Dynamic Framework Research Articles

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.

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.

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)

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.

Excessive bank risk-taking in an infinite horizon economy

We develop a dynamic framework to study banks’ incentives to take excessive risk in an emerging economy, where bank default probability and excess bank risk-taking are modeled endogenously. We calibrate it for the 1998 Peruvian economy. We find that the infinite-period feature amplifies banks’ incentives to take excessive risk. When we simulate the sudden stop that hit Peru in 1998, the model accurately predicts the substantial short-term rise in the non-performing loans ratio through the rise of the bank default probability.

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

Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin Framework

This paper addresses the intricate challenges of coastal management, particularly in rapidly forming tidal flats, emphasizing the need for innovative monitoring strategies. The dynamic coastal topography, exemplified by a newly formed tidal flat in Shanghai, underscores the urgency of advancements in coastal risk recognition. By utilizing a digital twin framework integrated with state-of-the-art unmanned aerial vehicles (UAVs), we systematically evaluate three configurations and identify the optimal setup incorporating real-time kinematics (RTK) and light detection and ranging (LiDAR). This UAV configuration excels in efficiently mapping the 3D coastal terrain. It has an error of less than 0.1 m when mapping mudflats at an altitude of 100 m. The integration of UAV data with a precise numerical ocean model forms the foundation of our dynamic risk assessment framework. The results showcase the transformative potential of the digital twin framework, providing unparalleled accuracy and efficiency in coastal risk recognition. Visualization through Unity Engine or Unreal Engine enhances accessibility, fostering community engagement and awareness. By predicting and simulating potential risks in real-time, this study offers a forward-thinking strategy for mitigating coastal dangers. This research not only contributes a comprehensive strategy for coastal risk management but also sets a precedent for the integration of cutting-edge technologies in safeguarding coastal ecosystems. The findings are significant in paving the way for a more resilient and sustainable approach to coastal management, addressing the evolving environmental pressures on our coastlines.

Leveraging Technology to Dismantle Systemic Racism: Modeling and Simulation with Planetary Chess

In the realm of Information Technology, Planetary Chess emerges as a paradigm-shifting solution to combat systemic racism, utilizing modeling and simulation techniques akin to iterative computational processes. This paper elucidates how Planetary Chess harnesses the power of technology, employing modeling and simulation methodologies for systemic evolution in the fight against racism. By integrating adaptive control principles and innovative IT solutions, Planetary Chess offers a dynamic framework for societal empowerment and change.

A Dynamic Prediction Framework for Urban Public Space Vitality: From Hypothesis to Algorithm and Verification

Predicting and assessing the vitality of public urban spaces is crucial for effective urban design, aiming to prevent issues such as “ghost streets” and minimize resource wastage. However, existing assessment methods often lack temporal dynamics or heavily rely on historical big data, limiting their ability to accurately predict outcomes for unbuilt projects. To address these challenges, this study integrates previous methodologies with observations of crowd characteristics in public spaces. It introduces the crowd-frequency hypothesis and develops an algorithm to establish a time-dimensional urban vitality dynamic prediction model. Through a case study of the Rundle Mall neighborhood in Adelaide, Australia, the effectiveness of the prediction model was validated using on-site observation sampling and comparative verification. The prediction model framework allows for the determination of urban vitality within specific time ranges by directly inputting basic information, providing valuable support to urban planners and government officials during the design and decision-making processes. It offers a cost-effective approach to achieve sustainable urban vitality construction. Furthermore, machine learning techniques, specifically the decision tree model, were applied to case data to develop a set of preliminary algorithm tools, which enable output of reference urban vitality levels (high-medium-low).

The Interplay of Historical Foundations, Resource Dynamics, and Ideological Frameworks in the Shaping of Tanzania's Foreign Policy Landscape: Practicability of the Dependency Theory

This article analyzes three key variables - ideology, history, and resources - to get a deeper insight into the intricate dynamics shaping Tanzania's foreign policy. The research examines how the country's diplomatic environment is influenced by resource considerations, historical legacies, and ideological shifts within the framework of dependence theory. The findings from the survey show various perspectives on the influence of history, which is consistent with the emphasis of dependence theory on historical variables shaping foreign policy. The examination of resource contributions emphasizes the pressing necessity for policy reform, endorsing the suggestion of dependence theory for strategic resource utilization to reduce external constraints. Tanzania's strategic focus on developing economic partnerships outside of Western markets is evident in its ideological aspect. This is in line with the suggestion of dependency theory to reduce reliance on a certain set of trading partners in order to enhance the country's ability to navigate changing global economic trends. Key Informant Interviews support the study's theoretical framework and demonstrate the ongoing influence of historical personalities such as Julius Nyerere on Tanzania's foreign policy. The study's findings resulted in three significant recommendations. To establish diplomatic partnerships that honour past legacies while adapting to current geopolitical circumstances, it is advisable to take a sophisticated approach to historical connections. Implementing thorough policy change is recommended to strategically protect and utilize national resources in global contexts. Finally, it is recommended to take a proactive approach to diverse economic relationships in alignment with the broader pan-African objective of reducing dependence on specific trade partners. valuable guidance for decision-makers navigating the complex process of developing foreign policy.

Banking on resilience: EU macroprudential policy and systemic risk

This study examines the effectiveness of macroprudential policies on banking systemic risk for 172 European banks across 20 EU countries from 2000 to 2017. Using a dynamic panel framework, it assesses the effectiveness of policy on two dimensions of systemic risk: banks' contribution to aggregate systemic risk (ΔCoVaR) and their vulnerability in stressed markets (Exposure-ΔCoVaR). Results show policy actions reduce bank systemic risk, especially in terms of Exposure-ΔCoVaR. However, this effect varies between tightening and loosening measures. Interestingly, the analysis indicates that policies don't significantly affect banks' contribution to systemic risk as measured by ΔCoVaR. Borrower-based policies and exposure limits in particular enhance bank resilience. This study has important implications for policymakers regarding the calibration and evaluation of macroprudential measures.

Women Directors: An Empirical Test of Critical Mass Hypothesis

This paper offers integrated theoretical-based empirical evidence regarding the role of female directors in promoting corporate common good using resource dependency model built on critical mass hypothesis. Using panel regression involving 220 firm-year observations from 2011 to 2021, the paper empirically assesses the moderating impacts of diversity and social inclusion policy, and gender-based power separation in determining the direction of causality between composition of female directors and foreign capital importation by the top 20 commercial banks in Nigeria. With approximately 30 per cent female director representation in the sampled banks (i.e., optimal gender threshold), the paper offers support to critical mass hypothesis and validates intrinsic benefits of women in corporate boardroom. The empirical result shows that female director representation in boards with strong diversity and social inclusion policy and greater independent non-executive directors, is positively linked to resource dependency role of foreign capital importation. Diversity of power separation is found to be detrimental to such board tasks due to overwhelming tokenism effect that surrounds gender-based power delineation. These key findings are statistically significant and robust to a series of iterated sensitivity tests. In addition to offering emerging market contributions to the growing literature on critical mass theory application, findings from this study demonstrate the inherent value of combining multiple governance theories (such as resource dependency and critical mass models) and the dynamic research framework opportunities it offers for robust empirical testing.

Design and analysis of self-priming extension DNA hairpin probe for miRNA detection based on a unified dynamic programming framework

MicroRNAs (miRNAs) are potential biomarkers for cancer diagnosis and prognosis, methods for detecting miRNAs with high sensitivity, selectivity, and stability are urgently needed. Various nucleic acid probes that have traditionally been for this purpose suffer several drawbacks, including inefficient signal-to-noise ratios and intensities, high cost, and time-consuming method establishment. Computing tools used for investigating the thermodynamics of DNA hybridization reactions can accurately predict the secondary structure of DNA and the interactions between DNA molecules. Herein, NUPACK was used to design a series of nucleic acid probes and develop a phosphorothioated-terminal hairpin formation and self-priming extension (PS-THSP) signal amplification strategy, which enabled the ultrasensitive detection of miR-200a in serum samples. The free and binding energies of the DNA detection probes calculated using NUPACK, as well as the biological experimental results, were considered synthetically to select the best sequence and experimental conditions. A unified dynamic programming framework, NUPACK analysis and the experimental data, were complementary and improved the designed model in all respects. Our study demonstrates the feasibility of using computer technology such as NUPACK to simplify the experimental process and provide intuitive results.

The Influence of Slim Tube Length on the Minimum Miscibility Pressure of CO2 Gas–Crude Oil

This study focuses on the Bozhong 25-1 oilfield formation oil as the experimental subject, systematically investigating the influence of different slim tube lengths (1 m, 12.5 m, 20 m, and 25 m) on the minimum miscibility pressure (MMP) of the CO2 and formation oil mixture system. Through slim tube experiments, the interaction process of CO2 with formation oil in slim tubes of different lengths was simulated, with a particular focus on analyzing how changes in slim tube length affect the MMP. The experiments revealed an important phenomenon: as the slim tube length gradually increased from shorter dimensions, the MMP showed a decreasing trend; when the slim tube length reached 12.5 m, this trend stabilized, meaning that further increasing the slim tube length no longer led to significant changes in the MMP, with its stable value determined to be 27.86 MPa. This phenomenon can be explained within the theoretical framework of fluid dynamics and interfacial science, where several key factors play a significant role. Firstly, the flow characteristics of the fluid inside the slim tube significantly influence it; secondly, the interfacial tension between phases is also a decisive factor; lastly, the impact of the internal microstructure of the slim tube cannot be overlooked. These aspects together form the basis for understanding the impact of slim tube length on MMP and reveal the underlying mechanisms. This research is significant for deeply understanding and quantifying this effect, providing a solid theoretical basis for optimizing CO2 flooding technology and guiding more precise operational strategies in oilfield development practices to enhance oil displacement efficiency and economic benefits.