: the significance of data elements in fostering green regional development is increasing; the multi-collaborative innovation configuration is facilitating the green development of the eastern and central regions, whereas the western and northeastern regions are progressing at a relatively slow pace. This study provides theoretical and practical insights to promote the integration of digital innovation and green development.

Уровень и темпы старения населения северных регионов России по новому пенсионному возрасту

The article is devoted to the peculiarities of demographic ageing in the northern regions of Russia in accordance with the new economic threshold of old age. The relevance of the research is conditioned by the gradual increase of the retirement age in Russia and preservation of the favorable retirement age in the North. The information base is the results of the population censuses and the official data of Rosstat. The dynamic and comparative statistical analysis and demographic research methods are used. Regularities of ageing of the Russian population by new retirement age in different inter-census periods are revealed. The period of 19591970 is characterized by “ageing from below” due to the transition to limited fertility, and “ageing from above” under conditions of increasing life expectancy of the population. The periods of 19701979 and 19892002 are characterized by “ageing from below”. Within the periods of 19791989 and 20022010, there was a decrease in the level of ageing of the population in Russia. The last intercensal period of 20102021 is the only one for which the definition of “ageing from above” is suitable. By 1989, the North zone had a noticeably younger age structure of the population than in the country as a whole. The migration outflow that began in the late 1980s caused the increased rates of demographic ageing of the Northern regions, which also continued in 20022010. As a result, in Karelia and the Arkhangelsk Oblast, the share of the population above the new retirement age in 2021 already noticeably exceeds the average Russian level, while in the Sakhalin Oblast, the Komi Republic and the Murmansk Oblast, it is close to the national level.

Analysis of characteristics of ground motion and typical bridge performance in the Baoshan earthquake

A Ms5.2 earthquake occurred in Longyang District, Baoshan City, Yunnan Province on 2 May 2023. The earthquake caused some degree of damage to power facilities and roads. Since then, small and medium sized earthquakes have occurred frequently in this region. In order to better analyze the characteristics of ground motion in this area and the coping strategies of related bridges, the response spectra, omnidirectional response spectra and omnidirectional representations of other indexes of intensity measure from records of strong ground motion were analyzed using the seismic records recorded by stations near the epicenter. Four typical bridges near the epicenter were selected for modeling and seismic response analysis to derive their damages under the action of ground motion, and structural displacements of the bridges with the strong ground motion were compared to analyze directional linkages. The Incremental Dynamic Analysis (IDA) method was used to analyze the fragility of the four bridges, and the seismic capacity of different bridges under the ground motion was compared. The omnidirectional displacement response spectra of the two stations and the omnidirectional representations of other indexes of intensity measure have obvious directionality, and the predominant direction is perpendicular to the direction of the fault, reflecting the rupture directionality of the earthquake. Comparing the corresponding period of the structural displacement response with seismic action with the omnidirectional displacement response spectra, it was found that the bridge structural displacement is correlated with the predominant cycle and predominant direction of the omnidirectional displacement response spectra. Under the same seismic action, the maximum moment of the deck arch bridge in the transverse direction is larger than that in the bridge direction; the larger the span of the deck arch bridge, the larger the maximum moment at the arch footing. According to the analysis of fragility, it can be seen that the seismic capacity of the continuous rigid frame bridge is much lower than that of the deck steel truss arch bridge under this ground motion. Compared to the deck-type concrete arch bridge, the deck steel truss arch bridge has greater seismic capacity. In this paper, based on the structural analysis and fragility analysis of these four bridges close to the fault, the structural damage and damage levels in the future earthquake are speculated, which provide suggestions for the follow-up maintenance and reinforcement work, and are also conducive to the resilience evaluation and rapid repair work after the real earthquake damage.

Research and Simulation Application of Automatic Generation of Judging Curves for IoT Mobile Devices in Train Driving Operations Incorporating Hybrid Genetic Algorithms

A control system for automatically generating a train operation evaluation curve based on a hybrid genetic algorithm has been proposed in order to improve the safety of automatic train operation. The system is based on Internet of Things (IoT) mobile devices and utilizes various sensors such as accelerometers, gyroscopes, barometers, and GPS to collect real-time data on the driver’s acceleration, angular velocity, air pressure, and position, among other parameters. 5G wireless communication technology is used to achieve high-speed data transmission and real-time communication with the cloud. Based on cloud data, a spatial grid area planning model is constructed to automatically generate the train operation evaluation curve. Using a spatial dynamic programming method, the entire network of Electric Multiple Unit (EMU) trains is treated as a whole, and a dynamic model of the EMU train is constructed. The spatial area parameters of the EMU train’s automatic driving operation evaluation curve are combined with the dynamic model analysis method. By identifying and analyzing environmental parameters such as train speed and distance, the EMU train’s automatic driving operation evaluation curve is optimized. A hybrid genetic evolution learning optimization algorithm is used to fit the motion spatial parameters of the EMU train’s automatically generated driving operation evaluation curve, and a spatial behavior analysis simulation is created for the EMU train’s automatically generated control driving operation curve. Through the use of hybrid genetic evolution learning optimization technology, adaptive control and automatic driving operation curve simulation planning for the automatically generated driving operation evaluation curve of the maglev train are achieved, as well as simulation and algorithm optimization design for the automatically generated driving operation evaluation curve of the maglev train. The simulation results show that the method has good adaptability and strong automatic control capability. The experimental results demonstrate the control effect of the proposed method on energy consumption and train stopping error, indicating that the proposed method can effectively improve the parameter adjustment and offset correction ability of the evaluation curve generated by the high-speed train driving operation.

ANALYSIS AND COMPARISON OF MACHINE LEARNING METHODS FOR MALWARE DETECTION

Our study aims to analyze and evaluate modern machine learning methods for detecting malware, a critical challenge given the increasing complexity and volume of cyber threats. We explore various algorithms, including Support Vector Machines, Random Forest, Logistic Regression, Decision Trees, and K-Means Clustering, comparing their effectiveness in identifying and classifying malware. Our methodology combines static, dynamic, and memory-based analysis techniques, offering a comprehensive approach to understanding malware behavior. Key findings reveal that Decision Trees and Random Forests demonstrate impressive accuracy in both binary and multi-class classification tasks. We also highlight novel methods such as the Self-Organizing Incremental Neural Network (SOINN), which effectively handles evolving malware threats. The integration of static and dynamic analysis methods deepens insights into malware behavior. This research underscores the importance of advancing machine learning techniques to enhance cybersecurity measures against evolving global malware threats, offering valuable insights for future research directions.

A boundary element solution for free vibration of FGM beams

The main purpose in this article is to derive a new solution based on Boundary Element Method (BEM) for dynamic analysis of Euler-Bernoulli beams made of composites such as Functionally Graded Materials (FGM) that have properties that vary continuously through the thickness direction according to the volume fraction of constituents. In contrast, laminated composite materials are oriented plies bonded together, where each ply may be made of a different material. A boundary element solution for any problem is obtained using integral equations and fundamental solutions defined on continuum, and algebraic equations for the discretized problem. In this paper, original discussions on deriving both integral equations and fundamental solutions for dynamic composite beam problems are properly made. Algebraic representation is also obtained, incorporating rigid and elastic end supports into the BEM solution. Numerical results of different cases of mechanical properties and boundary conditions are considered and compared to other published works.