Dynamic Approach Research Articles

Entropy as a Window Into Behavioral Phase Transitions: Unveiling Contextual Dynamics in Affordance-Based Reaching.

Prior work has demonstrated the presence of hysteresis effects in the control of affordance-guided behavior, in that behavioral transitions around a critical action boundary vary with directions of change in said action boundary. To date, research on this topic has overlooked the influence of the global context on these phenomena. We employ an affordance-based reaching task, whereby participants were asked to move a target to a goal by passing through one of two apertures (size variable or size constant). It was found that the direction of change in the size of the variable aperture influenced the point of behavioral transitions, and this effect interacted with the location of a given goal. In addition, we considered fluctuations in the entropy of participants' reach trajectories as a window into the nature of the behavioral phase transitions. Differences in the structure of entropy were found depending on the direction of change in the size variable aperture. These results are discussed in light of a dynamical systems approach, and recommendations for future work are made.

Climate change scenario simulations for urban flood resilience with system dynamics approach: a case study of smart city Shanghai in Yangtze River Delta region

With the rapid advancement of technology, cities are increasingly integrating smart designs to enhance their urban resilience, particularly against natural disasters such as floods. This study employs an integrated approach to simulate the social, economic, and environmental conditions under various climate change scenarios to strengthen urban flood resilience in smart cities. The CMIP5 BCC-CSM 1-1 model, using the RCP4.5 scenario, is utilized to project future climate changes. Shanghai, a highly developed smart city in the Yangtze River Delta, was selected as the case study. Five scenarios are examined through a system dynamics model spanning from 2015 to 2030, encompassing current continuation, economic priority, social priority, natural priority, and coordinated development scenarios. The model is built upon a conceptual framework known as PSR-SENCE (Pressure-State-Response model and Social-Economic-Natural Complex Ecosystem theory). The findings reveal a consistent improvement in resilience within the response dimension facilitated by modern technology. Notably, the coordinated development scenario emerges as the most effective in bolstering urban flood resilience, surpassing scenarios prioritizing economic, social, or natural development individually. Smart cities like Shanghai must prioritize technological innovation alongside enhancing organizational governance to address these challenges effectively.

Nexus Among Administrative Environmental Innovation and Firm Financial Performance

This paper explores the effect of administrative environmental innovation (AEI) on firm financial performance (FFP) by considering the dynamic capability theory and resource-based view. Specifically, this study tests the linkages between administrative environmental innovation (AEI) and firm financial performance (FFP)by using worldwide data from 685 companies from 42 countries for the period 2004 to 2020. In the final sample, we have 4368 year-annual observations. In this study, AEI is segregated into internal AEI and external AEI. The findings of this study show that Internal AEI and external AEI both enhance FFP under a dynamic capability approach. In this way, this study provides some managerial implications such as the implementation of AEI helps in green innovation strategy that can raise firm profitability in the long run period.

The Future of Performance Management: Shifting from Annual Reviews to Continuous Feedback

Abstract: The landscape of performance management is undergoing a significant transformation, moving away from the traditional model of annual reviews toward a more dynamic and continuous feedback approach. This shift is driven by the need to adapt to the rapidly changing work environment, where agility, employee engagement, and real-time performance improvement are becoming increasingly critical. This chapter explores the evolution of performance management, examining the limitations of annual reviews and the growing demand for more frequent, constructive feedback mechanisms. Continuous feedback not only fosters a culture of ongoing development but also aligns employee objectives with organizational goals in realtime, enhancing overall productivity and job satisfaction. The discussion highlights the technological advancements, such as performance management software and AI-driven analytics, that are enabling this shift, making it easier for organizations to implement and sustain continuous feedback systems. Additionally, the chapter delves into the challenges and considerations that come with this transition, including the need for managerial training, the potential for feedback overload, and the importance of maintaining a balanced approach that supports both individual growth and team collaboration. Through a combination of theoretical insights and practical case studies, this chapter provides a comprehensive overview of the future of performance management, offering actionable strategies for organizations looking to foster a more responsive and employee-centric work culture. The move toward continuous feedback represents not just a trend but a fundamental rethinking of how performance is managed, with the potential to significantly enhance both individual and organizational success in the modern workplace

Effect of domestic material production on environmental sustainability in EU countries under changing financial development: a dynamic panel threshold regression approach

Effect of domestic material production on environmental sustainability in EU countries under changing financial development: a dynamic panel threshold regression approach

Evaluating Quinolines: Molecular Dynamics Approach to Assess Their Potential as Acetylcholinesterase Inhibitors for Alzheimer's Disease.

Evaluating Quinolines: Molecular Dynamics Approach to Assess Their Potential as Acetylcholinesterase Inhibitors for Alzheimer's Disease.

Dynamic Approach to Update Utility and Choice by Emerging Technologies to Reduce Risk in Urban Road Transportation Systems

Dynamic Approach to Update Utility and Choice by Emerging Technologies to Reduce Risk in Urban Road Transportation Systems

Dynamic decoupling and compensation based on the inverted network technique and experiment research for multi-axis hydraulic road simulator

This paper presents a dynamic decoupling and compensation approach to eliminate channel interaction for a multi-axis hydraulic road simulator. Misalignment of the concentrated moving mass centroid on the entire test bench with the control point and inconsistencies in actuator dynamics leads to dynamic coupling, which deteriorates the motion performance of the system. To solve the problem, an inverted decoupling and compensation network based on an identification model is proposed. Firstly, the system is modeled using the recursive extended least square (RELS) identification method. In the feedforward compensation modeling process, the steady-state inverse is obtained using zero-magnitude error technology control (ZMETC). In addition, a finite impulse response (FIR) filter with an adaptive algorithm is employed to reduce the adverse impact of the modeling error. The proposed decoupling strategy is validated by conducting experiments using a multi-axis hydraulic road simulator. The experimental results indicate that the designed compensator can effectively decrease the cross-coupling of multi-input and multi-output (MIMO) systems. The suggested approach is also applicable to applications that necessitate the elimination of interactions between different control variables.

Market resilience in turbulent times: a proactive approach to predicting stock market responses during geopolitical tensions

PurposeThe Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.Design/methodology/approachHybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.FindingsOur study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.Originality/valueTo the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

International youth literature in the Chinese EFL classroom

Abstract This qualitative case study examines the role of international youth literature in enhancing the English language learning and global competence of Chinese university students. Through engaging in meaningful literacy activities, this study reveals the potential of a critical-global-literacies-based approach to foster cultural sensitivity and global awareness. By investigating how students gained knowledge of cultural diversity and developed a critical understanding of our interconnected world, this paper advocates for a dynamic approach to literature-oriented English language education, thus prompting a paradigm shift in teaching practices.

Path planning of manure-robot cleaners using grid-based reinforcement learning

The use of a robot cleaner for manure removal improves housing conditions for dairy cows in the face of labor shortages. However, current robot cleaners follow programmed fixed routes without considering the dynamic behaviors of cows. This cleaning approach is less efficient and leads to more cow-robot encounters or collisions, thus affecting animal welfare. To address these issues, this paper (1) developed heatmap models for cow locations and defecation behaviors; (2) proposed a dynamic path planning approach for the manure robot cleaner using Grid-based Reinforcement Learning; (3) incorporated cow location information and defecation behavior into the path planning process; (4) compared the performance of the proposed approach with two different cleaning methods: the current fixed programmed cleaning in practice and the ideal path produced by simulated annealing for traveling salesman problem. The simulations mimic the situation in a barn at Dairy Campus of Wageningen Livestock Research located in Leeuwarden (the Netherlands). Obviously, the best performance was achieved when the route was executed without cows present, resulting in no cow-robot collision. However, with cows present, the proposed dynamic path planning strategy achieved a 67.6% reduction in cow-robot encounters while maintaining 85.4% of the cleaning performance compared to the current programmed fixed routes. Compared to the ideal path produced by simulated annealing for traveling salesman problem, the proposed dynamic path planning approach achieved 5% better cleaning performance, at the cost of 25% more cow-robot encounters due to its longer working path. We conclude the proposed grid-based Reinforcement Learning solution for manure robots in barns cleaned most efficient with the least interference with cow traffic.

Knowledge hierarchy-based dynamic multi-objective optimization method for AUV path planning in cooperative search missions

This study focuses on path planning for Autonomous Underwater Vehicles (AUVs) in underwater cooperative search missions. The complexities of the ocean environment, the uncertainty of target movements, and limited communication capabilities render underwater cooperative search missions dynamic multi-objective optimization challenges. Studies focusing on communication-constrained search strategies still lack reliable metrics for assessing underwater communication quality and do not consider communication dead zones. Addressing these deficiencies, this paper introduces a novel communication optimization strategy utilizing packet error rate as a metric for assessing communication efficacy, complemented by a potential field-based method for navigating out of communication dead zones. To tackle the inherently dynamic nature of cooperative search missions for mobile underwater targets, we propose a dynamic multi-objective optimization algorithm that employs a knowledge hierarchy strategy. This method enhances the NSGA-II algorithm's efficiency by extracting effective gene segments from historical Pareto solution set and generating a new initial population through recombination, hierarchy, and prediction. Distinct from other advanced dynamic multi-objective optimization approaches that are limited to theoretical problems, our approach is directly applicable to practical scenarios. The effectiveness and practicality of the proposed method are validated through a series of simulation experiments considering the impact of underwater acoustic communication. These results demonstrate that this research is not only innovative in theory but also holds significant engineering value and practical prospects in real-world applications.

From disorder to order: A dynamic approach to mesophase formation in soft sphere model.

This study explores the dynamics of self-assembly and mesophase formation through molecular dynamics simulations of hexagonal and lamellar systems using a simplified coarse-grained model. We focus on characterizing the order-disorder transitions driven by temperature variations and emphasize the often overlooked disordered regime, which serves as a precursor to periodic mesoscale ordering. Our findings not only underscore the morphological richness of the disordered regime, comparable to that of its periodic counterparts, but also reveal the presence of clustering regimes within isotropic phases, thus corroborating prior experimental and theoretical observations. By employing the dynamic correlation coefficient, this work introduces a novel approach to understanding the fundamental mechanisms of mesophase formation, providing new insights into the complex dynamics of self-assembly.

The evolution of two‐stage production and order equilibrium in a random yield supply chain with demand information updating

AbstractThe mismatch between production, order, and demand seriously affects supply chain performance. However, most research focus on the mismatch between the retailer's order and customer's demand, which ignores the influence of the supplier's random yield on supply chain members' decision‐making. This paper investigates a two‐stage optimization problem within a two‐echelon supply chain, featuring a supplier with random yield and a retailer updating demand information in real‐time. Faced with a long production lead time, the retailer can either place advance orders at the production season's onset (first‐stage advance order) or opt for instant orders at the beginning of the sales season (second‐stage instant order). To ensure timely order fulfillment, the supplier initially employs a cost‐effective regular production mode with random yield during the production season. If yields are insufficient during sales, a pricier emergency production mode with guaranteed output becomes available. Utilizing a dynamic programming approach, we formulate the two‐stage optimization problem to derive optimal production and order decisions. Our analysis uncovers how realized random yield and stochastic market signals influence emergency production and instant order quantities in the second stage. We compare expected profits in scenarios with perfect and imperfect market signals, probing the members' preferences regarding order strategies. An intriguing finding emerges: as instant wholesale prices rising, the supplier's preferred order strategy diverges from the retailer. By strategic adjustments to the instant wholesale price, we demonstrate the potential for unanimous agreement on preferred order strategies among supply chain members — a quality enhancing the chain's flexibility and performance. Moreover, we extend the model to hybrid order strategies and identify conditions for unanimous preference among the three strategies. To bolster our theoretical findings, we provide numerical examples, lending practical support to our study.

Pore-to-Core Upscaling of Two-Phase Flow in Mixed-Wet Porous Media: Part II-A Dynamic Pore-Network Modeling Approach

Pore-to-Core Upscaling of Two-Phase Flow in Mixed-Wet Porous Media: Part II-A Dynamic Pore-Network Modeling Approach

A dynamic and critical approach to belonging as a dimension of occupation

ABSTRACT Doing, being, becoming, and belonging have been theorized as dimensions of occupation, yet belonging has received comparatively less attention from occupational scientists. The occupation-based literature tends to equate belonging with connectedness and to take a sedentarist approach in which (transnational) mobilities are considered as a challenge for occupational engagement and belonging. This paper critically examines belonging as developed through occupations and considers its constructed, dynamic, and multifaceted nature in the context of migration. Our theoretical framework is informed by transnational approaches, the mobilities paradigm, and transculturality. We draw from a cross-national comparative ethnography including 86 interviews with recent Vietnamese migrants in Vancouver, Canada, and Paris, France, as well as returning migrants in Vietnam. Our findings address participants’ belonging with regards to 1) routines, social ties, and familiarity; 2) mobile and immobile occupations; and 3) (post-)colonial politics of belonging. This study deepens understandings of the dynamic nature of occupation and belonging as well as how both are shaped by and can reproduce power dynamics. First, we contend that occupation and belonging are inherently situated, yet are not necessarily bounded to a single location. Belonging develops through routine occupations involving familiar people, objects, and amenities, which can themselves be mobile or accessible in multiple settings. Second, we emphasize how some occupations can become benchmarks for belonging, particularly in Western immigrant-receiving countries influenced by assimilationist and (post-)colonial dynamics. Lastly, we underscore individuals’ agency in employing occupations in ways to negotiate and claim belonging to their receiving and/or sending countries.

Remaking urban village through culture: the politics of urban aesthetics in Shenzhen, China

ABSTRACT This paper delves into the evolving governance dynamics of urban villages in China through a case study of the heritage-rich Nantou urban village in Shenzhen. Drawing on the Rancierian politics of aesthetics, the study examines the government’s repositioning of urban villages as cultural and aesthetic assets. The research highlights the state’s emerging approach to urban village redevelopment which combines aesthetics regime and cultural governance. The intricate interplay between consensus construction and dissensus destruction reveals the fluid and porous boundaries between policing and politicization. The findings contribute to a more dynamic approach to analyzing urban village governance in China, expanding the frameworks for understanding the diverse possibilities inherent in urban redevelopment.

A Resonant Lyapunov Centre Theorem with an Application to Doubly Periodic Travelling Hydroelastic Waves

We present a Lyapunov centre theorem for an antisymplectically reversible Hamiltonian system exhibiting a nondegenerate 1 : 1 or 1:-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1:-1$$\\end{document} semisimple resonance as a detuning parameter is varied. The system can be finite- or infinite-dimensional (and quasilinear) and have a non-constant symplectic structure. We allow the origin to be a ‘trivial’ eigenvalue arising from a translational symmetry or, in an infinite-dimensional setting, to lie in the continuous spectrum of the linearised Hamiltonian vector field provided a compatibility condition on its range is satisfied. As an application, we show how Kirchgässner’s spatial dynamics approach can be used to construct doubly periodic travelling waves on the surface of a three-dimensional body of water (of finite or infinite depth) beneath a thin ice sheet (‘hydroelastic waves’). The hydrodynamic problem is formulated as a reversible Hamiltonian system in which an arbitrary horizontal spatial direction is the time-like variable, and the infinite-dimensional phase space consists of wave profiles which are periodic (with fixed period) in a second, different horizontal direction. Applying our Lyapunov centre theorem at a point in parameter space associated with a 1 : 1 or 1:-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1:-1$$\\end{document} semisimple resonance yields a periodic solution of the spatial Hamiltonian system corresponding to a doubly periodic hydroelastic wave.

Uncovering naringin’s anticancer mechanisms in glioblastoma via molecular docking and network pharmacology approaches

Naringin, a flavonoid, exhibits diverse therapeutic properties and has been proven to exert cytotoxic effects on cancer cells. Nevertheless, the precise mechanism of naringin maintaining its cytotoxic effect on glioblastoma (GBM) remains unknown. Thus, the current study aimed to establish a plausible cellular mechanism for Naringin’s inhibition of GBM. We employed various system biology techniques to forecast the primary targets, including gene ontology and cluster analysis, KEGG enrichment pathway estimation, molecular docking, MD (molecular dynamic) simulation and MMPBSA analysis. Glioblastoma target sequences were obtained via DisGeNet and Therapeutic Target Prediction, aligned with naringin targets, and analyzed for gene enrichment and ontology. Gene enrichment analysis identified the top ten hub genes. Further, molecular docking was conducted on all identified targets. For molecular dynamics modelling, we selected the two complexes that exhibited the most docking affinity and the two most prominent genes of the hub identified through analysis of the enrichment of genes. The PARP1 and ALB1 signalling pathways were found to be the main regulated routes. Naringin exhibited the highest binding potential of − 12.90 kcal/mol with PARP1 (4ZZZ), followed by ABL1 (2ABL), with naringin showing a − 8.4 kcal/mol binding score, as determined by molecular docking. The molecular dynamic approach and MM-PBSA investigation along with PCA study revealed that the complex of Naringin, with 4ZZZ (PARP1) and, 2ABL (ABL1), are highly stable compared to that of imatinib and talazoparib. Analyses of the signalling pathway suggested that naringin may have anticancer effects against GBM by influencing the protein PARP and ALB1 levels. Cytotoxicity assay was performed on two different glioblastoma cell lines C6 and U87MG cells. Naringin demonstrates a higher cytotoxic potency against U87MG human glioblastoma cells compared to C6 rat glioma cells.

Interval efficiency estimation using relational dynamic DEA approach: Case of Indian banks

Interval efficiency estimation using relational dynamic DEA approach: Case of Indian banks