Complex Dynamics Research Articles

The Gendered Allure of Nang Kwak: From Statuettes to NFTs in Thailand

Nang Kwak, the “Beckoning Lady”, encapsulates the convergence of commercial ambition and spiritual devotion in Thailand. This article explores her role as a popular deity of prosperity and amid the country’s rich genderscape, how her allure is shaped by traditional female tropes of beauty, family devotion, and reassurance. By analyzing her depictions in lore, sacred objects, and contemporary media like NFTs, alongside insights from shopkeepers and amulet collectors, this study examines how her mythical representations as a daughter and worshipped mother intersect with Thai Buddhism. Nang Kwak’s potency works through her charm or sane (เสน่ห์), nuancing conventional notions of power and reflecting complex dynamics of gender, religion, and cultural reproduction.

Dynamics of Soil Carbon Average Content at Different Depths: Insights from a Global Approach to Climate Change Mitigation

ABSTRACT This study provides an in-depth examination of the average values of gravimetric carbon content, measured in grams per kilogram (g/kg), and the organic Carbon (OC) content, quantified in kilograms per square meter (kg/m2), within various soil classifications and depths. Highlighting the relevance of such research, it delves into the intricacies of soil OC dynamics across diverse depth strata and offers a comparative analysis of different soil types, each with distinct carbon sequestration capacities. Utilizing the latest version of the world soil database, the research integrates three interconnected data sets: soil classification, density, and OC. In total, the study includes 51,507 soil profile layers to calculate the average gravimetric OC content in the fine-earth fraction. In parallel, the average OC content in the fine-earth fraction was determined across 6,197 soil profile layers. This comprehensive data is organized into 34 separate soil units, each dissected across four depth categories: 0–30 cm, 0–100 cm, 0–200 cm and 0–2590 cm. The findings from this analysis reveal a consistent pattern: as depth increases, both the gravimetric content and average OC content tend to decrease. However, it is noteworthy that this trend is not universal. Certain soil units demonstrate an opposing behavior, with an increase in average OC content observed at greater depths, contradicting the prevailing trend. This divergence underscores the complexity of soil OC dynamics and the inherent variability across different soil units.

Temporal Analysis of Social Media Trends: A Computational Perspective

The ubiquitous nature of social media systems has redefined the panorama of conversation, with customers producing great amounts of statistics in actual-time. This evaluate paper delves into the temporal evaluation of social media developments from a computational angle, providing a complete exploration of methodologies, challenges, and improvements. By scrutinizing temporal trend detection algorithms, sentiment evaluation through the years, the impact of activities on social media discourse, and the evolving position of system getting to know, this paper ambitions to illuminate the elaborate dynamics of temporal trends on social media systems. Through case studies and discussions on challenges and ethical concerns, the assessment underscores the transformative potential of computational approaches in information the temporal evolution of social media trends. The exploration concludes with the aid of outlining destiny instructions, emphasizing the interdisciplinary nature of this subject and the continuing necessity for studies to navigate the complexities of social media temporal dynamics in the digital age.

Exploring the complexity of multilingual spaces: embracing diverse perspectives of linguistic non-understanding

Abstract In our article, we investigate the complex dynamics of linguistic understandings and mis- or non-understanding within multilingual contexts. Through the lens of sociolinguistic exploration, we navigate the multifaceted landscapes of language use, applying a multi-perspective approach and the triangulation method to explore the depths of linguistic non-understanding (LNU). Drawing on ethnographic data collection and multi-perspective analysis, we highlight the challenges and opportunities inherent in incorporating LNUs into research practice. Embracing a non-positivist stance, we acknowledge the role of being a ‘non-knower’ in our pursuit of understanding. Our methodology section briefly demonstrates the application of the triangulation method for researching multilingualism, while our empirical findings unveil various instances of LNU across everyday language use. We emphasise the importance of researchers’ reflexivity and the recognition of repertoire users as experts in their linguistic practices. Ultimately, our study offers insights into the analytical advantages of adopting an in-depth and multi-perspective approach to research in multilingual environments.

Understanding the Impacts of Arctic Climate Change Through the Lens of Political Ecology

ABSTRACTClimate change is a critical global issue with far‐reaching implications for the environment, society, and economy. Political ecology examines the relationship between political systems, social inequalities, and ecological concerns in relation to climate change. It focuses on how power dynamics, resource allocation, and political decisions influence vulnerability, adaptation, and mitigation efforts, highlighting the intersectionality between politics, ecology, and climate change impacts. Climate change in the Arctic is having profound geopolitical, environmental, and socioeconomic impacts on Indigenous Peoples. However, few, if any, studies have examined these interactions from a political ecology standpoint. Herein, we review and analyze the complex relationships and power dynamics that shape and are shaped by climate change in the Arctic through a political ecology lens, developing an understanding of how political, economic, and social factors interact to drive climate change impacts and responses. We introduce the term Arctic Political Ecology to understand these dynamics. The paper examines the significance of Indigenous knowledge, environmental governance, and Indigenous Peoples' sovereignty in control over productive resources, promoting sustainable practices, and addressing the challenges posed by climate change. We highlight the need for a comprehensive approach that considers the political ecology of climate change in the Arctic to understand the interplay of capitalism, colonialism, and resource exploitation.

Intersecting dimensions: advanced analytical approach to school climate and injury prevention in health policy

BackgroundChild safety in schools is paramount for decision-makers globally, with a focus on ensuring children return home safely. However, the prevalent issue of injuries across educational systems demands a comprehensive investigation into their causes, incorporating interdisciplinary perspectives and social dynamics, to develop effective prevention strategies. The objective of this study is to comprehensively analyze the factors contributing to school-related injuries and examine the impact of school climate on student safety. By employing advanced data analysis techniques, the research aims to develop targeted, effective strategies to enhance child safety in educational settings. This research aims to develop a multidimensional taxonomy to understand child injuries in elementary schools better, enhancing precision in decision-making.MethodsData from 363 Israeli primary schools and 10,855 school injuries attended to by MDA, the principal EMS provider, were analyzed. The study utilized a two-level taxonomy, employing clustering methodology to classify schools into distinct climate "patterns," with each pattern further delineating school injury characteristics into sub-patterns. The chosen method proved effective in revealing nuanced relationships between school injuries and climate characteristics.ResultsAnalysis revealed five school climate clusters, ranging from "good" to "bad," each exhibiting two homogeneous sub-clusters of school injuries. Schools with a "positive" climate witnessed boys predominantly experiencing head injuries during breaks, while girls often sustained limb injuries from playing in corridors. Conversely, within the "negative" climate cluster, subgroups emerged based on injury nature, whether linked to playing or falling from a height.ConclusionThe research delineates a nuanced association between school climate and injury rates, emphasizing the necessity for sophisticated analytical techniques beyond conventional methodologies. Utilizing a diverse dataset from various disciplines, the study highlights the multidimensional aspects of school health. The developed taxonomy reveals the complex dynamics within school environments, advocating for customized health policies to mitigate injuries. Critical findings prompt a reevaluation of established assumptions about the school climate-injury relationship, informing strategic policymaking. For example, it suggests collaboration to enhance school safety through targeted, gender-sensitive interventions and improvements. Integrating different data sources offers a holistic understanding crucial for effective health policy formulation in educational contexts.

Complexity Analysis and Control of Output Competition in a Closed-Loop Supply Chain of Cross-Border E-Commerce Under Different Logistics Modes Considering Chain-to-Chain Information Asymmetry

To investigate the dynamic complexity of chain-to-chain output decisions in a closed-loop supply chain system of cross-border e-commerce (CBEC), this study decomposes the system into four product–market (PM) chains, based on the e-commerce platform’s information-sharing strategy and the manufacturer’s selected logistics mode (direct mail or bonded warehouse). By combining game theory with complex systems theory, discrete dynamic models for output competition among PM chains under four scenarios are constructed. The Nash equilibrium solution and stability conditions of the models are derived according to the principles of nonlinear dynamics. The stability of the model under the four scenarios, as well as the impacts of the initial output level and comprehensive tax rates on the stability and stability control of the system, are analyzed using numerical simulation methods. Our findings suggest that maintaining system stability requires controlling the initial output levels, the output adjustment speeds, and tariff rates to remain within specific thresholds. When these thresholds are exceeded, the entropy value of the model increases, and the system outputs decisions to enter a chaotic or uncontrollable state via period-doubling bifurcations. When the output adjustment speed of the four PM chains is high, the direct-mail logistics mode exhibits greater stability. Furthermore, under increased tariff rates for CBEC, the bonded warehouse mode has a stronger ability to maintain stability in system output decisions. Conversely, when the general import tax rate increases, the direct-mail mode demonstrates better stability. Regardless of the logistics mode, the information-sharing strategy can enhance the stability of system output decisions, while increased e-commerce platform commission rates tend to reduce stability. Interestingly, the use of a non-information-sharing strategy and the direct-mail logistics mode may be more conducive to increasing the profit levels of overseas manufacturers. Finally, the delayed feedback control method can effectively reduce the entropy value, suppress chaotic phenomena in the system, and restore stability to output decisions from a fluctuating state.

An AI-Driven Particle Filter Technology for Battery System State Estimation and RUL Prediction

The increasing demand for reliable and safe Lithium-ion (Li-ion) batteries requires more accurate estimation of state of health (SOH) and remaining useful life (RUL) prediction. However, the inherent complexity and non-linear dynamics of Li-ion batteries present specific challenges to traditional methods of SOH modeling. Although particle filter (PF) techniques can handle nonlinear dynamics, they still face challenges, including particle degeneracy and loss of diversity, that reduce their ability to effectively model the nonlinear degradation mechanisms of batteries. To tackle these limitations, this paper presents a novel artificial intelligence-driven PF (AI-PF) technology for battery health modeling and prognosis. The main contributions of the AI-PF technique are as follows: (1) A novel dynamic sample degeneracy detection method is proposed to provide real-time assessment of particle weights so as to promptly identify degeneracy and improve computational efficiency. (2) An adaptive crossover and mutation strategy is proposed to reallocate low-weight particles and maintain particle diversity to improve modeling and RUL forecasting accuracy. The effectiveness of the AI-PF framework is validated through systematic evaluations carried out using benchmark models and well-recognized battery datasets.

Exploration of the Spatiotemporal Characteristics and Driving Mechanisms of Vegetation Greenness Changes in Laos

In the context of climate change, vegetation changes in Laos have attracted widespread attention, especially the profound impact of its greenness changes on ecosystems, water cycles, and climate feedback. However, our understanding of the driving factors of vegetation greenness changes in different latitudes is still limited. This study utilized EVI and climate factor data from 2001 to 2023, employing trend analysis, correlation analysis, and machine learning methods to investigate the spatiotemporal patterns of vegetation greenness changes across Laos and their responses to climate factors. Results revealed an overall increasing trend in vegetation greenness, with 75% of the area exhibiting annual increases, primarily in northern, central, and parts of the southern regions. Conversely, 24.8% of the area experienced declines, concentrated near Vientiane and certain southern regions. Seasonal trends during the wet season largely aligned with annual patterns, although reduced rainfall negatively impacted some areas. The dry season exhibited the most pronounced changes, with 70% of the area showing increased greenness, especially in northern and central regions, despite localized rainfall constraints. Minimum temperature (TMMN) emerged as the most influential factor, with importance values of 0.42 for annual changes and 0.37 for dry season changes, while precipitation impacts varied across space and time. High temperatures affected vegetation more significantly in low-latitude regions, whereas high-latitude areas relied on changes in DSR. This significant finding underscores the differential impact of climate factors on vegetation greenness across latitudes, which is crucial for understanding the complex dynamics of tropical inland ecosystems under climate change and for developing targeted conservation and adaptation strategies.

An Effective Method for Generating Isolated Attosecond Pulses from a Solid Crystal Film

Solids subjected to strong-field laser excitation can produce high harmonics, making high-order harmonic generation (HHG) one of the most effective methods for creating ultrafast coherent light sources, such as isolated attosecond pulses (IAPs). While extensive research has been conducted on generating IAPs through HHG in gaseous media, studies focusing on solid media are relatively limited. In crystals, the presence of numerous ionization and recombination sites, combined with high density and periodic structure, results in more complex interference dynamics. This complexity paves the way for unique applications in generating IAPs. Using an argon (Ar) crystal as a specific example, we have proposed and theoretically demonstrated an innovative approach for generating IAPs from a solid crystal film using a multi-cycle conventional driving laser pulse.

A Study on the Relationship between Family Shareholding and Company’s Operating Performance - Evidences from a Small Open Economy

Our study is to investigate how the ownership structure and financial aspects of family-controlled firms influence their operating performance. The interests of major shareholders are aligned, this could positively impact the company's operating performance. Conversely, dominant shareholders might entrench themselves in a way that doesn't necessarily benefit the operating performance. The presence of a second largest shareholder acts as a check and balance on the first largest shareholder. We reveal an inverted U-shaped relationship between the shareholding ratio of foreign institutional investors (SRFII) and operating performance. It suggests that as SRFII increases, operating performance initially improves but might decline after reaching a certain threshold. This finding highlights the importance of maintaining balance in SRFII to ensure alignment of interests between company leadership and the company's overall goals. Excessive foreign ownership could potentially lead to divergent interests that may not be aligned with the company's best interests. We also find that higher equity pledge ratio among key leadership roles might adversely affect operating performance. Pledged shares could restrict decision-making flexibility and potentially lead to conflicts of interest. These findings shed light on the complex dynamics within family-controlled firms, particularly how ownership structure and financial arrangements could influence operational outcomes.

Hybrid Crow Search Algorithm–LSTM System for Enhanced Stock Price Forecasting

This study presents a hybrid crow search algorithm–long short-term memory (CSLSTM) system for forecasting stock prices. This system allows investors to effectively avoid risks and enhance profits by predicting the closing price the following day. This method utilizes a stacking ensemble of long short-term memory (LSTM) networks, with the crow search algorithm (CSA) optimizing the weights assigned to the predictions from multiple LSTM models. To improve the overall accuracy, this system leverages three distinct datasets: technical analysis indicators; price fluctuation limits; and variation mode decomposition (VMD) subsignal sequences. The predictions for the three reference-data types are more comprehensive than single-model or single-data-type approaches. The prediction accuracies of the recurrent neural network, gate recurrent unit, and the LSTM network for five stocks were compared. The proposed CSLSTM system outperforms the other standalone models. Furthermore, we conducted backtesting to demonstrate that the prediction information from our model could generate profit in the stock market, enabling users to benefit from complex stock-market dynamics. The stock prices in this study are expressed in New Taiwan Dollars (TWD), the official currency of Taiwan.

Agent-based modelling of Mycobacterium tuberculosis transmission: a systematic review

BackgroundTraditional epidemiological models tend to oversimplify the transmission dynamics of Mycobacterium tuberculosis (M.tb) to replicate observed tuberculosis (TB) epidemic patterns. This has led to growing interest in advanced methodologies like agent-based modelling (ABM), which can more accurately represent the complex heterogeneity of TB transmission.ObjectivesTo better understand the use of agent-based models (ABMs) in this context, we conducted a systematic review with two main objectives: (1) to examine how ABMs have been employed to model the intricate heterogeneity of M.tb transmission, and (2) to identify the challenges and opportunities associated with implementing ABMs for M.tb.Search methodsWe conducted a systematic search following PRISMA guidelines across four databases (MEDLINE, EMBASE, Global Health, and Scopus), limiting our review to peer-reviewed articles published in English up to December 2022. Data were extracted by two investigators using a standardized extraction tool. Prospero registration: CRD42022380580.Selection criteriaOur review included peer-reviewed articles in English that implement agent-based, individual-based, or microsimulation models of M.tb transmission. Models focusing solely on in-vitro or within-host dynamics were excluded. Data extraction targeted the methodological, epidemiological, and computational characteristics of ABMs used for TB transmission. A risk of bias assessment was not conducted as the review synthesized modelling studies without pooling epidemiological data.ResultsOur search initially identified 5,077 studies, from which we ultimately included 26 in our final review after exclusions. These studies varied in population settings, time horizons, and model complexity. While many incorporated population heterogeneity and household structures, some lacked essential features like spatial structures or economic evaluations. Only eight studies provided publicly accessible code, highlighting the need for improved transparency.Authors’ conclusionsABMs are a versatile approach for representing complex disease dynamics, particularly in cases like TB, where they address heterogeneous mixing and household transmission often overlooked by traditional models. However, their advanced capabilities come with challenges, including those arising from their stochastic nature, such as parameter tuning and high computational expense. To improve transparency and reproducibility, open-source code sharing, and standardised reporting are recommended to enhance ABM reliability in studying epidemiologically complex diseases like TB.

Inferring in vivo murine cerebrospinal fluid flow using artificial intelligence velocimetry with moving boundaries and uncertainty quantification.

Cerebrospinal fluid (CSF) flow is crucial for clearing metabolic waste from the brain, a process whose dysregulation is linked to neurodegenerative diseases like Alzheimer's. Traditional approaches like particle tracking velocimetry (PTV) are limited by their reliance on single-plane two-dimensional measurements, which fail to capture the complex dynamics of CSF flow fully. To overcome these limitations, we employ artificial intelligence velocimetry (AIV) to reconstruct three-dimensional velocities, infer pressure and wall shear stress and quantify flow rates. Given the experimental nature of the data and inherent variability in biological systems, robust uncertainty quantification (UQ) is essential. Towards this end, we have modified the baseline AIV architecture to address aleatoric uncertainty caused by noisy experimental data, enhancing our measurement refinement capabilities. We also implement UQ for the model and epistemic uncertainties arising from the governing equations and network representation. Towards this end, we test multiple governing laws, representation models and initializations. Our approach not only advances the accuracy of CSF flow quantification but also can be adapted to other applications that use physics-informed machine learning to reconstruct fields from experimental data, providing a versatile tool for inverse problems.

Exploring Factors Influencing Speeding on Rural Roads: A Multivariable Approach

Speeding is one of the main contributing factors to road crashes and their severity; therefore, this study aims to investigate the complex dynamics of speeding and uses a multivariable analysis framework to explore the diverse factors contributing to exceeding vehicle speeds on rural roads. The analysis encompasses diverse measured variables from Croatia’s secondary road network, including time of day and supplementary data such as average summer daily traffic, roadside characteristics, and settlement location. Measuring locations had varying speed limits ranging from 50 km/h to 90 km/h, with traffic volumes from very low to very high. In this study, modeling of influencing factors on speeding was carried out using conventional and more advanced methods with speeding as a binary dependent variable. Although all models showed accuracy above 74%, their sensitivity (predicting positive cases) was greater than specificity (predicting negative cases). The most significant factors across the models included the speed limit, distance to the nearest intersection, roadway width, and traffic load. The findings highlight the relationship between the variables and speeding cases, providing valuable insights for policymakers and law enforcement in developing measures to improve road safety by determining locations where speeding is expected and planning further measures to reduce the frequency of speeding vehicles.

Exact analytic technique for designing controller architecture in multi-axis active magnetic bearings with rotor eccentricities

Multi-coil active magnetic bearing systems require an efficient controller design to maintain stability, maximize performance, and improve flexibility in high-speed transport systems, particularly those used in electric cars, military, and aerospace. By employing Fourier-based frameworks rather than conventional Laplace-based methods, the work reported here simplifies the AMBs controllers, offering more flexibility to complex dynamics and adaptability to non-linearity. For computational and mathematical brevity, the entire formulation has been linearized around a specific range of uncertainty in this work. The magnetic field pattern with rotor eccentricities is characterized by using a subdomain technique and a perturbation methodology. Complex magnetic field formulations are obtained by the estimation of zeroth and first-order formulae in polar coordinates. For every coil, predictive model-based interpolation functions are constructed and optimal controller settings for stability are found by eigenvalue evaluation. The study enhances accuracy and effectiveness by validating these analytical methods against numerical results, offering deeper insights into rotor dynamics and controller adjustments.

From Ratings to Results: How Feedback Orientation Bridges Rater Behavior and Job Performance

This study investigates the effects of raters’ effective performance management behaviors on ratee job performance, with feedback orientation mediating this relationship. Grounded in social exchange theory, this dyadic study collected data from 301 rater-ratee dyads in the telecom sector Islamabad using a purposive sample and time-lag design. Self-administered questionnaires were used to gather data from both raters and ratees at two separate time points (Time 1 and Time 2). The data were analyzed using MPlus software, revealing that effective rater performance management behaviors positively impact ratee job performance. Feedback orientation emerged as significant mediators highlighting the importance of feedback processes in enhancing ratee performance. This study contributes to the existing literature by elucidating the complex dynamics of rater performance management behaviors, and feedback processes, providing valuable insights for organizations seeking to develop targeted interventions and improve rateeperformance. The findings suggest that organizations should prioritize training rater to effectively manage performance and foster open feedback cultures.

A Hybrid Approach to Mountain Torrent-Induced Debris Flow Prediction Combining Experiments and Gradient Boosting Regression

Debris flows are highly unpredictable and destructive natural hazards that present significant risks to both human life and infrastructure. Despite advances in machine learning techniques, current predictive models often fall short due to the insufficient and low-quality historical data available for training. In this study, we introduce a hybrid approach that combines physical model experiments with a gradient boosting regression model to enhance the accuracy and reliability of debris flow predictions. By integrating experimental data that closely simulate real-world flow conditions, the gradient boosting regression model is trained on a more robust foundation, enabling it to capture the complex dynamics of debris flows under various conditions. Selecting slide mass, slope length, yield stress, and slope angle as explanatory variables, we focus on quantify two critical debris flow parameters—frontal thickness and velocity—at indicated locations within the flow. The model demonstrates strong predictive performance in forecasting these key parameters, achieving coefficients of determination of 0.938 for slide thickness and 0.934 for slide velocity. This hybrid approach not only simplifies the prediction process but also significantly improves its precision, offering a valuable tool for real-time risk assessment and mitigation strategies in debris flow-prone regions.

Single inertial neuron with forced bipolar pulse: chaotic dynamics, circuit implementation, and color image encryption

Abstract The bipolar pulse current can effectively mimic the external time-varying stimulus of neurons, and its effect of neuronal dynamics has rarely been reported. To this end, this paper reports the effects of bipolar pulses on a two-dimensional single inertial neuron model, showcasing the chaotic dynamics of hidden attractors and coexisting symmetric attractors, which is of significant importance for understanding the complex behaviors of neuron dynamics under time-varying external stimuli and its application. Firstly, the mathematical model of the single intertial neuron model with forced bipolar pulse is presented, and then the equilibrium states behaving as unstable saddle point (USP), stable node-focus (SNF), and stable node point (SNP) are analyzed. Additionally, by using multiple dynamical methods including bifurcation plots, basins of attraction, and phase plots, complex dynamics of interesting bifurcation behaviors and coexisting attractors are revealed, which are induced by the forced bipolar pulse current as well as initial values, both. In addition, such effets are well valideted via a simple multiplerless electronic neuron circuit. The implementation circuit of presented model is constructed on the analog level and executed using PSIM circuit platform. The measurement results verified the double-scroll chaotic attractors and the coexisting period/chaos behaviors. Finally, the chaotic sequences of the model are applied to color image encryption for the benefit of requirements on modern security field. The encryption effectiveness is demonstrated through various evaluation indexes, including histogram analysis, information entropy, correlation coefficient, plaintext sensitivity, and resistance to noise attacks.

Dependence of metrics and noise intensity on escape dynamics: mean first exit time and escape probability

Abstract This paper focuses on analyzing two deterministic quantities of the dynamics of stochastic systems, mean first exit time and escape probability. Through a detailed exploration of the escape phenomenon, we have successfully fitted the complex dynamics of the mean first exit time as a function of interval length (area size) and noise intensity, respectively. We have found that the mean first exit time tends to increase monotonically with the interval length (area size). Meanwhile, we also accurately captured the dynamical behavior of the minimum escape probability under various parameter cases. The relationship between them displays a range of patterns when we observed different intervals. Our research highlights the importance of noise effects when considering escape processes, especially in the context of interaction of interval length (area size) and noise intensity. A concrete error analysis is conducted to ensure the accuracy and generalization ability of the model’s predictions.