Dynamic Framework Research Articles

Energy-Efficient Framework for Green Cloud Computing in Dynamic Resource Allocation

Cloud Computing has been a trending technology for a few years supporting computational services over internet. But ever since its adoption, cloud’s consistent challenge is in its dynamic resource allocation. The existing cloud model details the online and offline algorithms used to decide the dynamic resource allocation. The goal is to have a dynamic resource allocation framework that aligns to cloud data management’s objective of maximizing revenue with minimum cost. This encourages both consumers and cloud providers not only with energy-efficient power usage but also high CPU utilization. This article discusses the impediments of migrating to Public Cloud, what is dynamic resource allocation, HPC workloads with complex communication path on cloud platform, and the benefits of bare metal platform for latency-sensitive applications. We shed light on trade- offs (compute balance) between Private and Public Cloud, how existing resources can be leveraged, Random forest (RF) solutions including a study on hybrid cloud computing capacity optimization framework. Understanding RL architecture, problem solving approach, learning structure and Hybrid Cloud Management Architecture framework are also explored. Also given are a few RL implemented gaming examples on how it makes an impact. Lastly, we shall do the comparisons of RL with other Machine Learning (ML) approaches.

On the provision of excludable public goods: General taxes or user prices?

AbstractThis paper compares some of the most common ways of financing the provision of publicly provided impure public goods/services in a unified dynamic general equilibrium framework with heterogeneous income groups. The focus is on the effects of user prices on individual incentives and how the latter shape aggregate outcomes and distribution. We show that a market‐like mechanism of user prices not only improves individual incentives and enhances aggregate efficiency, but it is also Pareto improving. However, such a mechanism is not beneficial or simply collapses when social externalities are strong, when the government adopts a mixed system that combines user prices with heavy subsidization of user costs, and when there is a minimum amount of the good that everybody needs.

Effects of moving vehicles and running lane on the dynamic response of a long-span floating suspension bridge

ABSTRACT Long-span floating bridges are not as robust as bottom-fixed bridges under moving vehicle loads due to weaker foundation stiffness. Understanding the effect of vehicles on the vehicle-induced dynamic response is essential for structural and operational safety assessments. This paper developed a dynamic analysis framework for floating bridges under moving vehicles, including different running lanes. The effects of moving vehicle speed, weight, running lane, vehicle number, spacing in the fleet, and vehicle fleet arrangement on the dynamic response of the example floating suspension bridge were investigated. The primary research outcomes include the following: 1) The developed framework can efficiently compute the dynamic response of the example floating bridge under moving vehicles. Compared with the bottom-fixed bridge scheme, the floating bridge under the same moving vehicle has larger dynamic responses, especially the lateral vibration response of the girder. 2) The vertical response of the floating bridge is mainly affected by the vehicle weight. The offset distribution of vehicle running lanes exaggerates the lateral vibrations of the bridge, of which the magnitude is similar to the vertical response. 3) Increasing the vehicle spacing in the fleet and implementing two-way opposing fleet traffic can effectively reduce the vehicle-induced lateral vibrations of the long-span floating suspension bridge. The design of the floating bridge can benefit from a better understanding of its dynamic response under moving vehicles.

A dynamic exploratory hybrid modelling framework for simulating complex and uncertain system

Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time, leading to significant deep uncertainty. Due to deep uncertainty, decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future, thus making it difficult to specify appropriate models and parameters to describe the system of interest. In this paper, we propose a dynamic exploratory hybrid modeling framework that fits data, models, and computational experiments together to simulate complex systems with deep uncertainty. In the framework, one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experiments to explore the diversity of future scenarios. Real-time data is then incorporated into diverse forecasts to dynamically adjust the simulation system. This ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually mitigated. Our approach has been applied to a human-involved car-following system simulation under complex traffic conditions. The results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.

Economic optimization of Wolbachia-infected Aedes aegypti release to prevent dengue.

Dengue virus, primarily transmitted by the Aedes aegypti mosquito, is a major public health concern affecting ≈3.83 billion people worldwide. Recent releases of Wolbachia-transinfected Ae. aegypti in several cities worldwide have shown that it can reduce dengue transmission. However, these releases are costly, and, to date, no framework has been proposed for determining economically optimal release strategies that account for both costs associated with disease risk and releases. We present a flexible stochastic dynamic programming framework for determining optimal release schedules for Wolbachia-transinfected mosquitoes that balances the cost of dengue infection with the costs of rearing and releasing transinfected mosquitoes. Using an ordinary differential equation model of Wolbachia and dengue in a hypothetical city loosely describing areas at risk of new dengue epidemics, we determined that an all-or-nothing release strategy that quickly brings Wolbachia to fixation is often the optimal solution. Based on this, we examined the optimal facility size, finding that it was inelastic with respect to the mosquito population size, with a 100% increase in population size resulting in a 50-67% increase in optimal facility size. Furthermore, we found that these results are robust to mosquito life-history parameters and are mostly determined by the mosquito population size and the fitness costs associated with Wolbachia. These results reinforce that Wolbachia-transinfected mosquitoes can reduce the cost of dengue epidemics. Furthermore, they emphasize the importance of determining the size of the target population and fitness costs associated with Wolbachia before releases occur. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Tensor analysis of tornadoes: a new analytical and numerical model

This research proposes a new mathematical formulation of tornadoes based on the theory of tensor analysis and simulation in a non-inertial dynamics framework, both in two and three dimensions. This model may show the spherical upward movement of air in a tornado without taking into account vertical convection. A tornado requires several elements, including geocentric latitude, the Coriolis effect, increased airspeed in the upper atmosphere, and increased air pressure. Computing the three-dimensional location of the tornado or hurricane, as well as the mathematical models of airflow motion and the Earth's rotation in three-dimensional (3D) space, can determine a tornado's airflow characteristics. To show tornado patterns, we employed computer software that computed motion dynamics and did numerical computations. The results of 2-D modeling and simulation indicated that the greater the initial tornado angular speed, the larger the tornado area. Three-dimensional modeling and simulation also show that tornadoes are more powerful at higher geocentric latitude angles. The novelty of this study is that this model can be used to explain tornado patterns. In our research, we combine tensor analysis, computational modeling, as well as 2D and 3D simulations for simulating tornadoes for the first time. The scientific application of this finding is that researchers at the Meteorology, Climatology, and Geophysics Agency will be able to analyze a tornado and geophysical phenomena more readily with simulations and models.

Dynamic Teacher’s Technology Adoption During the COVID-19 Pandemic

Understanding the teacher’s technology adoption process is essential to comprehend and narrow the digital divide in the post-epidemic age. During the pandemic, the stay-at-home orders not only intervened schooling and teaching but also increased digital accessibility to teachers. This research studies teacher heterogeneity and adoption controls in the epidemic to simultaneously affect teacher’s underlying intention and adoption behavior based on a dynamic framework under the theory of planned behavior. We present a quantitative framework for modeling the teachers’ adoption behavior of a technology conditioned on intention, defined as latent dynamic processes via a hidden Markov model. This model allows us to examine the effects of three concerned adoption controls: epidemic, community, and experience. We also explicitly characterized teachers’ digital traits as the estimated results accounts for teacher’s heterogeneity. The implicit quality of digital teaching artifacts is examined to correlate the dynamic analyses with the qualitative supports. We collected data from four primary schools in Shanghai over 173 weeks, using an after-school activity management system. The data collection spanned periods both before and after the school closure caused by the epidemic, providing us with a dynamic view of technology adoption patterns under different circumstances. Our results suggest that the interventions derived from the controls of the epidemic did not significantly narrow the digital gap. In particular, well-prepared teachers may be more sensitive to adjusting their usage to meet the evolving standards. The inexperienced teacher struggles to maintain a high level of adoption once the passive external pressure is eliminated. Even the compulsory policy can temporarily change their adoption behavior. These implications highlight the second-order digital divide problem.

Diversifying Quranic revision methods using gamification-based teaching material for tahfiz education

This study discusses the development procedure of the global tahfiz game (GTG) as a teaching aid for the Quranic revision subject for tahfiz students based on the gamification concept. Comprehensive design and development research (DDR) was used as the research method. The analysis includes conceptual and material development, expert evaluation, testing, and the final product. The development of concepts and materials used in this gamification was based on the theoretical framework of mechanics, dynamics, and aesthetics (MDA), with the application of tahfiz education elements. To examine the ability of GTG prototype to tackle the users’ needs, we have conducted the usability analysis. The expert teacher's mean score for the trial run on this gamification was high at 4.85. The trial run score for student participants was also high (4.45). The result demonstrated that most of the constructs are perceived at a high level by respondents. Thus, this indicated the high usability of GTG to be used as a teaching aid for a Quranic revision subject. The gamification of Quranic tahfiz based on board games has been finalized through recommendations and improvements based on the evaluation. The study findings are expected to be useful for educational institutions or researchers in developing teaching materials using the MDA framework for gamification, particularly in the field of Islamic education.

Human–structure interaction effects on lightweight footbridges with tuned mass dampers

The aim of the work is to explore how the interaction between pedestrians and a bridge deck could affect the effectiveness of tuned mass dampers (TMD) in suppressing the vibrations of lightweight footbridges. To this end, a human–structure interaction (HSI) model is built in a modal dynamic analysis framework, and it is applied to a lightweight footbridge subject to a large number of pedestrian arrangements with near-resonant walking frequencies. Compared with the response ignoring HSI, the results show that the interaction between the dynamic response of the pedestrians and the structure can reduce the vibrations in the deck, but they exceed comfort limits based on peak and root mean square (RMS) acceleration criteria. In order to reduce the oscillations to a comfortable level, a TMD located at midspan has been designed following classical optimisation methods that neglect HSI. The efficiency of the damper in resonant conditions is demonstrated in the time and frequency domains, even under a large number of synchronised pedestrians interacting with the structure, suggesting that crowds do not introduce important detuning effects in the TMD.

Dynamic Graph Construction Framework for Multimodal Named Entity Recognition in Social Media

Dynamic Graph Construction Framework for Multimodal Named Entity Recognition in Social Media

MDG: A Multi-Task Dynamic Graph Generation Framework for Multivariate Time Series Forecasting

MDG: A Multi-Task Dynamic Graph Generation Framework for Multivariate Time Series Forecasting

The impact of AI assimilation on firm performance in small and medium-sized enterprises: A moderated multi-mediation model

Artificial intelligence (AI) and other advanced technologies are increasingly recognized as essential catalysts for enhancing productivity due to their capability to transform nearly all operations within and outside firms. However, the empirical research on how AI assimilation may promote firm-level outcomes such as absorptive capacity (AC), customer agility (CA), and firm performance (FP) is still in its infancy. Drawing from the dynamic capability view and using 417 valid responses collected through cross-sectional methods from small and medium-sized enterprises (SMEs) in Lebanon, this study examines the effect of AI assimilation on firm performance. The mediating roles of AC and CA were investigated. The moderating role of organizational agility (OA) was also explored. The findings support the hypothesized assumptions that continual advancement of technology evolves the industrial organizations' performance with CA and AC as parallel mediators, partially mediating the link between AI assimilation and FP and OA as a moderator, moderating the positive relationship between AI and CA and between AI and FP. The findings provide crucial insights for practitioners and advance the dynamic capability view framework. They provide compelling evidence that enriches the understanding of AI assimilation, demonstrating its positive impact on critical organizational outcomes and yielding performance benefits for SMEs.

SeaTE: Subjective ex ante Treatment Effect of Health on Retirement

The paper studies the effect of health on work among older workers by eliciting two- and four-year-ahead subjective probabilities of working under alternative health states. These measures predict work outcomes. Person-specific differences in working probabilities across health states can be interpreted as Subjective ex ante Treatment Effects (SeaTEs) in a potential outcomes framework and as taste for work within a discrete choice dynamic programming framework. There is substantial heterogeneity in expectations of work conditional on health. The paper shows how heterogeneity in taste for work correlated with health can bias regression estimates of the effect of health on retirement. (JEL D84, I12, J14, J22, J26)

IJCM_214A: Digital interventions for childhood malnutrition in a tribal area of Andhra Pradesh: Understanding the problem through a health system perspective

Background: Childhood malnutrition is a major public health concern in the tribal areas of Andhra Pradesh, India, particularly in the region of ITDA Rampachodavaram. The integration and effectiveness of digital health interventions in these underserved regions are vital for addressing this endemic issue. However, the unique challenges posed by the remote and resource- limited settings of tribal areas make the implementation of such interventions particularly complex. Moreover, the varying complexity of the digital health ecosystem in these areas adds another layer of concern to deploying digital health solutions. Objectives: 1. To explore digital health interventions related to childhood malnutrition in the Integrated Tribal Development Agency (ITDA) of Rampachodavaram. 2. To understand the dynamics of the local health system and the digital health ecosystem in the implementation of these interventions. 3. To examine the role of street-level bureaucrats in implementing digital interventions and identify the barriers and facilitators affecting this process Methodology: The research was conducted in ITDA Rampachodavaram, involving interviews with health system officials and Focus Group Discussions (FGDs) with frontline health workers. The Health System Dynamics (HSD) framework and Lipsky’s theory on street-level bureaucrats were utilized, and enriched with insights into the digital health ecosystem. Results: The study reveals significant systemic, contextual, and human factors influencing the implementation of digital health interventions. Challenges include the complex role of street-level bureaucrats and systemic barriers like inadequate training, technological limitations, and disparities in digital access and literacy. Conclusion: This study highlights the potential of digital health interventions in improving childhood malnutrition outcomes in tribal areas. It stresses the importance of adaptive, inclusive strategies and advocates for digital health equity. Empowering frontline health workers and tailoring interventions to the specific health system dynamics and digital ecosystem of tribal regions are key for effective implementation.

A dynamic driving-style analysis method based on drivers’ interaction with surrounding vehicles

The ability to recognize different driving styles of surrounding vehicles is crucial to determine the safest and most efficient driving decisions, prevent accidents, and analyze the causes of traffic accidents. Understanding if the surrounding vehicle is aggressive or cautious can greatly assist in the decision making of vehicles in terms of whether and when it is appropriate to make particular maneuvers. A driver’s driving style usually changes with the environment, which brings great challenges to the current research. To this end, a dynamic driving-style analysis framework, in which drivers’ interactions with other vehicles are considered, is proposed in this article. First, by analyzing common traffic scenarios, five surrounding vehicles are selected as the environmental vehicles to be considered. Time headway (THW) and time to collision (TTC) that can consider the relative speed and position with the ego vehicle are selected as the clustering indicators. Then, a Bayesian nonparametric learning method based on a hierarchical Dirichlet-process hidden semi-Markov model (HDP-HSMM) is introduced to extract primitive driving patterns from time-series driving data without prior knowledge of the number of these patterns. Then the driving pattern is scored according to the risk degree. A driver’s aggressiveness is scored and drivers are divided into different styles based on the frequency distribution of driving patterns. The effectiveness of the proposed method is demonstrated on a real-world vehicle trajectory data set where results show that driving pattern switches and more complex driving behaviors can be better captured and understood semantically.

Prediction of Coaxial Rotor Hub Flow Using Mercury Framework

Rotor hubs are predominantly responsible for the parasitic drag encountered by high-speed rotorcraft. To gain a deeper understanding of the fluid dynamics around rotor hubs, simulations of counter-rotating coaxial rotor-hub flows were conducted, which was subsequently followed by a corroborative experiment at Pennsylvania State University. The simulation process employed a computational fluid dynamics framework termed “Mercury,” which utilizes an unstructured/Cartesian multimesh paradigm. This process incorporated Spalart???Allmaras delayed detached eddy simulation turbulence modelling for an accurate representation of the flow. The investigation into the coaxial hub flow physics was conducted at two different advance ratios: 0.25 and 0.6, constructed by building up the hub components. The interaction between the hub and fairing components led to an increase in the average hub drag and caused unsteady harmonics in the hub and fairing drags. Furthermore, it was noted that different advance ratios affected the drag and wake structures. The complete hub model was then simulated in a water tunnel with an advance ratio of 0.25. Predictions of mean and unsteady drag, as well as mean wake velocity fields, were compared with the experimental results. Overall, the mean wake velocity field from the simulation qualitatively aligned with the experimental results, especially in the near-wake region. Additionally, the buildup model analysis significantly aided in understanding the intricate wake structures surrounding the hub.

The evolution of whole-brain turbulent dynamics during recovery from traumatic brain injury.

It has been previously shown that traumatic brain injury (TBI) is associated with reductions in metastability in large-scale networks in resting-state fMRI (rsfMRI). However, little is known about how TBI affects the local level of synchronization and how this evolves during the recovery trajectory. Here, we applied a novel turbulent dynamics framework to investigate whole-brain dynamics using an rsfMRI dataset from a cohort of moderate to severe TBI patients and healthy controls (HCs). We first examined how several measures related to turbulent dynamics differ between HCs and TBI patients at 3, 6, and 12 months post-injury. We found a significant reduction in these empirical measures after TBI, with the largest change at 6 months post-injury. Next, we built a Hopf whole-brain model with coupled oscillators and conducted in silico perturbations to investigate the mechanistic principles underlying the reduced turbulent dynamics found in the empirical data. A simulated attack was used to account for the effect of focal lesions. This revealed a shift to lower coupling parameters in the TBI dataset and, critically, decreased susceptibility and information-encoding capability. These findings confirm the potential of the turbulent framework to characterize longitudinal changes in whole-brain dynamics and in the reactivity to external perturbations after TBI.

WAYS TO INCREASE THE EFFECTIVENESS OF STATE REGULATION OF THE SOCIO-ECONOMIC DEVELOPMENT OF UKRAINE AND ARTISTIC ACTIVITY, TAKING INTO ACCOUNT THE ENVIRONMENTAL STIMULATION OF SOCIETY DURING THE WAR

This paper explores the effectiveness of state regulatory strategies in fostering socio-economic development and enhancing artistic activity in Ukraine during ongoing military conflict and environmental pressures. It delves into the crucial balance between state control and market autonomy, utilizing both historical insights and modern global practices to suggest improvements in regulatory frameworks. The focus is on a cohesive governance approach that integrates economic stabilization measures, cultural enrichment, and environmental sustainability initiatives to enhance societal resilience and well-being. This paper aims to explore strategic approaches that can enhance the effectiveness of such regulations, specifically focusing on socio-economic growth and the flourishing of artistic activities, while simultaneously considering the vital role of environmental stimulation as a catalyst for societal endurance and morale. The methodology of the conducted research involves the use of methods of comparative analysis, historical and strategic analysis, forecasting. The used methods made it possible to propose directions for solving the tasks and strategic directions. The study demonstrates that specific economic policies, such as tax breaks for key sectors and increased financial support for the arts, have significantly strengthened economic stability and continuity. State-sponsored artistic endeavors have rejuvenated Ukraine's cultural landscape, boosting both national spirit and international cultural engagement. Additionally, environmental measures, like establishing urban green zones and promoting community farming, have been shown to improve mental health and social unity, showcasing the benefits of incorporating environmental aspects into governance during times of war. The paper addresses the complexities of integrating diverse policy areas, managing resources efficiently, and sustaining public involvement under conflict conditions. It proposes an adaptable, dynamic regulatory framework to tackle these challenges. Recommendations include adopting a comprehensive policy approach, enhancing governance flexibility, and seeking international collaboration to optimize state regulation. The findings emphasize the critical role of state intervention in aligning socio-economic and cultural development, offering valuable guidance for policymakers dealing with the intricacies of governance in conflict-impacted regions.

Chatbot Based Online Shopping Web Application

This study presents an innovative chatbot system aimed at transforming product recommendation and customization. It integrates feature-based input analysis and user-driven updates to provide tailored product recommendations. Users can input specific product features, leading to personalized suggestions that align precisely with their preferences. Utilizing a dynamic customization framework, the chatbot consistently refines recommendations based on user interests, ensuring a personalized and evolving user experience. The project seeks to reshape the realm of product recommendation systems by providing a seamless and adaptable interface, enhancing the exploration and acquisition of products that precisely match individual needs and preferences.

Derivation of Growth of Import Demand: A Time Series Analysis of Less Developed Countries

In this article, growth of import demand is estimated in a dynamic optimisation framework. Since a conventional micro-theoretic import demand function cannot capture the issue of the growth of import demand, it is derived in a macro-theoretic framework of equilibrium import demand function. Growth rate of import demand in a dynamic optimisation framework (considering utility maximisation problem) has not yet been found in the literature. The main focus of this article is to derive this. It is found that growth rate of imports is the function of the growth of domestic income or expenditure, the growth rate of relative price, which is nothing but the real exchange rate, and productivity of capital. The theoretically derived import growth equation was empirically verified for 35 less developed countries. It is observed that for most of the less developed countries, independent variables are statistically significant with expected sign. JEL Codes: F130, F14