Nonlinear Dynamics Approach Research Articles

CANDA: computer-assisted nonlinear dynamic approach for the L2 teaching in blended and distance learning

ABSTRACT Second language teaching approaches that were originally proposed for classroom context are used for blended learning context as well as distance learning contexts regardless of their fundamental differences in terms of applicability, accessibility, objectives, designs and theoretical bases. Such theoretical and practical incompatibilities served as the rationale to bridge the gap of a basically devised second language (L2) teaching approach for blended and distance learning contexts. The proposed computer-assisted nonlinear dynamic approach (CANDA) enables L2 learners and teachers to improve psychological factors and communicative skills by suggesting the use of a variety of technology-based apps for a multitude of learning styles. The CANDA, as an L2 teaching approach has significant theoretical and pedagogical implications for educational app designers, computer-assisted teaching programmers, blended and distance learning researchers and officials.

Sequential coding of maternal sensitivity: application of nonlinear dynamic analyses and reliability

In infant development, maternal sensitivity is a central construct in the study of the processes involved in the caregiver-infant relationship and child attachment. The relevance of these dyadic processes for infant and later child development requires the advancing in the measurement of maternal sensitivity. The measurement of this construct, that reveals its complexity through different ‘windows’ using different approaches and operational definitions, has brought researchers’ attention into the methodological arena. Mother–infant interaction has been mainly quantified through rating scales and sequential strategies using observational methodology. The present study is part of a research program whose purpose is to measure maternal sensitivity dimensions using sequential coding in real time, the free-play paradigm and the early mother–child interaction coding system (CITMI-R: by its Spanish initials) instrument. This study was designed to further the analysis of the quality of the data obtained by this coding system. For this purpose, six trained observers coded fifty-seven mother–infant free-play episodes. The reliability of their coding was estimated: firstly, examining the inter-coder agreement with the Alignment Kappa, thus, incorporating the temporal dimension of the measurement. Secondly, analyzing the inter-coder agreement on parameters that consider the sequential and temporal organization by applying variables derived from the State Space Grid methodology and the nonlinear dynamic system approach: Contingency, through dyadic events, visits to them, predictability and duration of event, visit and dyadic behavioral states. The results indicated acceptable inter-observer reliability values based on the standards for use on parameters of interest in the study of sensitivity.

Trunk Dynamic Stability Assessment for Individuals With and Without Nonspecific Low Back Pain During Repetitive Movement.

This study aimed to employ nonlinear dynamic approaches to assess trunk dynamic stability with speed, symmetry, and load during repetitive flexion-extension (FE) movements for individuals with and without nonspecific low back pain (NSLBP). Repetitive trunk FE movement is a typical work-related LBP risk factor contingent on speed, symmetry, and load. Improper settings/adjustments of these control parameters could undermine the dynamic stability of the trunk, hence leading to low back injuries. The underlying stability mechanisms and associated control impairments during such dynamic movements remain elusive. Thirty-eight male volunteers (19 healthy, 19 NSLBP) enrolled in the current study. All participants performed repetitive trunk FE movements at high/low speeds, in symmetric/asymmetric directions, with/without a wearable loaded vest. Trunk instantaneous rotation angle was computed for each trial to be assessed in terms of local and orbital stability, using maximum finite-time Lyapunov exponents (LyEs) and Floquet multipliers (FMs), respectively. Both groups demonstrated equivalent competency in terms of trunk control and stability, suggesting functional adaptation strategies may be used by the NSLBP group. Wearing the loaded vest magnified the effects of trunk control impairment for the NSLBP group. The combined presence of high-speed and symmetrical FE movements was associated with least trunk local stability. Nonlinear dynamic techniques, particularly LyE, are potentially effective for assessing trunk dynamic stability dysfunction for individuals with NSLBP during various activities. This work can be applied toward the development of quantitative personalized spinal evaluation tools with a wide range of potential occupational and clinical applications.

A nonlinear dynamical approach to the path correction of multi-steering articulated vehicles

ABSTRACT When designing articulated tractor-trailer systems, engineers should take care to contrast some undesired phenomena associated with the lateral dynamics: the path off-tracking, namely the trajectory deviation of the points of the two units compared to the midpoint of the tractor fore; the rearward amplification of the transverse acceleration. The off-tracking may lead the vehicle tail to invade the opposite direction lane or the emergency lane, whereas the rearward amplification may involve the danger of the trailer rollover. Nonetheless, the arrangement of multi-steering axle systems with the proper correlation among the steering angles may substantially cancel the off-tracking, both along a path with constant curvature and while entering or leaving the road bend. Moreover, the proper control of the increase and decrease of the steering angle during the transient phases can restrain the lateral acceleration of the towed unit. The present analysis considers several nonlinear effects of the vehicle lateral dynamics, such as large steering angles, nonlinear cornering characteristics and changes of the cornering forces because of the centrifugal effect. The goal is a practical formulation of the correlation among the steering angles to correct the vehicle run and ensure lateral stability.

New nonlinear and dynamic avenues for the study of work and organizational psychology: an introduction to the special issue

ABSTRACT Nonlinear and dynamic approaches are two main principles of complex systems. The work and organizational psychology field has recently applied these concepts to understand human behaviour at work. In this introductory paper, we describe how human behaviour at work can be understood from a nonlinear and dynamic perspective, developing and applying the advantages of this approach to continue moving beyond the frontiers of our knowledge. We focus on the theoretical, methodological, and epistemological implications of the nonlinear dynamic approach in the field. Then, we present the five papers included in this special issue. These papers have applied nonlinear and dynamic concepts to understand a wide range of phenomena: subordinates-supervisors relationships, team behaviour, deviant workplace behaviour, transformational leadership, and training transfer. Overall, these papers demonstrate the utility of nonlinear dynamics in our field and how we can create more time-sensitive and complex knowledge by paying special attention to when and how phenomena happen and how such phenomena interrelate over time.

Ultra-Short-Term Photovoltaic Power Prediction Model Based on the Localized Emotion Reconstruction Emotional Neural Network

Due to the intermittency and randomness of photovoltaic (PV) power, the PV power prediction accuracy of the traditional data-driven prediction models is difficult to improve. A prediction model based on the localized emotion reconstruction emotional neural network (LERENN) is proposed, which is motivated by chaos theory and the neuropsychological theory of emotion. Firstly, the chaotic nonlinear dynamics approach is used to draw the hidden characteristics of PV power time series, and the single-step cyclic rolling localized prediction mechanism is derived. Secondly, in order to establish the correlation between the prediction model and the specific characteristics of PV power time series, the extended signal and emotional parameters are reconstructed with a relatively certain local basis. Finally, the proposed prediction model is trained and tested for single-step and three-step prediction using the actual measured data. Compared with the prediction model based on the long short-term memory (LSTM) neural network, limbic-based artificial emotional neural network (LiAENN), the back propagation neural network (BPNN), and the persistence model (PM), numerical results show that the proposed prediction model achieves better accuracy and better detection of ramp events for different weather conditions when only using PV power data.

A nonlinear dynamical approach to analysis of emotions using EEG signals based on the Poincaré map function and recurrence plots.

Dynamic variations of electroencephalogram (EEG) contain significant information in the study of human emotional states. Transient time methods are well suited to evaluate short-term dynamic changes in brain activity. Human affective states, however, can be more appropriately analyzed using chaotic dynamical techniques, in which temporal variations are considered over longer durations. In this study, we have applied two different recurrence-based chaotic schemes, namely the Poincaré map function and recurrence plots (RPs), to analyze the long-term dynamics of EEG signals associated with state space (SS) trajectory of the time series. Both approaches determine the system dynamics based on the Poincaré recurrence theorem as well as the trajectory divergence producing two-dimensional (2D) characteristic plots. The performance of the methods is compared with regard to their ability to distinguish between levels of valence, arousal, dominance and liking using EEG data from the "dataset for emotion analysis using physiological" database. The differences between the levels of emotional feelings were investigated based on the analysis of variance (ANOVA) test and Spearman's statistics. The results obtained from the RP features distinguish between the emotional ratings with a higher level of statistical significance as compared with those produced by the Poincaré map function. The scheme based on RPs was particularly advantageous in identifying the levels of dominance. Out of the 32 EEG electrodes examined, the RP-based approach distinguished the dominance levels in 23 electrodes, while the approach based on the Poincaré map function was only able to discriminate dominance levels in five electrodes. Furthermore, based on nonlinear analysis, significant correlations were observed over a wider area of the cortex for all affective states as compared with that reported based on the analysis of EEG power bands.

Assessment of reduced-order modeling strategies for convective heat transfer

An assessment of physics-based and data-driven reduced-order models (ROMs) is presented for the study of convective heat transfer in a rectangular cavity. Despite the simple geometrical configuration, the current setup offers increasingly rich dynamics as the thermal forcing is increased, thus making it a suitable candidate to evaluate the performance of ROMs. First, flow simulations are performed using a high-order spectral element method that will feed the ROMs with well-resolved temporal and spatial information. Proper orthogonal decomposition (POD) is applied to reduce the problem dimensionality for all models. The class of tested physics-based models include the Galerkin and least-squares Petrov–Galerkin (LSPG) methods that rely on projection of the Navier–Stokes and energy equations being solved. On the other hand, the data-driven methods applied in this work rely on regression of the governing equations, which are treated as a nonlinear dynamical system. The data-driven methods tested here include the sparse identification of nonlinear dynamics (SINDy) approach and a method recently proposed in literature based on deep neural networks (DNNs). All ROMs are able to represent the periodical temporal dynamics of a low Rayleigh number flow. However, the physics-based approaches demonstrate a better performance for a moderate Rayleigh number case with more complex flow dynamics, when several frequencies are excited in a non-periodical fashion.

Optimization of dynamic incentive for the deployment of carbon dioxide removal technology: A nonlinear dynamic approach combined with real options

Due to the high adoption cost, large uncertainty, and ignorance of the positive externalities for private entities, additional incentives are needed for the development of carbon dioxide removal (CDR) technology. And there is a trade-off between the government and investors on how to ensure the effectiveness of the incentive policy and optimally allocate subsidized capital. This paper proposes a nonlinear dynamic programming model that combines real options method to study the optimization of dynamic subsidies for CDR technology. Using the endogenous learning effect, technological advance, and technology applicability, we modeled the investor decisions under uncertainty, as well as the government's effective use of incentive policies. Our model is available for deriving the development path of CDR technology with optimized subsidies and research and development (R&D) input across multiple periods. We use China's carbon capture and storage (CCS) development as a case study. The results show that, unlike other kinds of low-carbon technology such as renewable energy, the subsidy level of CCS may not decrease in the future because of rising trend of fuel costs and worse technology applicability in large-scale deployment. The achievement of large-scale CCS development will rely more on second-generation CCS. The levelized policy cost of incentivizing CCS technology in China can be high, and thus the target should be prudently set based on an evaluation of its socioeconomic burden. A supplementary measure that caps the CCS installation in each period is recommended to prevent excessive development.

Disentangling nonlinear geomagnetic variability during magnetic storms and quiescence by timescale dependent recurrence properties

Understanding the complex behavior of the near-Earth electromagnetic environment is one of the main challenges of Space Weather studies. This includes both the correct characterization of the different physical mechanisms responsible for its configuration and dynamics as well as the efforts which are needed for a correct forecasting of several phenomena. By using a nonlinear multi-scale dynamical systems approach, we provide here new insights into the scale-to-scale dynamical behavior of both quiet and disturbed periods of geomagnetic activity. The results show that a scale-dependent dynamical transition occurs when moving from short to long timescales, i.e., from fast to slow dynamical processes, the latter being characterized by a more regular behavior, while more dynamical anomalies are found in the behavior of the fast component. This suggests that different physical processes are typical for both dynamical regimes: the fast component, being characterized by a more chaotic and less predictable behavior, can be related to the internal dynamical state of the near-Earth electromagnetic environment, while the slow component seems to be less chaotic and associated with the directly driven processes related to the interplanetary medium variability. Moreover, a clear difference has been found between quiet and disturbed periods, the former being more complex than the latter. These findings support the view that, for a correct forecasting in the framework of Space Weather studies, more attention needs to be devoted to the identification of proxies describing the internal dynamical state of the near-Earth electromagnetic environment.

Radio Frequency Fingerprint Extraction Based on Multi-Dimension Approximate Entropy

With the advance in wireless network technique, its security becomes of paramount importance. Radio Frequency Fingerprint (RFF) is the underlying characteristic of hardware chains in transmitters, which can be used as a unique ID for specific emitter identification (SEI) and a non-cryptographic access authentication technology in physical layer to enhance wireless network security. To date, few studies have extracted the inevitable non-linearity in the transmitter as RFF features. Hence, this letter provides a novel nonlinear dynamics approach based on Multi-dimension Approximate Entropy (MApEn) for SEI. Specifically, this method utilizes the steady-state portion of the preamble structure of Standard IEEE802.11b/g to obtain the nonlinear properties of wireless network cards. The experimental results demonstrate that the proposed identification algorithm outperforms the existing steady-state methods in terms of the identification accuracy.

Dynamic interfacial debonding in sandwich panels

A nonlinear dynamic analytical approach is formulated aiming to investigate the dynamic interfacial debonding mechanism in sandwich panels. The approach is derived using the variational principle of virtual work and it incorporates the Extended High-Order Sandwich Panel Theory with a cohesive interface modeling for the interfaces between the face sheets and the core. The model adopts the first-order shear deformation theory kinematic assumptions for the face sheets along with a geometrically nonlinear behavior. The high-order small deformations kinematic assumptions that account for out-of-plane compressibility are considered for the core layer. The two interfaces link the three components of the panel together and the nonlinear traction-displacement laws introduce the interfacial nonlinearity into the model. The model considers dynamic effects in order to assess the influence of the inertial terms on the interfacial debonding mechanism and to examine the coupling between the two. The results of the dynamic analysis are compared with results of a static analysis for two cases: a sandwich panel with a pre-existing delamination subjected to an end-shortening loading and an intact sandwich panel subjected to three-point-bending. In the second case, the interfacial debonding triggers a dynamic response that substantially affects the structural behavior. The dynamic results, the assessment of the time scales of the process, and the comparison with the static results shed light on the significance of the dynamic nature of the interfacial debonding failure mechanism.

Nonlinear Static and Dynamic Analysis of Rocking Masonry Corners Using Rigid Macro-Block Modeling

The corner failure is one of the most typical local mechanisms in masonry buildings vulnerable to earthquakes. The seismic assessment of this mechanism is poorly studied in the literature and in this paper it is addressed by means of both nonlinear static and dynamic analyses of rocking rigid blocks. The static approach is based on the displacement-based method and is aimed at predicting the onset of the 3D failure mechanism and its evolution through incremental kinematic analysis. This approach also considers the presence of a thrusting roof and the stabilizing contribution of frictional resistances exerted within interlocked walls. The capacity in terms of both forces and displacements is compared with the seismic demand through the construction of acceleration–displacement response spectra, with some originality. The nonlinear dynamic approach is based on the seminal Housner’s work on rocking rigid blocks and considers the influence of transverse walls, roof overloads and outward thrust, all included in an updated equation of one-sided motion. In particular, the process of defining an equivalent prismatic block, representative of the original corner geometry, is presented to convert the 3D dynamic problem into a 2D rocking motion. The wide suitability and advantage of such modeling approaches to assess the seismic response of rocking masonry structures with reference to specific limit states are demonstrated through a real case study, i.e. the collapse of a corner in a masonry school building during the 2016–2017 Central Italy seismic sequence. The compared results provide a good agreement of predictions in terms of both onset and overturning conditions, for which the static model appears to be more conservative than the dynamic one.

Time-Shift Multi-scale Weighted Permutation Entropy and GWO-SVM Based Fault Diagnosis Approach for Rolling Bearing.

Multi-scale permutation entropy (MPE) is an effective nonlinear dynamic approach for complexity measurement of time series and it has been widely applied to fault feature representation of rolling bearing. However, the coarse-grained time series in MPE becomes shorter and shorter with the increase of the scale factor, which causes an imprecise estimation of permutation entropy. In addition, the different amplitudes of the same patterns are not considered by the permutation entropy used in MPE. To solve these issues, the time-shift multi-scale weighted permutation entropy (TSMWPE) approach is proposed in this paper. The inadequate process of coarse-grained time series in MPE was optimized by using a time shift time series and the process of probability calculation that cannot fully consider the symbol mode is solved by introducing a weighting operation. The parameter selections of TSMWPE were studied by analyzing two different noise signals. The stability and robustness were also studied by comparing TSMWPE with TSMPE and MPE. Based on the advantages of TSMWPE, an intelligent fault diagnosis method for rolling bearing is proposed by combining it with gray wolf optimized support vector machine for fault classification. The proposed fault diagnostic method was applied to two cases of experimental data analysis of rolling bearing and the results show that it can diagnose the fault category and severity of rolling bearing accurately and the corresponding recognition rate is higher than the rate provided by the existing comparison methods.

Nonlinear approach to study the acute effects of static and dynamic stretching on local dynamic stability in lower extremity joint kinematics and muscular activity during pedalling

Researchers have reported contradictory results on the effect of static and dynamic stretching on subsequent performance. Due to the importance of performance through static and dynamic stretching, the aim of this study is to investigate the acute effects of static and dynamic stretching protocols on local dynamic stability in lower extremity joint kinematics and muscular activities during pedalling using a nonlinear dynamics approach. Using a randomised crossover trial design, fifteen active males participated voluntarily in this research (mass: 69.02 ± 10.52 kg, height: 174.00 ± 6.74 cm, and age: 21.20 ± 1.47 years) and completed a pedalling trial in situations of without stretching (WS), after static (SS), and dynamic stretching (DS) of lower extremity. The lower extremity joint angles in the sagittal plane and the electrical activity of soleus, gastrocnemius medialis, tibialis anterior, vastus medialis, biceps femoris, and rectus femoris muscles were collected during 30 pedalling cycles at 70 rates per minute. The results of the repeated measure ANOVA indicated that the knee and ankle angle largest LyE was significantly lower in DS compared to WS and SS. The largest LyE in muscle activity is also significantly lower for all the muscles after DS compared to WS and SS (P ≤ 0.05). Regarding the positive effects of DS on the joints and the muscle activity local dynamic stability, it is suggested to use DS than SS in the warm-up program before repetitive activities like pedalling.

Nonlinear dynamic approaches to identify atrial fibrillation progression based on topological methods

In recent years, atrial fibrillation (AF) development from paroxysmal to persistent or permanent forms has become an important issue in cardiovascular disorders. Information about AF pattern of presentation (paroxysmal, persistent, or permanent) was useful in the management of algorithms in each category. This management is aimed at reducing symptoms and stopping severe problems associated with AF. AF classification has been based on time duration and episodes until now. In particular, complexity changes in Heart Rate Variation (HRV) may contain clinically relevant signals of imminent systemic dysregulation. A number of nonlinear methods based on phase space and topological properties can give more insight into HRV abnormalities such as fibrillation. Aiming to provide a nonlinear tool to qualitatively classify AF stages, we proposed two geometrical indices (fractal dimension and persistent homology) based on HRV phase space, which can successfully replicate the changes in AF progression. The study population includes 38 lone AF patients and 20 normal subjects, which are collected from the Physio-Bank database. “Time of Life (TOL)” is proposed as a new feature based on the initial and final Čech radius in the persistent homology diagram. A neural network was implemented to prove the effectiveness of both TOL and fractal dimension as classification features. The accuracy of classification performance was 93%. The proposed indices provide a signal representation framework useful to understand the dynamic changes in AF cardiac patterns and to classify normal and pathological rhythms.

A Novel Geometric Hierarchical Approach for Dynamic Visual Servoing of Quadrotors

This paper proposes a novel nonlinear geometric hierarchical dynamic visual servoing approach to drive a quadrotor to the desired pose defined by a previously captured image of a planar target. Different from existing works, the key novelty is to extend the positionbased nonlinear hierarchical control to image-based nonlinear hierarchical control. More specifically, by seamlessly integrating the nonlinear hierarchical control with the geometric control, and taking full advantage of the cascade property of the system, the proposed visual servoing strategy does not require the thrust force or its derivative to be measurable when compared with the existing backstepping methods, which brings much convenience for practical applications. For the attitude loop, the axis-angle rotation representation is adopted to design a tracking control law on the vector space s (3). In the outer loop, perspective image moments in the virtual image plane are employed as image feedback to construct the outer-loop image-based visual servoing controller with geometric control and backstepping techniques. Based on Lyapunov techniques and the theory of cascade systems, it is rigorously proven that the proposed image-based controller achieves asymptotic stability. Comparative experiments are conducted to show that the proposed approach has advantages of better transient performance, better steady-state performance, and stronger robustness.

Development of digital twin of CNC unit based on machine learning methods

Introduction. It is shown that the digital twin (electronic passport) of a CNC machine is developed as a cyber-physical system. The work objective is to create neural network models to determine the operation of a CNC machine, its performance and dynamic stability under cutting.Materials and Methods. The development of mathematical models of machining processes using a sensor system and the Industrial Internet of Things is considered. Machine learning methods valid for the implementation of the above tasks are evaluated. A neural network model of dynamic stability of the cutting process is proposed, which enables to optimize the machining process at the stage of work preparation. On the basis of nonlinear dynamics approaches, the attractors of the dynamic cutting system are reconstructed, and their fractal dimensions are determined. Optimal characteristics of the equipment are selected by input parameters and debugging of the planned process based on digital twins.Research Results. Using machine learning methods allowed us to create and explore neural network models of technological systems for cutting, and the software for their implementation. The possibility of applying decision trees for the problem of diagnosing and classifying malfunctions of CNC machines is shown.Discussion and Conclusions. In real production, the technology of digital twins enables to optimize processing conditions considering the technical and dynamic state of CNC machines. This provides a highly accurate assessment of the production capacity of the enterprise under the development of the production program. In addition, equipment failures can be identified in real time on the basis of the intelligent analysis of the distributed sensor system data.

Forward and Inverse Dynamics of a Unicycle-Like Mobile Robot

In this research work, a new method for solving forward and inverse dynamic problems of mechanical systems having an underactuated structure and subjected to holonomic and/or nonholonomic constraints is developed. The method devised in this paper is based on the combination of the Udwadia-Kalaba Equations with the Underactuation Equivalence Principle. First, an analytical method based on the Udwadia-Kalaba Equations is employed in the paper for handling dynamic and control problems of nonlinear nonholonomic mechanical systems in the same computational framework. Subsequently, the Underactuation Equivalence Principle is used for extending the capabilities of the Udwadia-Kalaba Equations from fully actuated mechanical systems to underactuated mechanical systems. The Underactuation Equivalence Principle represents an efficient method recently developed in the field of classical mechanics. The Underactuation Equivalence Principle is used in this paper for mathematically formalizing the underactuation property of a mechanical system considering a particular set of nonholonomic algebraic constraints defined at the acceleration level. On the other hand, in this study, the Udwadia-Kalaba Equations are analytically reformulated in a mathematical form suitable for treating inverse dynamic problems. By doing so, the Udwadia-Kalaba Equations are employed in conjunction with the Underactuation Equivalence Principle for developing a nonlinear control method based on an inverse dynamic approach. As shown in detail in this investigation, the proposed method can be used for analytically solving in an explicit manner the forward and inverse dynamic problems of several nonholonomic mechanical systems. In particular, the tracking control of the unicycle-like mobile robot is considered in this investigation as a benchmark example. Numerical experiments on the dynamic model of the unicycle-like mobile robot confirm the effectiveness of the nonlinear dynamic and control approaches developed in this work.

Культурно-диалогическая интердетерминистская перспектива интегрального синтеза в «очеловечивании» психологического знания: к 80-летию В. Н. Панфёрова

Based on the analysis of Vladimir Panferov’s heritage the author suggests a cultural-dialogical perspective for the development of ideas for integral synthesis of human psychology. Attention is focused on the current uncertainty in the development of psychological knowledge, which is determined by the need to re-evaluate traditional ideas induced by scientific, technological and social progress. Today, an individual faces an increasing complexity of surrounding reality represented not only by the natural and social environment, but also by the increasing influence of technogenic and cyber reality. The paper articulates the impasse of deterministic and indeterministic trends in the development of psychological knowledge and proposes a movement towards cultural-dialogical interdeterminism as an alternative. The heterogeneous nature of psychological phenomenology is presented in the form of three four-dimensional continua of heteroqualitative nature. The perspective of its consideration is substantiated from the standpoint of nonlinear dynamic system approach, which allows us to consider the specificity of heterogeneity and the dynamic nature of psychological phenomenology with allocation of bifurcation points characterising the transition from one qualitative state to another. A general description of cultural-dialogical interdeterminist metatheory of psychological knowledge integration is provided, and its heuristic potential is demonstrated. The author defines the reciprocal and changing nature of human interaction with the internal and external environment, which leads to establishing a qualitative state of balance that ensures optimal interaction in a particular space-time location. The specificity of interdetermination manifests itself in the acquisition of a new quality that cannot be reduced to a simple sum of its parts. The aspect of mutual adaptation between an individual and the environment is emphasized. Mankind not only adapts to external changes, but also adapts the external environment for themselves. Attention is drawn to the fact that an ill-considered adaptation of this kind can lead to destructive consequences for one’s mental activity and for their life as a whole. In the presented context, the author substantiates that psychological phenomenology should be analyzed in the context of qualitative states of balance (imbalance) of bio-psycho-socio-consciously-unconsciouslyexistential interdeterminants interacting in specific environmental settings, taking into account individual psychological characteristics of an individual performing certain activities, a combination of which provides an actual quality condition. Dialogue is defined as a mechanism for achieving balance, and conditions for its effectiveness are also determined. The paper offers examples of practical application of cultural dialogic interdeterminism.