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COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

We consider an acyclic network of single-server queues with heterogeneous processing rates. It is assumed that each queue is fed by the superposition of a large number of i.i.d. Gaussian processes with stationary increments and positive drifts, which can be correlated across different queues. The flow of work departing from each server is split deterministically and routed to its neighbors according to a fixed routing matrix, with a fraction of it leaving the network altogether. We study the exponential decay rate of the probability that the steady-state queue length at any given node in the network is above any fixed threshold, also referred to as the ‘overflow probability’. In particular, we first leverage Schilder’s sample-path large deviations theorem to obtain a general lower bound for the limit of this exponential decay rate, as the number of Gaussian processes goes to infinity. Then, we show that this lower bound is tight under additional technical conditions. Finally, we show that if the input processes to the different queues are nonnegatively correlated, non-short-range dependent fractional Brownian motions, and if the processing rates are large enough, then the asymptotic exponential decay rates of the queues coincide with the ones of isolated queues with appropriate Gaussian inputs.

Taking two susceptible groups into account, we formulate a modified subhealthy-healthy-infected-recovered (SHIR) model with time delay and nonlinear incidence rate in networks with different topologies. Concretely, two dynamical systems are designed in homogeneous and heterogeneous networks by utilizing mean field equations. Based on the next-generation matrix and the existence of a positive equilibrium point, we derive the basic reproduction numbers \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_{0}^{1}$$\end{document}R01 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_{0}^{2}$$\end{document}R02 which depend on the model parameters and network structure. In virtue of linearized systems and Lyapunov functions, the local and global stabilities of the disease-free equilibrium points are, respectively, analyzed when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_{0}^{1}1$$\end{document}R01>1. Finally, numerical simulations are performed to conduct sensitivity analysis and confirm theoretical results. Moreover, some conjectures are proposed to complement dynamical behavior of two systems.

The health, economic, and social impact of COVID-19 has been significant across the world. Our objective was to evaluate the association between air pollution (through NO2 and PM2.5 levels) and COVID-19 mortality in Spanish provinces from February 3, 2020, to July 14, 2020, adjusting for climatic parameters. An observational and ecological study was conducted with information extracted from Datadista repository (Datadista, 2020). Air pollutants (NO2 and PM2.5 levels) were analyzed as potential determinants of COVID-19 mortality. Multilevel Poisson regression models were used to analyze the risk of mortality after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Models were adjusted by four climatic variables (hours of solar radiation, precipitation, daily temperature and wind speed) and population size. The mean levels of PM2.5 and NO2 across all provinces and time in Spain were 8.7 μg/m3 (SD 9.7) and 8.7 μg/m3 (SD 6.2), respectively. High levels of PM2.5 (IRR = 1.016, 95% CI: 1.007–1.026), NO2 (IRR = 1.066, 95% CI: 1.058–1.075) and precipitation (IRRNO2 = 0.989, 95% CI: 0.981–0.997) were positively associated with COVID-19 mortality, whereas temperature (IRRPM2.5 = 0.988, 95% CI: 0.976–1.000; and IRRNO2 = 0.771, 95% CI: 0.761–0.782, respectively) and wind speed (IRRNO2 = 1.095, 95% CI: 1.061–1.131) were negatively associated with COVID-19 mortality. Air pollution can be a key factor to understand the mortality rate for COVID-19 in Spain. Furthermore, climatic variables could be influencing COVID-19 progression. Thus, air pollution and climatology ought to be taken into consideration in order to control the pandemic. Supplementary Information The online version contains supplementary material available at 10.1007/s11869-021-01062-2.

The ongoing COVID-19 pandemic has affected most of the countries on Earth. It has become a pandemic outbreak with more than 50 million confirmed infections and above 1 million deaths worldwide. In this study, we consider a mathematical model on COVID-19 transmission with the prosocial awareness effect. The proposed model can have four equilibrium states based on different parametric conditions. The local and global stability conditions for awareness-free, disease-free equilibrium are studied. Using Lyapunov function theory and LaSalle invariance principle, the disease-free equilibrium is shown globally asymptotically stable under some parametric constraints. The existence of unique awareness-free, endemic equilibrium and unique endemic equilibrium is presented. We calibrate our proposed model parameters to fit daily cases and deaths from Colombia and India. Sensitivity analysis indicates that the transmission rate and the learning factor related to awareness of susceptibles are very crucial for reduction in disease-related deaths. Finally, we assess the impact of prosocial awareness during the outbreak and compare this strategy with popular control measures. Results indicate that prosocial awareness has competitive potential to flatten the COVID-19 prevalence curve.

This paper develops asymptotics and approximations for ruin probabilities in a multivariate risk setting. We consider a model in which the individual reserve processes are driven by a common Markovian environmental process. We subsequently consider a regime in which the claim arrival intensity and transition rates of the environmental process are jointly sped up, and one in which there is (with overwhelming probability) maximally one transition of the environmental process in the time interval considered. The approximations are extensively tested in a series of numerical experiments.

Microzonation is one of the essential tools in seismology to mitigate earthquake damage by estimating the near-surface velocity structure and developing land usage plans and intelligent building design. The number of microzonation studies increased in the last few years as induced seismicity becomes more relevant, even in low-risk areas. While of vital importance, especially in densely populated cities, most of the traditional techniques suffer from different shortcomings. The microzonation technique presented here tries to reduce the existing ambiguity of the inversion results by the combination of single-station six-component (6C) measurements, including three translational and three rotational motions, and more traditional H/V techniques. By applying this new technique to a microzonation study in the downtown area of Munich (Germany) using an iXblue blueSeis-3A rotational motion sensor together with a Nanometrics Trillium Compact seismometer, we were able to estimate Love and Rayleigh wave dispersion curves. These curves together with H/V spectral ratios are then inverted to obtain P- and S-wave velocity profiles of the upper 100 m. In addition, there is a good correlation between the estimated velocity models and borehole-derived lithology, indicating the potential of this single-station microzonation approach., European Research Council https://doi.org/10.13039/501100000781, Bundesministerium für Wirtschaft und Energie https://doi.org/10.13039/501100006360

Whenever a disease emerges, awareness in susceptibles prompts them to take preventive measures, which influence individuals’ behaviors. Therefore, we present and analyze a time-delayed epidemic model in which class of susceptible individuals is divided into three subclasses: unaware susceptibles, fully aware susceptibles, and partially aware susceptibles to the disease, respectively, which emphasizes to consider three explicit incidences. The saturated type of incidence rates and treatment rate of infectives are deliberated herein. The mathematical analysis shows that the model has two equilibria: disease-free and endemic. We derive the basic reproduction number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document}R0 of the model and study the stability behavior of the model at both disease-free and endemic equilibria. Through analysis, it is demonstrated that the disease-free equilibrium is locally asymptotically stable when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_01$$\end{document}R0>1, and linearly neutrally stable when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0=1$$\end{document}R0=1 for the time delay \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varrho >0$$\end{document}ϱ>0. Further, an undelayed epidemic model is studied when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0=1$$\end{document}R0=1, which reveals that the model exhibits forward and backward bifurcations under specific conditions, which also has important implications in the study of disease transmission dynamics. Moreover, we investigate the stability behavior of the endemic equilibrium and show that Hopf bifurcation occurs near endemic equilibrium when we choose time delay as a bifurcation parameter. Lastly, numerical simulations are performed in support of our analytical results.

Misconceptions about energy conservation abound due to the gap between physics and secondary school chemistry. This paper surveys this difference and its relevance to the 1690s–2010s Leibnizian argument that mind-body interaction is impossible due to conservation laws. Justifications for energy conservation are partly empirical, such as Joule’s paddle wheel experiment, and partly theoretical, such as Lagrange’s statement in 1811 that energy is conserved if the potential energy does not depend on time. In 1918 Noether generalized results like Lagrange’s and proved a converse: symmetries imply conservation laws and vice versa. Conservation holds if and only if nature is uniform. The rise of field physics during the 1860s–1920s implied that energy is located in particular places and conservation is primordially local: energy cannot disappear in Cambridge and reappear in Lincoln instantaneously or later; neither can it simply disappear in Cambridge or simply appear in Lincoln. A global conservation law can be inferred in some circumstances. Einstein’s General Relativity, which stimulated Noether’s work, is another source of difficulty for conservation laws. As is too rarely realized, the theory admits conserved quantities due to symmetries of the Lagrangian, like other theories. Indeed General Relativity hasmoresymmetries and hence (at least formally)moreconserved energies. An argument akin to Leibniz’s finally gets some force. While the mathematics is too advanced for secondary school, the ideas that conservation is tied to uniformities of nature and that energy is in particular places, are accessible. Improved science teaching would serve the truth and enhance the social credibility of science.

Signal overlapping is a major bottleneck for protein NMR analysis. We propose a new method, stable-isotope-assisted parameter extraction (SiPex), to resolve overlapping signals by a combination of amino-acid selective isotope labeling (AASIL) and tensor decomposition. The basic idea of Sipex is that overlapping signals can be decomposed with the help of intensity patterns derived from quantitative fractional AASIL, which also provides amino-acid information. In SiPex, spectra for protein characterization, such as 15N relaxation measurements, are assembled with those for amino-acid information to form a four-order tensor, where the intensity patterns from AASIL contribute to high decomposition performance even if the signals share similar chemical shift values or characterization profiles, such as relaxation curves. The loading vectors of each decomposed component, corresponding to an amide group, represent both the amino-acid and relaxation information. This information link provides an alternative protein analysis method that does not require “assignments” in a general sense; i.e., chemical shift determinations, since the amino-acid information for some of the residues allows unambiguous assignment according to the dual selective labeling. SiPex can also decompose signals in time-domain raw data without Fourier transform, even in non-uniformly sampled data without spectral reconstruction. These features of SiPex should expand biological NMR applications by overcoming their overlapping and assignment problems. Electronic supplementary material The online version of this article (10.1007/s10858-019-00295-9) contains supplementary material, which is available to authorized users.

Booth (Solar Phys.296, 108, 2021; hereafter B21) is essentially a critique of the Hilbert transform techniques used in our paper (Leamon et al., Solar Phys.295, 36, 2020; hereafter L20) to predict the termination of solar cycles. Here we respond to his arguments; our methodology and parameter choices do extract a mathematically robust signature of terminators from the historical sunspot record. We agree that the attempt in L20 to extrapolate beyond the sunspot record gives a failed prediction for the next terminator of May 2020, and we identify both a possible cause and remedy here. However, we disagree with the B21 assessment that the likely termination of Solar Cycle 24 is two years after the date predicted in L20, and we show why.

Measurements from NASA's Van Allen Probes have transformed our understanding of the dynamics of Earth's geomagnetically-trapped, charged particle radiation. The Van Allen Probes were equipped with the Magnetic Electron Ion Spectrometers (MagEIS) that measured energetic and relativistic electrons, along with energetic ions, in the radiation belts. Accurate and routine measurement of these particles was of fundamental importance towards achieving the scientific goals of the mission. We provide a comprehensive review of the MagEIS suite's on-orbit performance, operation, and data products, along with a summary of scientific results. The purpose of this review is to serve as a complement to the MagEIS instrument paper, which was largely completed before flight and thus focused on pre-flight design and performance characteristics. As is the case with all space-borne instrumentation, the anticipated sensor performance was found to be different once on orbit. Our intention is to provide sufficient detail on the MagEIS instruments so that future generations of researchers can understand the subtleties of the sensors, profit from these unique measurements, and continue to unlock the mysteries of the near-Earth space radiation environment. Supplementary Information The online version contains supplementary material available at 10.1007/s11214-021-00855-2.

The paper represents an analysis of convective instability in a vertical cylindrical porous microchannel performed using the Galerkin method. The dependence of the critical Rayleigh number on the Darcy, Knudsen, and Prandtl numbers, as well as on the ratio of the thermal conductivities of the fluid and the wall, was obtained. It was shown that a decrease in permeability of the porous medium (in other words, increase in its porosity) causes an increase in flow stability. This effect is substantially nonlinear. Under the condition Da > 0.1, the effect of the porosity on the critical Rayleigh number practically vanishes. Strengthening of the slippage effects leads to an increase in the instability of the entire system. The slippage effect on the critical Rayleigh number is nonlinear. The level of nonlinearity depends on the Prandtl number. With an increase in the Prandtl number, the effect of slippage on the onset of convection weakens. With an increase in the ratio of the thermal conductivities of the fluid and the wall, the influence of the Prandtl number decreases. At high values of the Prandtl numbers (Pr > 10), its influence practically vanishes.

The van der Waals (vdW) interaction between nanoparticles (NPs) in general, and especially between metal NPs, may be appreciable, and may result in nanoparticle aggregation. In biofluids, NPs become rapidly surrounded by a protein corona (PC). Here, the vdW and hydration interaction of NPs with and without PC are compared in detail. The focus is on two widely used types of NPs fabricated of SiO2 and Au and possessing weak and strong vdW interactions, respectively. For SiO2, the presence of PC increases the vdW interaction, but it remains relatively weak and insufficient for aggregation. For Au, the presence of PC decreases the vdW interaction, and in the case of small NPs (≤ 40 nm in diameter) it may become insufficient for aggregation as well while the larger NPs can aggregate.

Bayesian analysis often concerns an evaluation of models with different dimensionality as is necessary in, for example, model selection or mixture models. To facilitate this evaluation, transdimensional Markov chain Monte Carlo (MCMC) relies on sampling a discrete indexing variable to estimate the posterior model probabilities. However, little attention has been paid to the precision of these estimates. If only few switches occur between the models in the transdimensional MCMC output, precision may be low and assessment based on the assumption of independent samples misleading. Here, we propose a new method to estimate the precision based on the observed transition matrix of the model-indexing variable. Assuming a first-order Markov model, the method samples from the posterior of the stationary distribution. This allows assessment of the uncertainty in the estimated posterior model probabilities, model ranks, and Bayes factors. Moreover, the method provides an estimate for the effective sample size of the MCMC output. In two model selection examples, we show that the proposed approach provides a good assessment of the uncertainty associated with the estimated posterior model probabilities.

Apodization weighted acquisition is a simple approach to enhance the sensitivity of multidimensional NMR spectra by scaling the number of scans during acquisition of the indirect dimension(s). The signal content of the resulting spectra is identical to conventionally sampled data, yet the spectra show improved signal-to-noise ratios. There are no special requirements for data acquisition and processing: the time-domain data can be transformed with the same schemes used for conventionally recorded spectra, including Fourier transformation. The method is of general use in multidimensional liquid and solid state NMR experiments if the number of recorded transients per sampling point is bigger than the minimum required phase cycle of the pulse sequence. Electronic supplementary material The online version of this article (10.1007/s10858-019-00243-7) contains supplementary material, which is available to authorized users.

For the discrete Laguerre operators we compute explicitly the corresponding heat kernels by expressing them with the help of Jacobi polynomials. This enables us to show that the heat semigroup is ultracontractive and to compute the corresponding norms. On the one hand, this helps us to answer basic questions (recurrence, stochastic completeness) regarding the associated Markovian semigroup. On the other hand, we prove the analogs of the Cwiekel-Lieb-Rosenblum and the Bargmann estimates for perturbations of the Laguerre operators, as well as the optimal Hardy inequality., Comment: 22 pages; new Section 5.3 with the Hardy-type inequality for Laguerre operators

In this paper, phase space reconstruction from stereo-electroencephalography data of ten patients with focal epilepsy forms a series of graphs. Those obtained graphs reflect the transition characteristics of brain dynamical system from pre-seizure to seizure of epilepsy. Interestingly, it is found that the rank of Laplacian matrix of these graphs has a sharp decrease when a seizure is close to happen, which thus might be viewed as a new potential biomarker in epilepsy. In addition, the reliability of this method is numerically verified with a coupled mass neural model. In particular, our simulation suggests that this potential biomarker can play the roles of predictive effect or delayed awareness, depending on the bias current of the Gaussian noise. These results may give new insights into the seizure detection.

The goal of the present work is the study and use of the cooperative effects of bacteria located in the evanescent field of biomaterials during Raman interaction of the light in UV region. A model is proposed based upon existing experiments, which takes into account the local symmetry of biomolecules. In this paper is presented the quantum correlations which can be obtained in the processes of Raman scattering in two photon lasing. An interesting behavior of Stokes and anti-Stokes generated field in the Raman processes can be observed for the small number of pump field in nonlinear media. The photons statistic is proposed for the diagnosis of the new collective processes. The set of functions was presented, which describes the probability of simultaneous existence of (2j + 1) Dicke states in the scattering process.

In this article we introduce and study oscillating Gaussian processes defined by$$X_t = \alpha _+ Y_t \mathbf{1}_{Y_t >0} + \alpha _- Y_t\mathbf{1}_{Y_tXt=α+Yt1Yt>0+α-Yt1Yt<0, where$$\alpha _+,\alpha _->0$$α+,α->0are free parameters andYis either stationary or self-similar Gaussian process. We study the basic properties ofXand we consider estimation of the model parameters. In particular, we show that the moment estimators converge in$$L^p$$Lpand are, when suitably normalised, asymptotically normal.

The living state is low entropy, highly complex organization, yet it is part of the energy cycle of the environment. Due to the recurring presence of the resting state, stimulus and its response form a thermodynamic cycle of perception that can be modeled by the Carnot engine. The endothermic reversed Carnot engine relies on energy from the environment to increase entropy (i.e., the synaptic complexity of the resting state). High entropy relies on mental energy, which represents intrinsic motivation and focuses on the future. It increases freedom of action. The Carnot engine can model exothermic, negative emotional states, which direct the focus on the past. The organism dumps entropy and energy to its environment, in the form of aggravation, anxiety, criticism, and physical violence. The loss of mental energy curtails freedom of action, forming apathy, depression, mental diseases, and immune problems. Our improving intuition about the brain's intelligent computations will allow the development of new treatments for mental disease and novel find applications in robotics and artificial intelligence.

In this paper, we construct an inertial two-neuron system with multiple delays, which is described by three first-order delayed differential equations. The neural system presents dynamical coexistence with equilibria, periodic orbits, and even quasi-periodic behavior by employing multiple types of bifurcations. To this end, the pitchfork bifurcation of trivial equilibrium is analyzed firstly by using center manifold reduction and normal form method. The system presents different sequences of supercritical and subcritical pitchfork bifurcations. Further, the nontrivial equilibrium bifurcated from trivial equilibrium presents a secondary pitchfork bifurcation. The system exhibits stable coexistence of multiple equilibria. Using the pitchfork bifurcation curves, we divide the parameter plane into different regions, corresponding to different number of equilibria. To obtain the effect of time delays on system dynamical behaviors, we analyze equilibrium stability employing characteristic equation of the system. By the Hopf bifurcation, the system illustrates a periodic orbit near the trivial equilibrium. We give the stability regions in the delayed plane to illustrate stability switching. The neural system is illustrated to have Hopf-Hopf bifurcation points. The coexistence with two periodic orbits is presented near these bifurcation points. Finally, we present some mixed dynamical coexistence. The system has a stable coexistence with periodic orbit and equilibrium near the pitchfork-Hopf bifurcation point. Moreover, multiple frequencies of the system induce the presentation of quasi-periodic behavior. The system presents stable coexistence with two periodic orbits and one quasi-periodic behavior.

The study of heat and electroosmotic characteristics in the flow of a third-order fluid regulated by peristaltic pumping is examined by using governing equations, i.e., the continuity equation, momentum equation, energy equation, and concentration equation. The wavelength is considered long compared to its height and a low Reynolds number is assumed. The velocity slip condition is employed. Analytical solutions are performed through the perturbation technique. The expressions for the dimensionless velocity components, temperature, concentration, and heat transfer rate are obtained. Pumping features were computed numerically for discussion of results. Trapping and heat transfer coefficient distributions were also studied graphically. The findings of the present study can be applied to design biomicrofluidic devices like tumor-on-a-chip and organ-on-a-chip.

Well-constrained marine sediment characteristics (sediment thickness and shear wave velocity) are important not only for the study of climate over geologic times scales but also for correcting and accounting for its presence in seismic data used to investigate deeper structures. We use data from the PI-LAB (Passive Imaging of the Lithosphere Asthenosphere Boundary) experiment, which consisted of 39 broadband ocean bottom seismometers deployed at the Equatorial Mid-Atlantic Ridge near the Chain fracture zone covering 0–80 Myr old seafloor. We compute admittance between the pressure to the vertical displacement at the seafloor at frequencies between 0.1 and 0.2 Hz for microseism-generated Rayleigh waves for 18 stations where data quality is good to determine the sediment thickness and shear wave velocity. We find a general trend of increasing sediment thickness with the seafloor ages, as expected with sediment thicknesses that range from 10–450 m and, shear wave velocities that range from 0.05–0.34 km/s. We find sediment thickness varies almost uniformly across both sides of the ridge, and it indicates that both sides experienced a similar sedimentation process. Our results are in good agreement with the global sediment model that is based on drilling cores and active source experiments, but thinner by up to 50 m at several stations on seafloor older than 25 My. Overlap of the 95% confidence regions between admittance and Ps estimates for thickness and shear velocity is found at 15 stations where we have both Ps and admittance estimates. It suggests that both methods yield accurate estimates for sediment thickness. In addition, our admittance result extends the lateral resolution of sediment characteristics to stations that were not previously resolved by Ps.

In a motor brain-machine interface system, since the electroencephalogram signal is changing through out the process of the arm movement, the offline trained decoder with fixed weights is often unable to convert the electroencephalogram signal accurately, resulting in poor recovery of joint motor function. In this paper, a receding horizon optimization strategy is chosen to online update the decoder weights and design an adaptive Wiener-filter-based decoder. Firstly, a classical Wiener-filter-based decoder with fixed weights is brief reviewed. Secondly, the weights in Wiener-filter-based decoder are updated by minimizing the cost function, which is composed by the sum of squared position errors in the given horizon at each sampling time. The simulation shows that the recovery effect of joint motor function and neuron activity in the BMI system with the adaptive decoder are both better than that in the BMI system with the fixed decoder.

Forced convection of nanofluid in a vented cavity with elastic bottom wall is studied by using an inner conductive L-shaped object and magnetic field. Simulations are performed using the finite element method when the impacts of various pertinent parameters, such as Reynolds number (between 100 and 500), Hartmann number (between 0 and 40), elastic modulus of the flexible wall (between $$10^5$$ and $$10^9$$), solid nanoparticle volume fraction (between 0 and 0.04), size (between 0.1 and 0.4H), inclination (between − 90 and 90) and location ($$x_\mathrm{c}$$ between 0.25 and 0.75 H and $$y_\mathrm{c}$$ between 0.15 and 0.65H) of the L-shaped object on the fluid flow and heat transfer features, are investigated. It was observed that wall flexibility effects are influential for the configuration with strong convection and maximum of $$11\%$$ enhancement in the average heat transfer rate for the bottom wall is achieved. Suppression of the recirculations in the vented cavity and around the L-shaped object is observed with magnetic field. It is observed that impact of magnetic field on heat transfer enhancement is different for different segments of hot wall. When the cases with the highest magnetic field and in the absence of magnetic field are compared, the average heat transfer enhancement of $$5.5\%$$ is achieved for bottom elastic wall while $$24.5\%$$ of reduction in the average heat transfer is seen for upper hot wall. The overall Nusselt number reduces slightly when the magnetic field strength is increased. Significant impacts of the size, inclination and location of the of the L-shaped conductive object on the fluid flow such as branching of the main flow stream, size of the vortex below the inlet port and heat transfer are observed. $$31.6\%$$ rise of the average heat transfer for left vertical wall and $$34.6\%$$ reduction of average heat transfer for bottom wall are achieved when the minimum and maximum of the orientation angles are compared. The location of the L-shaped object has a significant impact on the flow and thermal pattern variations. The highest variation in the contribution to the overall heat transfer is seen for right vertical hot wall segment when the Nusselt numbers at the lowest and highest values of the horizontal and vertical locations of the object are compared. L-shaped object was found to be an efficient tool to control the heat transfer features of the vented cavity. Nanofluid inclusion resulted in heat transfer enhancement in the range of 8.5–16.5% while amount of enhancement is different for different hot wall segments either in the absence or in the presence of magnetic field effects. Finally, a polynomial-type correlation for the average Nusselt number of each hot wall segments of the vented cavity is proposed for water and for nanofluid at $$\phi =0.04$$.

In this chapter we look at random fields from a perspective that is common in statistical physics but not so much in spatial data analysis. This perspective is useful, because it can lead to computationally efficient methods for spatial prediction, while it is also related with Markovian random fields. In addition, it enables the calculation of new forms of covariance functions and provides a link with stochastic partial differential equations.

This chapter focuses on the modeling of non-Gaussian probability distributions. The main reason for discussing non-Gaussian models is the fact that spatial data often exhibit properties such as (i) strictly positive values (ii) asymmetric (skewed) probability distributions (iii) long positive tails (e.g., power-law decay of the pdf) and (iv) compact support.

Materials for energy storage have been a subject of high interest in recent times. The development of new materials with high dielectric constant and low losses is one of the main goals in scientific research for electronic applications. These properties allow reduction of the size and weight of the electronic devices. In this work, powders of sodium ferrite were prepared by sol-gel, using iron nitrate and sodium acetate as raw materials, according to the Pechini route. In order to optimize the synthesis parameters, the materials were heat-treated, and structural, morphological and dielectric characterization was performed to find the most suitable ones for storing energy. The sample structure was characterized by X-ray diffraction (XRD), Raman spectroscopy and the morphology surface by scanning electron microscopy (SEM). The dielectric properties were studied in a frequency range between 100 Hz and 1 MHz at temperatures from 200 up to 370 K by impedance spectroscopy measurements. The complex permittivity at 2.7 GHz and 5 GHz (T = 300 K) was determined using the small perturbation method through resonant cavities. The sample of sodium ferrite heat-treated at 1100 °C is the most promising for energy storage with a dielectric constant of ≈818 (f = 1 kHz; T = 300 K) and ≈6 (f = 5 GHz; T = 300 K), with low losses. The dielectric constant increases with the presence of Na3Fe5O9 phase and with the grain size and consequently porosity decreases. At low frequencies, this sample presents two relaxation processes described by Cole-Cole model and the relaxation time versus temperature has an Arrhenius behaviour.

This work presents a study of the structural, electrical, and dielectric properties of polymethylmethacrylate/polypyrrole composites. Structural analysis was performed using X-ray diffraction, showing an increase in the crystallinity index with the increasing of filler concentrations. The electrical conductivity mechanism and the dielectric relaxation process of these composites were studied in the frequency range from 100 Hz to 1 MHz and temperature range from 290 to 380 K, using impedance spectroscopy. The frequency-dependence of the conductivity is analyzed using the Jonscher power law. The values of the n exponent in this law are superior to 1, which is an indication that electron hopping occurs between neighboring sites. The Nyquist representations of the complex impedance spectra are modeled using the Cole-Cole model. The temperature dependence of both DC conductivity and relaxation process behaviors, using the Arrhenius equation, indicates that the conduction process is thermally activated.

Gaussian random fields have a long history in science that dates back to the research of Andrey Kolmogorov and his group. Their investigation remains an active field of research with many applications in physics and engineering. The widespread appeal of Gaussian random fields is due to convenient mathematical simplifications that they enable, such as the decomposition of many-point correlation functions into products of two-point correlation functions. The simplifications achieved by Gaussian random fields are based on fact that the joint Gaussian probability density function is fully determined by the mean and the covariance function.

The impurity ordering effects on graphene electron properties within a one-band model of strong coupling are studied. We carried out analytical and numerical calculations of the energy spectrum of electrons in the cases of low and high orderings. The limiting case of weak scattering for the varying degrees of scattering potential is analyzed. The ordering of impurity atoms causes the appearance of a gap in the energy spectrum of electrons. The gap width is proportional to the ordering parameter and the scattering potential for moderate magnitudes of the latter, but, as the scattering potential increases, its more complex behavior is observed. It is established that the regions of localization of electronic states are at the edges of the gap and the edges of the energy spectrum. The case of weak scattering potential allows the analytic investigation at the gap edges. Within the Lifshitz one-electron tight-binding model, the electrical conductivity of graphene is investigated. When the Fermi level enters the gap region of the energy spectrum, the electrical conductivity becomes zero, and the metal-dielectric transition occurs. If the Fermi level is in the region of the energy band, the electron relaxation time and electrical conductivity go to infinity, when the order parameter reaches its maximum value.

We now turn our attention to specialized topics of random field modeling that include ergodicity, the concept of isotropy, the definition of different types of anisotropy, and the description of the joint dependence of random fields at more than two points. Ergodicity, isotropy and anisotropy are properties that have significant practical interest for the modeling of spatial data. On the other hand, the joint N-point dependence is a more advanced topic, primarily of modeling importance for non-Gaussian random fields. In the case of Gaussian random fields the N-point moments can be expressed in terms of the first and second-order moments.

An investigation of the thermal, electrical and dielectric properties of original nanocomposite materials based on the incorporation of carbon dots, synthesized in organic solvents, in a poly(methyl methacrylate) (PMMA) is presented. Thermal analysis was performed using differential scanning calorimetry. Electrical and dielectric measurements were carried out in the frequency range from 100 Hz to 1 MHz and at temperatures between 200 and 400 K. The data were analyzed using two formalisms: (i) AC conductivity that has been found to follow the Jonscher’s power law with double exponents, and (ii) electric modulus that permits to identify two dielectric relaxation processes. The first one, appearing at low-frequency, was attributed to the conduction effect which is consistent with the Havriliak-Negami model, and the second one, appearing at high-frequency was associated with the interfacial polarization effect. Furthermore, the analysis of the temperature dependence of AC conductivity using the Arrhenius representation indicated the existence of two mechanisms basically governing the conductivity.

The trial of Galileo has long been used by historians, by philosophers, and by scientists as a classic example of intellectual martyrdom, resembling somewhat the trial of Socrates. Since that is the way “the Galileo story” is usually represented, it turns out replete with villains and heroes. The scenario depends on who the scriptwriter happens to be.

The goal of our recent project is to develop cheap but effective photonic crystal structures formed by a periodic distribution of nanoparticles in polymer matrix for highly sensitive detection of chemical and biological agents. The volume photonic crystal structures are fabricated using holographic method in original nanocomposites developed by authors. Project main steps are: (i) theoretical analysis and design; (ii) fabrication and characterization of label-free sensors; (iii) functionalization of photonic crystal structures with graphene nanoflakes, and (iv) testing of enhancement effects in Raman spectroscopy. The project realization will promote emerging nanotechnologies for early detection of environmental contamination.

This chapter deals with the main concepts and mathematical tools that help to describe and quantify the shapes of random fields. The geometry of Gaussian random functions is to a large extent determined by the mean and the two-point correlation functions. The classical text on the geometry of random fields is the book written by Robert Adler [10]. The basic elements of random field geometry are contained in the technical report by Abrahamsen [3]. A more recent and mathematically advanced book by Taylor and Adler exposes the geometry of random field using the language of manifolds [11].

A new method of synthesis was developed for the preparation of basic bismuth nitrate [Bi6O5(OH)3](NO3)5·2H2O (ECBBN). Electrochemical synthesis of the material was carried out by galvanostatic electrodeposition from an acidic Bi(III) solution on a Ti substrate and further thermal treatment in air at 200 °C. Characterization of ECBBN was conducted by employing SEM–EDX, N2 adsorption, XRD and FTIR, and its pI was also determined. The analyses showed that the material obtained was pure [Bi6O5(OH)3](NO3)5·2H2O. Morphologically, ECBBN aggregates were composed of crystals, some smaller than 50 nm. Electrochemically synthesized sorbent (ECBBN) was used for the removal of the textile dye Reactive Blue 19 (RB19) from deionized water and model solutions of polluted river water, and it showed considerably superior sorption performance compared to other inorganic sorbents synthesized by conventional methods reported in the literature. A kinetic study suggests that the sorption process is both under reaction and diffusion control. Equilibration of the sorption process was attained in several minutes, i.e. the sorption process is very fast. The sorption equilibrium data were well interpreted by the Langmuir, Redlich–Peterson and Brouers–Sotolongo isotherm. Using Langmuir isotherm, the maximum sorption capacity of ECBBN was reached at pH 2 and was 1049.19 mg g−1.

In thirty years the science of elementary particles has made few achievements compared with its unsuccessful essays. The recent works of Schwinger, Tomonaga, Feynman and Dyson, however, have had some success.1 We have here a hint that progress is being made on the formal side of the discipline — though even this work is profoundly disturbing in some of its purely mathematical aspects.2 There could be no better time to review the situation from a physical and philosophical standpoint, even if this proves to be an over-ambitious undertaking.

To understand the impacts of adding twisted tape inside the tube with hybrid nanomaterial, the present paper has been examined with considering FVM. New testing fluid instead of water leads to lower exergy loss. Modeling outputs were carried out for different pitch ratio and Reynolds number. Decrement trend for secondary flow was reported when pitch ratio increases, and for this reason, convective flow reduces with rise of P which results in greater exergy drop. Turbulence intensity improves with augment of Re which provides stronger interaction of nanomaterial and tube wall. So, thinner boundary layer appears with rise of Re and exergy loss deteriorates. © 2020, Akadémiai Kiadó, Budapest, Hungary.