Phenomena In Natural Sciences Research Articles

Stability estimate for an inverse stochastic parabolic problem of determining unknown time-varying boundary *

Stochastic parabolic equations are widely used to model many random phenomena in natural sciences, such as the temperature distribution in a noisy medium, the dynamics of a chemical reaction in a noisy environment, or the evolution of the density of bacteria population. In many cases, the equation may involve an unknown moving boundary which could represent a change of phase, a reaction front, or an unknown population. In this paper, we focus on an inverse problem with the goal is to determine an unknown moving boundary based on data observed in a specific interior subdomain for the stochastic parabolic equation. The uniqueness of the solution of this problem is proved, and furthermore a stability estimate of log type is derived. This allows us, theoretically, to track and to monitor the behavior of the unknown boundary from observation in an arbitrary interior domain. The primary tool is a new Carleman estimate for stochastic parabolic equations. As a byproduct, we obtain a quantitative unique continuation property for stochastic parabolic equations.

Сигнатура мятежа: как теория СОК позволяет обнаруживать признаки протестной мобилизации в Сети

The purpose of this study is to detect signs of political mobilization in social networks during the period of intensification of internal political struggle (including street actions of the opposition) at the beginning of 2021 in Russia. This study suggests a new quantitative indicator to reveal online communities (networks) characterized by strong user engagement and high level of political mobilization. VK-communities were mapped and analyzed in order to demonstrate the capability of this tool and determine levels of mobilization. The hypothesis of the study was that mobilized communities are able to turn virtual grievances into street protests. The object of study is a set (cluster) of related protest VK-communities (groups/hubs) through which messages from the most influential information donors are brought to public notice on the Internet. The proposed indicator for the calculation of mobilization level is based on the theory of self-organized criticality (SOC). The theory of SOC was initially created to describe a broad class of natural science phenomena, yet it has recently made its way into humanities and social sciences. Timeline of work: from January 10 to April 30, 2021. It was found that some groups (about 10%) in the considered cluster had a high mobilization index during this period. Many of the mobilized groups were very influential: they were both large-scale information donors and highly specialized protest communities. The practical significance of this study lies in improving the methods of monitoring network activity to identify groups that can influence the behavior of their members (both online and on the streets). This study also has theoretical significance related to the accumulation of knowledge about SOC effects in social systems.

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11th International Conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production (HPCST-2021). May 21-22, 2021, Barnaul, Russia The 11th International Conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production (HPCST-2021) took place at the Altai State University on May 21 - 22, 2021. Altai State University (AltSU) is located in the center of Barnaul city - the capital of Altai region of the south-west of Siberia. Figure 1. Main Complex of Two Joint Buildings of Altai State University: left - Learning Campus; right - University Administration Building.HPCST is a regular scientific meeting that is held annually since 2011. It attracts specialists in the various fields of modern computer and information science, and their applications in automation of control and production, in mathematical modelling and computer simulation of processes and phenomena in natural sciences by means of high-performance computing.The goal of the conference is to present state-of-art approaches and methods for solving contemporary scientific problems and to exchange the latest research results obtained by scientists from both universities and research institutions. All the reported results are valuable contributions to the field of applied information and computer science.Due to the global COVID 2019 pandemic, travel restrictions were still in place in Russia, so the committee decided to hold the conference in a semi-virtual format. Specifically for the purpose, several rooms in AltSU main building were supplied with the equipment for videoconferencing. HPCST-2021 virtual meetings took place in the conference room (figures 2 and 3) of the university administration building using the Zoom remote videoconferencing system.List of BOARD OF THE CONFERENCE, Photos are available in this pdf.

Preface

10th International Conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production (HPCST-2020). May 15-16, 2020, Barnaul, Russia The 10th International Conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production (HPCST-2020) took place at the Altai State University on May 15 - 16, 2020. Altai State University (AltSU) is located in the center of Barnaul city - the capital of Altai region of the south-west of Siberia.HPCST is a regular scientific meeting that is held annually since 2011. It attracts specialists in the various fields of modern computer and information science, and their applications in automation of control and production, in mathematical modelling and computer simulation of processes and phenomena in natural sciences by means of parallel computing.The aim of the traditional annual conference is to present state-of-art approaches and methods for solving contemporary scientific problems, to exchange the latest research results obtained by scientists both from universities and research institutions. All the results are valuable contributions to the modern computer and information science and to their applications and technology.Taking into account the COVID-19 pandemic, the HPCST-2020 conference was held in the remote mode as a video conference with a presentation of materials on the conference’s online resources. HPCST-2020 virtual meetings took place in the conference room (figures 2-3) of the university administration building using the Zoom remote videoconferencing system (figure 4).List of Conference Photographs are available in this PDF.

Use of measurement theory for operationalization and quantification of psychological constructs in systems dynamics modelling

The analytical tools available to social scientists have traditionally been adapted from tools originally designed for analysis of natural science phenomena. This article discusses the applicability of systems dynamics - a qualitative based modelling approach, as a possible analysis and simulation tool that bridges the gap between social and natural sciences. After a brief overview of the systems dynamics modelling methodology, the advantages as well as limiting factors of systems dynamics to the potential applications in the field of social sciences and human interactions are discussed. The issues arise with regards to operationalization and quantification of latent constructs at the simulation building stage of the systems dynamics methodology and measurement theory is proposed as a ready and waiting solution to the problem of dynamic model calibration, with a view of improving simulation model reliability and validity and encouraging the development of standardised, modular system dynamics models that can be used in social science research.

Learning and teaching natural science in the early years: A case study of three different contexts

Currently many children in early childhood education cannot be accommodated in provincial department schools. Consequently, different non-governmental institutions offer Grade R programmes in an attempt to support the DBE. Pre-primary schools that traditionally took responsibility for early childhood education also offer Grade R education. The recent policy decision to include Grade R in the primary school is an innovation, which is still in its infancy. It is against this background that the national South African Curriculum (NCS) has to be implemented. This paper focuses on the teaching of natural science in Grade R and attempts to determine if the teaching and learning of natural science has different outcomes in the different contexts described above. An oral questionnaire was administered to capture children’s understanding of natural science phenomena, while interviews provided data with regard to teachers’ understanding of natural science in the foundation phase. The results show that there are differences in children’s understanding of natural phenomena in the different contexts and these differences are related to teachers’ understanding of the curriculum, as well as their views of the nature of science.

Children Explain the Rainbow: Using Young Children’s Ideas to Guide Science Curricula

This study examines young children’s ideas about natural science phenomena and explores possibilities in starting investigations in kindergarten from their ideas. Given the possibilities inherent in how young children make sense of their experiences, we believe it is critical to take children’s perspectives into consideration when designing any activities, and ideally, to design activities from their perspectives and understandings. Specifically, this research focuses on 5- and 6-year old children’s explanations of rainbows, and there are three main findings. First, our analysis demonstrates that opportunities to discuss their ideas revealed children’s different perceptions of the phenomena of rainbows. Secondly, this research emphasizes that peer-to-peer interaction in the co-construction of science concepts provided support to the children to learn from, and with, each other. Third, children’s initial explanations provided the teacher-researcher (second author) with a starting point to scaffold her teaching from. Although rainbows are quite an abstract topic to try to reproduce in the classroom, the children demonstrated their often sophisticated understandings of natural science phenomena, as well as their creative ideas as related to rainbows. In order to foster an appreciation of themes in natural science, it is crucial to build from what children already know and can do, and to use these emergent theories and considerations in designing curriculum. Thus, we draw implications for the importance of teaching science at the early childhood level and for using children’s ideas as starting points in planning instruction.

A bump on a bump? Emerging intuitions concerning the relative difficulty of the sciences.

In 4 studies, the authors examined how intuitions about the relative difficulties of the sciences develop. In Study 1, familiar everyday phenomena in physics, chemistry, biology, psychology, and economics were pretested in adults, so as to be equally difficult to explain. When participants in kindergarten, Grades 2, 4, 6, and 8, and college were asked to rate the difficulty of understanding these phenomena, children revealed a strong bias to see natural science phenomena as more difficult than those in psychology. The perceived relative difficulty of economics dropped dramatically in late childhood. In Study 2, children saw neuroscience phenomena as much more difficult than cognitive psychology phenomena, which were seen as more difficult than social psychology phenomena, even though all phenomena were again equated for difficulty in adults. In Study 3, we explored the basis for these results in intuitions about common knowledge and firsthand experience. Study 4 showed that the intuitions about the differences between the disciplines were based on intuitions about difficulty of understanding and not on the basis of more general intuitions about the feasibility or truth of the phenomena in question. Taken together, in the studies, the authors find an early emerging basis for judgments that some sciences are intrinsically more difficult than others, a bias that may persevere in adults in subtler forms in such settings as the courtroom.

DESCRIPTIVE ANALYSIS: QUANTIFICATION AND EXAMINATION OF BEHAVIOR—ENVIRONMENT INTERACTIONS

It has been argued that the study of behavior is much like the study of other natural-science phenomena. Like physical principles and laws of physics, there are behavioral principles and related laws. Understanding behavioral principles allows an applied behavior analyst to better understand social and environmental influences on behavior and to solve problems related to aberrant behavior. Most of us are familiar with the law of gravity in physics: Under certain conditions (e.g., in a vacuum tube in a laboratory), any two items (e.g., a feather and a wrench) dropped simultaneously from a given height will both reach the ground at the same time. Similarly, behavior analysts understand that the principle of reinforcement is in effect when a stimulus follows a response and, as a result, that response is strengthened in terms of rate or some other dimension (e.g., duration). This principle has been demonstrated innumerable times in experimental preparations with humans and nonhuman animals in laboratories and natural settings. Less well understood, however, is how to observe and quantify the effects of reinforcement in behavior–environment interactions as they naturally unfold in nonexperimentally manipulated conditions. In the field of applied physics, scientists take the laws of gravity and then adapt them to account for variables that, at times, exert influence outside the highly controlled laboratory environment. For example, we do not live in a vacuum; therefore, objects fall around us at varying speeds. Thus, there is a formula in physics that takes into account the effects of air resistance. To better understand the influence of particular variables on behavior and to systematically and effectively address severe behavior problems, behavior analysts have isolated and manipulated environmental variables in experimental analyses of behavior. Further, functional analysis of behavior, described by Iwata, Dorsey, Slifer, Bauman, and Richman (1982/1994) to specify the operant reinforcers for aberrant behavior, has evolved for use in a variety of settings in treating a variety of target behaviors with a variety of populations. These analyses all involve the isolation and manipulation of variables for the purposes of controlling the independent variables and predicting their effects. However, if laws and principles of behavior are like physical principles and laws of physics, then it seems that we should be able to observe and quantify their effects in natural, nonexperimental conditions. The emerging practice of conducting quantitative descriptive analyses is an example of applied behavior analysts' attempts to account for and understand the effects of social and environmental influences on behavior as behavior–environment exchanges unfold naturally in day-to-day interactions, and how those interactions influence patterns of behavior (Martens, DiGennaro, Reed, Szczech, & Rosenthal, 2008). This special section on descriptive analysis consists of three different kinds of papers. The first paper by McComas et al. presents technical discussions of descriptive analysis methods with illustrative data sets. The next set of papers detail various approaches to the analysis and interpretation of descriptive data and the implications of each approach (Camp, Iwata, Hammond, & Bloom; Pence, Roscoe, Bourret, & Ahern; Samaha et al.). The final two papers seek to answer specific applied questions regarding environmental influences on behavior, using descriptive methods (Addison & Lerman; Hughes). It is our hope that these papers will (a) improve understanding of different approaches to the conduct of descriptive analyses and their implications, (b) increase capacity to conduct descriptive analyses, and (c) stimulate readers to consider new possibilities for systematically examining the influence of social and environmental influences on behavior.

From discrete actors to goal-directed actions: toward a process-based methodology for psychology

Studying social phenomena is often assumed to be inherently different from studying natural science phenomena. In psychology, this assumption has led to a division of the field into social and experimental domains. The same kind of division has carried over into ecological psychology, despite the fact that Gibson clearly intended his theory for both social and natural phenomena. In this paper, we argue that the social/natural science dichotomy can be derived from a distinction between hermeneutics and science that is deeply rooted in the atomistic, structuralist ontological tradition. We show that, from a process-based perspective, the central questions of hermeneutics (action of an individual within a context of possible actions), ecological psychology (behavior of an organism in an ecological niche) and physics (motion of a particle in a field) share a similar structure. Building on these ideas, we propose a common, process-based methodology for psychology that integrates field theory with insights from quantum mechanics to accommodate traditionally problematic concepts in natural science such as teleology and values. To demonstrate the feasibility of this approach, empirical findings on the paradigmatic problem of prospective control (such as gaze control in automobile driving in relation to perceptual tuning) are presented.

Critical Phenomena in Natural Sciences: Chaos, Fractals, Selforganization and Disorder: Concepts and Tools

Since the discovery of the renormalization group theory in statistical physics, the realm of applications of the concepts of scale invariance and criticality has pervaded several fields of natural and social sciences. This is the leitmotiv of Didier Sornette's book, who in Critical Phenomena in Natural Sciences reviews three decades of developments and applications of the concepts of criticality, scale invariance and power law behaviour from statistical physics, to earthquake prediction, ruptures, plate tectonics, modelling biological and economic systems and so on. This strongly interdisciplinary book addresses students and researchers in disciplines where concepts of criticality and scale invariance are appropriate: mainly geology from which most of the examples are taken, but also engineering, biology, medicine, economics, etc. A good preparation in quantitative science is assumed but the presentation of statistical physics principles, tools and models is self-contained, so that little background in this field is needed. The book is written in a simple informal style encouraging intuitive comprehension rather than stressing formal derivations. Together with the discussion of the main conceptual results of the discipline, great effort is devoted to providing applied scientists with the tools of data analysis and modelling necessary to analyse, understand, make predictions and simulate systems undergoing complex collective behaviour. The book starts from a purely descriptive approach, explaining basic probabilistic and geometrical tools to characterize power law behaviour and scale invariant sets. Probability theory is introduced by a detailed discussion of interpretative issues warning the reader on the use and misuse of probabilistic concepts when the emphasis is on prediction of low probability rare---and often catastrophic---events. Then, concepts that have proved useful in risk evaluation, extreme value statistics, large limit theorems for sums of independent variables with power law distribution, random walks, fractals and multifractal formalisms, etc, are discussed in an immediate and direct way so as to provide ready-to-use tools for analysing and representing power law behaviour in natural phenomena. The exposition then continues discussing the main developments, allowing the reader to understand theoretically and model strongly correlated behaviour. After a concise, but useful, introduction to the fundamentals of statistical physics a discussion of equilibrium critical phenomena and the renormalization group is proposed to the reader. With the centrality of the problem of non-equilibrium behaviour in mind, a discussion is devoted to tentative applications of the concept of temperature in the off-equilibrium context. Particular emphasis is given to the development of long range correlation and of precursors of phase transitions, and their role in the prediction of catastrophic events. Then, basic models such as percolation and rupture models are described. A central position in the book is occupied by a chapter on mechanisms for power laws and a subsequent one on self-organized criticality as a general paradigm for critical behaviour as proposed by P Bak and collaborators. The book concludes with a chapter on the prediction of fields generated by a random distribution of sources. The book maintains the promise of the title of providing concepts and tools to tackle criticality and self-organization. The second edition, while retaining the structure of the first edition, considerably extends the scope with new examples and applications of a research field which is constantly growing. Any scientific book has to solve the dichotomy between the depth of discussion, the pedagogical character of exposition and the quantity of material discussed. In general the book, which evolved from a graduate student course, favours these last two aspects at the expense of the first one. This makes the book very readable and means that, while complicated concepts are always explained by means of simple examples, important results are often mentioned but not derived or discussed in depth. Most of the time this style of exposition manages to successfully convey the essential information, other times unfortunately, e.g. in the case of the chapter on disordered systems, the presentation appears rather superficial. This is the price we pay for a book covering an impressively vast subject area and the huge bibliography (more than 1000 references) furnishes a necessary guide for acquiring the working knowledge of the subject covered. I would recommend it to teachers planning introductory courses on the field of complex systems and to researchers wanting to learn about an area of great contemporary interest.

FROM MY PERSPECTIVE: VIII. Continuum of change: Perspectives on physical or “hard” sciences

This article, the eighth in a series on the new millennium, describes timelines that provide the setting and context for basing very long forecasts. The longer and more complete the historic patterns of past and present, coupled with conceptual possibilities and prospective developments, the clearer the pattern of ongoing incremental change becomes. Likely patterns of continuing change literally “pop out” when assessing such chronologies. Feasibility, credibility and practicality of 1000 year forecasting typically is dismissed out-of-hand. To help dispel such mindsets, timelines covering wide-ranging natural science phenomena spanning thousands to billions of years are presented here. Depicting the coming and going of familiar natural science phenomena in this manner reveals how the future fate of the world we live in is predetermined. These very long horizons suggest potentials for projecting physical science possibilities a mere 1000 years into the future.

Extreme events as foundation of Lévy walks with varying velocity

Abstract In this work we study the role of extreme events [E.W. Montroll, B.J. West, in: J.L. Lebowitz, E.W. Montrell (Eds.), Fluctuation Phenomena, SSM, vol. VII, North-Holland, Amsterdam, 1979, p. 63; J.-P. Bouchaud, M. Potters, Theory of Financial Risks from Statistical Physics to Risk Management, Cambridge University Press, Cambridge, 2001; D. Sornette, Critical Phenomena in Natural Sciences. Chaos, Fractals, Selforganization and Disorder: Concepts and Tools, Springer, Berlin, 2000] in determining the scaling properties of Levy walks with varying velocity. This model is an extension of the well-known Levy walks one [J. Klafter, G. Zumofen, M.F. Shlesinger, in M.F. Shlesinger, G.M. Zaslavsky, U. Frisch (Eds.), Levy Flights and Related Topics ion Physics, Lecture Notes in Physics, vol. 450, Springer, Berlin, 1995, p. 196; G. Zumofen, J. Klafter, M.F. Shlesinger, in: R. Kutner, A. Pȩkalski, K. Sznajd-Weron (Eds.), Anomalous Diffusion. From Basics to Applications, Lecture Note in Physics, vol. 519, Springer, Berlin, 1999, p. 15] introduced in the context of chaotic dynamics where a fixed value of the walker velocity is assumed for simplicity. Such an extension seems to be necessary when the open and/or complex system is studied. The model of Levy walks with varying velocity is spanned on two coupled velocity–temporal hierarchies: the first one consisting of velocities and the second of corresponding time intervals which the walker spends between the successive turning points. Both these hierarchical structures are characterized by their own self-similar dimensions. The extreme event, which can appear within a given time interval, is defined as a single random step of the walker having largest length. By finding power-laws which describe the time-dependence of this displacement and its statistics we obtained two independent diffusion exponents, which are related to the above-mentioned dimensions and which characterize the extreme event kinetics. In this work we show the principal influence of extreme events on the basic quantities (one-step distributions and moments as well as two-step correlation functions) of the continuous-time random walk formalism. Besides, we construct both the waiting-time distribution and sojourn probability density directly in a real space and time in the scaling form by proper component analysis which takes into account all possible fluctuations of the walker steps in contrast to the extreme event analysis. In this work we pay our attention to the basic quantities, since the summarized multi-step ones were already discussed earlier [Physica A 264 (1999) 107; Comp. Phys. Commun. 147 (2002) 565]. Moreover, we study not only the scaling phenomena but also, assuming a finite number of hierarchy levels, the breaking of scaling and its dependence on control parameters. This seems to be important for studying empirical systems the more so as there are still no closed formulae describing this phenomenon except the one for truncated Levy flights [Phys. Rev. Lett. 73 (1994) 2946]. Our formulation of the model made possible to develop an efficient Monte Carlo algorithm [Physica A 264 (1999) 107; Comp. Phys. Commun. 147 (2002) 565] where no MC step is lost.

Virtual spiders guide robotic control design

Traditionally, researchers have studied natural science phenomena by describing observations and by experimentally isolating and analyzing pertinent parameters and factors. As computational power has mushroomed in recent years, analysis of biological systems has increasingly involved formal models coupled with computer simulations. Lately, investigators have turned to individual-based models that mimic the complexity of biological processes by using repeated local interactions between units, described in few simple behavior rules. By mimicking plant morphology or the problem-solving capabilities of social robots, computer scientists can use a comparable concept for simulation, applied chaos theory, artificial intelligence, and artificial life. In our approach, called TheseusV, we used this approach to mimic the spatial orientation of spiders during web construction. As this article shows, behavioral principles of arthropods (spiders and insects) during orientation are interesting not only for research into the behavior of real animals, but also because their robustness and simplicity make them potentially quite useful for controlling autonomous agents such as insect robots. After all, most real bugs are notoriously good at coping with unpredictable environments, so lessons we learn from animal models can also enhance artificial-life models and offer new insight into spatial orientation to AI researchers.

Global Environmental Change: Management Under Long-range Uncertainty

This chapter provides an overview of social science challenges in global environmental research. After a sketch of backgrounds, the 'social dilemma' is introduced as a major research question. It is asserted that uncertainties over global environmental change provoke innovative studies on risk behaviour and management of long-range, largely unpredictable, phenomena. Problems of global environmental change are not isolated from the way individuals, groups and governments take decisions in our society. The essence of social science research questions in the area of global environmental change is the so-called 'social dilemma'. Various economists have tried to cope with the social dilemma by looking for correction mechanisms which would fully incorporate all, marginal, social costs of global environmental decay. A prominent focal point in global environmental change research would have to be uncertainty and risk assessment. It is often taken for granted that uncertainty is lower in natural science phenomena governed by fixed laws and regularities.