Dynamic Framework Research Articles

A thermodynamic motivated RCCM damage interface model in an explicit transient dynamics framework

A thermodynamic motivated RCCM damage interface model in an explicit transient dynamics framework

Dynamic capabilities and competitiveness: Relationship among industrial policy, structural transformation, and economic growth in Indonesian regional industries

The complex interactions of industrial Policy, structural transformation, economic growth, and competitive strategy within regional industries are examined in this research. Using a dynamic capabilities framework, the study examines the mediating roles of organizational innovation and adaptability in the link between competitiveness and macroeconomic variables. A two-way fixed effects model is used in this study to examine the influence of structural transformation (ST) on Industrial Policy (IP). Using regional data covering the years 2010 to 2022, the research undertaken in this paper explores the dynamics of the Indonesian economy by empirically assessing the consequences of structural change on industrial Policy. In order to establish a comprehensive model that clarifies the mechanisms through which industrial policies and structural shifts impact the development of dynamic capabilities, ultimately influencing competitiveness strategies, this research draws on a large amount of empirical data and integrates insights from seminal works. Our research adds to our knowledge of strategic management in regional industries by providing detailed information on how economic development and policy interventions influence businesses’ ability to adapt and gain a competitive edge. In addition to advancing scholarly discourse, this study offers business executives and politicians valuable insights for managing the intricacies of global economic processes.

Western US intraplate deformation controlled by the complex lithospheric structure

The western United States is one of Earth’s most tectonically active regions, characterized by extensive crustal deformation through intraplate earthquakes and geodetic motion. Such intracontinental deformation is usually ascribed to plate boundary forces, lithospheric body forces, and/or viscous drag from mantle flow. However, their relative importance in driving crustal deformation remains controversial due to inconsistent assumptions on crustal and mantle structures in prior estimations. Here, we utilize a fully dynamic three-dimensional modeling framework with data assimilation to simultaneously compute lithospheric and convective mantle dynamics within the western United States. This approach allows for quantitative estimations of crustal deformation while accounting for the realistic three-dimensional lithospheric structure. Our results show the critical role of the complex lithospheric structure in governing intraplate deformation. Particularly, the interaction between the asthenospheric flow and lithospheric thickness step along the eastern boundary of the Basin and Range represents a key driving mechanism for localized crustal deformation and seismicity.

Improvement of Student Interaction Analysis in Online Education Platforms Through Interactive Mobile Technology and Machine Learning Integration

The emergence of online education platforms, driven by interactive mobile technology, has significantly reshaped traditional educational paradigms and underscored the critical need for advanced analysis and improvement of student interactions. Effective analysis of student interaction is crucial for enhancing teaching quality and optimizing the learning experience in these digitally enriched environments. Traditional analysis frameworks often face challenges such as inaccuracies in anomaly detection and inefficiencies in data handling, particularly when handling extensive datasets typical of online platforms. This study introduces a novel approach to enhancing student interaction analysis systems by leveraging the synergy between machine learning and advanced interactive mobile technologies. Initially, the study proposes an advanced anomaly detection method tailored for identifying irregular student interactions. This method utilizes a blend of machine learning algorithms and the real-time data processing capabilities of mobile technology. Furthermore, to address the complexities of data transmission in mobile-based online education ecosystems, a state-ofthe- art congestion control algorithm has been developed. This algorithm optimizes data flow, significantly enhancing transmission stability and efficiency. The integration of interactive mobile technology with machine learning offers a robust and dynamic framework for analyzing student interactions, thereby facilitating a more engaging and effective online educational experience. This research contributes to the advancement of online education quality and efficiency by emphasizing the role of interactive mobile technology in shaping future learning environments.

Enhancing SMEs resilience through digital innovation: a stage-based analysis

PurposeThe COVID-19 pandemic has profoundly impacted small and medium-sized enterprises (SMEs), inherently vulnerable entities, prompting a pivotal question of how to enhance SMEs’ organizational resilience (OR) to withstand discontinuous crises. Although digital innovation (DI) is widely acknowledged as a critical antecedent to OR, limited studies have analyzed the configurational effects of DI on OR, particularly stage-based analysis.Design/methodology/approachUnderpinned by the dynamic capabilities view, this study introduces a multi-stage dynamic capabilities framework for OR. Employing Latent Dirichlet Allocation (LDA), digital product innovation (DPI), digital services innovation (DSI) and digital process innovation (DCI) are further deconstructed into six dimensions. Furthermore, we utilized fuzzy-set qualitative comparative analysis (fsQCA) to explore the configuration effects of six DI on OR at different stages, using data from 94 Chinese SMEs.FindingsFirst, OR improvement hinges not on a singular DI but on the interactions among various DIs. Second, multiple equivalent configurations emerge at different stages. Before the crisis, absorptive capability primarily advanced through iterative DPI and predictive DSI. During the crisis, response capability is principally augmented by the iterative DPI, distributed DCI, and integrated DCI. After the crisis, recovery capability is predominantly fortified by the iterative DPI, expanded DPI and experiential DSI. Third, iterative DPI consistently assumes a supportive role in fortifying OR.Originality/valueThis study contributes to the extant literature on DI and OR, offering practical guidance for SMEs to systematically enhance OR by configuring DI across distinct stages.

A dynamic system optimal dedicated lane design for connected and autonomous vehicles in a heterogeneous urban transport network

Numerous contemporary studies have posited that connected and autonomous vehicles (CAVs) hold the potential to enhance traffic safety and augment efficiency substantially. One widely discussed approach to optimize CAV operations within urban traffic networks involves the implementation of dedicated lanes (DLs). This study aims to assist system planners in optimally deploying DLs within heterogeneous urban traffic networks of CAVs and Human-Driven Vehicles (HDVs). In pursuit of this objective, we have introduced a multi-class dynamic traffic assignment framework that enhances network performance and offers insights into traffic dynamics. Additionally, our methodology considers dynamic routing behaviour while devising DLs, formulating and approximating the problem as a mixed-integer linear program (MILP). The resulting strategy delineates the temporal and spatial aspects of the deployment of DLs for CAVs, specifying the quantity and locations of these lanes. Subsequently, we assessed our framework using test-bed networks of varying sizes and demand profiles, evaluating the solution’s quality and the model’s adaptability to diverse traffic conditions. Our findings indicate that implementing DLs for CAVs can bolster vehicular throughput across the network while neglecting dynamic capacity variation in mixed traffic may yield misleading outcomes.

Theoretical frameworks for regulatory compliance in Fintech innovation: A comparative analysis of Africa and the United States

This review paper conducts a comparative analysis of the theoretical frameworks for regulatory compliance in fintech innovation between Africa and the United States. It explores the regulatory objectives, compliance mechanisms, enforcement strategies, and the role of innovation in shaping the fintech landscape. The study highlights key similarities and differences, emphasizing the importance of adaptability, consumer protection, and the promotion of financial inclusion. It also discusses the implications for policymakers and regulatory bodies, recommending strategies for fostering innovation while ensuring robust compliance. The paper concludes by identifying future research directions, focusing on the impact of emerging technologies and international regulatory cooperation. This analysis underlines the need for dynamic regulatory frameworks that balance innovation with financial stability and consumer protection in the rapidly evolving fintech sector. Keywords:  Fintech Innovation, Regulatory Compliance, Financial Inclusion, Consumer Protection, Emerging Technologies, International Cooperation.

Infection-induced cascading failures – impact and mitigation

In the context of epidemic spreading, many intricate dynamical patterns can emerge due to the cooperation of different types of pathogens or the interaction between the disease spread and other failure propagation mechanism. To unravel such patterns, simulation frameworks are usually adopted, but they are computationally demanding on big networks and subject to large statistical uncertainty. Here, we study the two-layer spreading processes on unidirectionally dependent networks, where the spreading infection of diseases or malware in one layer can trigger cascading failures in another layer and lead to secondary disasters, e.g., disrupting public services, supply chains, or power distribution. We utilize a dynamic message-passing method to devise efficient algorithms for inferring the system states, which allows one to investigate systematically the nature of complex intertwined spreading processes and evaluate their impact. Based on such dynamic message-passing framework and optimal control, we further develop an effective optimization algorithm for mitigating network failures.

Dynamic two-dimensional covalent organic frameworks.

Porous covalent organic frameworks (COFs) enable the realization of functional materials with molecular precision. Past research has typically focused on generating rigid frameworks where structural and optoelectronic properties are static. Here we report dynamic two-dimensional (2D) COFs that can open and close their pores upon uptake or removal of guests while retaining their crystalline long-range order. Constructing dynamic, yet crystalline and robust frameworks requires a well-controlled degree of flexibility. We have achieved this through a 'wine rack' design where rigid π-stacked columns of perylene diimides are interconnected by non-stacked, flexible bridges. The resulting COFs show stepwise phase transformations between their respective contracted-pore and open-pore conformations with up to 40% increase in unit-cell volume. This variable geometry provides a handle for introducing stimuli-responsive optoelectronic properties. We illustrate this by demonstrating switchable optical absorption and emission characteristics, which approximate 'null-aggregates' with monomer-like behaviour in the contracted COFs. This work provides a design strategy for dynamic 2D COFs that are potentially useful for realizing stimuli-responsive materials.

Communication dynamics of a two-agent interaction model with applications to human-autonomy teaming

ABSTRACT Communication is pivotal for the emergence of coordination and cooperation within teams. As a result, communication plays an important role in team dynamics as better communication between teammates can lead to more efficient and successful teams. To better understand communication dynamics and their impact on team performance, we develop a modelling framework of two-agent interaction dynamics in a discrete-time fashion. Our proposed model considers the communications between two agents based on each agent's personality and their individual impact on any received communication. Combined with data, we perform mathematical analysis and bifurcation diagrams to study how agents' personality and training may impact the quality of the team's communications and therefore their performance in task completion. The validations and parameter estimations of our model from data could potentially help us to select team members that could work together efficiently, and train members in the established team to collaborate better.

Analyzing Destination Resilience From A Spatiotemporal Perspective

The tourism industry is vulnerable to external shocks. Various crises inevitably impact the tourism industry and tourist destinations negatively but at the same time bring opportunities to examine destination resilience in response to a real shock that is hard to simulate. To manage a crisis more effectively, two critical issues should be addressed: the duration of the impact of the crisis (i.e., temporal perspective) and the affected geographical scale (i.e., spatial perspective), which have been neglected in previous studies on destination resilience. To address the above gaps, this research develops a comprehensive, multi-stage, dynamic spatiotemporal analytical framework to firstly measure two aspects of tourism resilience (i.e., resistance and recovery), and secondly analyze the influencing factors of tourism resilience. The empirical context of international tourism in Europe during the COVID-19 pandemic is used to demonstrate the applicability of the developed framework and relevant policy implications.

Application of energy scheduling algorithm based on energy consumption prediction in smart home energy scheduling

To effectively optimize household energy management, enhance user comfort, and ensure the stability of the power grid, this paper directs its focus towards household equipment and presents inter-household and indoor energy scheduling algorithms. Grounded in a two-dimensional dynamic device priority framework and predictive device operation modeling, this study demonstrates that the utilization of scheduling algorithms can avert grid overload scenarios during periods of low electricity prices. This results in more consistent hourly electricity consumption and a substantial peak-to-average ratio reduction of up to 44.86%. Additionally, the application of the continuous wavelet neural network algorithm proves to be proficient in accurately forecasting home device operations. When the time was 17.5 hours, the actual energy consumption of the water heater was 7.12 kWh, which was 0.78 kWh more than the predicted energy consumption. Utilizing scheduling algorithms, as opposed to not employing them, led to notable reductions in electricity expenses, demonstrating a cost savings rate of 15.32% on the third day. Additionally, it facilitated decreased usage of the power grid. When the time was 18 o'clock, the power consumption of the grid using scheduling algorithms was 0.24 kWh, which was 0.25 kWh lower than that without scheduling algorithms. Following the implementation of the scheduling algorithm, minimal deviation in device startup times was observed during periods of high comfort, with offsets ranging from 0 hours. The research methods employed demonstrate efficacy in orchestrating energy distribution among households and indoor spaces, thereby striking a harmonious balance between energy conservation and user comfort.

A budgeting resource allocation model for capacity expansion

The reconfiguration of the global supply chain has created opportunities for newly industrialized economies (NIEs) to strengthen and expand their production capacities. We develop a dynamic modeling framework to study a firm’s resource allocation decisions while building capacity for a portfolio of production lines over multiple decision periods. Our model adds to the literature on versions of this problem in which distributions of market demands are incorporated into the investment-return function, and where no partial order fulfillment is possible. We consider two specific cases: one involving exogenously defined demand distributions, and one where the distributions of the parameters of the demand distribution are updated based on Bayesian inference. For each case, we first identify optimal capacity levels as a function of unit investment cost, holding cost, and the demand distribution of each production line. We then characterize the optimal path to reach these levels under different market conditions by deriving closed-form optimal resource allocation policies.

Synthetic wind speed time series generation by dynamic factor model

The global transition to renewable energy is driven by the fight against climate change. Wind power plays a crucial role in reducing dependence on fossil fuels and greenhouse gas emissions. Therefore, addressing uncertainties in wind speed variations requires innovative solutions. This study proposes a Bayesian-based approach using a Dynamic Factor Model to generate synthetic monthly average wind speed series. The Dynamic Factor Model framework captures temporal and spatial correlations, improving wind resource representation in operational planning models. The model's autoregressive configuration with common factors, prior distributions, and Bayesian inference techniques enhances predictive capabilities. Validation exercises confirm the model's reliability, accurately capturing seasonal oscillations and spatial correlations across eight wind farms. The study highlights the usefulness of the Dynamic Factor Model in evaluating wind projects and optimizing energy generation strategies, effectively mitigating wind uncertainty, and facilitating renewable energy integration in Brazil's power mix.

Learning dynamic graph representations through timespan view contrasts

The rich information underlying graphs has inspired further investigation of unsupervised graph representation. Existing studies mainly depend on node features and topological properties within static graphs to create self-supervised signals, neglecting the temporal components carried by real-world graph data, such as timestamps of edges. To overcome this limitation, this paper explores how to model temporal evolution on dynamic graphs elegantly. Specifically, we introduce a new inductive bias, namely temporal translation invariance, which illustrates the tendency of the identical node to keep similar labels across different timespans. Based on this assumption, we develop a dynamic graph representation framework CLDG that encourages the node to maintain locally consistent temporal translation invariance through contrastive learning on different timespans. Except for standard CLDG which only considers explicit topological links, our further proposed CLDG＋＋additionally employs graph diffusion to uncover global contextual correlations between nodes, and designs a multi-scale contrastive learning objective composed of local-local, local–global, and global-global contrasts to enhance representation capabilities. Interestingly, by measuring the consistency between different timespans to shape anomaly indicators, CLDG and CLDG＋＋are seamlessly integrated with the task of spotting anomalies on dynamic graphs, which has broad applications in many high-impact domains, such as finance, cybersecurity, and healthcare. Experiments demonstrate that CLDG and CLDG＋＋both exhibit desirable performance in downstream tasks including node classification and dynamic graph anomaly detection. Moreover, CLDG significantly reduces time and space complexity by implicitly exploiting temporal cues instead of complicated sequence models. The code and data are available at https://github.com/yimingxu24/CLDG.

A minimal dynamical model of Intonation: Tone contrast, alignment, and scaling of American English pitch accents as emergent properties

The pitch accent system of Mainstream American English (MAE) is one of the most well-studied phenomena within the Autosegmental-Metrical (AM) approach to intonation. In this work we present an explicit model grounded in dynamical theory that predicts both qualitative phonological and quantitative phonetic generalizations about the MAE system. While the traditional AM account separates a phonological model of the structure of the accents from the F0 algorithm that interprets the phonological specification, we propose a unified dynamical model that encompasses both. The proposed model is introduced incrementally, one dynamical term at a time, to arrive at the minimal model needed to account for observed empirical generalizations, avoiding unnecessary complexity. The quantitative and qualitative properties of the MAE system that inform the dynamical model are based on an analysis of a large database of productions of the four most well-studied pitch accents of American English: three rising accents (H*, L+H*, L*+H) and a low-falling accent (L*). The dynamic model highlights the importance of velocity-based measures of F0, not typically invoked in intonational research, as key to understanding F0 differences among pitch accent categories. Although the focus of this work is on the MAE pitch accent system, suggestions are made for how the unified phonetic-phonological dynamical framework presented can be further developed to account for other pitch-based phenomena in a variety of languages.

Dynamical quantum state tomography with time-dependent channels

Abstract In this paper, we establish a dynamical quantum state tomography framework. Under this framework, it is feasible to obtain complete knowledge of any unknown state of a $d$-level system via only one operator of a special type of positive operator-valued measure (POVM) in dimension $d$. We define a new channel, referred to as the time-dependent average channel. Utilizing this channel, we show that we can acquire a collection of projective operators that is informationally complete (IC) and thus obtain the corresponding informationally complete POVMs (IC-POVMs). Zauner conjectured that for any dimension $d$ there exists a fiducial vector, such that all remaining $d^{2}-1$ elements of the desired symmetric informationally complete POVM (SIC-POVM) can be obtained by acting on said vector with unitary matrices representing elements of the Weyl-Heisenberg group. We show that under certain condition, it is possible to obtain infinite families of projective operators that are IC, and obtain infinite families of corresponding IC-POVMs; otherwise, Zauner's conjecture is incorrect. We also show how to simulate a SIC-POVM on any unknown quantum state by using the time-dependent average channel.

Historical Territorialization of Mining Companies in The State of Bahia

Purpose: This article proposes to develop a territorial, historical, dynamic, open and plural analytical framework, referring to mining companies operating in the State of Bahia.   Theoretical Framework: Through socioeconomic, ecological and historical analyses, we clarify multiple relationships between past and contemporary facts, based on the theoretical and conceptual perspectives of the cartographic exercise of New Social Cartography, developed by professor Alfredo Wagner.   Method: The methodological instrument is New Social Cartography, through which we seek to highlight companies, agents of mineral exploration, highlighting phenomena inherent to their activities. Using methods that involve the leading roles of communities in the construction of knowledge.   Results and conclusions: We observed that in Bahia, as, generally speaking, in Brazil, sophisticated models of mining colonization were established, with embryonic approvals from the Kandir Law, one of the legal provisions that must be urgently questioned and repudiated, in the name of freedom and lives.   Research implications: Exposure and elucidation of global policies involving mining processes, presenting a worrying dynamism, with major ruptures and structural transformations (economic, political, cultural and mental).   Originality/value: we contrast political categories and reified legal norms, demystifying them, presenting immeasurable losses inherited by human populations, their sociocultural frameworks, living organisms and the abiotic environments of ecosystems.

A Blended Teaching Model of College English Based on Deep Learning

Blending traditional face-to-face instruction with online learning components, this model aims to enhance student engagement and improve learning outcomes. This paper introduces a novel blended teaching model for college English courses, integrating Conditional Random Field (CRF) techniques with deep learning frameworks. Blended learning, combining traditional face-to-face instruction with online learning components, has gained traction in higher education for its potential to enhance student engagement and learning outcomes. CRF blended teaching integrates the principles of conditional random fields, a powerful probabilistic graphical model, with traditional teaching methodologies to enhance learning outcomes. Unlike conventional teaching approaches that often rely on linear or static instructional methods, CRF blended teaching embraces a more dynamic and adaptive framework. By leveraging CRF, which models the dependencies between different instructional variables, educators can design personalized learning experiences tailored to individual student needs and learning styles. This approach enables the seamless integration of diverse instructional modalities, including face-to-face instruction, online resources, and interactive learning activities. The proposed model CRF algorithms to effectively capture the sequential dependencies inherent in language learning tasks, such as sentence parsing and part-of-speech tagging. By integrating deep learning architectures, including recurrent neural networks (RNNs) and transformers, model with large-scale linguistic corpora to improve accuracy and generalization. The model achieves a 15% increase in students' English proficiency scores and a 25% improvement in comprehension compared to traditional teaching methods, underscoring its effectiveness in enhancing learning outcomes.

Modeling of Wind Power Plants and their Impact on Economic and Environmental Development

Abstract This research addresses the environmental dynamics of wind power plants and their impact on the economy and environment. A system dynamics framework is used as a tool for model development since it accommodates relationships between complex and nonlinear variables affecting the wind power plants and their impact on the economy and environment. The scientific contribution of this research is the creation of scenario modeling that describes the interrelationships of variables and parameters affecting wind power plants and their impact. Total wind energy depends on density, wind speed, blade cross-sectional area, and the efficiency of the Betz limit. The average fulfillment ratio after wind turbine installations is projected to increase from 0.047 to 0.117 due to the increase in wind energy production. Meanwhile, total CO2 emissions are projected to decrease from an average of 67,306.48 tons to 36,830.57 tons due to the use of solid direct air capture.