Scientific Problems Research Articles

Simulation-based inference of single-molecule experiments.

Single-molecule experiments are a unique tool to characterize the structural dynamics of biomolecules. However, reconstructing molecular details from noisy single-molecule data is challenging. Simulation-based inference (SBI) is a powerful framework for analyzing complex experimental data, integrating statistical inference, physics-based simulators, and machine learning. Recent advances in deep learning have accelerated the development of new SBI methods, enabling the application of Bayesian inference to an ever-increasing number of scientific problems. Here, we review the nascent application of SBI to the analysis of single-molecule experiments. We introduce parametric Bayesian inference and discuss its limitations. We then overview emerging deep learning-based SBI methods to perform Bayesian inference for complex models encoded in computer simulators. We illustrate the first applications of SBI to single-molecule force spectroscopy and cryo-electron microscopy experiments. SBI allows us to leverage powerful computer algorithms modeling complex biomolecular phenomena to connect scientific models and experiments in a principled way.

Generalization of Liu–Zhou Method for Multiple Roots of Applied Science Problems

Some optimal and non-optimal iterative approaches for computing multiple zeros of nonlinear functions have recently been published in the literature when the multiplicity θ of the root is known. Here, we present a new family of iterative algorithms for multiple zeros that are distinct from the existing approaches. Some special cases of the new family are presented and it is found that existing Liu-Zhou methods are the special cases of the new family. To check the consistency and stability of the new methods, we consider the continuous stirred tank reactor problem, isentropic supersonic flow problem, eigenvalue problem, complex root problem, and standard test problem in the numerical section and we find that the new methods are more competitive with other existing fourth-order methods. In the numerical section, the error of the new methods confirms their robust character.

Solving the relevance problem with predictive processing

ABSTRACT The frame or relevance problem is a classic problem in cognitive science and philosophy. We attempt to resolve this problem by appealing to predictive processing, a growing theory of cognition. As such, it ought to explain one of the central processes of cognition, that is, how an agent context-sensitively determines relevance. Our solution begins by appealing to Bayesian prior probabilities, which intuitively reflect relevance for a predictive agent. However, prior probabilities are necessary but insufficient for solving the problem with predictive processing. We then turn to the broader predictive processing toolbox, leveraging the concepts of prediction, prediction error, and precision in order to explain relevance. This move reveals that the processes that optimize for prediction error minimization are crucial for realizing relevance. Although, they do not yet solve the entire problem, which also demands an agent select relevant actions, based on considerations about their consequences. By appealing to active inference, decision-making, and planning can be brought to bear on relevance, in addition to perceptual inference. With this final inclusion of action (as inference), we suggest predictive processing has the tools to comprehensively solve the problem of relevance.

Sarcopenia and COVID-19 - a complex pathological duet

March 11, 2020 The World Health Organization has declared the New Coronavirus Infection 2019 (COVID-19) a pandemic. The risk of complications in the acute period of COVID-19 was obvious almost immediately. However, back in 2020, during the onset and height of the pandemic, health professionals began to think about the possible negative long-term and delayed consequences of COVID-19. Until now, the world is facing the long-term consequences of COVID-19 among survivors, who often have multisystem pathological manifestations. The study of the consequences of COVID-19 continues to be an urgent problem of modern medical science. Currently, increased attention is being paid to sarcopenia, which occurred in patients after COVID-19. On the other hand, sarcopenia is a predictor of adverse outcomes in patients with new coronavirus infection. In 2020, a self-isolation regime (lockdown) was introduced in many countries as an effective anti-epidemic strategy. Unfortunately, the self-isolation regime has led to a decrease in physical activity, especially in elderly and senile people. Therefore, physical inactivity against the background of self-isolation itself was a risk factor for sarcopenia. In addition, during self-isolation, many patients experienced an increase in the caloric content of their diet and the development of obesity. Rehabilitation after COVID-19 necessarily includes measures for the prevention and treatment of sarcopenia. Thus, this literature review is devoted to the study of the relationship between sarcopenia and COVID-19.

Creative Thinking in Science: Influence of Ethnicity, Gender and Grade Level in Co-educational Schools, Kenya

The purpose of the study was to investigate the influence of ethnicity, gender and grade level on creative thinking in science among co-educational secondary school students. The study employed cross-sectional design with purposive sampling through which 363 students were sampled from 2 co-educational secondary schools in Kakamega (n = 198) and Siaya (n = 165) counties of Kenya. The entire sample was made of grade 9 (n = 95), grade 10 (n = 99), grade 11 (n = 75), and grade 12 (n = 94) students. The same sample consisted of 168 males and 195 females. Students Science Creativity Questionnaire (SSCQ) was adapted from Smith (2019), piloted and used to measure creative thinking in science. The instrument measured four dimensions of divergent creative thinking as fluency, flexibility, elaboration and originality. The data were analysed using a 4 x 2 x 2 MANOVA. The findings indicated statistically significant ethnic differences between Luo students from Siaya county and Luhya students from Kakamega county with regard to fluency and flexibility in favor of Luhya students. There were no statistically significant gender differences in creative thinking in science and statistically significant grade level differences in the dimensions of fluency, elaboration and originality. The findings have implications for pedagogic practice. Science teachers could deliberately make learning environments characterised creative learning activities which require learners to to generate multiple ideas and encourage students to solve scientific problems. Such learning environments could be made to encourage gender inclusive pedagogic practices and tailor creative learning experiences to grade level of learners. The discussion, conclusions and recommendations of the study are herein explained.

The Impact of Sustainable Practices on Organizational Reputation: The Mediating Role of Social Responsibility in Saudi Organizations

This research paper deals with a study on the relationship between sustainable practices and organizational reputation through the mediating role of social responsibility in Saudi organizations. The main purpose of the research is to contribute to understanding how sustainable practices can enhance organizational reputation. The study uses a quantitative research design, and surveys data collected from a sample of 304 administrators working in a number of Saudi organizations. The organization of literature sources and problem-solving methods indicate that the importance of this scientific problem lies in the need for an empirical examination of how sustainable practices can improve organizational reputation through the mediating effect of social responsibility. The methodological tools of the research included the use of SPSS v.23 and AMOS v.23 to test the hypotheses. The results of the empirical analysis show that sustainable practices positively affect organizational reputation, and this relationship is mediated by social responsibility. The results of the study confirm that the application of sustainable practices can enhance the organization's reputation by enhancing its social responsibility. The results also confirm that there are no gender differences in the perception of the practices of the study variables. The results of this study can also be useful to policy makers and university administrators who seek to promote sustainable practices and enhance the organization's reputation through effective social responsibility initiatives. Keywords: corporate social responsibility; environmental sustainability; organizational image; reputational management; sustainable business practices.

Human motivation is organized hierarchically, from proximal (means) to ultimate (ends).

Murayama and Jach raise a key problem in behavioral sciences, to which we suggest evolutionary science can provide a solution. We emphasize the role of adaptive mechanisms in shaping behavior and argue for the integration of hierarchical theories of goal-directed cognition and behavioral flexibility, in order to unravel the motivations behind actions that, in themselves, seem disconnected from adaptive goals.

Mental computational processes have always been an integral part of motivation science.

Some constructs in motivation science are certainly underdeveloped and some motivation researchers may work with underspecified constructs, as suggested by Murayama and Jach (M&J). However, this is not indicative of a general problem in motivation science. Many motivation theories focus on specific mechanisms underlying motivated behavior and thus have already adopted the computational process perspective that M&J call for.

To the History of the Discovery and Study of the New Siberian Islands: Expedition of M.M. Gedenstrom 1809-1810

This article is devoted to a little-studied aspect of the history of Russian exploration of the North - one of the first Arctic expeditions of Russians at the beginning of the 19th century. The appeal to the historical experience of the discovery and study of the New Siberian Islands archipelago is determined by the growing interest in it on the part of world powers in connection with the development of coastal shelf resources that began here and the resumption of work on the Northern Sea Route. In Russian historiography the name M.M. Gedenshtrom for a long time remained a figure of silence in comparison with such researchers as F.P. Wrangel, P.F. Anjou, G.A. Sarychev, E.V. Toll, who studied this region of the Arctic. At the same time, the expedition of M.M. Gedenshtrom is marked not only by its connection with the specific tasks of Russian foreign policy and attempts to solve scientific problems that were pressing for that time, but also with the interests of developing business activity on the islands, the depths of which are still rich in valuable mineral raw materials. MM. Gedenshtrom was the first to create a general map of the New Siberian Islands, describe them, describe the coast between the mouths of the Yana and Kolyma rivers, and correct inaccuracies on previously compiled maps.

Content analysis of biology book for the fourth scientific grade according to the levels of depth of knowledge

The research aimed to analyze the content of the biology book for 4th scientific grade in high school according to the levels of (DOK), to know the extent of the availability of indicators of these levels in the scientific content of the book, which would achieve a deep understanding of students in biological concepts and develop strategic and extended thinking in solving scientific problems in a sustainable style. The researchers used a descriptive-analytical approach, and the content of the biology book represented the research community. The sample was made up of the first six chapters. The research tool was built and included (22) indicators and was presented to several arbitrators in teaching methods to verify the validity of the tool. researchers analyzed the content of the biology book, depending on the explicit and implicit idea as a unit of recording, and the Holsti formula was used to calculate the reliability of the analysis. The results showed that the biology book does not contain all indicators, and the levels appeared to vary. level (Recall and reproduction) obtained (63.49%), level (Skills and concepts) obtained (19.07%), level (Strategic thinking) obtained (8.84%), and level (Extended thinking) obtained (8.60%), accordingly the researchers presented several recommendations.

A Comprehensive Review on insights of Ancient Vedic physics

This Article provides a deep analysis of Vedic physics by investigating its prime ideas and their relation with contemporary scientific theories. By analyzing ancient Indian Vedic text like the Vedas and Upanishads, the composition explores key ideas such as cyclic time, akasha (ether), and how consciousness shapes reality. To find similarities between these ancient discoveries and modern scientific theories, a multidisciplinary method that includes textual analysis, comparative research, and philosophical investigation is used. Past Research enables that, there are many notable similarities between the quantum vacuum and the Vedic text about akasha, which indicates that the ancient Indian saints believed in a movable ground that supported matter and energy. The oscillatory cosmos and other contemporary cosmological ideas are contrasted with cyclic notion of time (kalpas and yugas) found in the Vedas such as the oscillatory universe and Big Bang scenarios presenting diverse perspectives on the origins and history of the cosmos. Again quantum physics and the study defining that how the observer shapes physical reality are in line with the Vedic emphasis on consciousness as a unifying field. The study emphasizes how these ancient insights could enhance contemporary scientific paradigms, despite the interpretation difficulties posed by the symbolic language of Vedic texts. The Vedic text provides a combination of philosophical and empirical prescript, which works as a catalyst in finding the creative answers for unspecified scientific problems like the nature of energy, unified forces and dark matter. Present composition stimulates an interdisciplinary conversation which works like a bridge between tradition and modernity along with highlighting the timeless value of early scientific ideas. The composition aims to provide a unified vision of science and spirituality by fusing Vedic wisdom with modern approaches to shed light on new paradigms for comprehending the universe and humanity's place within it.

Studying Critical Parameters of Superconductor via Diamond Quantum Sensors

Abstract Critical parameters are the key to superconductivity research, and reliable instrumentations can facilitate the study. Traditionally, one has to use several different measurement techniques to measure critical parameters separately. In this work, we develop the use of a single species of quantum sensor to determine and estimate several critical parameters with the help of independent simulation data. We utilize the nitrogen-vacancy (NV) center in the diamond, which recently emerged as a promising candidate for probing exotic features in condensed matter physics. The non-invasive and highly stable nature provides extraordinary opportunities to solve scientific problems in various systems. Using a high-quality single-crystalline YBa2Cu4O8 (YBCO) as a platform, we demonstrate the use of diamond particles and a bulk diamond to probe the Meissner effect. The evolution of the vector magnetic field, the H-T phase diagram, and the map of fluorescence contour are studied via NV sensing. Our results reveal different critical parameters, including lower critical field Hc1 , upper critical field Hc2 , and critical current density jc , as well as verifying the unconventional nature of this high-temperature superconductor YBCO. Therefore, NV-based quantum sensing techniques have huge potential in condensed matter research.

Neural Associations between Inhibitory Control and Counterintuitive Reasoning in Science and Maths in Primary School Children.

Emerging evidence suggests that inhibitory control (IC) plays a pivotal role in science and maths counterintuitive reasoning by suppressing incorrect intuitive concepts, allowing correct counterintuitive concepts to come to mind. Neuroimaging studies have shown greater activation in the ventrolateral and dorsolateral pFCs when adults and adolescents reason about counterintuitive concepts, which has been interpreted as reflecting IC recruitment. However, the extent to which neural systems underlying IC support science and maths reasoning remains unexplored in children. This developmental stage is of particular importance, as many crucial counterintuitive concepts are learned in formal education in middle childhood. To address this gap, fMRI data were collected while fifty-six 7- to 10-year-olds completed counterintuitive science and math problems, plus IC tasks of interference control (Animal Size Stroop) and response inhibition (go/no-go). Univariate analysis showed large regional overlap in activation between counterintuitive reasoning and interference control, with more limited activation observed in the response inhibition task. Multivariate similarity analysis, which explores fine-scale patterns of activation across voxels, revealed neural activation similarities between (i) science and maths counterintuitive reasoning and interference control tasks in frontal, parietal, and temporal regions, and (ii) maths reasoning and response inhibition tasks in the precuneus/superior parietal lobule. Extending previous research in adults and adolescents, this evidence is consistent with the proposal that IC, specifically interference control, supports children's science and maths counterintuitive reasoning, although further research will be needed to demonstrate the similarities observed do not reflect more general multidemand processes.

О соотношении понятий «агрогеосистема», «агроландшафт» и «агроэкосистема» как основных дефиниций в геоэкологических исследованиях

Definition is important for the development of the theory of any science. It allows us to define a clear conceptual and terminological framework of scientific knowledge. The scientific article discusses the main definitions used in geoecological research related to agricultural production: «agrogeosystem», «agrolandscape» and «agroecosystem». The importance of the conceptual and terminological apparatus for solving a scientific problem is emphasized. The main differences of the considered terms are outlined.

Teachers' and Students' Feedback on Sociocultural Interactive Digital Modules for Science Literacy and Problem-Solving: A Transformative Learning Approach

Purpose of the study: This article examines the practicality of sociocultural-based interactive digital modules in improving students' science literacy and problem-solving skills by integrating a transformative learning approach. Methodology: This descriptive research analyzes students' and teachers' responses to the developed interactive digital module. The study was conducted in junior high schools in Merauke Regency and Boven Digoel Regency, South Papua Province, totaling six schools. Data were collected through responses from 6 science teachers and 60 students involved in learning science with the module. Main Findings: The results showed that this module is efficient for use in science learning, according to the responses from teachers and students. Feedback from teachers and students indicated that this module is efficient to use in science learning, as seen from the average total percentage given by teachers of 95% in the efficient category and provided by students of 95.53% in the efficient category. Through this article, the developed interactive digital module is feasible to use in science learning to improve science literacy and problem solving skills of junior high school students.. Novelty/Originality of this study: The module is designed with an approach that combines Papuan local wisdom values and transformative learning principles. This approach is rarely implemented simultaneously in the context of science learning to improve students' understanding of science. In addition, the interactive digital module not only focuses on understanding science materials but also connects them with local wisdom to increase the relevance of learning to students' daily lives.

Determining interaction directionality in complex biochemical networks from stationary measurements

Revealing interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Some methods may reveal undirected network topology, e.g., using node-node correlation. Yet, the direction of the interaction, thus a causal inference, remains to be determined - especially in steady-state observations. We introduce a method to infer the directionality within this network only from a “snapshot” of the abundances of the relevant molecules. We examine the validity of the approach for different properties of the system and the data recorded, such as the molecule’s level variability, the effect of sampling and measurement errors. Simulations suggest that the given approach successfully infer the reaction rates in various cases.

Characterizing out-of-distribution generalization of neural networks: application to the disordered Su–Schrieffer–Heeger model

Abstract Machine learning (ML) is a promising tool for the detection of phases of matter. However, ML models are also known for their black-box construction, which hinders understanding of what they learn from the data and makes their application to novel data risky. Moreover, the central challenge of ML is to ensure its good generalization abilities, i.e. good performance on data outside the training set. Here, we show how the informed use of an interpretability method called class activation mapping, and the analysis of the latent representation of the data with the principal component analysis can increase trust in predictions of a neural network (NN) trained to classify quantum phases. In particular, we show that we can ensure better out-of-distribution (OOD) generalization in the complex classification problem by choosing such an NN that, in the simplified version of the problem, learns a known characteristic of the phase. We also discuss the characteristics of the data representation learned by a network that are predictors of its good OOD generalization. We show this on an example of the topological Su–Schrieffer–Heeger model with and without disorder, which turned out to be surprisingly challenging for NNs trained in a supervised way. This work is an example of how the systematic use of interpretability methods can improve the performance of NNs in scientific problems.

Donat Semenovich Sarkisov: on Pathological Anatomy, Experimental Medicine and Philosophy

D.S. Sarkisov is an outstanding pathologist, scientist, researcher and philosopher. The works of D.S. Sarkisov do not lose their relevance in the 21st century, when medicine is moving far forward thanks to modern high-tech techniques. However, turning to the yellowed pages of D.S. Sarkisov’s works, one can find discussions on topical topics in medicine, such as the formulation of a diagnosis, which is impossible not to argue about today. Thanks to his philosophical approach to the organization of work, D.S. Sarkisov was able to create generalizing works, for example, in the field of experimental medicine, as a result of which we can, after reading one of his books, get a general idea of the current state of the issue. This rare ability to rise above all pressingmedical issues and the scientific data accumulated on them allowed Donat Semenovich to win the true respect and love of fellow clinicians and pathologists. I.V. Davydovsky had another equally extraordinary approach to work, the similarity of opinions with whom the authors discuss within the article. The desire for a comprehensive discussion of medical and scientific problems brought D.S. Sarkisov not only to face-to-face conferences, but also to the pages of well-deserved scientific publications. The discussions of D.S. Sarkisov and colleagues serve as a brilliant example of scientific dialogue. In personal and work relationships, D.S. Sarkisov also left an indelible impression on people who were lucky enough to communicate with him in person, as evidenced by touching articles dedicated to his memory.

Research on Optimization Design of Intelligent Aircraft

Demand analysis and concept research of intelligent deform-able aircraft, overall and subsystem design technology of intelligent deform-able aircraft. From the perspective of basic scientific problems and bottleneck technologies, the main problems lie in two aspects, namely: aerodynamics, flight mechanics and flight control of deform-able aircraft, deform-able structure, drive and deformation control. The connotation of intelligent deform-able aircraft, deform-able technical indicators, deform-able materials and structures, and cost-effectiveness analysis. Technical difficulties for each performance indicator mentioned above, some novel technologies and methods are used to improve the performance of cross-medium aircraft, which are mainly divided into variable wing technology, variable blade technology, variable density technology and structural buffer technology.

A comprehensive analysis of R's application in ecological research from 2008 to 2023

Abstract The field of ecology has been greatly enhanced by the integration of computational tools and statistical methods, with the programming language R emerging as a pivotal and flexible tool for ecological inquiry. As ecological research accelerates, understanding the prevalent trends and specific usage patterns of R in recent studies is crucial. Our investigation explored the use of R and its packages in 125,494 scholarly articles across 40 ecology journals from 2008 to 2023. A considerable number of articles, 52,658 (41.96%), designated R as their principal analytical tool, demonstrating a steady linear growth in its utilization from 10.31% in 2008 to 66.88% in 2023. Twelve R packages, including "lme4," "vegan," "nlme," "MuMIn," "ape," "ggplot2," "car," "mgcv," "MASS," "raster," "multcomp," and "lmerTest," each played a pivotal role in contributing to more than 1,000 scholarly articles. The highest usage rate of the lme4 package indicates that mixed-effect models have a particularly important role in ecological research, and the application of these models has helped ecologists solve many important scientific problems. Journal-specific package preferences aligned with their scientific domains, while the rise in the average number of R packages per article signals a trend towards more complex analytical methods in ecology. Our findings indicate a reciprocal relationship between the development of R and ecological research, underscoring the need for collaboration among quantitative ecologists, R developers, and ecologists to further advance both the language and the field.