Dynamic Approach Research Articles

Establishing an Empirical link between Financial Development, CO2 Emissions, Tourist Arrivals and Economic Growth: A Dynamic Panel Data Approach

The study is an attempt to see the impact of changes in environmental, economic, trading, and financial conditions on tourist arrivals of 123 countries from Europe, America, Asia and Africa. The countries were distributed in seven distinct regions on the four continents. In order to perform an effective empirical analysis, panel data from these countries was obtained from World Development Indicators from 1999 to 2018. The results of panel cointegration revealed the presence of a long-run relationship between tourist arrivals and the explanatory variables included in the model. Environmental conditions were found to adversely affect tourist arrivals with rising CO2 emissions, whereas improvements in financial development contributed towards increased tourist arrivals. The results of the study encouraged the implication of eco-friendly policies and financial development for tourism promotion in selected 123 countries from seven regions.

Modeling Determinants of Profitability in Ethiopia Manufacturing Companies: A Dynamic Panel Model Approach

Background: Profitability is the primary measure of the overall success of company. The analysis of profitability ratios is important for the shareholders, creditors, prospective investors, manufacturers and government alike. Objective: The objective of this study was fitted a dynamic panel regression model that efficiently fit the data and further to identify the determinants contributing significantly to profitability in Ethiopia. Methods: This study was conducted on secondary data of 32 sample manufacturing companies collected from the audited financial statements of large tax payers’ office and national bank of Ethiopia. The study was covered a period of seven years from 2011 to 2017. The panel unit root test of Levin-Lin-Chu tests was made for each variable and applied first difference transformation for the variables that had unit root. A dynamic panel regression model was utilized for data handling technique using generalized method of moments (GMM) estimation. Results: We compared the results when one unit increase in lagged profitability, managerial efficiency, capital intensity, GDP, exchange rate and one ratio decrease in leverage, ceteris paribus, turn out were found to increase the profitability of manufacturing companies by around 0.69, 0.179, 4.52E-06, 3.844, 0.04 and 0.393 ratio, respectively. The model reveled that previous profitability, leverage, capital intensity, managerial efficiency, GDP and exchange rate had a statistically significant (P<0.05) effect on companies’ profitability in Ethiopia. Conclusion: As per our findings, the manufacturing companies should minimize leverage financing from its capitals and should emphasize the management of appropriate financing to increase profitability. Finally, we recommended that policy makers should coming up with better policies on improvement of profitability.

Dynamic Modeling and Experimental Modal Analysis for the Central Rod-Fastened Rotor With Hirth Couplings Based on Fractal Contact Theory

Abstract The central rod-fastened rotor of gas turbine exhibits pronounced noncontinuous characteristics due to the large number of contact interfaces between the compressor and turbine disks. It is necessary to establish an accurate dynamic modeling method for the central rod-fastened rotor that fully considers the contact surface effect. In this work, the contact behavior of the rough surface is characterized by the fractal theory. The normal and tangential contact stiffness models are developed, and the influence of fractal parameters is discussed. Besides, the finite element model for the central rod-fastened rotor is established by developing an improved contact element considering the equivalent stiffness segment of Hirth couplings. Finally, the proposed model is verified by conducting the modal testing and measuring the first four modes of natural frequencies and modal shapes of the central rod-fastened rotor. The results show that the numerical results are in good agreement with the experimental ones, and the fractal contact model can effectively predict the connection stiffness of Hirth couplings, which in turn improves the simulation accuracy for the modal characteristics of the central rod-fastened rotor and provides a dynamic modeling approach with high efficiency and less computational complexity.

Green-Kubo paralell tempering molecular dynamics determination of the thermal conductivity for the wurtzite ZnO: statistical problems of the method

The thermal conductivity for the wurtzite ZnO is determined in the temperature range from 300 to 1100 K by using parallel tempering molecular dynamics within the Green-Kubo approach and a classical Morse-Born-Mayer-Coulomb hybrid interaction potential. Compared to other previous calculations for the thermal conductivity of common crystals within the same Green-Kubo and molecular dynamics approach, the used parallel tempering scheme shows some appealing improvements in the calculation of the time self-correlation of the heat flux vector, although at the price of using a relatively large number of total computational steps. However, in spite of the found improvements for the calculation of the self-correlation of the heat flux vector, some statistical problems on this point remain on the particular application of the method. Finally, even with the presence of a clear statistical noise, the obtained values and temperature trend of the calculated thermal conductivity shows the classical 1/T decaying behavior reported in previous works for wurtzite ZnO and other related semiconductor systems using the alternative Boltzmann transport equation theory.

Dynamic simulation-assisted Gaussian mixture alignment approach for fault diagnosis of rotation machinery under small samples

Abstract Obtaining a substantial number of actual samples for rotating machinery in an industrial setting can be challenging, particularly when faulty samples are acquired under hazardous working conditions. The issue of insufficient samples hinders the effective training of reliable fault diagnosis models, impeding the industrial implementation of advanced intelligent methods. This study proposes an innovative Dynamic Simulation-assisted Gaussian Mixture Alignment model (DSGMA) to address the challenge of applying fault diagnosis technologies, with its performance mined by advanced transfer algorithms. Specifically, we establish a fault dynamics model for rotating machinery and acquire a substantial amount of simulated data as the source domain to facilitate the training of the deep neural network model. Subsequently, we propose a Gaussian mixture-guided domain alignment approach that assigns a domain-independent Gaussian distribution to each category as prior knowledge, with the parameters calculated using limited actual samples. Diagnostic knowledge is transferred from the source domain to the target domain by minimizing the Kullback-Leibler divergence between the features of the simulated samples and the Gaussian mixture priors. Furthermore, the DSGMA model incorporates Gaussian clustering loss to augment the clustering capability of samples belonging to the same category from real devices and enhances the computational stability of the parameters in the Gaussian mixture model. The efficacy of the DSGMA method is validated using three publicly available datasets and compared against five widely adopted methods. The experimental findings illustrate that DSGMA exhibits superior diagnostic and robust capabilities, facilitating efficient fault diagnosis under scenarios of small samples.

An ecological dynamics approach to promote physical literacy and well-being of primary school children: a cluster randomised controlled trial study protocol

BackgroundLittle is known about the impact of an ecological dynamics (ED) intervention (EDI) on primary school children’s physical literacy and well-being in the Hong Kong context. The aim of this...

Herding behaviour surrounding the Russo–Ukraine war and COVID-19 pandemic: evidence from energy, metal, livestock and grain commodities

PurposeThis study examines herding behaviour in commodity markets amid two major global upheavals: the Russo–Ukraine conflict and the COVID-19 pandemic.Design/methodology/approachBy analysing 18 commodity futures worldwide, the study examines herding trends in metals, livestock, energy and grains sectors. The applied methodology combines static and dynamic approaches by incorporating cross-sectional absolute deviations (CSAD) and a time-varying parameter (TVP) regression model extended by Markov Chain Monte Carlo (MCMC) sampling to adequately reflect the complexity of herding behaviour in different market scenarios.FindingsOur results show clear differences in herd behaviour during these crises. The Russia–Ukraine war led to relatively subdued herding behaviour in commodities, suggesting a limited impact of geopolitical turmoil on collective market behaviour. In stark contrast, the outbreak of the COVID-19 pandemic significantly amplified herding behaviour, particularly in the energy and livestock sectors.Originality/valueThis discrepancy emphasises the different impact of a health crisis versus a geopolitical conflict on market dynamics. This study makes an important contribution to the existing literature as it is one of the first studies to contrast herding behaviour in commodity markets during these two crises. Our results show that not all crises produce comparable market reactions, which underlines the importance of the crisis context when analysing financial market behaviour.

Development of the dynamic approach for detection of Salmonella Typhi biofilm via optimizations of interaction with safranin dye

Development of the dynamic approach for detection of Salmonella Typhi biofilm via optimizations of interaction with safranin dye

A Review of Computational Fluid Dynamics Approaches Used to Investigate Lubrication of Rolling Element Bearings

Abstract Optimizing bearing performance is based on effective lubrication, especially in high-speed machinery, where minimizing churning and drag losses is of significant importance. Over the past few decades, extensive research has been conducted into the better understanding of different aspects of bearing lubrication. These investigations have employed a combination of experimental methods and advanced Computational Fluid Dynamics (CFD) models. This article provides a comprehensive overview of critical aspects of bearing lubrication, with a specific emphasis on recent advances in CFD models. Lubricant flow and distribution patterns are discussed while examining their impact on drag and churning losses. An extensive discussion is provided on the meshing strategies and modeling approaches used to simulate various flow phenomena within bearings. In addition, relevant trends and impacts of cage design on bearing lubrication and fluid friction have been explored, along with a discussion of prevailing limitations that can be addressed in future bearing CFD models.

Performance of Dynamic Retry Over Static Towards Resilience Nature of Microservice

Objectives: The main objective of this study is to enhance the flexibility of microservice-based cloud applications, so they can cope with transient or short-term failure conditions more effectively, providing a higher throughput of cloud applications as a result. Methods: The comparison is done between the existing or manual resilient patterns such as circuit breaker and retry with a dynamically proposed retry patterns using a microservice application. Findings: The short-term or transient failures in cloud based microservice applications can occur for many reasons. Examples such as Network glitches, service failures, timeouts of requests etc. These failures can bring down the entire application resulting in a cascading of failures. Resiliency patterns are used to protect these applications from failures. The widely used patterns are circuit breaker and retry, with static configurations. It is possible that the static configurations used in resiliency patterns might not support all types of failures. Hence, a dynamic approach has been proposed to satisfy all these transient failures of cloud. The analysis proves that the application efficiency is balanced in dynamic retry than the static configurations of resiliency pattern. Hence, performance can be increased up to 34.3%, which means the availability can be ensured, in transient failure cases. Very little research has been carried out on this resiliency pattern, and there are no standards available for this pattern. When comparing with the existing adaptive retry pattern the performance is increased up to 88.52%, while using a microservice application. Novelty: A dynamically modified resilient pattern is been proposed by incorporating additional parameters such as execution time to the existing parameters of the static retry pattern. This is an individual resilience pattern, which can perform independently when compared with other dynamic resilience patterns. Keywords: Dynamic retry, Request time, Static circuit breaker, Resilience, Static retry

Reinforcement-Learning-Based Visual Servoing of Underwater Vehicle Dual-Manipulator System

As a substitute for human arms, underwater vehicle dual-manipulator systems (UVDMSs) have attracted the interest of global researchers. Visual servoing is an important tool for the positioning and tracking control of UVDMSs. In this paper, a reinforcement-learning-based adaptive control strategy for the UVDMS visual servo, considering the model uncertainties, is proposed. Initially, the kinematic control is designed by developing a hybrid visual servo approach using the information from multi-cameras. The command velocity of the whole system is produced through a task priority method. Then, the reinforcement-learning-based velocity tracking control is developed with a dynamic inversion approach. The hybrid visual servoing uses sensors equipped with UVDMSs while requiring fewer image features. Model uncertainties of the coupled nonlinear system are compensated by the actor–critic neural network for better control performances. Moreover, the stability analysis using the Lyapunov theory proves that the system error is ultimately uniformly bounded (UUB). At last, the simulation shows that the proposed control strategy performs well in the task of dynamical positioning.

Learning unbounded-domain spatiotemporal differential equations using adaptive spectral methods

AbstractRapidly developing machine learning methods have stimulated research interest in computationally reconstructing differential equations (DEs) from observational data, providing insight into the underlying mechanistic models. In this paper, we propose a new neural-ODE-based method that spectrally expands the spatial dependence of solutions to learn the spatiotemporal DEs they obey. Our spectral spatiotemporal DE learning method has the advantage of not explicitly relying on spatial discretization (e.g., meshes or grids), thus allowing reconstruction of DEs that may be defined on unbounded spatial domains and that may contain long-ranged, nonlocal spatial interactions. By combining spectral methods with the neural ODE framework, our proposed spectral DE method addresses the inverse-type problem of reconstructing spatiotemporal equations in unbounded domains. Even for bounded domain problems, our spectral approach is as accurate as some of the latest machine learning approaches for learning or numerically solving partial differential equations (PDEs). By developing a spectral framework for reconstructing both PDEs and partial integro-differential equations (PIDEs), we extend dynamical reconstruction approaches to a wider range of problems, including those in unbounded domains.

Creating advanced photonics AI assistants

AbstractThe AI assistants that have emerged in the past couple of years open up new domains of knowledge, allowing us to be educated and guided in many new and versatile domains. However, for domain experts such as optical designers, scientists, and photonics engineers, they often have limitations in their information accuracy and scope. 3DOptix has developed a dynamic approach to overcome these limitations that enables the creation of a proficient optical design copilot. This copilot assists experts in every step of the design process, including conducting precise calculations, searching for components, and simulating actions.

Time-dependent Kohn-Sham electron dynamics coupled with nonequilibrium plasmonic response via atomistic electromagnetic model.

Computational modeling of plasmon-mediated molecular photophysical and photochemical behaviors can help us better understand and tune the bound molecular properties and reactivity and make better decisions to design and control nanostructures. However, computational investigations of coupled plasmon-molecule systems are challenging due to the lack of accurate and efficient protocols to simulate these systems. Here, we present a hybrid scheme by combining the real-time time-dependent density functional theory (RT-TDDFT) approach with the time-domain frequency dependent fluctuating charge (TD-ωFQ) model. At first, we transform ωFQ in the frequency-domain, an atomistic electromagnetic model for the plasmonic response of plasmonic metal nanoparticles (PMNPs), into the time-domain and derive its equation-of-motion formulation. The TD-ωFQ introduces the nonequilibrium plasmonic response of PMNPs and atomistic interactions to the electronic excitation of the quantum mechanical (QM) region. Then, we combine TD-ωFQ with RT-TDDFT. The derived RT-TDDFT/TD-ωFQ scheme allows us to effectively simulate the plasmon-mediated "real-time" electronic dynamics and even the coupled electron-nuclear dynamics by combining them with the nuclear dynamics approaches. As a first application of the RT-TDDFT/TD-ωFQ method, we study the nonradiative decay rate and plasmon-enhanced absorption spectra of two small molecules in the proximity of sodium MNPs. Thanks to the atomistic nature of the ωFQ model, the edge effect of MNP on absorption enhancement has also been investigated and unveiled.

Novel physiological analysis using blood flow velocity for colonic polyps: Pilot study.

Real-time visualization of red blood cell flow inside subepithelial microvessels is performed with magnifying endoscopy. However, microvascular blood flow velocity in the colorectum has not been investigated. Here, we aimed to evaluate the blood flow velocity of microvessels of colonic polyps and to compare it with that of surrounding mucosa. We examined 50 lesions, including 30 adenomas (ADs) and 20 hyperplastic polyps (HPs). Blood flow velocities of lesions and their surrounding mucosa were evaluated using magnifying blue laser imaging (BLI) prior to endoscopic resection. Calculation of mean blood flow velocities was based on mean movement distance of one tagged red blood cell using split video images of magnifying BLI. Mean microvascular blood flow velocity was significantly lower in ADs (1.65±0.66 mm/sec; range 0.46-2.90) than in HPs (2.83±1.10 mm/sec; 1.07-4.50) or the surrounding mucosa (3.73±1.11 mm/sec; 1.80-6.20; P <0.001). The blood flow velocity rate compared with the surrounding mucosa was significantly lower in ADs (0.41±0.16; 0.10-0.82) than in HPs (0.89±0.25; 0.46-1.51; P <0.001). We found that mean microvascular blood flow velocity was significantly lower in ADs than in HPs and the surrounding non-neoplastic mucosa. These findings indicate that a novel dynamic approach with microvascular blood flow velocity using magnifying endoscopy may be useful in assessing physiological differences between ADs and HPs.

Comprehensive review of load balancing in cloud computing system

Load balancing plays a critical role in optimizing resource utilization and enhancing performance in cloud computing systems. As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of load balancing techniques in cloud computing systems, with a focus on their applicability, advantages, and limitations. The review encompasses both static and dynamic load balancing approaches, evaluating their effectiveness in addressing the challenges posed by cloud infrastructure, such as heterogeneity, scalability, and variability in workload demands. Furthermore, the review examines load balancing algorithms considering factors such as resource utilization, response time, fault tolerance, and energy efficiency. Additionally, the impact of load balancing on cloud performance metrics, including throughput, latency, and scalability, is analyzed. This review aims to provide insights into the state-of-the-art load balancing strategies and serve as a valuable resource for researchers, practitioners, and system designers involved in the development and optimization of cloud computing systems.

Study on Dynamic and Accurate 6-DOF Measurement System and Approach

Study on Dynamic and Accurate 6-DOF Measurement System and Approach

The Authenticity of Organizational-Level Visual Identity in the Context of Strategic Communication

ABSTRACT With the purpose of addressing authenticity as a central concept for organizational-level visual identity, the paper contributes to growing scholarly interest in the role of symbols and images in the context of strategic communication. The paper theoretically and empirically explores conceptualizations and assumptions related to visual identity authenticity. Theoretically, it demonstrates how the dominant approach to authenticity rests on three interrelated assumptions: that a real organizational identity essence exists; that it can and should be aligned with a corresponding visual identity; and that such visual identity can transmit the essence as part of strategic communication. However, additional approaches to authenticity challenge and nuance the essentialist approach by operating with alternative, processual assumptions related to the nature of organizational identity, visual identity, and strategic communication. Empirically, the paper develops the identified theoretical approaches though practitioner insights. In particular, the empirical work consists of interviews with ten agency strategists focused on how they understand visual identity authenticity. Combined, the theoretical and empirical work offer a complex perspective on organizational-level visual identity authenticity, which goes beyond the essentialist approach by adding dynamic, aspirational, and consumer-driven approaches. These different approaches, in turn, have distinctive implications for visual strategic communication.

Employing an Interpretive Structural Modeling–System Dynamics Approach for Deep Foundation Pit Risk Assessment Model

Employing an Interpretive Structural Modeling–System Dynamics Approach for Deep Foundation Pit Risk Assessment Model

Numerical simulation analysis of wind field above pavement surface in different landforms by computational fluid dynamics approach

The temperature and moisture of the pavement structure can be greatly influenced by the wind speed above pavement surface. The wind speed above pavement surface not only is dominated by the wind speed of atmosphere, but also it is highly related to the landform and buildings around road. However, currently there are no studies about the wind field above pavement surface in consideration of the effect of the landform and buildings. A simulation method, which is combined with geographic information system (GIS), wind data from meteorological observatory and computational fluid dynamics (CFD) software, is employed to study the effect of the landform and the wind speed of atmosphere on the wind field above pavement surface. Three cases are studied, including an urban road, a coastal road and a mountainous road. Furthermore, the wind field distribution above road surface in different wind directions was studied in our work. Results indicate that the wind field above pavement surface can be greatly affected by the landforms, buildings and wind direction. This simulation method can provide reliable results for the wind field above pavement surface. The maximum relative errors between simulated and measured wind speed can be less than 20% in the analysis of the three cases. It is recommended that the CFD simulation method is a good tool to accurately know the wind field above pavement surface.