Showing papers in "Frontiers in Physics in 2022"
TL;DR: The physics processes that have been newly released or are currently implemented in the code distributed by the FLUKA are illustrated, to modernize its structure to integrate independent contributions more easily and to formalize quality assurance through state-of-the-art software deployment techniques.
Abstract: FLUKA is a general purpose Monte Carlo code able to describe the transport and interaction of any particle and nucleus type in complex geometries over an energy range extending from thermal neutrons to ultrarelativistic hadron collisions. It has many different applications in accelerator design, detector studies, dosimetry, radiation protection, medical physics, and space research. In 2019, CERN and INFN, as FLUKA copyright holders, together decided to end their formal collaboration framework, allowing them henceforth to pursue different pathways aimed at meeting the evolving requirements of the FLUKA user community, and at ensuring the long term sustainability of the code. To this end, CERN set up the FLUKA.CERN Collaboration 1 . This paper illustrates the physics processes that have been newly released or are currently implemented in the code distributed by the FLUKA.CERN Collaboration 2 under new licensing conditions that are meant to further facilitate access to the code, as well as intercomparisons. The description of coherent effects experienced by high energy hadron beams in crystal devices, relevant to promising beam manipulation techniques, and the charged particle tracking in vacuum regions subject to an electric field, overcoming a former lack, have already been made available to the users. Other features, namely the different kinds of low energy deuteron interactions as well as the synchrotron radiation emission in the course of charged particle transport in vacuum regions subject to magnetic fields, are currently undergoing systematic testing and benchmarking prior to release. FLUKA is widely used to evaluate radiobiological effects, with the powerful support of the Flair graphical interface, whose new generation (Available at http://flair.cern) offers now additional capabilities, e.g., advanced 3D visualization with photorealistic rendering and support for industry-standard volume visualization of medical phantoms. FLUKA has also been playing an extensive role in the characterization of radiation environments in which electronics operate. In parallel, it has been used to evaluate the response of electronics to a variety of conditions not included in radiation testing guidelines and standards for space and accelerators, and not accessible through conventional ground level testing. Instructive results have been obtained from Single Event Effects (SEE) simulations and benchmarks, when possible, for various radiation types and energies. The code has reached a high level of maturity, from which the FLUKA.CERN Collaboration is planning a substantial evolution of its present architecture. Moving towards a modern programming language allows to overcome fundamental constraints that limited development options. Our long term goal, in addition to improving and extending its physics performances with even more rigorous scientific oversight, is to modernize its structure to integrate independent contributions more easily and to formalize quality assurance through state-of-the-art software deployment techniques. This includes a continuous integration pipeline to automatically validate the codebase as well as automatic processing and analysis of a tailored physics-case test suite. With regard to the aforementioned objectives, several paths are currently envisaged, like finding synergies with Geant4, both at the core structure and interface level, this way offering the user the possibility to run with the same input different Monte Carlo codes and crosscheck the results.
306 citations
TL;DR: In this article , the main advantages in the use of SiC detectors and the current state of research in this field are summarized, with particular emphasis on how these can be related to detector performance.
Abstract: Silicon Carbide (SiC) is a wide bandgap semiconductor with many excellent properties that make it one of the most promising and well-studied materials for radiation particle detection. This review provides an overview of the main advantages in the use of SiC detectors and the current state of research in this field. Key aspects related to material properties, growth techniques, doping, defects, electrical contacts, and characterization methods are summarized, with particular emphasis on how these can be related to detector performance. The most recent and significant experimental results on the use of SiC diodes for the detection of electrons, protons, alpha, ions, UV radiation, x/γ-rays, and neutrons are discussed. The effects of high temperature operation and radiation damage on detector performance are outlined.
75 citations
TL;DR: In this article , a simple and illustrative example of stochastic processes in the form of a particle undergoing standard Brownian diffusion, with the additional feature of the particle resetting repeatedly and at random times to its initial condition.
Abstract: Stochastic processes offer a fundamentally different paradigm of dynamics than deterministic processes, the most prominent example of the latter being Newton's laws of motion. Here, we discuss in a pedagogical manner a simple and illustrative example of stochastic processes in the form of a particle undergoing standard Brownian diffusion, with the additional feature of the particle resetting repeatedly and at random times to its initial condition. Over the years, many different variants of this simple setting have been studied, all of which serve as illustrations of non-trivial and interesting static and dynamic features that characterize stochastic dynamics at long times. We will provide in this work a brief overview of this active and rapidly evolving field by considering the arguably simplest example of Brownian diffusion in one dimension. Along the way, we will learn about some of the general techniques that a physicist employs to study stochastic processes. Relevant to the special issue, we will discuss in detail how introducing resetting in an otherwise diffusive dynamics provides an explicit optimization of the time to locate a target through a special choice of the resetting protocol. We also discuss thermodynamics of resetting, and provide a bird's eye view of some of the recent work in the field of resetting.
74 citations
TL;DR: In this paper , the properties of electromagnetic absorption by structures known as metamaterial absorbers (MMAs) are discussed, which are used in a myriad of applications, including invisibility cloaking, perfect lensing, perfect absorption, and sensing.
Abstract: Metamaterials (MM) are artificially designed materials that possess unique properties due to their geometrical design. They also display some peculiar properties, such as negative refractive index, Snell’s law reversal, Doppler effect reverse, and left-handed behavior. MMs are used in a myriad of applications, including invisibility cloaking, perfect lensing, perfect absorption, and sensing. In this review article, the property of electromagnetic absorption by structures known as metamaterial absorbers (MMAs) is discussed. An MMA is a composite made up of many layers of metallic patterns separated by dielectric. This novel device helps in achieving near-unity absorption by various mechanisms, which are investigated in this article. The MMAs are classified based on their absorption characteristics, such as polarization tunability, broadband operation, and multiband absorption, in different frequency regimes.
68 citations
TL;DR: In this paper , a review of the research progress of terahertz spectroscopic techniques for the detection and recognition of substances is presented, where metamaterials are also investigated for the applications in teraspectroscopic detection and classification of substances.
Abstract: Recently, terahertz spectroscopy has received a lot of attention because of its unique properties such as biosafety, fingerprint spectrum, and good penetration. In this review, we focus on the research progress of terahertz spectroscopic techniques for the detection and recognition of substances. First, we describe the fundamentals of terahertz spectroscopy. Then, we outline the applications of terahertz spectroscopy in biomedicine, agriculture, food production, and security inspection. Subsequently, metamaterials, which have recently received extensive attention, are also investigated for the applications in terahertz spectroscopic detection and recognition of substances is illustrated. Finally, the development trend of terahertz spectroscopy for substance detection and recognition is also prospected.
66 citations
TL;DR: In this paper , the authors review the recent development in polarimetric imaging techniques that address the challenges of image degradation in scattering media and provide a model selection guideline and future research directions.
Abstract: Imaging in scattering media has been a challenging and important subject in optical science. In scattering media, the image quality is often severely degraded by the scattering and absorption effects owing to the small particles and the resulting nonuniform distribution of the intensity or polarization properties. This study reviews the recent development in polarimetric imaging techniques that address these challenges. Specifically, based on the polarization properties of the backscattering light, polarimetric methods can estimate the intensity level of the backscattering and the transmittance of the media. They can also separate the target signal from the undesired ones to achieve high-quality imaging. In addition, the different designs of the polarimetric imaging systems offer additional metrics, for example, the degree/angle of polarization, to recover images with high fidelity. We first introduce the physical degradation models in scattering media. Secondly, we apply the models in different polarimetric imaging systems, such as polarization difference, Stokes vector, Mueller matrix, and deep learning-based systems. Lastly, we provide a model selection guideline and future research directions in polarimetric imaging.
65 citations
TL;DR: In this paper , a review of recent developments on nonrelativistic string theory in flat spacetime is presented, including the appropriate target-space geometry that non-relativism string couple to.
Abstract: We review recent developments on nonrelativistic string theory. In flat spacetime, the theory is defined by a two-dimensional relativistic quantum field theory with nonrelativistic global symmetries acting on the worldsheet fields. This theory arises as a self-contained corner of relativistic string theory. It has a string spectrum with a Galilean dispersion relation, and a spacetime S-matrix with nonrelativistic symmetry. This string theory also gives a unitary and ultraviolet complete framework that connects different corners of string theory, including matrix string theory and noncommutative open strings. In recent years, there has been a resurgence of interest in the non-Lorentzian geometries and quantum field theories that arise from nonrelativistic string theory in background fields. In this review, we start with an introduction to the foundations of nonrelativistic string theory in flat spacetime. We then give an overview of recent progress, including the appropriate target-space geometry that nonrelativistic strings couple to. This is known as (torsional) string Newton–Cartan geometry, which is neither Lorentzian nor Riemannian. We also give a review of nonrelativistic open strings and effective field theories living on D-branes. Finally, we discuss applications of nonrelativistic strings to decoupling limits in the context of the AdS/CFT correspondence.
64 citations
TL;DR: In this paper , the authors summarized recent work on the many-body (beyond density functional theory) electronic structure of layered rare-earth nickelates, both in the context of the materials themselves and in comparison to the high-temperature superconducting (high-T c ) layered copper-oxide compounds.
Abstract: This article summarizes recent work on the many-body (beyond density functional theory) electronic structure of layered rare-earth nickelates, both in the context of the materials themselves and in comparison to the high-temperature superconducting (high-T c ) layered copper-oxide compounds. It aims to outline the current state of our understanding of layered nickelates and to show how the analysis of these fascinating materials can shed light on fundamental questions in modern electronic structure theory. A prime focus is determining how the interacting physics defined over a wide energy range can be estimated and “downfolded” into a low energy theory that would describe the relevant degrees of freedom on the ∼ 0.5 eV scale and that could be solved to determine superconducting and spin and charge density wave phase boundaries, temperature dependent resistivities, and dynamical susceptibilities.
61 citations
TL;DR: In this article , the electronic structure of nickelate superconductors with and without effects of electronic correlations is reviewed, and a minimal model for the Ni 3 d x 2 − y 2 orbital plus a pocket around the A-momentum is identified.
Abstract: We review the electronic structure of nickelate superconductors with and without effects of electronic correlations. As a minimal model, we identify the one-band Hubbard model for the Ni 3 d x 2 − y 2 orbital plus a pocket around the A-momentum. The latter, however, merely acts as a decoupled electron reservoir. This reservoir makes a careful translation from nominal Sr-doping to the doping of the one-band Hubbard model mandatory. Our dynamical mean-field theory calculations, in part already supported by the experiment, indicate that the Γ pocket, Nd 4f orbitals, oxygen 2p, and the other Ni 3d orbitals are not relevant in the superconducting doping regime. The physics is completely different if topotactic hydrogen is present or the oxygen reduction is incomplete. Then, a two-band physics hosted by the Ni 3 d x 2 − y 2 and 3 d 3 z 2 − r 2 orbitals emerges. Based on our minimal modeling, we calculated the superconducting T c vs. Sr-doping x phase diagram prior to the experiment using the dynamical vertex approximation. For such a notoriously difficult to determine quantity as T c , the agreement with the experiment is astonishingly good. The prediction that T c is enhanced with pressure or compressive strain has been confirmed experimentally as well. This supports that the one-band Hubbard model plus an electron reservoir is the appropriate minimal model.
55 citations
TL;DR: In this article , the authors discussed the important advances in TDLAS detection sensitivity, including the selection of absorption lines, the improvement of diode lasers, the design of effective optical paths, data demodulation, and the suppression of background interference.
Abstract: Tunable Diode Laser Absorption Spectroscopy (TDLAS), a trace gas sensing technology based on infrared absorption spectroscopy, has been developed rapidly in the past few decades. The advantages of low cost and easy miniaturization could be applied in real-time monitoring. As an important factor, the detection sensitivity of TDLAS has been improved by a variety of methods. In this review paper, the important advances in TDLAS detection sensitivity are discussed, including the selection of absorption lines, the improvement of diode lasers, the design of effective optical paths, data demodulation, and the suppression of background interference. For gases with high application values, such as CH4, CO2, and NO, we summarize the detection sensitivity that the existing TDLAS system has been achieved, combined with the above-improved process. However, considering the principle of infrared absorption, the increase in detection sensitivity could reach an ultra-limit. Therefore, the hypothesis of the sensitivity limit of TDLAS is proposed at the end of the paper, through the quantization analysis.
54 citations
TL;DR: Wang et al. as mentioned in this paper found that the easier contagion of fake news online is positively associated with the greater anger it carries and that mutations in emotions like increasing anger will progressively speed up the information spread.
Abstract: Fake news that manipulates political elections, strikes financial systems, and even incites riots is more viral than real news online, resulting in unstable societies and buffeted democracy. While factor that drives the viral spread of fake news is rarely explored. In this study, it is unexpectedly found that the easier contagion of fake news online is positively associated with the greater anger it carries. The same results in Twitter and Weibo indicate that this correlation is independent of the platform. Moreover, mutations in emotions like increasing anger will progressively speed up the information spread. Increasing the occupation of anger by 0.1 and reducing that of joy by 0.1 are associated with the generation of nearly six more retweets in the Weibo dataset. Offline questionnaires reveal that anger leads to more incentivized audiences in terms of anxiety management and information sharing and accordingly makes fake news more contagious than real news online. Cures such as tagging anger in social media could be implemented to slow or prevent the contagion of fake news at the source.
TL;DR: A new 6D fractional-order memristive Hopfield neural network is presented and pinched hysteresis loops (PHL) are used to prove the memristor characteristics of the model and it is proved that the encryption algorithm using this 6D-FMHNN is safe and sensitive to the key.
Abstract: This paper proposes a new memristor model and uses pinched hysteresis loops (PHL) to prove the memristor characteristics of the model. Then, a new 6D fractional-order memristive Hopfield neural network (6D-FMHNN) is presented by using this memristor to simulate the induced current, and the bifurcation characteristics and coexistence attractor characteristics of fractional memristor Hopfield neural network is studied. Because this 6D-FMHNN has chaotic characteristics, we also use this 6D-FMHNN to generate a random number and apply it to the field of image encryption. We make a series of analysis on the randomness of random numbers and the security of image encryption, and prove that the encryption algorithm using this 6D-FMHNN is safe and sensitive to the key.
TL;DR: Ab initio has been used as a label in nuclear theory for over two decades and its meaning has evolved and broadened over the years as discussed by the authors , and its present-day relation to theoretical uncertainty quantification is discussed.
Abstract: Ab initio has been used as a label in nuclear theory for over two decades. Its meaning has evolved and broadened over the years. We present our interpretation, briefly review its historical use, and discuss its present-day relation to theoretical uncertainty quantification.
TL;DR: A quantum machine learning approach based on quantum convolutional neural networks for solvingMulticlass classification, a common task in computer vision, where one needs to categorize an image into three or more classes is proposed.
Abstract: Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we propose a quantum machine learning approach based on quantum convolutional neural networks for solving the multiclass classification problem. The corresponding learning procedure is implemented via TensorFlowQuantum as a hybrid quantum-classical (variational) model, where quantum output results are fed to the softmax activation function with the subsequent minimization of the cross entropy loss via optimizing the parameters of the quantum circuit. Our conceptional improvements here include a new model for a quantum perceptron and an optimized structure of the quantum circuit. We use the proposed approach to solve a 4-class classification problem for the case of the MNIST dataset using eight qubits for data encoding and four ancilla qubits; previous results have been obtained for 3-class classification problems. Our results show that accuracies of our solution are similar to classical convolutional neural networks with comparable numbers of trainable parameters. We expect that our finding provide a new step towards the use of quantum neural networks for solving relevant problems in the NISQ era and beyond.
TL;DR: In this article , the authors review the history of Newton-Cartan (NC) gravity with an emphasis on recent developments, including the covariant, off-shell large speed of light expansion of general relativity.
Abstract: This study reviews the history of Newton–Cartan (NC) gravity with an emphasis on recent developments, including the covariant, off-shell large speed of light expansion of general relativity. Depending on the matter content, this expansion leads to either NC geometry with absolute time or NC geometry with non-relativistic gravitational time dilation effects. The latter shows that non-relativistic gravity (NRG) includes a strong field regime and goes beyond Newtonian gravity. We start by reviewing early developments in NC geometry, including the covariant description of Newtonian gravity, mainly through the works of Trautman, Dautcourt, Künzle, and Ehlers. We then turn to more modern developments, such as the gauging of the Bargmann algebra and describe why the latter cannot be used to find an off-shell covariant description of Newtonian gravity. We review recent work on the 1/c expansion of general relativity and show that this leads to an alternative “type II” notion of NC geometry. Finally, we discuss matter couplings, solutions, and odd powers in 1/c and conclude with a brief summary of related topics.
TL;DR: In this article , a comprehensive examination aimed to explore the important features of spinning flows of chemically reactive Newtonian nanofluids over a uniformly revolving disk in the presence of a radially applied magnetic field along with an exponentially decaying space-dependent heat source, in the case where the disk surface is heated convectively and unaffected by the vertical nanoparticles' mass flux.
Abstract: Owing to the various physical aspects of nanofluids as thermally enhanced working fluids and the significance of swirling flows in rheological devices as well as in the spin coating and lubrication applications, the current comprehensive examination aimed to explore the important features of spinning flows of chemically reactive Newtonian nanofluids over a uniformly revolving disk in the existence of a radially applied magnetic field along with an exponentially decaying space-dependent heat source, in the case where the disk surface is heated convectively and unaffected by the vertical nanoparticles’ mass flux. Based on feasible boundary layer approximations and Buongiorno’s nanofluid formulation, the leading coupled differential equations are stated properly in the sense of Arrhenius’s and Von Kármán’s approaches. By employing an advanced generalized differential quadrature algorithm, the obtained boundary layer equations are handled numerically with a higher order of accuracy to generate adequate graphical and tabular illustrations for the different values of the influencing flow parameters. As findings, the graphical results confirm that the nanofluid motion decelerates meaningfully thanks to the resistive magnetic influence. A significant thermal amelioration can be achieved by strengthening the magnetic impact, the generation of heat, the thermal convective process, and the thermophoresis mechanism. Moreover, it is found that the thermo-migration of nanoparticles can be reinforced more via the intensification in the convective process, the thermo-migration of nanoparticles, and the activation energy.
TL;DR: In this paper, the authors established a model to analyze the changing law of the temperature profile inside the production string of a high-pressure/high-temperature gas well (HPHT gas well).
Abstract: The temperature profile plays an important role in well integrity, flow assurance, and well test. Meanwhile, the impact of engineering conditions should not be ignored while calculating the well temperature profile. Therefore, in this study, we established a model to analyze the changing law of the temperature profile inside the production string of a high-pressure/high-temperature gas well (HPHT gas well). The proposed model considers the flow friction caused by a high production rate. Meanwhile, the variations in gas properties are taken into account to increase the model accuracy, including gas density, flow velocity, and viscosity. The analysis indicates that the temperature in the production string decreases more and more quickly from the reservoir to the wellhead. The wellhead temperature changes more and more slowly with time. When the reservoir temperature is too low to maintain production, it is useful to regulate the production rate or inject the thermal insulating fluid into the annulus to avoid the block caused by wax deposition or hydrate deposition. Considering the sensitivity, feasibility, and cost, it is recommended to change the well temperature profile by adjusting the production rate. If not applicable, the thermal conductivity can also be optimized to change the temperature profile.
TL;DR: Carroll symmetry arises from Poincaré symmetry upon taking the limit of vanishing speed of light as mentioned in this paper , which is relevant for dark energy and inflation, and it has been shown that for energy-momentum tensors of perfect fluid form, these imply an equation of state for energy density plus pressure.
Abstract: Carroll symmetry arises from Poincaré symmetry upon taking the limit of vanishing speed of light. We determine the constraints on the energy-momentum tensor implied by Carroll symmetry and show that for energy-momentum tensors of perfect fluid form, these imply an equation of state E + P = 0
TL;DR: The presented polarimetric model was able to support the physical interpretation of the results from previous studies and relate the latter to the samples’ health condition, respectively.
Abstract: In biophotonics, novel techniques and approaches are being constantly sought to assist medical doctors and to increase both sensitivity and specificity of the existing diagnostic methods. In such context, tissue polarimetry holds promise to become a valuable optical diagnostic technique as it is sensitive to tissue alterations caused by different benign and malignant formations. In our studies, multiple Mueller matrices were recorded for formalin-fixed, human, ex vivo colon specimens containing healthy and tumor zones. The available data were pre-processed to filter noise and experimental errors, and then all Mueller matrices were decomposed to derive polarimetric quantities sensitive to malignant formations in tissues. In addition, the Poincaré sphere representation of the experimental results was implemented. We also used the canonical and natural indices of polarimetric purity depolarization spaces for plotting our experimental data. A feature selection was used to perform a statistical analysis and normalization procedure on the available data, in order to create a polarimetric model for colon cancer assessment with strong predictors. Both unsupervised (principal component analysis) and supervised (logistic regression, random forest, and support vector machines) machine learning algorithms were used to extract particular features from the model and for classification purposes. The results from logistic regression allowed to evaluate the best polarimetric quantities for tumor detection, while the use of random forest yielded the highest accuracy values. Attention was paid to the correlation between the predictors in the model as well as both losses and relative risk of misclassification. Apart from the mathematical interpretation of the polarimetric quantities, the presented polarimetric model was able to support the physical interpretation of the results from previous studies and relate the latter to the samples’ health condition, respectively.
TL;DR: In this paper , the challenges in the field of low temperature plasmas (LTPs) are discussed with a focus on health, energy, and sustainability, and the authors discuss the challenges being faced in particular for atmospheric pressure Plasmas.
Abstract: Low temperature plasmas (LTPs) enable to create a highly reactive environment at near ambient temperatures due to the energetic electrons with typical kinetic energies in the range of 1 to 10 eV (1 eV = 11600K), which are being used in applications ranging from plasma etching of electronic chips and additive manufacturing to plasma-assisted combustion. LTPs are at the core of many advanced technologies. Without LTPs, many of the conveniences of modern society would simply not exist. New applications of LTPs are continuously being proposed. Researchers are facing many grand challenges before these new applications can be translated to practice. In this paper, we will discuss the challenges being faced in the field of LTPs, in particular for atmospheric pressure plasmas, with a focus on health, energy and sustainability.
TL;DR: In this paper , inelastic neutron scattering (INS) spectroscopy was used to probe intermolecular interactions in the water-deep eutectic solvent mixtures for the cases of choline chloride (the hydrogen bond acceptor) and three different hydrogen bond donors, with different degrees of acidity.
Abstract: The effect of water on the physicochemical properties of deep eutectic solvents (DES) is a trending research topic. In this work, inelastic neutron scattering (INS) spectroscopy, was used to probe intermolecular interactions in the water-deep eutectic solvent mixtures for the cases of choline chloride (the hydrogen bond acceptor) and three different hydrogen bond donors, with different degrees of acidity: urea, glycerol and lactic acid. It was found that quenching samples in liquid nitrogen is a procedure that may retain the liquid phase morphology of DES at the low temperatures required by INS spectroscopy. The three studied systems share the preference of water molecules to bind to chloride anion, as predicted by numerous molecular dynamics simulations. Despite this similarity, the three systems present several distinct INS features upon water addition that are related to their unique properties and structure at the molecular level. In the choline chloride:urea system, water molecules promote a strengthening of hydrogen bonds with the NH and OH donors, while for the choline chloride:lactic acid system INS probed the existence of solvated DES clusters instead of specifically interfering water molecules. This study takes advantage from the unique capabilities of INS and paves the way for future studies in these systems.
TL;DR: In this article , a method of modeling analysis and parameter estimation of hydrological runoff and discharge relationship based on machine learning is designed, which can accurately evaluate and quantitate the property of the rainfall-runoff, and many traditional classic models are proposed to study the characteristic of rainfall runoff.
Abstract: In actual engineering fields, the bearing capacity of a rock is closely related to the pore water pressure in the rock. Studies have shown that the pore water in the rock has a great relationship with the change in runoff. Thus, it has crucial meaning to accurately evaluate and quantitate the property of the rainfall–runoff, and many traditional classic models are proposed to study the characteristic of rainfall–runoff. While considering the high uncertainty and randomness of the rainfall–runoff property, more and more artificial neural networks (ANN) are used for the rainfall–runoff modeling as well as other fields. Among them, the long short-term memory (LSTM), which can be trained for sequence generation by processing real data sequences one step at a time and has good prediction results in other engineering fields, is adopted in this study to investigate the changes of rainfall–runoff values and make a prediction. In order to ensure the accuracy of the trained model, the cross-validation method is used in this study. The training data set is divided into 12 parts. The monthly forecast results from 2014 to 2015 show that the model can well reflect the peaks and troughs. In a recent study, the relationship between the rainfall–runoff and discharge are commonly based on the current measured data, while the prediction results are adopted to analyze the relation of these parameters, and considering that the existing methods have fuzzy relationship between runoff and discharge, which leads to a high risk of forecasting and dispatching. A method of modeling analysis and parameter estimation of hydrological runoff and discharge relationship based on machine learning is designed. From the experimental results, the average risk of this method is 61.23%, which is 15.104% and 13.397% less than that of the other two existing methods, respectively. It proves that the method of hydrological runoff relationship modeling and parameter estimation integrated with machine learning has better practical application effect.
TL;DR: In this paper , the exact solutions to the fractional Fitzhugh-Nagumo (FN) equation, fractional Newell-Whitehead-Segel (NWS) equation and fractional Zeldovich equation were obtained within a time-fractional conformable derivative.
Abstract: The Fitzhugh–Nagumo equation is an important non-linear reaction–diffusion equation used to model the transmission of nerve impulses. This equation is used in biology as population genetics; the Fitzhugh–Nagumo equation is also frequently used in circuit theory. In this study, we give solutions to the fractional Fitzhugh–Nagumo (FN) equation, the fractional Newell–Whitehead–Segel (NWS) equation, and the fractional Zeldovich equation. We found the exact solutions of these equations by conformable derivatives. We have obtained the exact solutions within the time-fractional conformable derivative for these equations.
TL;DR: In this paper , the E-type α-attraction model of inflation is extended to include the formation of primordial black holes (PBHs) and the power spectrum of scalar perturbations is derived.
Abstract: We propose and study the new (generalized) E-type α-attractor models of inflation, in order to include formation of primordial black holes (PBHs). The inflaton potential has a near-inflection point where slow-roll conditions are violated, thus leading to large scalar perturbations collapsing to PBHs later. An ultra-slow roll (short) phase exists between two (longer) phases of slow-roll inflation. We numerically investigate the phases of inflation, derive the power spectrum of scalar perturbations and calculate the PBHs masses. For certain values of the parameters, the asteroid-size PBHs can be formed with the masses of 1017 ÷ 1019 g, beyond the Hawking evaporation limit and in agreement with current Cosmic Microwave Background observations. Those PBHs are a candidate for (part of) dark matter in the present Universe, while the gravitational waves induced by the PBHs formation may be detectable by the future space-based gravitational interferometers.
TL;DR: In this paper , the authors studied how voluntary health-protective behaviour and vaccination willingness impact the long-term dynamics of COVID-19 and highlighted the importance of appropriate mandatory NPIs to maximise the impact of individual voluntary actions in pandemic control.
Abstract: Pharmaceutical and non-pharmaceutical interventions (NPIs) have been crucial for controlling COVID-19. They are complemented by voluntary health-protective behaviour, building a complex interplay between risk perception, behaviour, and disease spread. We studied how voluntary health-protective behaviour and vaccination willingness impact the long-term dynamics. We analysed how different levels of mandatory NPIs determine how individuals use their leeway for voluntary actions. If mandatory NPIs are too weak, COVID-19 incidence will surge, implying high morbidity and mortality before individuals react; if they are too strong, one expects a rebound wave once restrictions are lifted, challenging the transition to endemicity. Conversely, moderate mandatory NPIs give individuals time and room to adapt their level of caution, mitigating disease spread effectively. When complemented with high vaccination rates, this also offers a robust way to limit the impacts of the Omicron variant of concern. Altogether, our work highlights the importance of appropriate mandatory NPIs to maximise the impact of individual voluntary actions in pandemic control.
TL;DR: An overview of the different cancers by LIBS is meant to summarize the research performed to date and suggest some suitable advanced chemometrics techniques and effective LIBS devices, if successfully implemented, would be significantly beneficial to the medical field in the future.
Abstract: Due to the new demonstrations of Laser-induced breakdown spectroscopy (LIBS) applicability in a surprisingly wide variety of applications, the use of LIBS as a medical diagnostic tool is steadily gaining momentum. Especially in different cancer diseases, LIBS has the potential to become a fast and valuable analytical tool. We addressed LIBS equipment and quantitative analytical procedures, and signal enhancement techniques for improving element detection. For detailed aspects of applications, we reviewed the recent progress of LIBS in different cancer diseases diagnoses by using different tissues and medical fluid as samples. To fulfill the high demands in the medical industry and overcome the severe tissue sample problem, it is proposed that the chemometric and signal amplification techniques for quantitative analysis should be employed, and robust and effective LIBS devices should be developed. This overview of the different cancers by LIBS is meant to summarize the research performed to date and suggest some suitable advanced chemometrics techniques and effective LIBS devices, if successfully implemented, would be significantly beneficial to the medical field in the future.
TL;DR: The 2019 Nobel Prize for physics was given to two climate scientists, Syukuro Manabe and Klaus Hasselmann, and the physicist Giorgio Parisi as mentioned in this paper , who developed and contributed to many complexity science methods which are nowadays widely used in climate science.
Abstract: The 2021 Nobel prize for physics was awarded to two climate scientists, Syukuro Manabe and Klaus Hasselmann, and the physicist Giorgio Parisi. While at first sight the work of Parisi seems not to be related to climate science, this is not the case. Giorgio Parisi developed and contributed to many complexity science methods which are nowadays widely used in climate science. Giorgi Parisi also was involved in the development of the “stochastic resonance” idea to explain paleoclimate variability, while Klaus Hasselmann developed stochastic climate models. Here we review and discuss their work from a complex and stochastic systems perspective in order to highlight those aspects of their work. For instance, fractal and multi-fractal analysis of climate data is now widely used and many weather prediction and climate models contain stochastic parameterizations, topics Parisi and Hasselmann have pioneered. Furthermore, Manabe’s work was key to understanding the effects of anthropogenic climate change by the development of key advances in the parameterization of convection and radiative forcing in climate models. We discuss also how their inventive research has shaped current climate research and is still influencing climate modeling and future research directions.
TL;DR: In this paper , the authors focus on the research done in the textile industry and food processing, how plasma brings in an effective change in these industrial sectors as well the as application of CAP in disinfection, sterilization, microbial inactivation, and surface modification to obtain desirable results.
Abstract: Cold atmospheric plasma (CAP) is a promising technology quite useful in many industries including biotechnology, biomedical, textiles, and food processing. The contrivance of plasma technology can be a potential game-changer to use in any possible way in these industries. This CAP technology is technically a green process with no generation of chemically harmful substances with more ecological and economic benefits. This review article will focus on the research done in the textile industry and food processing, how plasma brings in an effective change in these industrial sectors as well the as application of CAP in disinfection, sterilization, microbial inactivation, and surface modification to obtain desirable results. Recently, there have been reports of successful use of CAP technology for surface inactivation of SARS-CoV-2, plasma-activated water for disinfection of SARS-CoV-2. This article will streamline the innovations in textiles and food industries achieved using plasma technology and what gaps industries face while manufacturing. The focus will be on what research has already done while depicting the gaps and opportunities for using plasma technology in these industries and making use of it to achieve a circular economy, which is one of the major policies of the European countries. A circular economy enables manufactures to produce goods which can be reused, recycled, refurbished, and repaired rather than scrapping them after a single use. The reduction of harmful chemicals, wastewater treatment, and sterilization is achieved using plasma technology and allows reusing the resources which consequently helps to achieve most of the UN’s sustainable development goals and help society to live a sustainable and better life.
TL;DR: This paper proposes a novel image encryption scheme based on the memristive chaotic system and combining bidirectional bit-level cyclic shift and dynamic DNA-level diffusion (IES-M-BD), and demonstrates that this encryption scheme has a high security level and can resist various attacks.
Abstract: In recent years, many researchers have leveraged various memristors to design many novel memristive chaotic systems with complex dynamics. Compared with other chaotic systems, applying these memristive chaotic systems to image encryption is expected to solve some key problems in this field. Therefore, exploiting a recently reported memristive chaotic system, this paper proposes a novel image encryption scheme based on the memristive chaotic system and combining bidirectional bit-level cyclic shift and dynamic DNA-level diffusion (IES-M-BD). First, a discrete memristive chaotic map is employed to generate chaotic sequences. Then, the plaintext image is shifted circularly on bit-level according to chaotic sequences and the hash value of the plaintext image. After that, the shifted matrix is recombined on the bit plane and encoded dynamically by DNA encoding rules. Next, dynamic DNA-level diffusion and DNA-level permutation are carried out in two rounds. Finally, the encrypted image is obtained after dynamic DNA decoding. Simulation tests and performance analyses are also carried out in this paper. The simulation results and the security analyses demonstrate that this encryption scheme has a high security level and can resist various attacks.
TL;DR: This review proposes a complete overview of silicon SPADs characteristics and applications, and focuses on the development of SPAD arrays, presenting some of the most notable examples found in literature.
Abstract: The ability to detect single photons is becoming an enabling key capability in an increasing number of fields. Indeed, its scope is not limited to applications that specifically rely on single photons, such as quantum imaging, but extends to applications where a low signal is overwhelmed by background light, such as laser ranging, or in which faint excitation light is required not to damage the sample or harm the patient. In the last decades, SPADs gained popularity with respect to other single-photon detectors thanks to their small size, possibility to be integrated in complementary metal-oxide semiconductor processes, room temperature operability, low power supply and, above all, the possibility to be fast gated (to time filter the incoming signal) and to precisely timestamp the detected photons. The development of large digital arrays that integrates the detectors and circuits has allowed the implementation of complex functionality on-chip, tailoring the detectors to suit the need of specific applications. This review proposes a complete overview of silicon SPADs characteristics and applications. In the previous Part I, starting with the working principle, simulation models and required frontend, the paper moves to the most common parameters adopted in literature for characterizing SPAD performance and describes single pixels applications and their performance. In this Part II, the focus is posed on the development of SPAD arrays, presenting some of the most notable examples found in literature. The actual exploitation of these designs in real applications (e.g., automotive, bioimaging and radiation detectors) is then discussed.