Coherent Model Research Articles

Method for Determining Effective Frequency Range of Coherency Based on Seismic Energy

When using data recorded by dense instrument arrays to fit the coherency model of spatial variation of seismic ground motions, the selected frequency range may affect the fitting parameters, which also affects the synthesized time history of the ground motion field. In this study, the acceleration of the 5th and the 45th Earthquake of the SMART-1 array was selected. The Abrahamson and Loh models were used to perform the parameter fitting of the coherency for frequency ranges of 0–8, 0–16 and 0–24[Formula: see text]Hz, and the obtained results were different. The smaller the frequency range, the lower the fitted lagged coherency. The influence of frequency range on fitting parameters of Loh model is much greater than that of Abrahamson model. Based on the relative relationship of ground motion energy distribution in each frequency band represented by the power spectra, from the perspective of power spectrum energy, the energy ratio concept was introduced, i.e. the ratio of the power spectra for a specific frequency range to the total power spectra. Based on the energy ratio, a method for determining the effective frequency range of the coherency model was developed. Through the comparison and analysis of the horizontal-component acceleration of the SMART-1 array, it was found that when the frequency range was 0–5[Formula: see text]Hz, the energy ratio exceeded 95%, when the frequency range was 0–8[Formula: see text]Hz, the energy in the frequency band reached approximately 99% of the total energy. Therefore, it is recommended that when the parameters of the coherency model are fitted, the frequency corresponding to an energy ratio of 95% can be used as the effective frequency. Within this range, the contamination of high-frequency components on the fitting results is minimized.

A dynamical model of the immune system interaction in a melanoma

Melanoma is a concerning variety of skin cancer which prevalence arises in our society. Its lethality relies on its ability to migrate and metastasize. In this paper, we use a mathematical model of tumor growth and lysis, and we adjust it to model melanoma growth. Tumor growth is modeled through the action of a cellular automaton and two reaction–diffusion equations for nutrients distribution. In order to represent the melanoma, we have induced mutations in random cells, specifically in their reproductive capacity, so that tumor growth appears less structured and consistent. A coherent mathematical model for the decay of the melanoma has been created by means of an exponential function. The results show that the inner morphology plays an important role in cancer lysis: as the complexity increases, so does lysis time. We have also compared simulations to real melanomas with the aim of acknowledging the robustness of the melanoma modeling.

A Fourier series non-stationary coherency model with respect to site conditions for the horizontal component of ground motion

A Fourier series non-stationary coherency model with respect to the site conditions for the horizontal component was developed. The evolution of the model was achieved using parameters related to the site, inter-station distance, and time. First, based on the simulation of a non-stationary ground-motion field considering the wave-passage effect and site-response effect, the approach used for the estimation of coherency using wavelet transform is presented. Subsequently, the Fourier series non-stationary coherency model is formulated. The parameters are considered piecewise constant variables to represent the non-stationarity of the estimated coherency. The effects of the site and inter-station distance on the proposed model are presented. Parameters related to the site and inter-station distance were obtained. Finally, the proposed model is compared with the ground motion records at the SMART-1 array during Event 45, the stationary model, and the data from the Argostoli rock-site dense array. This indicates that the Fourier series non-stationary coherency model with respect to site conditions for the horizontal component can well match the correlation of the realistic spatially variable seismic ground motion, which is related to the site, time, and inter-station distance.

Three-dimensional aerodynamic lift on a rectangular cylinder in turbulent flow at an angle of attack

The aim of the present work is to derive the closed-form solution for the three-dimensional aerodynamic admittance (3D AAF) of the lift on a 5:1 rectangular cylinder in turbulent flow at an angle of attack (AoA), which can be used to study the unsteady effects of AoA. The separated one- and two-wavenumber aerodynamic admittances are introduced to assess the contributions of u and w components of turbulence to the lift force at an AoA. These admittances are divided into two parts: the two-dimensional aerodynamic admittance (2D AAF) for fully coherent gusts and the gust-related spanwise correction factors. The closed-form expressions for the one- and two-wavenumber spanwise correction factors of the 3D AAF are derived provided that the floating parameters in the coherence models of the lift force and turbulence are determined experimentally. Although the proposed 3D AAF model is a semi-analytical solution, it still provides explicit insight into the unsteady effects of u and w components of turbulence and the distortion of the free-stream turbulence. Based on theoretical and experimental investigations, it is shown that the unsteady effects of AoA on the coherence and spectra of the lift become more prominent with the increase of AoA, and reach their maximum as the AoA reaches 10°. This phenomenon can be attributed to the contribution of the u component, which plays a dominant role in determining the aerodynamic behaviour of the lift as the AoA approaches 10°. Notably, the spanwise correction factors of the 3D AAF will amplify the results based on the strip theory at low frequencies as the AoA is within 7°, and vice versa. In addition, it should be noted that the proposed approach allows us to quantitatively study the unsteady behaviour of the lift force on bluff bodies with complicated cross-sectional shapes, such as a streamlined bridge deck.

A hub-and-spoke model of geometric concepts

The cognitive basis of geometry is still poorly understood, even the ‘simpler’ issue of what kind of representation of geometric objects we have. In this work, we set forward a tentative model of the neural representation of geometric objects for the case of the pure geometry of Euclid. To arrive at a coherent model, we found it necessary to consider earlier forms of geometry. We start by developing models of the neural representation of the geometric figures of ancient Greek practical geometry. Then, we propose a related model for the earliest form of pure geometry – that of Hippocrates of Chios. Finally, we develop the model of the neural representation of the geometric objects of Euclidean geometry. The models are based on the hub-and-spoke theory. In our view, the existence of specific models opens the possibility of addressing the relationship between geometric figures and geometric objects, in a novel way, in terms of their neural representation.

RISK MANAGEMENT AS AN ELEMENT OF MANAGEMENT CONTROL IN A HEALTHCARE ENTITIES

The aim of this article is to indicate the importance of introducing management control in aspects of risk management. To this end, the article reviews legal acts and literature on the issue of management control. The article indicates the practical application of legislation and recommended standards from the perspective of risk management in healthcare entities. This article looks to discuss management control in healthcare entities. The Public Finance Act introduced in 2009 in Poland obliged all units of the public finance sector to introduce management control mechanisms in order to support the implementation of set goals and tasks in a lawful, effective, economical and timely manner. This proved helpful with regard healthcare entities, particularly when management control is properly applied and meets the assumed important goals. Management control standards define the basic requirements for management control. Management control standards are important in the implementation of assumptions; and constitute a set of guidelines that help the manager of the unit to create optimal solutions. The main objective of the standards is to promote the implementation of a coherent and uniform model of management control in the public finance sector, which will be compliant with international standards in this regard; taking into account the specific tasks of the unit that implements it and the conditions in which the unit operates. Taking into account the complexity of procedures and the huge amount of organizational work in healthcare entities, management control is all the more important. Management control is an important process for the proper functioning of healthcare entities. It helps eliminate or minimize risks, including the important risk of medical malpractice. Implemented management control standards will be shown to eliminate or reduce risks associated with the operation of a medical entity, including risks related to medical malpractice.

A novel retrieval model for soil salinity from CYGNSS: Algorithm and test in the Yellow River Delta

Soil salinization, which occurs mainly in arid and coastal regions, hampers agricultural development, and threatens food security in these regions. Therefore, acquiring the spatial distribution of soil salinization with high accuracy is paramount. However, obstacles continue to hinder attempts to use satellite remote sensing. Cyclone Global Navigation Satellite System (CYGNSS) provides a new alternative opportunity for soil salinization retrieval. In this study, soil electrical conductivity (EC) was chosen as the proxy of soil salinity. A CYGNSS-based model for soil EC retrieval (CIG) was proposed, and has been tested in the Yellow River Delta, China. The CIG model consisted of two modules. First, we modified the geometrical optics model to derive the surface’s Fresnel reflectivity from the incoherent scattering signal of the land surface with vegetation attenuation, and horizontal and vertical surface roughness calibrations. Second, soil EC was retrieved based on the gradient boost regression tree (GBRT) algorithm with the inputs of the surface’s Fresnel reflectivity and other ancillary variables. The in-situ measurements were used to compare the CIG retrieved soil EC against the retrievals from other classical machine learning approaches and the coherent model using CYGNSS data, and the optical SVIs model. CYGNSS signal was found to be highly sensitive to in-situ soil EC, and the proposed CIG model outperformed other models, with R = 0.730, RMSE = 1.318 mS/cm, and MAE = 0.570 mS/cm. In addition, several issues related to the model were also discussed, including the interactions between the incidence angle of the CYGNSS signal and soil EC, and the advantages and limitations of CYGNSS. The result suggested that the proposed CIG model, along with new CYGNSS data, can provide a promising method for monitoring land salinization on a large scale.

Money Growth and Inflation: A Wavelet Analysis for a Monetization Approach to COVID 19

COVID 19 pandemic will cause a global economic contraction. The magnitude of shocks will impact the fiscal deficit and the public debt in all Countries. This paper provides an insight into the dynamic relationship between money growth and inflation for several Countries. The Morlet wavelet coherence model is employed since it allows the simultaneous examination of lead-lag effects and co-movements between the couple of variables in the different Countries. The overall results do not evidence a clear impact of the money growth on inflation. This outcome has relevant economic policy consequences considering that the monetization of the public debt can sustain the economic recovery. Thus, the pure monetary nature of the inflationary phenomenon can hardly be considered as a constraint in designing the measures to counteract the issue.

Supplemental Material for Comparing Theories With the Ising Model of Explanatory Coherence

Supplemental Material for Comparing Theories With the Ising Model of Explanatory Coherence

Extended mean-field homogenization of unidirectional piezoelectric nanocomposites with generalized Gurtin-Murdoch interfaces

This paper presents for the first time an extended Mori-Tanaka approach aimed at identifying the little-explored piezoelectric response of unidirectional nanoporous composites with energetic surfaces. The interface is simulated using the generalized Gurtin-Murdoch coherent interface model considering nonvanishing in-plane surface stress and surface electric displacement. The analytical solutions for Eshelby’s inhomogeneity problems are obtained by solving four systems of equations, generated from the known macroscopic electromechanical loading conditions. The dilute concentration tensors for the fiber and energetic surface are evaluated exactly for coupled electromechanical fields, which are then utilized in the extended multiphysics Mori-Tanaka homogenization scheme. The reliability and accuracy of the extended theory are demonstrated through an extensive comparison with the predictions of the composite cylinder assemblage (CCA), the generalized self-consistent method (GSCM), as well as the finite-element (FE) homogenization technique. New results generated in this work demonstrate that, except for the transverse shear modulus, the extended Mori-Tanaka, CCA, and FE provide indistinguishable results under axisymmetric, axial shear, and electric loading, even at high volume fractions and small pore sizes where the surface piezoelectric effects are significant.

Angular analysis of the low K+K− invariant mass enhancement in B+→K+K−π+ decays

We study the decay B+→K+K−π+ and investigate the angular distribution of K+K− pairs with invariant mass below 1.1 GeV/c2. This region exhibits both a strong enhancement in signal and very large direct CP violation. We construct a coherent sum model for the angular distribution of the S- and P-wave, and report the ratio of their amplitudes, the relative phase and the forward-backward asymmetry. We also report absolute differential branching fractions and direct CP asymmetry for the decay in bins of MK+K− and the differential branching fractions in bins of MK+π−. The results are based on a data sample that contains 772×106 BB¯ pairs collected at the ϒ(4S) resonance with the Belle detector at the KEKB asymmetric-energy e+e− collider. The result favors the presence of S- and D-waves in low MK+K− region to the detriment of a P-wave.1 MoreReceived 24 June 2022Accepted 3 February 2023DOI:https://doi.org/10.1103/PhysRevD.107.032013Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasBranching fractionHadronic decaysParticle decaysRare decaysPhysical SystemsMesonsPropertiesCP violationParticles & Fields

Comparison of Coherent to Incoherent Kirchhoff-Ray-Mode (KRM) Models in Predicting Backscatter by Swim-Bladder-Bearing Fish

The Kirchhoff-Ray-Mode (KRM) model has been used in fishery acoustics for more than two decades to predict backscatter by individuals or aggregations of swimbladdered fish. Backscatter contributions from a fluid-like fish body and gas-filled swimbladder are coherently summed to estimate backscatter from the whole fish. For field applications, where morphologies and orientations of individual fish and their swimbladders are unknown, the coherent KRM model based on a limited number of X-ray images containing precise morphological information on swimbladders, body sizes and orientations may not provide statistically stable and robust backscatter predictions. An incoherent KRM model is proposed, which is independent of the exact knowledge of body–swimbladder acoustic interactions. When backscatter contributions from fish bodies and swimbladders are comparable, differences in coherent and incoherent KRM model target strength (TS) predictions can be as high as a few decibels. Incoherent TS predictions as a function of frequency and orientation are statistically more stable and less sensitive to variations in anatomy among individual fish than those predicted using the traditional coherent KRM model.

Towards soil moisture profile estimation in the root zone using L- and P-band radiometer observations: A coherent modelling approach

Precision irrigation management and crop water stress assessment rely on accurate estimation of root zone soil moisture. However, only the top 5 cm soil moisture can be estimated using the two current passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), which operate at L-band (wavelength of ∼21 cm). Since the contributing depth of the soil to brightness temperature increases with observation wavelength, it is expected that a P-band (wavelength of ∼40 cm) radiometer could potentially provide soil moisture information from deeper layers of the soil profile. Moreover, by combining both L- and P- bands, it is hypothesized that the soil moisture profile can be estimated even beyond their individual observation depths. The aim of this study was to demonstrate the potential of combined L-band and P-band radiometer observations to estimate the soil moisture profile under flat bare soil using a stratified coherent forward model. Brightness temperature observations at L-band and P-band from a tower based experimental site across a dry (April 2019) and a wet (March 2020) period, covering different soil moisture profile shapes, were used in this study. Results from an initial synthetic study showed that the performance of a combined L-band and P-band approach was better than the performance of using either band individually, with an average depth over which reliable soil moisture profile information could be estimated (i.e. with a target root mean square error (RMSE) of less than 0.04 m3/m3) being 20 cm for linear and 15 cm for second-order polynomial functions. Other functions were also tested but found to have a poorer performance. Applying the method to the tower-based brightness temperature achieved an average estimation depth of 28 cm (20 cm) and 5 cm (5 cm) during the dry and wet periods respectively when using a second-order polynomial (linear) function. These findings highlight the opportunity of a satellite mission with L-band and P-band observations to accurately estimate the soil moisture profile to as deep as 30 cm globally.

Linear uncertain modelling of LIDAR systems for robust wind turbine control design

Light detection and ranging (LIDAR) sensors measure the free wind ahead of the rotor, enabling the use of new feedforward control strategies. However, there exist some sources of error inherent to the measuring process that should be considered during the design of LIDAR-based controllers. Typically, the coherence function is used for that purpose, but it is not compatible with some robust design methodologies. This paper presents an analytic relation between the coherence function and a non-parametric uncertainty model of LIDAR sensors, suitable for the design of controllers via μ-synthesis or Quantitative Feedback Theory. Such a relation is applied to a realistic LIDAR simulator. First, the linear non-parametric uncertainty model is identified using simulation data obtained from the well-known NREL 5 MW wind turbine. Then, it is validated against the coherence model by comparing linear predictions of the simulation outputs.

On the correlated Gamma–Gamma distribution and coherent detection performance of optical spatial modulation

The performance of optical spatial modulation (OSM) is severely damaged by correlated atmospheric turbulence (AT) channels for coherent free-space optical (FSO) communication systems. In this paper, we assume correlated AT channels comply with Gamma–Gamma distribution and present a coherent FSO-OSM system model. Based on this, we derive the closed-form approximated expressions of average symbol error probability (ASEP), and discuss the interaction of signal constellation diagram and spatial constellation diagram of the OSM under different conditions. We further illustrate relations of the ASEP caused by signal constellation and spatial constellation to AT strength and correlation. In addition, we explore the impact trends of the correlation coefficients respectively resulted from large-scale and small-scale eddies on the performance of system under different AT conditions. Through simulations, we prove theoretical results and demonstrate that the rise of AT severity and correlation coefficients can degrade the performance of coherent OSM.

A confirmatory factor analysis of a cognitive model of COVID-19 related anxiety and depression

The mental health impact of the COVID-19 pandemic has been significant, with many regions across the globe reporting significant increases in anxiety, depression, trauma, and insomnia. This study aims to validate a potential cognitive model of maintenance factors of COVID-19 related distress by examining psychological predictors of distress, and their goodness-of-fit as a coherent model. Participants from the general population (n = 555) were recruited using a cross-sectional on-line survey design, assessing Demographic factors, Anxiety, Depression, Loneliness, COVID-19 related distress, Trauma Cognitions related to COVID-19, Rumination, Safety Behaviours, Personality Factors, and Mental Effort related to COVID-19. A series of stepwise linear regressions found that components of the model were significant and accounted for a large percentage of variance when examining Covid-19 related distress (R2 = 0.447 Covid Stress Scale), Anxiety (R2 = 0.536 DASS-Anxiety Subscale) and Depression (R2 = 0.596 Depression DASS-subscale). In a confirmatory factor analysis, Loneliness, Post-Traumatic Cognitions about Self, Post-Traumatic Cognitions about the World, Emotional Stability, and Mental Effort related to COVID-19 loaded onto a single factor. The final model showed adequate fit (CFI = 0.990, TLI = 0.983, RMSEA = 0.053 (0.027–0.080), GFI = 0.986, SRMR = 0.0216, χ2 = 23.087, p = .006). The results highlight the importance of cognitive factors, such as post-traumatic cognitions, rumination, and mental effort in maintaining COVID-19 related distress.

Spatial correlated incidence modeling with zero inflation.

Disease mapping models have been popularly used to model disease incidence with spatial correlation. In disease mapping models, zero inflation is an important issue, which often occurs in disease incidence datasets with high proportions of zero disease count. It is originated from limited survey coverage or unadvanced testing equipment, which makes some regions have no observed patients. Then excessive zeros recorded in the disease incidence dataset would mess up the true distributions of disease incidence and lead to inaccurate estimates. To address this issue, a zero-inflated disease mapping model is developed in this work. In this model, a zero-inflated process using Bernoulli indicators is assumed to characterize whether the zero inflation occurs for each region. For regions without zero inflation, a coherent and generative disease mapping model is applied for mapping the spatially correlated disease incidence. Independent spatial random effects are incorporated in both processes to account for the spatial patterns of zero inflation and disease incidence. External covariates are also considered in both processes to better explain the disease count data. To estimate the model, a Markov chain Monte Carlo algorithm is proposed. We evaluate model performance via a variety of simulation experiments. Finally, a Lyme disease dataset of Virginia is analyzed to illustrate the application of the proposedmodel.

Rules of transcriptional bursting revealed by absolute quantification of nascent RNA in living fly embryos.

Transcriptional bursting is a common regulatory strategy for controlling gene outputs in various organisms. Over the past 15 years, experiments that dissected transcriptional regulation into its smaller parts have helped us identify many molecular processes associated with burst modulation under various physiological conditions, such as enhancer regulation, transcription factor regulation, epigenetics modifications, etc. However, these reductionist insights have not yet been successful in adding up to a coherent biophysical model that links molecular mechanisms to empirical bursting dynamics. In this work, we provide a unified perspective on this problem by quantifying transcriptional activity in live Drosophila embryos with single RNA detection sensitivity. We reveal a common regulatory strategy that displays surprising constraints on transcriptional bursting under a whole variety of different experimental conditions, including cis- and trans-perturbations in mutant fly strains. We observe a massive data collapse across all conditions, hinting at general rules underlying the dynamics of transcriptional bursting that could be linked to evolutionarily conserved biophysical properties of the core eukaryotic transcriptional machinery.

A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array

Telescope arrays are receiving increasing attention due to their promise of higher resource utilization, greater sky survey area, and higher frequency of full space-time monitoring than single telescopes. Compared with the ordinary coordinated operation of several telescopes, the new astronomical observation mode has an order of magnitude difference in the number of telescopes. It requires efficient coordinated observation by large-domain telescopes distributed at different sites. Coherent modeling of various abstract environmental constraints is essential for responding to multiple complex science goals. Also, due to competing science priorities and field visibility, how the telescope arrays are scheduled for observations can significantly affect observation efficiency. This paper proposes a multilevel scheduling model oriented toward the problem of telescope-array scheduling for time-domain surveys. A flexible framework is developed with basic functionality encapsulated in software components implemented on hierarchical architectures. An optimization metric is proposed to self-consistently weight contributions from time-varying observation conditions to maintain uniform coverage and efficient time utilization from a global perspective. The performance of the scheduler is evaluated through simulated instances. The experimental results show that our scheduling framework performs correctly and provides acceptable solutions considering the percentage of time allocation efficiency and sky coverage uniformity in a feasible amount of time. Using a generic version of the telescope-array scheduling framework, we also demonstrate its scalability and its potential to be applied to other astronomical applications.

Deploying predictive analytics to enhance patient agility and patient value in hospitals: A position paper and research proposal

Patient value in hospital care has become increasingly important over the last decade. This paper argues that patient value could be ameliorated by investing in patient agility. Patient agility constitutes the capabilities that enable hospitals to sense the health service needs of their patients and respond to the changing health service demands of these patients. This paper further posits that hospitals’ current sensing and responding capabilities are inherently reactive as they detect, act upon and pursue occurrences and patterns in the needs and demands of patients after these have already manifested themselves. Hence, we assert that this reactive nature thwarts hospitals in preventing negative trends or capitalizing on positive trends necessitating a shift to sensing and responding capabilities that are more predictive. We postulate that this necessary organizational shift can be facilitated by implementing and utilizing the power of predictive analytics and that this particular issue has not yet been adequately addressed by existing scientific literature. Based on this argument, an initial research proposal is established centering around the role of predictive analytics in fostering patient agility and patient value in hospitals. The initial research proposal sets out to examine this topic in an inclusive, integrated and comprehensive manner as it outlines a coherent conceptual model, incorporates a multi-stakeholder perspective and integrates biomedical as well as health service needs and demands. The initial proposal is meant to serve as a conceptual framework for future research as well as an initial point of departure for further refinement, improvement, elaboration, and discussion.