Nonlinear Dynamics Psychology and Life Sciences最新文献

This study explores the hypothesis related to a punctuated equilibrium process for conceptual change in science learning, in conjunction with the effects of four cognitive variables: logical thinking and field-dependence/field-independence, divergent and convergent thinking. The participants were fifth and sixth-grade elementary school pupils involved in different tasks, who were asked to describe and interpret chemical phenomena. Latent Class Analysis (LCA) was applied to children's responses, and three clusters or latent classes (LC1, LC2, and LC3) were identified, corresponding to hierarchical levels of conceptual understanding. The ensued LCs align with the theoretical conjecture about a stepwise conceptual change process that might go through various stages or mental models. These levels or stages are conceptualized as attractors, and changes between them were modeled as cusp catastrophes using the four cognitive variables as controls. The analysis showed that logical thinking acted as asymmetry factor, while field-dependence/field-independence, divergent and convergent thinking acted as bifurcation variables. This analytic approach presents a methodology for investigating conceptual change as a punctuated equilibrium process that adds to the nonlinear dynamical research with important implications for theories of conceptual change in science education and psychology as well. Discussion on the new perspective embracing the meta-theoretical framework of complex adaptive systems (CDS) is provided.

The aim of the study is to evaluate the complexity matching between the HRVs of the group of Healers and the Healee during the various stages of the meditation protocol by employing a novel mathematical approach based on the H-rank algorithm. The complexity matching of heart rate variability is assessed before and during a heart-focused meditation in a close non-contact healing exercise. The experiment was conducted on a group of individuals (eight Healers and one Healee) throughout the various phases of the protocol over a ~75-minute period. The HRV signal for the cohort of individuals was recorded using high resolution HRV recorders with internal clocks for time synchronization. The Hankel transform (H-rank) approach was employed to reconstruct the real-world complex time series in order to measure the algebraic complexity of the heart rate variability and to assess the complexity matching between the reconstructed H-rank of the Healers and Healee during the different phases of the protocol. The integration of the embedding attractor technique was used to aid in the visualization of reconstructed H-rank in state space across the various phases. The findings demonstrate the changes in the degree of reconstructed H-rank (between the Healers and the Healee) during the heart-focused meditation healing phase by employing mathematically anticipated and validated algorithms. It is natural and thought-provoking to contemplate the mechanisms causing the complexity of the reconstructed H-rank to come closer; it can be explicitly stated that the purpose of the study is to communicate a clear idea that the H-rank algorithm is capable of registering subtle changes in the healing process, and that there was no intention of delving deep to uncover the mechanisms involved in the HRV matching. Therefore, the latter might be a distinct goal of future research.

We have until now focused solely on the non-coding, more precisely the non-protein-coding (npc), part of DNA of man and dog in the search for hidden y-texts written by means of y-words - spelled by nucleotides A, C, G, and T and delimited by stop-codons. In this paper we use the same methods to analyse the whole human and canine genome, but we divide the genome into the genetic part, the naturally occurring sequence of exons, and the non-protein-coding genome according to definitions. By use of the y-text-finder we determine the number of zipf-qualified and a-qualified texts hidden in each of these parts. We present the actual methods and procedures, and the results in twelve figures, six for Homo sapiens sapiens and six for Canis lupus familiaris. Results show that there are lots of y-texts in the genetic part of the genome just as there are in the npc-genome. There is even a non-negligible number of ?-texts hidden in the sequence of exons. In addition, we show how many genes we find included in or overlapping zipf-qualified and a-qualified y-texts in the one-stranded DNA of man and dog. We assume that all this information represents the cell's total ability to behave in all of life's situations and discuss briefly ?-text reading and disease aetiology; carcinogenesis are also discussed.

It is a commonplace perception that speed of time subjectively experienced by humans significantly differs from chronological (objective) time and shows a great deal of variability. An often cited example is the phenomenon of the time acceleration with age - subjectively, the time passes faster as we get older. While the exact mechanisms behind it are not yet fully established, here we consider three 'soft' (conceptual) mathematical models that might be applicable to the speeding time phenomenon: two proportionality theories widely discussed in the past and the original model that takes into account the novelty of experience effect. The latter is found the most plausible, as not only it satisfactorily describes the decadal subjective time acceleration, but also offers a reasonable explanation of the human life experience accumulation with age.

Theories and studies of corruption typically focus on individual ethics and agency problems in organizations. In this paper, we use concepts from complexity science to propose a process theory that describes how corruption risk emerges from conditions of uncertainty that are intrinsic in social systems and social interactions. We posit that our theory is valid across multiple levels of scale in social systems. We theorize that corruption involves dynamics that emerge when agents in a system take actions that exploit disequilibrium conditions of uncertainty and ethical ambiguity. Further, systemic corruption emerges when agent interactions are amplified locally in ways that create a hidden value sink which we define as a structure that extracts, or 'drains,' resources from the system for the exclusive use of certain agents. For those participating in corruption, the presence of a value sink reduces local uncertainties about access to resources. This dynamic can attract others to join the value sink, allowing it to persist and grow as a dynamical system attractor, eventually challenging broader norms. We close by identifying four distinct types of corruption risk and suggest policy interventions to manage them. Finally, we discuss ways in which our theoretical approach could motivate future research.

This paper is a top-down analysis of the non-protein-coding, canine genome. We demonstrate by use of the y-text-finder method, that the non-protein-coding genome contains lots of hidden y-texts, both short and long, proving that the non-protein-coding genome is the opposite of junk. They are written by means of a y-language of about 28 million y-words separated by stop codons and spelled by nucleotide letters A, C, G, and T. We use the Canis Lupus Familiaris reference genome, Roslin Institute, 2020, from which we select the non-protein-coding part. We show that 70-80 percent of chromosomal y-words are specific for the canine non-protein-coding chromosome, and we show how many y-words any non-protein-coding chromosome shares with any other non-protein-coding chromosome. We demonstrate the peculiar way by which the dog utilizes the nucleotide word-length of y-words to build up its y-language, moreover in a way it shares with the human non-protein-coding genome. In a large table we demonstrate how 18,398 Zipf-qualified y-texts/narratives are distributed over the 40 non-protein-coding chromosomes. 3,812 of these texts/narratives are alpha-qualified and similar in form to human novels. In the last table we compare selected, corresponding characteristics of the human and the canine non-protein-coding genome.

Comparison of some experimental data and deterministic dynamical models of heartbeat show that it is essential to consider stochastic mathematical models. The Zeeman heartbeat model is one of the main heartbeat models whose stochastic dynamics is less studied. Especially, investigating bifurcations in stochastic dynamical models can be useful for identifying abnormal cardiac rhythms. This paper is concerned with two essential features of the two dimensional stochastic Zeeman heartbeat model i.e., stability and bifurcation. To achieve this approach, Taylor expansion, polar coordinate transformation, and stochastic averaging procedure will be used to convert the classical system into an Ito averaging diffusion system. Furthermore, we consider several theorems which provide sufficient conditions of drift and diffusion coefficients to establish stochastic stability, D-bifurcation and phenomenological bifurcation of the model. In the end, numerical simulation plays an important role to show the influences of the noise severity and confirm our theoretical results.

Most scholarly discussions of novelty offer only partial or specified views of what it is or how it can be assessed particular to a discipline or are limited to individual studies. This article investigates novelty with the goal of offering a multi-systemic and generalizable definition and assessment process to be applicable across multiple disciplines. Novelty is proposed as a quality and state, which includes both a qualitative view that integrates subjective experience and a quantitative view that addresses nonlinear dynamical systems. It is also described as a comparative relatedness of space, time, and context. An 'outlier profile' is defined to search for features of the system that are original or unusual, and a 'low-recurrence profile' for features of the system that are unexpected or a surprise. A detailed research strategy is offered for novelty assessment with an example, and is usable across multiple disciplinary contexts in the sciences and humanities for studies of academic and practical use.

In the analysis of time series data, roughness is sometimes seen as a distinct feature of fractality. This paper seeks to distinguish it from other aspects of that construct (self-affinity and long-range memory processes) and it examines the reliability of the roughness measures currently available, i.e., Gneiting et al.'s (2010) fractal dimension and Marmelat et al.'s (2012) relative roughness. The response of these estimators is evaluated to simulations at varying levels of persistence, as specified by the Hurst exponent, and to the presence or absence of short-range ARMA processes. Four empirical time series datasets are subjected to roughness estimation: the flow of the river Nile, daily recordings of the number of births to teens in the state of Texas, daily school attendance rates at an urban middle school, and unemployment figures provided by the US Department of Labor. Results from the simulation study indicate that persistence levels are faithfully reproduced by both estimation techniques, which also show the (dis)attenuating effects of the short-range dependencies. Analysis of the empirical data indicates that the fractal dimension works best for non-stationary data, while relative roughness is more suitable for stationary data. In the simulations as well as the empirical situation, both estimations reliably identify randomness, and are therefore recommended as goodness of fit measures when time series are analyzed.

The present study investigated whether fractal dynamics can be observed during single-leg standing on a slackline. We also examined whether the temporal structure differs with skill level. To address these questions, we compared single-leg standing performance between novices (N=5) and experts (N=5) in terms of fractal dynamics in both ankles (i.e., stance and swing legs), center of mass, and head acceleration time series using detrended fluctuation analysis. Participants were required to perform single-leg standing on a slackline. To collect motion data while slacklining, we used a three-dimensional motion capture system and obtained time-series data on the position. We conducted detrended fluctuation analysis on the original acceleration time series and random shuffled time series to examine the fractal dynamics in each body part's fluctuation. Results suggest that experts showed persistent temporal structure in the swinging leg, center of mass, and head fluctuations in the horizontal direction, while that of novices did not differ from random fluctuations. These findings revealed that experts performing a single-leg standing task on a slackline show fractal dynamics. This might reflect their flexible or adaptive exploratory behavior in the performer-environment system and contribute to the dynamic stability of whole-body dynamic balancing.