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3.
J Chem Theory Comput ; 18(6): 3357-3363, 2022 Jun 14.
Artigo em Inglês | MEDLINE | ID: mdl-35657378

RESUMO

Widely applicable, modified Green-Kubo expressions for the local diffusion coefficient (Dl) are obtained using linear response theory. In contrast to past definitions in use, these expressions are statistical mechanical results. Molecular simulations of systems with anisotropic diffusion and an inhomogeneous density profile confirm the validity of the results. Diffusion coefficients determined from different expressions in terms of currents and velocity correlations agree in the limit of large systems. Furthermore, they apply to arbitrarily small local regions, making them readily applicable to nanoscale and inhomogeneous systems where knowledge of Dl is important.


Assuntos
Física , Anisotropia , Difusão
4.
J Radiol Prot ; 42(2)2022 Jun 28.
Artigo em Inglês | MEDLINE | ID: mdl-35705062

RESUMO

In this work, we conducted experiments to validate the proton physics models of Geant4 (version 10.6). The stopping power ratios (SPRs) of 11 inserts, such as acrylic, delrin, high density polyethylene, and polytetrafluoroethylene, etc, were measured using a superconducting synchrocyclotron that produces a scattering proton beam. The SPRs of the inserts were also calculated based on Geant4 simulation with six physics lists, i.e. QGSP_ FTFP_ BERT, QGSP_BIC_HP, QGSP_BIC, QGSP_FTFP_BERT, QSGP_BERT, and QBBC. The calculated SPRs were compared to the experimental SPRs, and relative per cent error was used to quantify the accuracy of the simulated SPRs of inserts. The comparison showed that the five physics lists generally agree well with the experimental SPRs with a relative difference of less than 1%. The lowest overall percentage error was observed for QGSP_FTFP_BERT and the highest overall percentage error was observed for QGSP_BIC_HP. The 0.1 mm range cut value consistently led to higher percentage error for all physics lists except for QGSP_BIC_HP and QBBC. Based on the validation, we recommend QGSP_BERT_HP physics list for proton dose calculation.


Assuntos
Terapia com Prótons , Prótons , Ciclotrons , Método de Monte Carlo , Física
5.
PLoS One ; 17(6): e0270131, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35737658

RESUMO

Science advances by pushing the boundaries of the adjacent possible. While the global scientific enterprise grows at an exponential pace, at the mesoscopic level the exploration and exploitation of research ideas are reflected through the rise and fall of research fields. The empirical literature has largely studied such dynamics on a case-by-case basis, with a focus on explaining how and why communities of knowledge production evolve. Although fields rise and fall on different temporal and population scales, they are generally argued to pass through a common set of evolutionary stages. To understand the social processes that drive these stages beyond case studies, we need a way to quantify and compare different fields on the same terms. In this paper we develop techniques for identifying common patterns in the evolution of scientific fields and demonstrate their usefulness using 1.5 million preprints from the arXiv repository covering 175 research fields spanning Physics, Mathematics, Computer Science, Quantitative Biology and Quantitative Finance. We show that fields consistently follow a rise and fall pattern captured by a two parameters right-tailed Gumbel temporal distribution. We introduce a field-specific re-scaled time and explore the generic properties shared by articles and authors at the creation, adoption, peak, and decay evolutionary phases. We find that the early phase of a field is characterized by disruptive works mixing of cognitively distant fields written by small teams of interdisciplinary authors, while late phases exhibit the role of specialized, large teams building on the previous works in the field. This method provides foundations to quantitatively explore the generic patterns underlying the evolution of research fields in science, with general implications in innovation studies.


Assuntos
Publicações , Redação , Física , Projetos de Pesquisa
6.
Artigo em Inglês | MEDLINE | ID: mdl-35742352

RESUMO

A thermogravimetric analysis is used to analyze the thermal kinetics and investigate the synergistic effects between Alternanthera philoxeroides (AP) and waste tires (WTS) in a temperature range of 50-900 °C under three heating rates (15, 25, and 35 °C/min). Two model-free methods (FWO and KAS) and a model-fitting method (CR) were applied to calculate the activation energy. Results revealed that heating rates had no significant effect on the pyrolysis operation. The addition of WTS improved the thermal degradation of the samples as the samples had more than one stage during the main reaction period. A promoting synergistic effect was found in the blend 75A25WT and obtained the lowest activation energy among all the blends without a catalyst, while the blend 50A50WT exhibited an inhibiting effect. On the other hand, the addition of HZSM-5 accelerated the reaction time and obtained the lowest activation energy among all the blends without a catalyst. Furthermore, ΔW of 75A25WT+C was the lowest, indicating that the blend with a catalyst exhibited the strongest synergistic effect. This research confirmed that the addition of WTS improved the thermal parameters of the samples and clarified the capacity of HZSM-5 to reduce the activation energy.


Assuntos
Calefação , Pirólise , Biomassa , Catálise , Cinética , Física , Termogravimetria
8.
Comput Biol Med ; 146: 105586, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-35751197

RESUMO

The development of computational modeling and simulation have immensely benefited the study of cardiac disease mechanisms and facilitated the optimal disease diagnosis and treatment design. The dynamic propagation of cardiac electrical signals are often described by electrophysiological models in the form of partial differential equations (PDEs), which are commonly solved by the finite element method (FEM). However, FEM-based simulation only provides the numerical solution of the PDEs and is incapable of incorporating real clinical measurements into the modeling for optimal decision making. Additionally, electrical signals from the heart are commonly collected through cardiac catheterization, which acquires cardiac signals from limited spatial locations. Such sparse sensor measurements significantly challenge traditional machine learning methods for reliable predictive modeling. This paper presents a physics-constrained deep active learning (P-DAL) framework to model spatiotemporal cardiac electrodynamics. Specifically, we adapt the physics-constrained deep learning (P-DL) framework developed in our prior work to integrate the physical laws of the cardiac electrical wave propagation with deep learning for robust predictive modeling of the heart electrical behavior from sparse sensor measurements. Furthermore, we develop a novel active learning strategy to seek the informative spatial locations on the heart surface for data collection to further increase the predictive power of the P-DL method. This active learning criterion combines both the prediction uncertainty of the P-DL and the space-filling design over the heart geometry. We evaluate the performance of the proposed framework to model cardiac electrodynamics in both healthy and diseased heart systems. Numerical results show that the proposed P-DL approach significantly outperforms traditional modeling methods. Specifically, P-DL achieves up to 48.3% and 28.0% reduction in the estimated Relative Error (RE) compared with that from the traditional spatiotemporal Gaussian process (STGP) models in the healthy and diseased systems, respectively. We also demonstrate the efficacy of the proposed active learning procedure by comparing it with traditional learning strategies. Specifically, RE generated from the proposed P-DAL achieves 16.3% and 28.0% (11.1% and 21.2%) reduction compared with RE generated from the P-DL method based on pure space-filling design (i.e., P-DSL) and random data sampling strategy (P-DRL) in the healthy (diseased) heart system, respectively.


Assuntos
Coração , Aprendizado de Máquina , Simulação por Computador , Distribuição Normal , Física
9.
Chaos ; 32(5): 051103, 2022 May.
Artigo em Inglês | MEDLINE | ID: mdl-35649977

RESUMO

During the last few years, statistical physics has received increasing attention as a framework for the analysis of real complex systems; yet, this is less clear in the case of international political events, partly due to the complexity in securing relevant quantitative data on them. Here, we analyze a detailed dataset of violent events that took place in Ukraine since January 2021 and analyze their temporal and spatial correlations through entropy and complexity metrics and functional networks. Results depict a complex scenario with events appearing in a non-random fashion but with eastern-most regions functionally disconnected from the remainder of the country-something opposing the widespread "two Ukraines" view. We further draw some lessons and venues for future analyses.


Assuntos
Física , Entropia , Ucrânia
10.
Chaos ; 32(5): 052102, 2022 May.
Artigo em Inglês | MEDLINE | ID: mdl-35649980

RESUMO

The Nobel Prize in Physics 2021 was awarded to Syukuro Manabe, Klaus Hasselmann, and Giorgio Parisi for their "groundbreaking contributions to our understanding of complex systems," including major advances in the understanding of our climate and climate change. In this Perspective article, we review their key contributions and discuss their relevance in relation to the present understanding of our climate. We conclude by outlining some promising research directions and open questions in climate science.


Assuntos
Mudança Climática , Prêmio Nobel , Física
11.
Phys Rev Lett ; 128(20): 208004, 2022 May 20.
Artigo em Inglês | MEDLINE | ID: mdl-35657869

RESUMO

We study the effect of spatial anisotropy on polar flocks by investigating active q-state clock models in two dimensions. In contrast to the equilibrium case, we find that any amount of anisotropy is asymptotically relevant, drastically altering the phenomenology from that of the rotationally invariant case. All of the well-known physics of the Vicsek model, from giant density fluctuations to microphase separation, is replaced by that of the active Ising model, with short-range correlations and complete phase separation. These changes appear beyond a length scale that diverges in the q→∞ limit, so that the Vicsek-model phenomenology is observed in finite systems for weak enough anisotropy, i.e., sufficiently high q. We provide a scaling argument which explains why anisotropy has such different effects in the passive and active cases.


Assuntos
Física , Anisotropia
12.
PLoS One ; 17(6): e0269845, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35767539

RESUMO

We propose a stochastic generative model to represent a directed graph constructed by citations among academic papers, where nodes and directed edges represent papers with discrete publication time and citations respectively. The proposed model assumes that a citation between two papers occurs with a probability based on the type of the citing paper, the importance of cited paper, and the difference between their publication times, like the existing models. We consider the out-degrees of citing paper as its type, because, for example, survey paper cites many papers. We approximate the importance of a cited paper by its in-degrees. In our model, we adopt three functions: a logistic function for illustrating the numbers of papers published in discrete time, an inverse Gaussian probability distribution function to express the aging effect based on the difference between publication times, and an exponential distribution (or a generalized Pareto distribution) for describing the out-degree distribution. We consider that our model is a more reasonable and appropriate stochastic model than other existing models and can perform complete simulations without using original data. In this paper, we first use the Web of Science database and see the features used in our model. By using the proposed model, we can generate simulated graphs and demonstrate that they are similar to the original data concerning the in- and out-degree distributions, and node triangle participation. In addition, we analyze two other citation networks derived from physics papers in the arXiv database and verify the effectiveness of the model.


Assuntos
Organizações , Física , Bases de Dados Factuais , Funções Verossimilhança , Distribuição Normal
15.
Igaku Butsuri ; 42(2): 88-105, 2022.
Artigo em Japonês | MEDLINE | ID: mdl-35768266

RESUMO

Details of Young Researchers' Association of Medical Physics (YRAMP) was introduced. In addition, several questionnaire surveys on medical physics education (MPE) or medical physicist training system (MPTS) in Japan have been conducted, none have targeted the current status and issues of MPE and MPTS. The purpose of this study was to investigate those from the perspective of researchers and students under 35-year-old (y.o.). The questionnaire survey was conducted between 14th September to 14th October 2021, for 112 members of the Young Researchers' Association of Medical Physics via Google Forms. The questionnaire was in two parts: MPE (Part1) and MPTS (Part2). Three subparts were constructed in Part1: Classroom lecture, Clinical training, Education course accredited by Japanese Board of Medical Physicist Qualification. Out of a total of 126 questions, 38 were mandatory to be answered. No personal information was collected. Ninety-three members (83.0%) were answered. The age structure of the respondents was as follows: 18-21, 22-26, 27-30, and 31-35 y.o.=5.4%, 36.6%, 39.8%, and 18.2%. Of the respondents, 74.2% and 11.8% answered that they first heard of "medical physics" or "medical physicist" when they were undergraduate students and in high school or younger, respectively. In Classroom lecture, 61.3%, 17.2%, and 21.5% of the respondents answered that they were "satisfied" or "moderately satisfied", "dissatisfied" or "moderately dissatisfied", and "Not sure" with the current MPE, respectively. In Clinical training, Education course, and MPTS, 58.1%, 21.5%, and 20.4% of the respondents answered that they were "satisfied" or "moderately satisfied", "dissatisfied" or "moderately dissatisfied", and "Not sure", respectively. In both MPE and MPTS, approximately 88% and 51% of the respondents answered that "holding lectures and study sessions for high school and undergraduate students" and "utilizing YouTube" would be useful in promoting MPE and MPTS in Japan, respectively. The results of the questionnaire survey will provide useful data for MPE and MPTS in Japan.


Assuntos
Educação Médica , Adulto , Humanos , Japão , Física/educação , Inquéritos e Questionários
16.
Nature ; 606(7916): 865-866, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35768588

Assuntos
Elétrons , Física
17.
Nature ; 606(7916): 896-901, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35676485

RESUMO

The observation of the Higgs boson solidified the standard model of particle physics. However, explanations of anomalies (for example, dark matter) rely on further symmetry breaking, calling for an undiscovered axial Higgs mode1. The Higgs mode was also seen in magnetic, superconducting and charge density wave (CDW) systems2,3. Uncovering the vector properties of a low-energy mode is challenging, and requires going beyond typical spectroscopic or scattering techniques. Here we discover an axial Higgs mode in the CDW system RTe3 using the interference of quantum pathways. In RTe3 (R = La, Gd), the electronic ordering couples bands of equal or different angular momenta4-6. As such, the Raman scattering tensor associated with the Higgs mode contains both symmetric and antisymmetric components, which are excited via two distinct but degenerate pathways. This leads to constructive or destructive interference of these pathways, depending on the choice of the incident and Raman-scattered light polarization. The qualitative behaviour of the Raman spectra is well captured by an appropriate tight-binding model, including an axial Higgs mode. Elucidation of the antisymmetric component is direct evidence that the Higgs mode contains an axial vector representation (that is, a pseudo-angular momentum) and hints that the CDW is unconventional. Thus, we provide a means for measuring quantum properties of collective modes without resorting to extreme experimental conditions.


Assuntos
Física , Análise Espectral Raman , Eletrônica , Movimento (Física)
18.
J R Soc Interface ; 19(191): 20220214, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35765805

RESUMO

Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behaviour. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here, we review and clarify the key concepts of connectivity, computation, criticality and coherence-the 4C's-and outline a potential role for metastability as a common denominator across these propositions. We analyse and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging, to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning.


Assuntos
Neuroimagem , Neurociências , Encéfalo/diagnóstico por imagem , Cabeça , Física
19.
Perception ; 51(7): 449-463, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-35578559

RESUMO

Illusions are commonly defined as departures of our percepts from the veridical representation of objective, common-sense reality. However, it has been claimed recently that this definition lacks validity, for example, on the grounds that external reality cannot possibly be represented truly by our sensory systems, and indeed may even be a fiction. Here, I first demonstrate how novelist George Orwell warned that such denials of objective reality are dangerous mistakes, in that they can lead to the suppression and even the atrophy of independent thought and critical evaluation. Second, anti-realists assume their opponents hold a fully reductionist metaphysics, in which fundamental physics describes the only ground truth, thereby placing it beyond direct human sensory observation. In contrast, I point to a more recent and commonly used alternative, non-reductive metaphysics. This ascribes real existence to many levels of dynamic systems of information, emerging progressively from the subatomic to the biological, psychological, social, and ecological. Within such a worldview the notion of objective reality is valid, it comes in part within the range of our senses, and thus a definition of illusions as kinds of deviations from veridical perception becomes possible again.


Assuntos
Ilusões , Humanos , Física
20.
PLoS One ; 17(5): e0268439, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35560322

RESUMO

Deep neural networks are widely used in pattern-recognition tasks for which a human-comprehensible, quantitative description of the data-generating process, cannot be obtained. While doing so, neural networks often produce an abstract (entangled and non-interpretable) representation of the data-generating process. This may be one of the reasons why neural networks are not yet used extensively in physics-experiment signal processing: physicists generally require their analyses to yield quantitative information about the system they study. In this article we use a deep neural network to disentangle components of oscillating time series. To this aim, we design and train the neural network on synthetic oscillating time series to perform two tasks: a regression of the signal latent parameters and signal denoising by an Autoencoder-like architecture. We show that the regression and denoising performance is similar to those of least-square curve fittings with true latent-parameters initial guesses, in spite of the neural network needing no initial guesses at all. We then explore various applications in which we believe our architecture could prove useful for time-series processing, when prior knowledge is incomplete. As an example, we employ the neural network as a preprocessing tool to inform the least-square fits when initial guesses are unknown. Moreover, we show that the regression can be performed on some latent parameters, while ignoring the existence of others. Because the Autoencoder needs no prior information about the physical model, the remaining unknown latent parameters can still be captured, thus making use of partial prior knowledge, while leaving space for data exploration and discoveries.


Assuntos
Redes Neurais de Computação , Física , Humanos , Conhecimento , Processamento de Sinais Assistido por Computador , Fatores de Tempo
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