Showing papers in "Frontiers of Physics in China in 2020"
TL;DR: A physics-informed neural network for cardiac activation mapping that accounts for the underlying wave propagation dynamics and quantifies the epistemic uncertainty associated with these predictions to open the door toward physics-based electro-anatomic mapping.
Abstract: A critical procedure in diagnosing atrial fibrillation is the creation of electro-anatomic activation maps. Current methods generate these mappings from interpolation using a few sparse data points recorded inside the atria; they neither include prior knowledge of the underlying physics nor uncertainty of these recordings. Here we propose a physics-informed neural network for cardiac activation mapping that accounts for the underlying wave propagation dynamics and we quantify the epistemic uncertainty associated with these predictions. These uncertainty estimates not only allow us to quantify the predictive error of the neural network, but also help to reduce it by judiciously selecting new informative measurement locations via active learning. We illustrate the potential of our approach using a synthetic benchmark problem and a personalized electrophysiology model of the left atrium. We show that our new method outperforms linear interpolation and Gaussian process regression for the benchmark problem and linear interpolation at clinical densities for the left atrium. In both cases, the active learning algorithm achieves lower error levels than random allocation. Our findings open the door towards physics-based electro-anatomic mapping with the ultimate goals to reduce procedural time and improve diagnostic predictability for patients affected by atrial fibrillation. Open source code is available at https://github.com/fsahli/EikonalNet.
218 citations
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TL;DR: Forecasts obtained with a simple iteration method that takes into account expected recoveries and deaths, and it determines maximally allowed daily growth rates that lead away from exponential increase toward stable and declining numbers.
Abstract: The World Health Organization declared the coronavirus disease 2019 a pandemic on March 11th, pointing to the over 118,000 cases in over 110 countries and territories around the world at that time At the time of writing this manuscript, the number of confirmed cases has been surging rapidly past the half-million mark, emphasizing the sustained risk of further global spread Governments around the world are imposing various containment measures while the healthcare system is bracing itself for tsunamis of infected individuals that will seek treatment It is therefore important to know what to expect in terms of the growth of the number of cases, and to understand what is needed to arrest the very worrying trends To that effect, we here show forecasts obtained with a simple iteration method that needs only the daily values of confirmed cases as input The method takes into account expected recoveries and deaths, and it determines maximally allowed daily growth rates that lead away from exponential increase toward stable and declining numbers Forecasts show that daily growth rates should be kept at least below 5% if we wish to see plateaus any time soon—unfortunately far from reality in most countries to date We provide an executable as well as the source code for a straightforward application of the method on data from other countries © Copyright © 2020 Perc, Gorisek Miksic, Slavinec and Stožer
211 citations
TL;DR: This review provides an overview of qudit-based quantum computing covering a variety of topics ranging from circuit building, algorithm design, to experimental methods and various physical realizations for qudit computation.
Abstract: Qudit is a multi-level computational unit alternative to the conventional 2-level qubit. Compared to qubit, qudit provides a larger state space to store and process information, and thus can provide reduction of the circuit complexity, simplification of the experimental setup and enhancement of the algorithm efficiency. This review provides an overview of qudit-based quantum computing covering a variety of topics ranging from circuit building, algorithm design, to experimental methods. We first discuss the qudit gate universality and a variety of qudit gates including the pi/8 gate, the SWAP gate, and the multi-level controlled-gate. We then present the qudit version of several representative quantum algorithms including the Deutsch-Jozsa algorithm, the quantum Fourier transform, and the phase estimation algorithm. Finally we discuss various physical realizations for qudit computation such as the photonic platform, iron trap, and nuclear magnetic resonance.
181 citations
TL;DR: Asymptotic safety is a theoretical proposal for the ultraviolet completion of quantum field theories, in particular for quantum gravity as discussed by the authors, and significant progress on this program has led to a first characterization of the Reuter fixed point.
Abstract: Asymptotic safety is a theoretical proposal for the ultraviolet completion of quantum field theories, in particular for quantum gravity. Significant progress on this program has led to a first characterization of the Reuter fixed point. Further advancement in our understanding of the nature of quantum spacetime requires addressing a number of open questions and challenges. Here, we aim at providing a critical reflection on the state of the art in the asymptotic safety program, specifying and elaborating on open questions of both technical and conceptual nature. We also point out systematic pathways, in various stages of practical implementation, towards answering them. Finally, we also take the opportunity to clarify some common misunderstandings regarding the program.
127 citations
TL;DR: In this paper, the authors presented a theoretical study on the swimming of migratory gyrotactic microorganisms in a non-Newtonian blood-based nanofluid via an anisotropic narrowing artery.
Abstract: In the present article, we have presented a theoretical study on the swimming of migratory gyrotactic microorganisms in a non-Newtonian blood-based nanofluid via an anisotropically narrowing artery. Sutterby fluid model is used to understand the rheology of the blood as a non-Newtonian fluid model. This fluid pattern has the ability to show Newtonian and non-Newtonian features. The mathematical formulation is performed via continuity, temperature, motile microorganism, momentum, and concentration equation. The series solutions are obtained using the perturbation scheme up to the third-order approximation. The resulting solutions are discussed with the help of graphs for all the leading parameters. The graphical results are also presented for non-tapered, diverging, and converging artery. We further discuss the velocity, temperature, swimming microorganism and temperature distribution. Moreover, the variation of impedance and the impact of wall shear stress are discussed and presented through the graphs.
126 citations
TL;DR: A mini review of the physical and biochemical mechanisms whereby low temperature plasma affects biological cells on macroscopic and microscopic scales can be found in this article, where a thorough understanding of these mechanisms is bound to lead to the development of novel plasma-based medical therapies.
Abstract: Low temperature plasmas that can be generated at atmospheric pressure and at temperatures below 40 oC have in the past couple of decades opened up a new frontier in plasma applications: biomedical applications. These plasma sources produce agents, such as reactive species (radicals and non-radicals), charged particles, photons, and electric fields, which have impactful biological effects. Investigators have been busy elucidating the physical and biochemical mechanisms whereby low temperature plasma affects biological cells on macroscopic and microscopic scales. A thorough understanding of these mechanisms is bound to lead to the development of novel plasma-based medical therapies. This mini review introduces the reader to this exciting multidisciplinary field of research.
121 citations
TL;DR: In this article, a Sardar-subequation method was proposed for solving distinct forms of Wazwaz-Benjamin-Bona-Mahony ((3+1)-Dimensional WBBM) equations.
Abstract: In this work, we suggest a Sardar-subequation method which powerful and efficient, for solving distinct forms of (3+1)-Dimensional Wazwaz-Benjamin-Bona-Mahony ((3+1)-Dimensional WBBM) equations. As a result, some new and more general solitary wave solutions (sws) are obtained including generalized hyperbolic and trigonometric functions. Our results demonstrate the power of the proposed method for the determination of sws of nonlinear evolution equations (NLEs), such as (3+1)-Dimensional WBBM equations which we will study in this work.
114 citations
TL;DR: In this paper, the exact solution is determined by solving only a sequence of linear boundary value problems of fractional-order, and an iterative algorithm that is also computationally efficient.
Abstract: The boundary value problems (BVPs) have attracted the attention of many scientists from both practical and theoretical points of view, for these problems have remarkable applications in different branches of pure and applied sciences Due to this important property, this research aims to develop an efficient numerical method for solving a class of nonlinear fractional BVPs The proposed method is free from perturbation, discretization, linearization, or restrictive assumptions, and provides the exact solution in the form of a uniformly convergent series Moreover, the exact solution is determined by solving only a sequence of linear BVPs of fractional-order Hence, from practical viewpoint, the suggested technique is efficient and easy to implement To achieve an approximate solution with enough accuracy, we provide an iterative algorithm that is also computationally efficient Finally, four illustrative examples are given verifying the superiority of the new technique compared to the other existing results
108 citations
TL;DR: In this paper, a new fractional derivative with a non-singular kernel involving exponential and trigonometric functions is proposed, which includes as a special case Caputo-Fabrizio fractional derivatives.
Abstract: A new fractional derivative with a non-singular kernel involving exponential and trigonometric functions is proposed in this paper. The suggested fractional operator includes as a special case Caputo-Fabrizio fractional derivative. Theoretical and numerical studies of fractional differential equations involving this new concept are presented. Next, some applications to RC-electrical circuits are provided.
108 citations
TL;DR: A broad survey of the current status of nuclear many-body approaches can be found in this paper, where the authors discuss both achievements and open issues that need to be addressed in the coming decade.
Abstract: Over the last decade, new developments in Similarity Renormalization Group techniques and nuclear many-body methods have dramatically increased the capabilities of ab initio nuclear structure and reaction theory. Ground and excited-state properties can be computed up to the tin region, and from the proton to the presumptive neutron drip lines, providing unprecedented opportunities to confront two- plus three-nucleon interactions from chiral Effective Field Theory with experimental data. In this contribution, I will give a broad survey of the current status of nuclear many-body approaches, and I will use selected results to discuss both achievements and open issues that need to be addressed in the coming decade.
102 citations
TL;DR: Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are crucially needed in combating the COVID-19 outbreak.
Abstract: Background: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) since the end of 2019 As of February 15, there were 56 COVID-19 cases confirmed in Hong Kong since the first case with symptom onset on January 23, 2020 Methods: Based on the publicly available surveillance data in Hong Kong, we identified 21 transmission events as of February 15, 2020 An interval censored likelihood framework is adopted to fit three different distributions including Gamma, Weibull, and lognormal, that govern the serial interval (SI) of COVID-19 We selected the distribution according to the Akaike information criterion corrected for small sample size (AICc) Findings: We found the lognormal distribution performed slightly better than the other two distributions in terms of the AICc Assuming a lognormal distribution model, we estimated the mean of SI at 4 9 days (95% CI: 3 6–6 2) and SD of SI at 4 4 days (95% CI: 2 9–8 3) by using the information of all 21 transmission events Conclusion: The SI of COVID-19 may be shorter than the preliminary estimates in previous works Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are crucially needed in combating the COVID-19 outbreak © Copyright © 2020 Zhao, Gao, Zhuang, Chong, Cai, Ran, Cao, Wang, Lou, Wang, Yang, He and Wang
TL;DR: In this article, the authors argue that the running of $\Lambda$ and $G$ found in Asymptotic Safety are not realized in the real world, with reasons which are relatively simple to understand.
Abstract: The present practice of Asymptotic Safety in gravity is in conflict with explicit calculations in low energy quantum gravity. This raises the question of whether the present practice meets the Weinberg condition for Asymptotic Safety. I argue, with examples, that the running of $\Lambda$ and $G$ found in Asymptotic Safety are not realized in the real world, with reasons which are relatively simple to understand. A comparison/contrast with quadratic gravity is also given, which suggests a few obstacles that must be overcome before the Lorentzian version of the theory is well behaved. I make a suggestion on how a Lorentzian version of Asymptotic Safety could potentially solve these problems.
TL;DR: In this paper, a new generation of nuclear forces derived in chiral effective field theory using the recently proposed semilocal regularization method is presented, and the conceptual foundations of nuclear chiral effect field theory are discussed.
Abstract: We review a new generation of nuclear forces derived in chiral effective field theory using the recently proposed semilocal regularization method. We outline the conceptual foundations of nuclear chiral effective field theory, discuss all steps needed to compute nuclear observables starting from the effective chiral Lagrangian and consider selected applications in the two- and few-nucleon sectors. We highlight key challenges in developing high-precision three-body forces, such as the need to maintain consistency between two- and many-body interactions and constraints placed by the chiral and gauge symmetries after regularization.
TL;DR: In this article, the dynamical properties of light fields with strong spatial inhomogeneoty of amplitude, phase, polarization and other parameters are discussed, and applications of structured light fields for optical manipulation, metrology, probing and data processing are described.
Abstract: The paper briefly presents some essential concepts and features of light fields with strong spatial inhomogeneoty of amplitude, phase, polarization and other parameters. It contains a characterization of optical vortices, speckle fields, polarization singularities. A special attention is paid to the field dynamical characteristics (energy, momentum, angular momentum and their derivatives) which are considered not only as mechanical attributes of the field but as its meaningful and application-oriented descriptive parameters. Peculiar features of the light dynamical characteristics in inhomogeneous and dispersive media are discussed. The dynamical properties of paraxial beams and evanescent waves (including surface plasmon-polaritons) are analyzed in more detail; in particular, a general treatment of the extraordinary spin and momentum, orthogonal to the main propagation direction, is outlined. Applications of structured light fields for optical manipulation, metrology, probing and data processing are described.
TL;DR: An extensive portrait of most recent MRI developments at low (1–199 mT) and ultra-low field (micro-Tesla range) outside of the commercial sphere is drawn, and their potential relevance in future clinical applications are proposed.
Abstract: For about 30 years, MRI set cruising speed at 1.5 T of magnetic field, with a gentle transition towards 3 T systems. In its first 10 years of existence, there was an open debate on the question of most relevant MRI field strengths considering the gain in T1 contrast, simpler cooling strategies, lower predisposition to generating image artifacts, and naturally cost reduction of small footprint low field systems. At the time, the inherent gain in sensitivity of high field, which would translate in more signal per unit time, quickly ended this debate. The promise of rapid exams or higher image resolution within a reasonable time won over other considerations and set the standards for MR value. Yet, many reasons bring low field MRI in a situation quite different from 40 years ago. From the achieved progress regarding all aspects of MRI technology, an MR scan at 1.5 T in the mid 1980s has very little in common with the equivalent scan in 2020. That clearly indicates that field strength alone is not what drives performance. It is also unlikely that the total number of machines worldwide will grow so to follow the increasing demand considering their overall cost ($1M/T). The natural trend is to better control medical expenses worldwide, and reconsidering low-field MRI could lead to the democratization of dedicated, point-of-care devices to decongest high-field clinical scanners. In the present article, we aim to draw an extensive portrait of most recent MRI developments at low (1-199 mT) and ultra-low field (micro-Tesla range) outside of the commercial sphere, and we propose to discuss their potential relevance in future clinical applications. We will cover a broad spectrum from pre-polarized MRI using ultra-sensitive magnetic sensors up to permanent and resistive magnets in compact designs.
TL;DR: It is shown that luminescent detectors have a key role to play in the development of FLASH, as the field rapidly progresses toward clinical adaptation, and the unique ability of certain luminescence-based methods to provide tumor oxygenation maps in real-time with submillimeter resolution can elucidate the radiobiological mechanisms behind the FLASH effect.
Abstract: While spatial dose conformity delivered to a target volume has been pushed to its practical limits with advanced treatment planning and delivery, investigations in novel temporal dose delivery are unfolding new mechanisms. Recent advances in ultra-high dose radiotherapy, abbreviated as FLASH, indicate the potential for reduction in healthy tissue damage while preserving tumor control. FLASH therapy relies on very high dose rate of > 40Gy/sec with sub-second temporal beam modulation, taking a seemingly opposite direction from the conventional paradigm of fractionated therapy. FLASH brings unique challenges to dosimetry, beam control, and verification, as well as complexity of radiobiological effective dose through altered tissue response. In this review, we compare the dosimetric methods capable of operating under high dose rate environments. Due to excellent dose-rate independence, superior spatial (~<1 mm) and temporal (~ns) resolution achievable with Cherenkov and scintillation-based detectors, we show that luminescent detectors have a key role to play in the development of FLASH-RT, as the field rapidly progresses towards clinical adaptation. Additionally, we show that the unique ability of certain luminescence-based methods to provide tumor oxygenation maps in real-time with submillimeter resolution can elucidate the radiobiological mechanisms behind the FLASH effect. In particular, such techniques will be crucial for understanding the role of oxygen in mediating the FLASH effect.
TL;DR: Andrade et al. as discussed by the authors proposed a nonlinear model to understand the mechanism of heat and mass transfer by contemplating various essential features of the proposed boundary layer and used the Keller-box technique to simulate the results.
Abstract: Computational fluid dynamics (CFD) [1] can be described as the set of techniques that assist the computer to provide the numerical simulation of the fluid flows. The three basic principles that can determine the physical aspects of any fluid are the i) energy conservation, ii) Newton’s second law, and the iii) mass conservation. These flow problem can be described in terms of these basic laws. Mathematical equations, which are usually in the form of partial differential equations, portrayed the fluid behavior in the flow domain. The solutions and interactive behavior of solid boundaries with fluid or interaction between the layers of the fluid while flowing are visualized using some CFD techniques. CFD helps replace these differential equations of fluid flow into numbers, and these numbers are beneficial in time and/or space which enable a numerical picture of the complete fluid flow. CFD is powerful in examining a system’s behavior, beneficial, and more innovative in designing a system [2]. Also, It is efficient in exploring the system’s performance metrics, whether it is for the yielding higher profit margins or in enhancing operational safety, and in various advantageous features [3]. Nowadays, CFD techniques are usually applied in various fields [4–8] i.e. car design, turbomachinery, ship design, and aircraft manufacturing. Moreover, it is beneficial in astrophysics, biology, oceanography, oil recovery, architecture, and meteorology. Numerous numerical Algorithm and software have been developed to perform CFD analysis. Due to the recent advancement in computer technology, numerical simulation for physically and geometrically complex systems can also be evaluated using PC clusters. Large scale simulations in different fluid flow on grids containing millions and trillions of elements can be achieved within a few hours via supercomputers. However, it is completely incorrect to think that CFD describes a mature technology, there are numerous open questions related to heat transfer, combustion modeling, turbulence, and efficient solution methods or discretization methods, etc. The coupling between CFD and other disciplines required further research, therefore, the main goal of this issue is to fill an essential gap that is greatly missed in this field. We sincerely hope that this issue will be beneficial to the readers to present the recent findings in the field and shed some light on the industrial sector. Rafique et al. [9] used Buongiorno model to discuss the Casson nanofluid boundary layer flow through an inclined surface under the impact of Dufour and Soret. This nonlinear model is beneficial to understand the mechanism of heat and mass transfer by contemplating various essential features of the proposed boundary layer. Further, the Keller-box technique has been used to simulate the results. The results show that the Dufour effect has a strong impact on the temperature profile and Edited and reviewed by: José S. Andrade Jr, Federal University of Ceara, Brazil
TL;DR: A novel neuro-swarming intelligence-based numerical computing solver is developed for solving second order non-linear singular periodic (NSP) boundary value problems (BVPs), i.e., ANN-PSO-IPS, which is compared with the available exact solutions to establish the worth of the solver in terms of accuracy and convergence.
Abstract: In the present investigation, a novel neuro-swarming intelligence-based numerical computing solver is developed for solving second order nonlinear singular periodic (NSP) boundary value problems (BVPs), i.e., NSP-BVPs, using modeling strength of artificial neural networks (ANN) optimized with global search efficacy of particle swarm optimization (PSO) supported with the methodology of rapid local search by interior-point scheme (IPS), i.e., ANN-PSO-IPS. In order to check the proficiency, robustness and stability of the designed ANN-PSO-IPS, two numerical problems of the NSP-BVPs have been presented for different number of neurons. The outcomes of proposed ANN-PSO-IPS are compared with the available exact solutions to establish worth of the solver in terms of accuracy and convergence, which is further endorsed through results of statistical performance metrics based on multiple implementations.
University of Nantes1, French Institute of Health and Medical Research2, First Faculty of Medicine, Charles University in Prague3, University of Tübingen4, University of British Columbia5, Medical University of Vienna6, Vienna University of Technology7, University of Vienna8, VRVis9, Bar-Ilan University10, University of Veterinary Medicine Vienna11, Austrian Institute of Technology12
TL;DR: A comprehensive overview of the field of CMI from preclinical hybrid imaging to correlative microscopy is presented, requirements for optimization and standardization are highlighted, and current efforts to bridge the gap between preclinical and biological imaging are focused on.
Abstract: The frontiers of bioimaging are currently being pushed towards the integration and correlation of several modalities to tackle biomedical research questions holistically and across multiple scales. Correlated Multimodal Imaging (CMI) gathers information about exactly the same specimen with two or more complementary modalities that – combined – create a composite and complementary view of the sample (including insights into structure, function, dynamics and molecular composition). CMI allows to describe biomedical processes within their overall spatio-temporal context and gain a mechanistic understanding of cells, tissues, diseases or organisms by untangling their molecular mechanisms within their native environment. The two best-established CMI implementations are hardware-fused platforms in (Pre)clinical Imaging (Hybrid Imaging) and Correlated Light and Electron Microscopy (CLEM) in biological imaging. Although the merits of Hybrid Imaging and CLEM are well established, both approaches would benefit from standardization of protocols, ontologies and data handling, and the development of optimized and advanced implementations. Specifically, CMI pipelines that aim at bridging preclinical and biological imaging beyond CLEM and Hybrid Imaging are rare but bear great potential to substantially advance both bioimaging and biomedical research. CMI faces three main challenges for its routine use in biomedical research: (1) Sample handling and preparation procedures that are compatible across modalities without compromising data quality, (2) soft- and hardware solutions to relocate the same region of interest (ROI) after transfer between imaging platforms including fiducial markers, and (3) automated software solutions to correlate complex, multiscale, multimodal and volumetric image data including reconstruction, segmentation and visualization. This review goes beyond preclinical imaging and puts its accessible information into a broader imaging context. We present a comprehensive overview of the field of CMI from PHI to correlative microscopy, highlight requirements for optimization and standardization, present a synopsis of current solutions to challenges of the field and focus on current efforts to bridge the gap between preclinical and biological imaging. The review is line with major European initiatives, such as COMULIS (CA17121), a COST Action to promote and foster Correlated Multimodal Imaging in Life Sciences.
TL;DR: In this article, the authors summarized the recent progress in Graphitic carbon nitride (g-C3N4)-based single-atom photocatalysts, mainly including preparation strategies, characterizations, and their applications.
Abstract: Single-atom photocatalysts, due to their high catalysis activity, selectivity and stability, become a hotspot in the field of photocatalysis. Graphitic carbon nitride (g-C3N4) is known as both a good support for single atoms and a star photocatalyst. Developing g-C3N4-based single-atom photocatalysts exhibits great potential in improving the photocatalytic performance. In this review, we summarize the recent progress in g-C3N4-based single-atom photocatalysts, mainly including preparation strategies, characterizations, and their photocatalytic applications. The significant roles of single atoms and catalysis mechanism in g-C3N4-based single-atom photocatalysts are analyzed. At last, the challenges and perspectives for exploring high-efficient g-C3N4-based single-atom photocatalysts are presented.
TL;DR: In this paper, the authors review the state-of-the-art of phenomenology of asymptotically safe gravity, focusing on the implications of the gravitational antiscreening in cosmology.
Abstract: According to the asymptotic-safety conjecture, the gravitational renormalization group flow features an ultraviolet-attractive fixed point that makes the theory renormalizable and ultraviolet complete. The existence of this fixed point entails an antiscreening of the gravitational interaction at short distances. In this paper we review the state-of-the-art of phenomenology of Asymptotically Safe Gravity, focusing on the implications of the gravitational antiscreening in cosmology.
TL;DR: In this paper, the authors used network-based, distributed models where the spread of the SARS-CoV-2 pandemic is described in distinct local cohorts with nested SE(A)IR models, i.e., modified SEIR models that include infectious asymptomatic individuals.
Abstract: Mathematical models of SARS-CoV-2 (the virus which causes COVID-19) spread are used for guiding the design of mitigation steps and helping identify impending breaches of health care system surge capacity The challenges of having only lacunary information about daily new infections and mortality counts are compounded by geographic heterogeneity of the population This complicates prediction, particularly when homogenized population models assume well mixed To address this problem, we account for the differences between rural and urban settings using network-based, distributed models where the spread of the pandemic is described in distinct local cohorts with nested SE(A)IR models, ie, modified SEIR models that include infectious asymptomatic individuals The model parameters account for the SARS-CoV-2 transmission mostly via human-to-human contact, and the fact that contact frequency among individuals differs between urban and rural areas, and may change over time The probability that the virus spreads into an uninfected community is associated with influx of individuals from communities where the infection is already present, thus each node is characterized by its internal contact and by its connectivity with other nodes Census and cell phone data used to set up the adjacency matrix of the network, which can be modified to simulate changes in mitigation measures Our network SE(A)IR model depends on easily interpretable parameters estimated from available community level data The parameters estimated with Bayesian techniques include transmission rate and the ratio asymptomatic to symptomatic infectious individuals The methodology predicts that the latter quantity approaches 05 as the epidemic reaches an equilibrium, in full agreement with the May 22, 2020 CDC modeling The network model gives rise to a spatially distributed computational model that explains the geographic dynamics of the contagion, eg, in larger cities surrounded by suburban and rural areas The time courses of the infected cohorts in the different counties predicted by the network model are remarkably similar to the reported observations Moreover, the model shows that monitoring the infection prevalence in each county, and adopting local mitigation measures as infections climb beyond a certain threshold, is almost as effective as blanket measures, and more effective than reducing inter-county mobility
TL;DR: The unified symmetric hyperbolic and thermodynamically compatible (SHTC) formulation of continuum mechanics developed by Godunov, Peshkov, and Romenski is presented, which allows to describe fluid and solid mechanics in one single and unified first orderhyperbolic system.
Abstract: In this paper we first review the development of high order ADER finite volume and ADER discontinuous Galerkin schemes on fixed and moving meshes, since their introduction in 1999 by Toro et al. We show the modern variant of ADER based on a space-time predictor-corrector formulation in the context of ADER discontinuous Galerkin schemes with a posteriori subcell finite volume limiter on fixed and moving grids, as well as on space-time adaptive Cartesian AMR meshes. We then present and discuss the unified symmetric hyperbolic and thermodynamically compatible (SHTC) formulation of continuum mechanics developed by Godunov, Peshkov and Romenski (GPR model), which allows to describe fluid and solid mechanics in one single and unified first order hyperbolic system. In order to deal with free surface and moving boundary problems, a simple diffuse interface approach is employed, which is compatible with Eulerian schemes on fixed grids as well as direct Arbitrary-Lagrangian-Eulerian methods on moving meshes. We show some examples of moving boundary problems in fluid and solid mechanics.
TL;DR: In this article, the authors proposed a q-statistical functional form that appears to describe satisfactorily the available data for all countries, based on the complex behavior of volumes of transactions of stocks at the NYSE and NASDAQ.
Abstract: The official data for the time evolution of active cases of COVID-19 pandemics around the world are available online. For all countries, a peak has been either observed (China and South Korea) or is expected in the near future. The approximate dates and heights of those peaks have important epidemiological implications. Inspired by similar complex behavior of volumes of transactions of stocks at the NYSE and NASDAQ, we propose a q-statistical functional form that appears to describe satisfactorily the available data for all countries. Consistently, predictions of the dates and heights of those peaks in severely affected countries become possible unless efficient treatments or vaccines, or sensible modifications of the adopted epidemiological strategies, emerge.
TL;DR: In this article, the current status of many-body perturbation theory in the field of nuclear structure is discussed and novel results are provided that highlight its power as a efficient and yet accurate (pre-processing) approach to systematically investigate medium-mass nuclei.
Abstract: In recent years many-body perturbation theory encountered a renaissance in the field of ab initio nuclear structure theory. In various applications it was shown that perturbation theory, including novel flavors of it, constitutes a useful tool to describe atomic nuclei, either as a full-fledged many-body approach or as an auxiliary method to support more sophisticated non-perturbative many-body schemes. In this work the current status of many-body perturbation theory in the field of nuclear structure is discussed and novel results are provided that highlight its power as a efficient and yet accurate (pre-processing) approach to systematically investigate medium-mass nuclei. Eventually a new generation of chiral nuclear Hamiltonians is benchmarked using several state-of-the-art flavors of many-body perturbation theory.
TL;DR: A minimal mathematical model of the interaction between social support for school and workplace closure and the transmission dynamics of SARS-CoV-2 finds that a second wave of COVID-19 occurs across a broad range of plausible model input parameters governing epidemiological and social conditions, on account of instabilities generated by behavior-disease interactions.
Abstract: In May 2020, many jurisdictions around the world began lifting physical distancing restrictions against the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) This gave rise to concerns about a possible second wave of coronavirus disease 2019 (COVID-19) These restrictions were imposed in response to the presence of COVID-19 in populations, usually with the broad support of affected populations However, the lifting of restrictions is also a population response to the accumulating socio-economic impacts of restrictions, and lifting of restrictions is expected to increase the number of COVID-19 cases, in turn This suggests that the COVID-19 pandemic exemplifies a coupled behavior-disease system where disease dynamics and social dynamics are locked in a mutual feedback loop Here we develop a minimal mathematical model of the interaction between social support for school and workplace closure and the transmission dynamics of SARS-CoV-2 We find that a second wave of COVID-19 occurs across a broad range of plausible model input parameters governing epidemiological and social conditions, on account of instabilities generated by behavior-disease interactions The second wave tends to have a higher peak than the first wave when the efficacy of restrictions is greater than 40% and when the basic reproduction number R0 is less than 2 4 Surprisingly, we also found that a lower R0 value makes a second wave more likely, on account of behavioral feedback (although a lower R0 does not necessarily cause more infections, in total) We conclude that second waves of COVID-19 can be interpreted as the outcome of non-linear interactions between disease dynamics and social behavior We also suggest that further development of mathematical models exploring behavior-disease interactions could help us better understand how social and epidemiological conditions together determine how pandemics unfold © Copyright © 2020 Pedro, Ndjomatchoua, Jentsch, Tchuenche, Anand and Bauch
TL;DR: In this paper, the authors study the quantile correlations of Bitcoin and two benchmarks (S\&P500 and VIX) and make comparison with gold as the traditional safe haven asset.
Abstract: Bitcoin being a safe haven asset is one of the traditional stories in the cryptocurrency community. However, during its existence and relevant presence, i.e. approximately since 2013, there has been no severe situation on the financial markets globally to prove or disprove this story until the COVID-19 pandemics. We study the quantile correlations of Bitcoin and two benchmarks -- S\&P500 and VIX -- and we make comparison with gold as the traditional safe haven asset. The Bitcoin safe haven story is shown and discussed to be unsubstantiated and far-fetched, while gold comes out as a clear winner in this contest even when a broader cryptocurrency index (CRIX) is considered.
TL;DR: Novel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles and demonstrated the results from the TCGA dataset that these models can produce accurate classification (above 94%), using cancer-specific markers genes.
Abstract: Background Cancer has been a leading cause of death in the United States with significant health care costs Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently Results In this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal Four GCNN models based on a co-expression graph, co-expression+singleton graph, protein-protein interaction (PPI) graph, and PPI+singleton graph have been designed and implemented They were trained and tested on combined 10,340 cancer samples and 731 normal tissue samples from The Cancer Genome Atlas (TCGA) dataset The established GCNN models achieved excellent prediction accuracies (899-947%) among 34 classes (33 cancer types and a normal group) In silico gene-perturbation experiments were performed on four models based on co-expression graph, co-expression+singleton, PPI graph, and PPI+singleton graphs The co-expression GCNN model was further interpreted to identify a total of 428 markers genes that drive the classification of 33 cancer types and normal The concordance of differential expressions of these markers between the represented cancer type and others are confirmed Successful classification of cancer types and a normal group regardless of normal tissues' origin suggested that the identified markers are cancer-specific rather than tissue-specific Conclusion Novel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles We demonstrated the results from the TCGA dataset that these models can produce accurate classification (above 94%), using cancer-specific markers genes The models and the source codes are publicly available and can be readily adapted to the diagnosis of cancer and other diseases by the data-driven modeling research community
TL;DR: The mechanisms and their potential overlap are reviewed along with discussions on the potential insights into mechanisms that magnetic resonance imaging sequences along with a multimodal stimulation approach involving electrical, magnetic, chemical, light, and mechanical stimuli can provide.
Abstract: Focused ultrasound (FUS) neuromodulation has shown that mechanical waves can interact with cell membranes and mechanosensitive ion channels, causing changes in neuronal activity. However, the thorough understanding of the mechanisms involved in these interactions are hindered by different experimental conditions for a variety of animal scales and models. While the lack of complete understanding of FUS neuromodulation mechanisms does not impede benefiting from the current known advantages and potential of this technique, a precise characterization of its mechanisms of action and their dependence on experimental setup (e.g., tuning acoustic parameters and characterizing safety ranges) has the potential to exponentially improve its efficacy as well as spatial and functional selectivity. This could potentially reach the cell type specificity typical of other, more invasive techniques e.g., opto- and chemogenetics or at least orientation-specific selectivity afforded by transcranial magnetic stimulation. Here, the mechanisms and their potential overlap are reviewed along with discussions on the potential insights into mechanisms that magnetic resonance imaging sequences along with a multimodal stimulation approach involving electrical, magnetic, chemical, light, and mechanical stimuli can provide.
TL;DR: In this article, the authors used computational fluid dynamics simulations to generate fluid flow simulations and represent them as synthetic 4D flow MRI data and trained a neural network to produce super-resolution 4D-flow phase images with upsample factor of 2.6 to 5.8%.
Abstract: 4D-flow magnetic resonance imaging (MRI) is an emerging imaging technique where spatiotemporal 3D blood velocity can be captured with full volumetric coverage in a single non-invasive examination. This enables qualitative and quantitative analysis of hemodynamic flow parameters of the heart and great vessels. An increase in the image resolution would provide more accuracy and allow better assessment of the blood flow, especially for patients with abnormal flows. However, this must be balanced with increasing imaging time. The recent success of deep learning in generating super resolution images shows promise for implementation in medical images. We utilized computational fluid dynamics simulations to generate fluid flow simulations and represent them as synthetic 4D flow MRI data. We built our training dataset to mimic actual 4D flow MRI data with its corresponding noise distribution. Our novel 4DFlowNet network was trained on this synthetic 4D flow data and was capable in producing noise-free super resolution 4D flow phase images with upsample factor of 2. We also tested the 4DFlowNet in actual 4D flow MR images of a phantom and normal volunteer data, and demonstrated comparable results with the actual flow rate measurements giving an absolute relative error of 0.6 to 5.8% and 1.1 to 3.8% in the phantom data and normal volunteer data, respectively.