Dynamic Approach Research Articles

From the Reflex Machine to the Changeable Brain

AbstractThe brain is exceedingly complex. Neuroscientists have used the tried and tested simplification methods of physics, such as mathematical idealization, in order to deal with neural complexity. This paper queries the appropriateness of certain physics-derived strategies within this biological science by presenting a historical case study (the reflex theory) in which scientists fell into the trap of over-simplifying the brain and nervous system. It then considers whether more recent computational and dynamical systems approaches in neuroscience are also at risk of abstracting away from critical features of neural functionality related to the inherent changeability of those systems.

Dynamic edge clustering and task scheduling for edge assisted metaverse system in the field of remote work and collaboration

SummaryThe metaverse, a future digital world for living, working, learning, and interacting, is rapidly gaining significance, particularly in the domain of remote work and collaboration. This emerging digital landscape demands high‐performance, low‐latency, and scalable services to provide an immersive user experience. The current technology used in metaverse systems has some limitations, which emphasize the importance of adopting emerging technologies like Edge Computing (EC). However, as the number of users and data volume increases, it can impact both system performance and scalability of the edge‐assisted metaverse system. Additionally, the uneven distribution of edge servers can cause inconsistencies and result in high latency. To overcome these challenges, this paper proposes a dynamic edge clustering and task scheduling approach for edge‐assisted metaverse systems (DTAM) in the field of remote work and collaboration. The proposed approach addresses the challenges of high user volume and uneven resource distribution by incorporating dynamic clustering and edge server assistance to improve clustering performance. Furthermore, a Prioritized Experience Replay‐based Deep Q‐learning algorithm with state augmentation (PERDQSA) for task scheduling is introduced to improve sample efficiency and performance. The performance of the proposed DTAM is evaluated against existing techniques, and experimental results demonstrate its significant superiority in terms of specific metrics such as bandwidth, task response time, energy efficiency, and latency. The experiments demonstrated that DTAM outperforms Transformation‐based Edge Computing Deep Q‐Learning (TransEC‐DQL) in several key metrics. Specifically, DTAM achieves 28.5% reduction in latency, 13.7% reduction in response time, and 6.4% improvement in bandwidth compared to TransEC‐DQL. These results signify that DTAM can deliver a significantly enhanced user experience in the metaverse, particularly in the context of remote work and collaboration.

Crop Phenology Estimation in Rice Fields Using Sentinel-1 GRD SAR Data and Machine Learning-Aided Particle Filtering Approach

Abstract. Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data.

Dynamic optimization of die-less spinning process through autonomous modeling of time-varying forming law

Die-less spinning is a typical incremental forming technology with flexible processing parameters, including the roller path and loading parameters, whose forming law changes dynamically and easy-to-produce defects of excessive thinning and flange wrinkling. This brings a great challenge to the process design of die-less spinning. In this study, we proposed a novel dynamic optimization approach and system considering the time-varying forming law for the processing parameters in die-less spinning. The idea of proposed approach is that simulating the spinning process step by step through autonomous finite element modeling, real-time extracting and modeling the simulated spinning status at each forming step to capture the time-varying forming law, then gradually optimizing the processing parameters through efficient multi-objective optimization algorithm. By conducting the proposed dynamic optimization approach to the first spinning pass of die-less spinning, series of polynomial response surface models (PRSM) of wall thickness and flange wrinkling at various forming steps were developed to describe the time-varying forming law. On this basis, the roller path and matching loading parameters in each step were gradually optimized through a differential evolution algorithm with the multi-objective of minimizing wall thickness reduction, reducing flange wrinkling and maximizing forming efficiency. The optimized process produced better spun part than the traditional spinning process, presenting the wall thickness reduction ≤5 % and the flange wrinkling ≤2.5 mm. Furthermore, the time-varying influence of processing parameters on the wall thickness and flange wrinkling were elucidated based on the PRSM of spinning status at various forming steps.

A Combined Molecular Dynamics and Hydropathic INTeraction (HINT) Approach to Investigate Protein Flexibility: The PPARγ Case Study

Molecular Dynamics (MD) is a computational technique widely used to evaluate a molecular system’s thermodynamic properties and conformational behavior over time. In particular, the energy analysis of a protein conformation ensemble produced though MD simulations plays a crucial role in explaining the relationship between protein dynamics and its mechanism of action. In this research work, the HINT (Hydropathic INTeractions) LogP-based scoring function was first used to handle MD trajectories and investigate the molecular basis behind the intricate PPARγ mechanism of activation. The Peroxisome Proliferator-Activated Receptor γ (PPARγ) is an emblematic example of a highly flexible protein due to the extended ω-loop delimiting the active site, and it is responsible for the receptor’s ability to bind chemically different compounds. In this work, we focused on the PPARγ complex with Rosiglitazone, a common anti-diabetic compound and analyzed the molecular basis of the flexible ω-loop stabilization effect produced by the Oleic Acid co-binding. The HINT-based analysis of the produced MD trajectories allowed us to account for all of the energetic contributions involved in interconverting between conformational states and describe the intramolecular interactions between the flexible ω-loop and the helix H3 triggered by the allosteric binding mechanism.

When We Are Happy, We Are the Same—Emotions as a Boundary Condition for the Impact of Cultural Differences on Strategic Decisions

AbstractStrategic decision-making research has mainly relied on the values-based approach to culture. However, the dynamic constructivist approach to culture has shown that cultural tendencies may also be altered by contingency factors in the decision-making process itself. We theorize based on the appraisal tendency framework as well as the concept of cultural affordances that emotions, such as happiness, can alter the cultural dispositions of managers from Western and East Asian contexts. To test our hypotheses, we conduct a lab-in-the-field experiment with 187 executives from China and Germany, and measure emotions based on participants’ psychophysiological skin conductance responses. Our results show that happiness moderates and can even reverse initial cultural dispositions in executives’ strategic decision-making behavior. These findings suggest that emotions may be important contingency factors that can alter the initial cultural dispositions of decision makers in the strategy process.

Understanding Unemployment Dynamics and Labor Market Behavior in South Korea: Employing Mccall Dynamic Programming Approach

This paper investigates labor market dynamics from 1990 to 2023 using the McCall dynamic programming search model, offering insights into individual decision-making, economic conditions, and policy implications. Convergence trends in wage predictions underscore the model's reliability, highlighting its utility for predicting wage outcomes over time. The policy implications emphasize the importance of balancing the duration of unemployment and the considerations of the wage level in policy design. In light of the analysis, policymakers and employers should consider the trade-offs between unemployment duration and wage level when designing policies and compensation practices. The insights provide guidance for policymakers, employers, and job seekers in navigating the complexities of the job market.

Spin-charge-lattice couplings and Co doping effect in Ba(Fe1-xCox)2As2: An ab initio molecular dynamics study

Abstract As one of the typical iron-based superconductors, Ba(Fe1-xCox)2As2 exhibits a dome-like evolution of superconducting transition temperature T c with the Co doping (x), which provides an ideal platform to explore the mechanism of unconventional superconductivity. Through ab initio molecular dynamics (AIMD) studies, we find that the spin, charge, and lattice degrees of freedom in Ba(Fe1-xCox)2As2 are intimately coupled with a characteristic frequency around 5.5 THz, which originates from the A1g phonon mode of the As atoms. Compared with the undoped BaFe2As2, the spin dynamics in Ba(Fe1-xCox)2As2 (x = 0.0625, 0.1250, 0.1875) shows enhanced spatial inhomogeneities, though the features of the charge dynamics are rarely modified. Along with the increasing Co doping, the spatial inhomogeneities of Fe spin dynamics display nonmonotonic changes, mean-while the lattice vibrational spectra first show peak broadening and then restore the main peak around 5.5 THz, being both consistent with the nonmonotonic behavior of superconducting T c. Our study provides a molecular dynamics approach to investigate the couplings of spin, charge, and lattice in doped BaFe2As2, which could also be applied to explore the dome-like evolution of superconductivity in other Fe-based superconductors.

Performance evaluation of the global airline industry under the impact of the COVID-19 pandemic: A dynamic network data envelopment analysis approach

The COVID-19 pandemic posed unprecedented challenges to the airline industry, necessitating a focus on maintaining high efficiency for profitability. This study assesses the efficiency of 26 international airlines from 2019 to 2022 using a dynamic network data envelopment analysis (DNDEA) methodology. The model accounts for the dynamic effect between two consecutive periods and incorporates an internal structure to evaluate airline performance across multiple dimensions. It enables the assessment of overall, period-specific, and stage-specific efficiencies. The findings reveal that while overall efficiency is moderately high on average, no airline achieved full efficiency during the pandemic. Efficiency decreased notably from 2019 to 2020, with a partial recovery but not a return to pre-pandemic levels by 2022. Operational performance remains satisfactory and stable, while service and financial performance exhibit lower efficiency, especially among low-cost airlines compared to full-service counterparts. Additionally, the study explores airlines' environmental impact by considering greenhouse gas emissions. Comparative analysis with a dynamic DEA model without internal structure highlights theoretical contributions, and the study offers managerial insights for airline leaders and policymakers.

Innovative decision-making for solar photovoltaic system installation in residential energy hubs: A probabilistic dynamic planning approach

This paper introduces an innovative probabilistic dynamic planning model tailored to optimize photovoltaic (PV) system installation within Residential Energy Hubs (REHs). The REH amalgamates diverse components, including PV systems, Combined Heat and Power (CHP) units, and gas boilers, to holistically fulfill residential thermal and electrical energy demands. The model's primary objective is to achieve an optimal equilibrium between minimizing initial investment costs associated with PV systems and managing ongoing energy expenses within the REH framework, encompassing potential PV energy losses over the planning horizon. To augment the flexibility and benefits of PV systems for residential consumers, the model integrates several pioneering features. Particularly notable is the introduction of a heating energy storage system, which complements the REH's thermal requirements and adeptly reacts to fluctuations in gas and electricity prices, thereby maximizing PV system benefits and ensuring homeowners' cost efficiency. Moreover, the model addresses the inherent uncertainty tied to PV generation by employing a two-point estimation method, offering a more realistic depiction of system performance and potential outcomes. To resolve the two-objective problem, an initial conversion into a single-objective problem via a weighted summation method precedes the application of an optimization algorithm inspired by symbiotic organisms. The paper demonstrates the practical application and efficacy of the proposed model through extensive simulations spanning a 10-year planning horizon. The results underscore the model's effectiveness and versatility in guiding residential consumers to make informed and economically viable decisions regarding PV system installation within the broader spectrum of their energy requirements. This research represents a significant stride towards empowering homeowners with invaluable tools for embracing sustainable energy solutions.

Breaking the plastic habit: Drivers of single-use plastic reduction among Thai university students.

This study investigated the decision-making dynamics for pro-environmental behavior among Thai university students, focusing on reducing the consumption of single-use plastics (SUP). By adopting a dynamic approach to the Theory of Planned Behavior (TPB), the research examined the influence of psychosocial factors, including attitudes, perceived behavioral control, and subjective norms, on SUP reduction intention at different phases of behavior change. Using structural equation modelling, we analyzed quantitative data (n = 317) from the selected universities. The results revealed that attitudes predicted behavioral intentions only among individuals in the contemplation phase of reducing SUP. Attitudes had a small but limited influence on the behavioral intentions of students who had not yet acted. Perceived behavioral control, on the other hand, significantly impacted behavioral intentions across all phases of behavior change, highlighting its importance in SUP reduction. The study also confirmed subjective norms' positive influence on students' behavioral intentions in the pre-contemplation phase. Practical implications suggested segmenting residents based on their behavior change phase so that public policymakers can allocate resources more efficiently and effectively by tailoring campaigns to specific behavior change phases, ultimately promoting sustainable behavior among university students.

Scalar Perturbations in Nonsingular Universes from Interacting Vacuum

Abstract In this paper we examine the stability of scalar perturbations in nonsingular models which emerge from an interacting vacuum component. The analysis developed in this paper relies on two phenomenological choices for the energy exchange between a nonrelativistic fluid and a vacuum component. In both scenarios it can be shown that closed models may furnish nonsingular orbits of physical interest in phase space once a decelerated past era is connected to a graceful exit 
to late-time acceleration. Regarding such configurations as background spacetimes we introduce
scalar perturbations in order to examine the stability of these models in a high energy domain.
We explicitly show that the vacuum perturbation is not an independent variable and diverges as
dynamics approaches the bounce. This feature assigns a rather unstable signature to the dynamics
making the choices for the energy transfer ill defined at least for nonsingular configurations
at the bounce scale.

The Shift to the 5.0 Revolution: The Challenges of Public Education in Costa Rica

Objective: The objective of this qualitative, exploratory, and descriptive study was to understand the causes and effects contributing to a specific phenomenon. Theoretical Framework: The study is contextualized within the realm of virtual education and digital transformation, emphasizing the need for a more active and dynamic approach in state support for students. Method: In-depth interviews and a bibliometric review were employed to identify key factors and comprehend the phenomenon under investigation. Result: It was found that students demand more active and dynamic state support, with measurable objectives, in line with the online teaching model and digital transformation goals. Discussion: It was concluded that the success of virtual education requires an agile, flexible, and dynamic teaching model that aligns with national objectives. Research Implications: This study suggests the need for reforms in virtual education to align with national digital transformation goals. Additionally, it underscores the importance of a more dynamic and measurable approach in state support for students. Originality/Value: The combination of qualitative methods and bibliometric review provides a unique perspective on the challenges and opportunities in virtual education, representing a significant contribution to both academia and educational policies.

Nexus between boardroom independence and firm financial performance: evidence from South Asian emerging market

This study empirically investigates the linkage between boardroom independence and the financial performance of non-financial firms in an emerging market featured by family-controlled businesses and concentrated ownership. The relationship is tested in a sample of 152 non-financial firms listed on the Pakistan Stock Exchange over a period from 2003 to 2018. Firms’ financial performance is measured through return on assets (ROA), return on equity (ROE), market-to-book ratio (MBR), and Tobin’s Q (TQ), while boardroom independence is measured through the proportion of non-executive directors on the corporate board. Using the dynamic GMM approach to address the possibility of endogeneity, it was found that boardroom independence is significantly negatively related to the financial performance of the sample firms. This negative impact is due to the reason of close ties of outside independent directors (non-executive directors) with dominant shareholders and management in personal, financial, and social terms. A significant negative influence of the board size and CEO duality on firms’ financial performance was also observed. The present study will add to the existing literature on corporate governance and firm financial performance using firm-level manually collected data. Further, our findings will also help the policymakers by providing empirical insights for strengthening corporate governance mechanisms in emerging market economies, specifically in the context of Pakistan.

Smart Defense: How Self-Learning AI Can Shield Bangladeshi Medical Records

The digitalization of healthcare records in Bangladesh presents both opportunities and challenges, particularly concerning the security and protection of sensitive patient information. As electronic health records (EHRs) become increasingly prevalent, the threat of cyberattacks targeting medical data escalates, necessitating innovative solutions to fortify the country's healthcare cybersecurity infrastructure. This paper investigates the efficacy of self-learning artificial intelligence (AI) systems in safeguarding Bangladeshi medical records against cyber threats. The traditional methods of securing medical records, such as firewalls and antivirus software, are proving inadequate against the evolving tactics of cybercriminals. Bangladesh faces unique challenges in this regard, including limited resources, lack of cybersecurity awareness among healthcare professionals, technological fragmentation, and an increasingly sophisticated threat landscape. To address these challenges, there is a growing imperative to explore novel approaches that can adapt and evolve in real-time to counter emerging cyber threats. Self-learning AI systems represent a promising frontier in healthcare cybersecurity. By leveraging advanced machine learning algorithms, these systems can analyze vast amounts of data to detect patterns indicative of cyber threats. Unlike static security measures, self-learning AI continuously learns from new information and adjust their defense strategies, accordingly, enabling them to stay ahead of evolving threats. Key functionalities of self-learning AI include anomaly detection, threat prediction, and adaptive defense mechanisms, all of which are essential for safeguarding medical records in Bangladesh's healthcare landscape. The implications of integrating self-learning AI into Bangladesh's healthcare cybersecurity framework are significant. Not only can these technologies enhance the detection and prevention of cyber threats, but they can also alleviate resource constraints and technical challenges faced by healthcare organizations. However, successful implementation requires comprehensive training, adherence to data privacy regulations, and ongoing monitoring to ensure the effectiveness and reliability of AI-driven security measures. The protection of medical records is paramount as Bangladesh continues its digital transformation in healthcare. Self-learning AI offers a dynamic and proactive approach to cybersecurity, empowering healthcare organizations to mitigate risks and preserve patient privacy in an increasingly digitized landscape. Embracing these innovative technologies is crucial for building a resilient healthcare ecosystem that prioritizes data security and patient trust.

A Separation Modeling Method for Morphing QUAV: Analytical Solutions for Constraint Forces Under Deformation

Abstract A morphing quadrotor unmanned aerial vehicle (QUAV) possesses the remarkable ability to alter its shape, enabling it to navigate through gaps smaller than its wingspan. However, these deformations result in changes to the system's center of gravity and moment of inertia, necessitating real-time computation of each state's variations. To address this challenge, we propose a dynamic modeling approach based on the Udwadia−Kalaba (U-K) method. The morphing QUAV is divided into three separate subsystems, with the dynamic modeling for each subsystem conducted independently. Subsequently, the QUAV's deformation states and inherent structure are introduced in the form of constraints, and the constrained forces are derived using the U-K equation. By combining these analytical solutions, the model of the QUAV under continuous deformation is obtained. This approach effectively simplifies the modeling computations caused by changes in the system's center of gravity and moment of inertia during deformation. A control approach is proposed to achieve attitude stabilization and altitude control for the morphing QUAV. Ultimately, the stable motion of the morphing QUAV is validated through numerical simulations.

Molecular Dynamics Simulation and the Regeneration and Diffusion Effects of Waste Engine Oil in Aged Asphalt Binder

In recent years, the potential of waste engine oil (WEO) as a rejuvenator for aged asphalt binders has gained significant attention. Despite this interest, understanding WEO’s regeneration mechanism within aged asphalt binders, particularly its diffusion behavior when mixed with both aged and virgin asphalt binders, remains limited. This study adopts a molecular dynamics approach to constructing models of virgin, aged, and rejuvenated asphalt binders with different WEO contents (3%, 6%, 9%, and 12%). Key properties such as the density, glass transition temperature, cohesive energy density, solubility parameter, viscosity, surface free energy, fractional free volume, and diffusion coefficient are simulated. Additionally, models of rejuvenated asphalt binder are combined with those of aged asphalt binder to investigate mutual diffusion, focusing on the impact of WEO on the relative concentration and binding energy. The findings reveal that WEO notably decreased the density, viscosity, and glass transition temperature of aged asphalt binders. It also improved the molecular binding within the asphalt binder, enhancing crack resistance. Specifically, a 9% WEO content can restore the diffusion coefficient to 93.17% of that found in virgin asphalt binder. Increasing the WEO content facilitates diffusion toward virgin asphalt binders, strengthens molecular attraction, and promotes the blending of virgin and aged asphalt binders.

Image types revisited. A texto-material approach for creating image types

In visual communication research there are already several methodological approaches for creating image types. These approaches work with creating typologies based on the representational features of visuals / image motifs, reducing the complexity of extensive visual material. However, we argue that in everyday visual communication, the meaning of images depends largely on the practices in which they are embedded and not on the motif. This paper’s aim is to propose a methodological design for an image type analysis that considers both the representational and the artifact-related component of images, following a texto-material understanding of images. We illustrate the procedure step by step through an empirical study that explores visuals in couple relationships in Switzerland. The method enables a qualitative analysis of a large number of visuals that combines a) the creation of image types and b) a dynamic approach to in-depth visual analysis. Image types resulting from this procedure are more heterogeneous in motifs compared to previous traditions of image type analysis but allow for considering both the visual and the material dimensions of visuals, e.g., practices in which images are embedded. The approach builds on existing methods and contributes to the advancements of methods within visual communication research.

Does energy transition reduce carbon inequality? A global analysis.

Energy transition from fossil fuels to renewables is instrumental in mitigating climate change. Low-income countries have a higher share of renewable energy in their total energy consumption than rich countries (WDI, 2023). Thus, it is imperative to examine the role of energy transition in affecting relative CO2 emissions between rich and poor sections of the societies across income groups of the countries. In this context, our study contributes by constructing the carbon inequality models with renewable and non-renewable energy consumption as prime explanatory variables separately for 114 countries over a data period 1990-2019. The models are estimated individually for high-middle-low-income countries by controlling for foreign direct investment (FDI), economic growth, and innovations. Starting with preliminary econometric operations, we employ the dynamic simulated panel autoregressive distributed lag approach and Driscoll-Kraay standard error regression for empirical investigation. We find that energy transition reduces carbon inequality globally. Innovation has a negative impact, economic growth has a positive impact on carbon inequality, and FDI has an asymmetric impact based on the income level of the countries. The crucial global policy implications are discussed.

Dynamic trust security approach for edge computing-based mobile IoT devices using artificial intelligence

The Industrial Internet of Things (IIoT) encompasses various applications such as smart cities, healthcare, and agriculture, which rely on a multitude of interconnected devices. These applications demand specific performance requirements, including low latency and sufficient bandwidth. Edge computing has emerged as a promising solution to address the challenges associated with data flow processing. Particularly, in scenarios like smart cities, where a massive number of IIoT devices are deployed, edge computing serves as the primary data processing layer. However, the limited resources of edge computing, compared to cloud computing, pose significant security concerns for IIoT applications. In fact, this paper proposes a novel security system to secure the relation between the IIoT devices and edge computing. Dynamic Trust Security Approach (DTSA) leverages Artificial Intelligence to analyze, evaluate and enhance the security and privacy aspects of the IIoT devices’ interaction with edge computing. DTSA improve the security level of the IIoT system and improve the performance with more than 45% compare to the traditional method.