A novel failure mode and effects analysis model enhanced with systems theory and artificial intelligence for dynamic positioning systems in offshore operations

Objective This pilot study aimed to assess the feasibility and informativeness of a mixed-method approach to investigate how the Self changes during psychotherapy within a Dynamic Systems Theory (DST) framework, addressing theoretical heterogeneity and the limits of conventional pre–post designs. Method Five participants undergoing psychotherapy were scheduled to complete weekly smartphone-based diary assessments over several months (≥30 scheduled prompts). Each assessment included 11 (nomothetic) ad hoc items and 2 person-specific (idiographic) items. Responses to the Narrative Assessment Interview (NAI) were analyzed using conventional qualitative content analysis to derive person-specific items and interpret quantitative trajectories (but were not analyzed as a separate qualitative outcome). Quantitative data were analyzed using time-series methods (i.e., trend analysis and ARIMA modeling). Results The Self exhibited various change patterns (e.g., linear, nonlinear, stationary, and non-stationary), characterized by the influence of past values (i.e., the autoregressive component) and innovations (the moving-average component). Moreover, participants exhibited different change dynamics. Therefore, the conditions required for ergodic generalization were often not met in these data. Conclusion The mixed-methods approach was informative, capturing the complexity of Self-change. The method was feasible in a real clinical setting, but compliance and the time required to perform the analyses were critical.

This paper advances the hypothesis that stellar plasma constitutes a candidate form of inorganic living matter when evaluated against a substrate-independent framework for life derived from first principles. The central epistemological argument is that every existing mainstream definition of life is contingently derived from a single biological data point — terrestrial carbon-based life — and therefore cannot legitimately function as a universal criterion. Building from this critique, five substrate-independent criteria for life are formalised using dynamical systems theory, nonequilibrium thermodynamics, Lyapunov stability analysis, Helmholtz decomposition, and information-theoretic transfer entropy. Each criterion is stated as a precise mathematical condition, connected to the organisational feature of living systems it captures, and assessed against the known physics of stellar plasma. The five criteria are formulated as jointly necessary for a physical system to qualify as living. They are not asserted as sufficient. A sufficiency claim is presented only as a testable conjecture. The application to plasma therefore demonstrates compliance with necessary conditions, while the broader question of sufficiency remains open and outside the scope of the results. Empirical support is drawn from Parker Solar Probe observational data, nucleosynthetic inheritance transmitted through stellar supernovae, and laboratory complex plasma experiments. A formal conjecture of joint sufficiency is stated. The framework is situated relative to Assembly Theory through a derived formal bridge between assembly index and transfer entropy. A continuum model of life is proposed, including resolution of the individuation problem through nested temporal scales. Falsifiable predictions are derived and the primary empirical gap identified.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting millions worldwide. Electroencephalography (EEG), a non-invasive, cost-effective, and safe diagnostic tool, is widely used for detecting neurological conditions. Existing EEG-based classification methods for AD diagnosis have limitations, particularly in adequately considering causal relationships between channels and implementing optimal feature selection, creating a need for highly interpretable feature screening mechanisms. This study presents EENet-RLA, a framework that integrates dynamical system theory with deep learning for AD classification, validated on the BrainLat EEG dataset. The framework operates in two stages, feature extraction and EEG classification, with the deep learning architecture serving primarily as a feature mapping and representation extractor. The core methodological contribution lies in the causal, stability-driven EEG channel selection strategy based on embedding entropy (EE), which quantifies nonlinear directional interactions between EEG channels. This strategy combines bootstrap resampling, multiple random seeds, and minimum connectivity thresholds to identify reproducible, informative channels under limited sample conditions. For classification, spatial and temporal EEG features are extracted using ResNet and LSTM respectively, then fused via a Multi-Head Attention mechanism to capture discriminative patterns. The proposed approach achieves 98.54% segment-level classification accuracy and perfect individual-level performance, demonstrating the discriminative potential of causality-informed feature selection in small-sample settings. While ensuring high accuracy, the method streamlines the analytical process and demonstrates the feasibility of causal-based EEG channel selection in AD characterization, with potential applicability to studying other neurological conditions with similar signal characteristics.

The Developmental Model of Intercultural Sensitivity (DMIS) has been widely used to conceptualise changes in how individuals experience cultural difference, typically described as a progression from ethnocentric toward ethnorelative orientations. While the model’s value is considerable, its common interpretation as a staged, linear pathway struggles to account for the variability, context dependence, and nonlinearity observed in real intercultural encounters. This paper argues that the DMIS is better understood through the lens of Complex Dynamic Systems Theory (CDST), which treats intercultural sensitivity as an emergent property of person-environment couplings evolving across multiple time scales. Reframing DMIS as a multistable, history-dependent system clarifies why people do not progress uniformly, why regressions and sudden leaps occur, and why change depends as much on context as on individual traits. Methodologically, we recommend person-focused, longitudinal designs and analytic tools that track change over time and embrace variability as signal rather than noise. Practically, we suggest that intercultural learning environments could cultivate productive variability, scaffold transitions, and design targeted perturbations that reconfigure the system toward more adaptive orientations. Altogether, this reframing preserves DMIS’s clarity while capturing the lived complexity of intercultural development.

Integrating dynamical systems theory and phenomenology to enhance early identification and treatment of psychotic disorders.

'Why I left the county': Exploring EFL teachers' emotional labour trajectories through complex dynamic systems theory.

ABSTRACT Frequent disruptions—such as those caused by the COVID-19 pandemic, geopolitical conflicts, and economic volatility—have exacerbated the mismatch between supply and demand, particularly in globally networked sectors like the textile industry. This study investigates how Dynamic Capabilities (DCs) can enhance Supply Chain Resilience (SCRes) in the context of Egypt’s textile sector. Grounded in System Dynamics (SD) theory and using a mixed-methods design, This Study develops and validates dynamic hypotheses. A case study of a medium-sized Egyptian textile manufacturer was used to construct Causal Loop Diagrams (CLDs) and simulate the system behaviour through Stock and Flow Diagrams (SFDs) over 3 years (2020–2023). The findings reveal that DCs influence SCRes through interconnected reinforcing and balancing feedback loops shaped by delays and nonlinear effects. Sensing capabilities—such as visibility and intra-departmental collaboration—foster financial preparedness. Seizing capabilities—such as agile decision-making, material flow restoration, and lead time reduction—enhance responsiveness. Reconfiguring capabilities—such as knowledge acquisition and resource integration—drive recovery and long-term growth. This study contributes a dynamic, empirical model that supports strategic capability development for resilience in emerging market supply chains.

ABSTRACT English-Medium Instruction (EMI) motivation research suffers from theoretical fragmentation and Western cultural bias that inadequately address student experiences in non-Western educational contexts. This study investigates motivational patterns among Vietnamese EMI students through Complex Dynamic Systems Theory, examining how contextual factors shape motivational dynamics in culturally distinct settings. A sequential explanatory mixed-methods design recruited 322 undergraduate students from three Vietnamese universities. Quantitative data collection employed adapted L2 Motivational Self System scales and contextually developed instruments, while qualitative semi-structured interviews with 45 purposively selected participants explored emergent patterns. Cluster analysis identified four distinct motivational profiles: Integrative-Intrinsic Learners (28.3%), Instrumentally-Driven Achievers (31.7%), Socially-Oriented Learners (24.2%), and Amotivated Strugglers (15.8%). Findings reveal that Vietnamese cultural values – particularly collectivism, hierarchical respect, and face-saving concerns – fundamentally moderate motivational orientations differently from Western contexts. Pedagogical quality, peer dynamics, and instructor rapport emerged as interactive contextual factors amplifying motivational patterns. These results challenge the universal applicability of Western-derived motivation theories in Asian EMI contexts and demonstrate the necessity of culturally grounded theoretical frameworks. Findings inform culturally responsive EMI pedagogy and advance understanding of motivation as a culturally embedded, dynamically evolving phenomenon in multilingual educational settings.

Living systems exhibit anticipation, adaptability, and resilience that cannot be fully explained by stimulus–response models, static homeostasis, or convergence-based optimization. This work addresses this gap by proposing a theoretical framework in which a central aspect of biological function is understood through the geometry and stability of distributions over unrealized but accessible future trajectories. We formalize these distributions as a counterfactual manifold, defined as a probabilistically supported subset of path space induced by a system’s effective internal dynamics. Using tools from information geometry and dynamical systems theory, we analyze adaptive systems that modify the laws governing their own future trajectories and construct explicit dual-channel adversarial dynamics that couple processes expanding future possibilities with antagonistic processes enforcing feasibility constraints. We show that adaptive systems of this kind are generically unstable, tending toward either collapse of accessible futures or unbounded sensitivity to perturbation. Constructive adversarial dynamics are sufficient to stabilize counterfactual geometry without requiring convergence to a fixed point. A minimal adversarial model reveals three generic regimes: collapse, runaway sensitivity, and bounded non-convergent regulation. The framework yields operational, falsifiable predictions through measurable proxies based on response diversity, perturbation sensitivity, recovery geometry, and boundary residence, allowing these regimes to be discriminated using finite observations without reconstructing underlying state-space dynamics. Interpreting disease as instability of counterfactual geometry provides a unifying language for understanding rigidity, volatility, and context dependence across biological domains. Rather than replacing mechanistic models, the proposed framework offers a higher-level geometric and dynamical perspective in which such models can be embedded and compared, shifting attention from component-level dysfunction to the stability of biological futures and establishing a principled foundation for analyzing disease, intervention, and adaptability across scales.

ABSTRACT In this article, we investigate the dynamical behavior and asymptotic profiles for a host‐pathogen epidemic model, where the different advection rates and degenerated heterogeneous diffusions are adopted and the total population is variable. First, the scalar equation of susceptible with diffusion and advection rate is investigated. The existence, global stability and prior estimations of positive steady state are established, and then the asymptotic properties of positive steady state are discussed as and approach to zero or infinity, respectively. Next, the well‐posedness of solutions for the model, including the global existence, nonnegativity and ultimate boundedness of solutions, and the existence of global attractor are established. Following, the display expression of the basic reproduction number is calculated by means of the variational method. The local reproduction number , the special forms , , of and the relationships between these reproduction numbers are presented. Then, the global dynamics of solutions for the model in terms with are established by using the comparison principle, properties of principle eigenvalue and the persistence theory of dynamical systems. That is, when the disease‐free steady state is globally asymptotic stable, otherwise when the disease is uniformly persistent. Furthermore, it is proved that is monotonically decreasing with respect to advection rate of infected individuals. The asymptotic profiles of in relation to the heterogeneous diffusion rates , and advection rates , approaching zero or infinity are discussed in detail by means of the display expression of and the corresponding principal eigenvalue and weight eigenvalue problems, including the eighteen limit cases of , , and , and involving single limits and double limits. Finally, some open questions are proposed for the model, left us to further explore. Compared with the existing results for the constant diffusion rates and common advection rate, our model is more general and more complicated, the results established in this paper are richer and more meaningful.

Previous research has shown that during interactions, partners adapt to (imitate, synchronize, complement) each other's behavior: a phenomenon often termed interpersonal coordination (IC). Approaches focusing on shared conceptual space suggested that the presence of synchronous or coordinated behaviors indicates the extent of conceptual alignment and thus, predicts communication success, while dynamical systems theory regards IC emerging from general coupling principles assuming no mechanistic role in the outcome of the interaction. Contrasting these two approaches, we tested whether IC appears in a wide variety of behaviors and how well various forms of IC predict the outcome of the interaction. Pairs of participants solved a computer-mediated communicative task involving verbal negotiation, while data of head motion, pupil size, and gaze direction were collected, and measures of prosody and structural speech characteristics were extracted from the recorded verbal interactions. Communication success was assessed using objective task performance measures and subjective evaluations from the participants. (1) Interlocutors coordinated multiple aspects of their behavior, (2) some of the objective measures of task performance were predicted by gaze pattern coordination, and (3) some forms of IC were positively, while other forms of IC were negatively associated with the participants' subjective experience of their partner and the interaction. The results indicating that interpersonal coordination between interlocutors appears across multiple modalities are fully compatible with dynamical systems theory. On the other hand, the presence of both positive and negative associations between IC and subjective outcomes of the interaction suggests that while a strict form of a theory suggesting that stronger alignment leads to better communication outcome is not supported by the data, it is compatible with an extended version of such a theory that acknowledges the potentially different roles of partners in a joint task situation.

Abstract This longitudinal study, grounded in Dynamic Systems Theory (DST), explores how language teachers’ integration of AI tools evolves over an 18-week period, revealing AI adoption as a complex pedagogical transformation rather than a simple technological shift. Drawing on these findings, the research introduces two models: (1) the DST-informed Pedagogical Content Knowledge (PCK) model, which specifies AI-empowered PCK by detailing the five domains of knowledge teachers draw upon, and how these inform teaching practice and scaffolding strategies for personalised student learning; and (2) the AI-in-PCK stage framework, which maps the trajectory of AI adoption, illustrating how teachers’ concerns and practices evolve from initial exploration and experimentation to strategic integration and ongoing learning, while responding to classroom realities and student feedback. Together, these models illuminate adaptive, multifaceted changes in PCK and teaching practice, highlighting how AI integration shapes decision-making and professional growth. The findings underscore critical implications for designing flexible, context-responsive professional learning and systemic support strategies, particularly in under-resourced rural contexts, and provide a foundation for future AI-in-PCK research.

While a substantial body of research on L2 oral performance has focused on aggregated outcomes, this study adopts a Complex Dynamic Systems Theory (CDST) framework to investigate the co-constructed processes inherent in dyadic interaction. Specifically, we explore the butterfly effect, wherein minor variations in initial conditions can lead to divergent interactional trajectories. A complex dynamic system comprises many interacting components, where micro-level interactions among components change their properties and, over time, create new properties at a macroscopic level (van Geert, 2011). From this perspective, participants are continuously influenced by their interlocutors through iterative processes of turn-taking, and a macro-pattern of discourse emerges as a result of dynamic co-regulation between the participants (Larsen-Freeman & Cameron, 2008). To investigate dyadic dynamics as a complex dynamic system, we conceptualized planning time (3 minutes vs. none) as the initial condition for a discussion task and re-analyzed the task-based interaction performed by 32 L2 learners in dyads (Nitta & Nakatsuhara, 2014). We employed a microgenetic approach, analyzing turn-taking dynamics and using Conversation Analysis to examine the qualitative features of the conversations to identify emergent interactional patterns under each condition. The analysis revealed a primary pattern observed in the majority of dyads: the non-planning condition fostered a highly collaborative mode of interaction, whereas the planning condition promoted a shift toward a sequence of monologues. However, a deviant pattern, identified in a minority of dyads, was characterized by consistent collaboration across both conditions. These findings suggest that while initial conditions like planning time are highly influential, their effects are not deterministic. We conclude that process-oriented, microgenetic methods are invaluable for understanding the nuanced dynamics of L2 interaction, and that CDST offers a promising theoretical lens for advancing both language learning research and pedagogical practice.

With the advancement of imaging technology, images present diverse and highly complex characteristics, and the theory of dynamical systems has great potential in image processing due to its unique mathematical properties. Therefore, this study proposes an IPT based on the reaction-diffusion equation. This technique combines cellular neural networks with the reaction-diffusion equation within the framework of dynamical system theory. Specifically, by introducing the Laplacian operator, the membrane potential in the FitzHugh-Nagumo equation is correlated with the spatiotemporal dynamics of the recovery variable. A new type of image processing model is constructed by mapping to the dynamic evolution of locally coupled mesh cells in convolutional neural networks. The theoretical framework of the technology is further improved through the dynamic analysis of the Turing instability of the model and the gradient changes of the reaction-diffusion system. The results showed that in the performance testing of the research model, the Edge Preservation Index (EPI) was 0.89 and the Pratt's Figure of Merit (PFOM) was 0.95, which were higher than the comparison models, indicating excellent model performance. Meanwhile, the time cost for the new model to complete one detection was only 0.9 seconds, and there were only 2 iterations, which was significantly better than other models. Research has shown that the new model has higher computational efficiency and better real-time performance. This study provides new ideas and methods for image processing and helps to promote the development of image processing algorithms towards high efficiency and intelligence.

Mobility Opportunities Via Education (MOVE) is a structured intervention to enhance independent mobility skills in individuals who are non-ambulatory. This study aims at identifying and mapping the literature related to the MOVE programme and to describe its content according to preselected categories, focusing on individuals with non-ambulatory cerebral palsy. A scoping review was conducted, with thirteen databases searched in May 2024, complemented by reference search and private databases; the search was updated in August 2025. Publications after 1985 were included without restrictions on language, population, or context. Two reviewers independently screened records and extracted data using qualitative content analysis. From 6794 records, 228 publications in 15 languages were included, mainly from the United States and Europe. MOVE was developed in the 1980s during a shift towards age-appropriate, functional interventions for individuals with severe disabilities. It is an early task-specific, activity-based and family-centred approach with retrospectively proposed foundations in dynamic systems theory and motor learning. Implementation follows a structured six-step process, embedding mobility training into daily routines. MOVE has been implemented across populations, settings, and countries, particularly for non-ambulatory individuals with cerebral palsy.

Background Shadowing-type properties play a fundamental role in the qualitative theory of dynamical systems, as they describe the relationship between approximate trajectories and exact orbits. In recent years, increasing attention has been given to extending these concepts to set-valued mappings, which naturally arise in various areas of mathematics and applied sciences. However, several shadowing-related notions for such mappings remain insufficiently explored. Methods In this work, we introduce precise definitions of the inverse shadowing property and the ergodic shadowing property for set-valued mappings. We analyse these properties within a general topological framework and examine their behaviour under the shift mapping on the inverse limit space. The relationships between inverse shadowing and ergodic shadowing are investigated using tools from topological dynamics. Results We establish connections between the inverse shadowing property and the ergodic shadowing property for set-valued mappings. In particular, we show how these properties interact when considered together with the shift mapping on the inverse limit space, and we identify conditions under which one property implies the other. Conclusions The results provide a clearer understanding of shadowing phenomena for set-valued mappings and highlight the role of inverse limit spaces in studying their dynamical behavior. This work contributes to the development of shadowing theory beyond single-valued dynamics and offers a foundation for further investigations in this direction.

Intent detection, a fundamental text classification task, aims to identify and label the semantics of user queries, playing a vital role in numerous business applications. Despite the dominance of deep learning techniques in this field, the internal mechanisms enabling recurrent neural networks (RNNs) to solve intent detection tasks are poorly understood. In this work, we apply dynamical systems theory to analyze how RNN architectures address this problem, using both the balanced SNIPS and the imbalanced ATIS datasets. By interpreting sentences as trajectories in the hidden state space, we first show that on the balanced SNIPS dataset, the network learns an ideal solution: the state space, constrained to a low-dimensional manifold, is partitioned into distinct clusters corresponding to each intent. The application of this framework to the imbalanced ATIS dataset then reveals how this ideal geometric solution is distorted by class imbalance, causing the clusters for low-frequency intents to degrade. Our framework decouples geometric separation from readout alignment, providing a novel, mechanistic explanation for real world performance disparities. These findings provide new insights into RNN dynamics, offering a geometric interpretation of how dataset properties directly shape a network’s computational solution.

This topic revisits and elucidates the impact of dynamical systems theory (DST) since the "dynamical hypothesis" was presented in the 1990s as an alternative to the information-processing approaches central to orthodox cognitive science. The dynamical hypothesis does not investigate cognition as necessitating explanations in computational or representational terms. Instead, DST is leveraged to approach cognition in temporal terms (e.g., continuous, self-organizing) that often encompass brain-body-environment systems. The contributions collected here examine DST's experimental, methodological, and theoretical roles across such areas as informatics, linguistics, neuroscience, philosophy, and psychology. They explore how DST reshapes debates about computation, representation, and embodiment, extending from individual cognition to artificial and social systems. Papers address advances in fractality, multiscale modeling, and nonlinear methods; applications to behavioral and neural coordination; and theoretical syntheses linking dynamics with cultural and symbolic processes. Together, they assess DST's growing influence in a "dynamical renaissance" within the cognitive and brain sciences, highlighting both its promise as a unifying framework and its conceptual challenges. Thus, this provides a comprehensive and critical overview of how the dynamical hypothesis continues to expand, guide, and refine the understanding of cognition as an emergent, temporally extended, and interactive process across biological, social, and artificial domains.

Abstract This study investigates the spectral and dynamic characteristics of turbulent fluctuations in the electric field within equatorial plasma bubbles using data from the Electric Field Detector aboard the China Seismo‐Electromagnetic Satellite‐01. We applied a novel analysis method, developed within the framework of dynamical systems theory, to high‐resolution electric field data, allowing us to resolve spatial scales down to just a few meters. This method evaluates the system's persistence in specific states and the instantaneous dimension of fluctuations. Our findings reveal a significant increase in the instantaneous dimension and a decrease in the extremal index , which measures local persistence, within the plasma bubbles. These results suggest a complex interplay of structures at different scales driven by turbulent dynamics characterizing these ionospheric plasma depletions. This study provides new insights into the turbulent processes within equatorial plasma bubbles, advancing our understanding of their underlying mechanisms.