Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical Simulation of Flow Interference Between Two Circular Cylinders

Viscous flow past two circular cylinders in tandem and side‐by‐side arrangements is investigated numerically. The solution to the Navier‐Stokes equations is obtained by means of a cell‐centred finite volume method (FVM) based on a structured Cartesian grid with collocated variable arrangement. The pressure‐velocity coupling is evaluated by using the SIMPLE algorithm. The 2D Cartesian cut cell mesh is generated on the horizontal plane, which is extended vertically to form the 3D grid. The simulations are based on the Reynolds number of 200, and the gap between the two cylinders are 1.5–3D for side‐by‐side arrangement and 3–4D for tandem. Instantaneous velocity contours of the flow around the cylinders and time histories of force coefficients are presented.
Estimation of Lévy-driven Ornstein–Uhlenbeck processes: application to modeling of CO 2 and fuel-switching

This paper proposes an estimation methodology for Lévy-driven Ornstein– Uhlenbeck processes. The estimation unfolds in two steps, with a least-squares method for a subset of parameters in the ﬁrst stage, and a constrained maximum likelihood for the remaining diffusion and Lévy distribution parameters. We develop this estimation procedure to demonstrate that the class of mean-reverting Lévy jump processes provides a better ﬁt of the electricity and CO 2 (carbon) market prices. In particular, we describe the dynamics of the fuel-switching price (from coal to gas) when taking into account carbon costs. Several stochastic processes are considered to model the fuel-switching price: (1) the Brownian motion, and (2) Poisson and a panel of Lévy jump processes. The results unambiguously point out the need to resort to jump modeling techniques to model satisfactorily the fuel-switching price. The Gaussianity assumption is also clearly rejected in favor of jump models, especially for pure-jump processes such as Lévy processes.
Attracting and Quasi-Invariant Sets of Cohen-Grossberg Neural Networks with Time Delay in the Leakage Term under Impulsive Perturbations

A class of impulsive Cohen-Grossberg neural networks with time delay in the leakage term is investigated. By using the method of -matrix and the technique of delay differential inequality, the attracting and invariant sets of the networks are obtained. The results in this paper extend and improve the earlier publications. An example is presented to illustrate the effectiveness of our conclusion.
Design and modeling of high-resolution multibit log-domain DR modulators

Log-domain Delta-Sigma (DR) modulators are attractive for implementing analog-to-digital (A/D) converters (ADCs) targeting low-power low-voltage applications. Previously reported log-domain DR modulators were limited to 1-bit quantization and, hence, could not benefit from the advantages associated with multibit quantization (namely, reduced in-band quantization noise, and increased modulator stability). Unlike classical DR modulators, directly extending a log-domain DR modulator with a 1-bit quantizer to a logdomain DR modulator with a multibit quantizer is challenging, in terms of CMOS circuit implementation. Additionally, the realization of log-domain DR modulators targeting highresolution applications necessitates minimization of distortion and noise in the log-domain loop-filter. This paper discusses the challenges of multibit quantization and digital-to-analog (D/A) conversion in the log-domain, and presents a novel multibit log-domain DR modulator, practical for CMOS implementation. SIMULINK models of log-domain DR modulator circuits are proposed, and the effects of various circuit non-idealities are investigated, including the effects of log-domain compression–expansion mismatch. Furthermore, this paper proposes novel low-distortion log-domain analog blocks suitable for high-resolution analog-to-digital (A/D) conversion applications. Circuit simulation results of a proposed third-order 3-bit class AB log-domain DR loop-filter demonstrate 10.4-bit signal-to-noise-and-distortion-ratio (SNDR) over a 10 kHz bandwidth with a 0:84 Vpp differential signal input, while operating from a 0.8 V supply and consuming a total power of 35:5 lW:
Study of a new cophasing system for hypertelescopes

As the next generation of giant optical interferometers, hypertelescopes will provide high resolution direct imaging of celestial sources by using the densification principle.1 In order to determine the technical requirements of such an instrument, an interferometric testbench, called SIRIUS (Patru et al. 2004), has been developed at the Observatoire de la Cote d'Azur, France. The active cophasing of the beams remains the most significant hard point to preserve the quality of the image. It has been shown that this cophasing should be at the level of λ/10 so that more than 90% of the energy remains in the central peak of the point spread function.2 In the current version of SIRIUS, the raw coherencing is done manually by adjusting air delay lines, whereas the cophasing is ensured by a fibered cophasing system. We present our study of an optimized cophasing system that we intend to develop on the SIRIUS testbench. One of the main goals is to be adaptable to any interferometer, whatever the configuration of the entrance pupil and the number of sub pupils. This new version will improve the cophasing system by using a derived version of the dispersed speckles method3 for fine cophasing. The observed images will then be stabilized during a longer period, allowing a more efficient analysis of the studied source.
Bayesian Spatio-Temporal Prediction and Counterfactual Generation: An Application in Non-Pharmaceutical Interventions in COVID-19

The spatio-temporal course of an epidemic (such as Covid-19) can be significantly affected by non-pharmaceutical interventions (NPIs), such as full or partial lockdowns. Bayesian Susceptible-Infected-Removed (SIR) models can be applied to the spatio-temporal spread of infectious disease (STIF) (such as Covid-19). In causal inference it is classically of interest to investigate counterfactuals. In the context of STIF it is possible to use nowcasting to assess the possible counterfactual realization of disease in incidence that would have been evidenced with no NPI. Classic lagged dependency spatio-temporal IF models will be discussed and the importance of the ST component in nowcasting will be assessed. The real example of lockdowns for Covid-19 in two US states during 2020 and 2021 is provided. The degeneracy in prediction in longer time periods is highlighted and the wide confidence intervals characterize the forecasts.
Hierarchical Bayesian Spatiotemporal Analysis of Childhood Cancer Trends

In this article, generalized additive mixed models are constructed for the analysis of geographical and temporal variability of cancer ratios. In this class of models, spatially correlated random effects and temporal components are adopted. Spatio-temporal models that use intrinsic conditionally autoregressive smoothing across the spatial dimension and B-spline smoothing over the temporal dimension are considered. We study the patterns of incidence ratios over time and identify areas with consistently high ratio estimates as areas for further investigation. A hierarchical Bayesian approach using Markov chain Monte Carlo techniques is employed for the analysis of the childhood cancer diagnoses in the province of Alberta, Canada during 1995–2004. We also evaluate the sensitivity of such analyses to prior assumptions in the Poisson context. En este articulo los autores construyen modelos aditivos generalizados mixtos (generalized additive mixed models) con el fin de analizar la variabilidad geografica y temporal en las tasas de incidencia de cancer. Este tipo de modelos emplean efectos aleatorios correlacionados espacialmente y componentes temporales. Los modelos espacio-temporales emplean un suavizado condicional intrinseco autorregresivo (conditionally autoregressive smoothing) a traves de la dimension espacial y un suavizado de tipo B-spline sobre la dimension temporal. Los autores examinan los patrones de las tasas de incidencia a traves del tiempo e identifican las areas con valores consistentemente altos con el fin de sugerir areas de investigacion para el futuro. El estudio utiliza un enfoque jerarquico bayesiano (hierarchical bayesian) que usa una cadena de Markov Monte Carlo para evaluar los diagnosticos de cancer infantil en la provincia de Alberta, Canada durante el periodo 1995–2004. Asimismo, tambien se evalua la sensibilidad de este tipo de analisis con respecto a los supuestos a-priori, en el contexto de los modelos tipo Poisson. 本文提出了广义可加和混合模型进行癌症比率的地理和时间变化分析。 在这类模型中引入了空间相关的随机效应和时间组分。 时空模型在空间维度上采用本征自回归条件平滑，而在时间维度上则使用了B样条平滑。 本文研究了疾病发生率模式，并识别出一直具有高比率估计的地区作为进一步调查区。 在1995–2004年加拿大亚伯达省儿童癌症的诊断中，采用了基于马尔科夫链-蒙特卡罗模型的分层贝叶斯方法，并且在泊松先验假设条件下评估了该类分析的敏感性。
A Fourier approximation method for steady water waves

A method for the numerical solution of steadily progressing periodic waves on irrotational flow over a horizontal bed is presented. No analytical approximations are made. A finite Fourier series, similar to Dean's stream function series, is used to give a set of nonlinear equations which can be solved using Newton's method. Application to laboratory and field situations is emphasized throughout. When compared with known results for wave speed, results from the method agree closely. Results for fluid velocities are compared with experiment and agreement found to be good, unlike results from analytical theories for high waves. The problem of shoaling waves can conveniently be studied using the present method because of its validity for all wavelengths except the solitary wave limit, using the conventional first-order approximation that on a sloping bottom the waves at any depth act as if the bed were horizontal. Wave period, energy flux and mass flux are conserved. Comparisons with experimental results show good agreement.
Utility of Matrix Metalloproteinases in the Diagnosis, Monitoring and Prognosis of Ovarian Cancer Patients

Abstract Ovarian cancer is one of the most common gynecologic malignancies. It is characterized by a high mortality rate, which is mainly due to the asymptomatic course of the disease. In light of the high mortality rate and increasing morbidity, new diagnostic methods are being explored to enable earlier detection, better monitoring, and improved prognosis. Such diagnostic methods include the assessment of tumor markers in various biological samples. Among the markers currently being investigated, extracellular matrix metalloproteinases (MMPs) are of particular interest. The objective of this article was to compile the existing knowledge of MMPs in ovarian cancer patients and to describe their potential diagnostic utility. Additionally, this article provides an overview of the symptoms, complications, and risk factors associated with ovarian cancer and the role of MMPs in physiology and pathology. Preliminary results indicate that tissue expression and blood and body fluid levels of MMPs may be different in ovarian cancer patients than in healthy women. The expression and concentration of individual MMPs have been shown to be correlated with cancer stage and disease severity. In addition, the preliminary value of some of these enzymes in predicting prognosis is discussed. However, as the amount of data is limited, more studies are needed to fully evaluate the potential function of individual MMPs in ovarian cancer patients. Based on the knowledge gathered for this article, it seems that MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, MMP-13, are tentatively the most useful. A thorough evaluation of their utility as modern biomarkers in ovarian cancer requires further investigation.
Androgen receptor activity in prostate cancer dictates efficacy of bipolar androgen therapy through MYC

Testosterone is the canonical growth factor of prostate cancer but can paradoxically suppress its growth when present at supraphysiological levels. We have previously demonstrated that the cyclical administration of supraphysiological androgen (SPA), termed bipolar androgen therapy (BAT), can result in tumor regression and clinical benefit for patients with castration-resistant prostate cancer. However, predictors and mechanisms of response and resistance have been ill defined. Here, we show that growth inhibition of prostate cancer models by SPA required high androgen receptor (AR) activity and were driven in part by downregulation of MYC. Using matched sequential patient biopsies, we show that high pretreatment AR activity predicted downregulation of MYC, improved clinical response, and prolonged progression-free and overall survival for patients on BAT. BAT induced strong downregulation of AR in all patients, which is shown to be a primary mechanism of acquired resistance to SPA. Acquired resistance was overcome by alternating SPA with the AR inhibitor enzalutamide, which induced adaptive upregulation of AR and resensitized prostate cancer to SPA. This work identifies high AR activity as a predictive biomarker of response to BAT and supports a treatment paradigm for prostate cancer involving alternating between AR inhibition and activation.
Knowledge Representation and Management: Notable Contributions in 2021

Summary Objectives : To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2021. Methods : Following the International Medical Informatics Association (IMIA) Yearbook guidelines, a comprehensive and standardized review of the biomedical informatics literature was performed to select the best KRM papers published in 2021, based on PubMed queries. Results : A total of 1,231 publications were retrieved from PubMed. We nominated 15 candidate best papers, and four of them were finally selected as the best papers in the KRM section. The topics covered by these papers include knowledge graph, ontology development, ontology alignment, and the International Classification of Diseases. Conclusion : In the KRM best paper selection for 2021, the candidate best papers covered a wider spectrum of topics compared to the last year’s significant focus on ontology curation. In particular, ontology development for specific domains (e.g., Alzheimer’s disease, infectious diseases, bioethics) has received the most attention.
A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

Summary Objectives : Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods. Methods : We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation. Results : We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field. Conclusions : We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.
Reconsidering Capacity to Appoint a Healthcare Proxy

Abstract Clinicians are often called upon to assess the capacity of a patient to appoint a healthcare agent. Although a consensus has emerged that the standard for such assessment should differ from that for capacity to render specific healthcare decisions, exactly what standard should be employed remains unsettled and differs by jurisdiction. The current models in use draw heavily upon analogous methods used in clinical assessment, such as the “four skills” approach. This essay proposes an alternative model that relies upon categorization and sliding scale risk assessment that can be used to determine to how much scrutiny the proxy appointment should be subjected and how much certainty of accuracy should be required in order to maximize the patient’s autonomy and ensure that her underlying wishes are met.
Magnetic acceleration of winds from solar-type stars

Magnetically coupled stellar winds are thought to provide the dominant mechanism for angular momentum loss over the entire main-sequence lifetime of stars possessing hydrogen convective zones. The associated loss of rotational kinetic energy can strongly affect the energetics of winds emanating from such stars, for sufficiently high rotation rates and magnetic field strengths. Using the Weber and Davis model of magnetohydrodynamic (MHD) winds, we show that the magnetic acceleration of an MHD wind is of importance when the loss of rotational kinetic energy due to magnetic braking is in excess of the energy flux due to thermal processes alone. Such an excess may plausibly occur at early times in the main-sequence evolution of stars of spectral type F5 and later, since such stars initially exhibit high angular momentum loss rates. For the magnetic acceleration of the solar wind to be of importance, the product of rotation rate . cap omega. and magnetic flux f/sub b/ must exceed its present solar value by a factor of approx.10--20. Assuming a linear dependence of f/sub b/ on . cap omega. , we construct a qualitative model for the main-sequence evolution of the solar rotation rate, and argue that in the first 4 x 10/sup 8/ yearsmore » of the Sun's main-sequence lifetime, its wind may have been magnetically accelerated to average velocities an order of magnitude or more above present values. (AIP)« less
Development of a discontinuous approach for modeling fluid flow in heterogeneous media using the numerical manifold method

In the numerical modeling of fluid flow in heterogeneous geological media, large material contrasts associated with complexly intersected material interfaces are challenging, not only related to mesh discretization but also for the accurate realization of the corresponding boundary constraints. To address these challenges, we developed a discontinuous approach for modeling fluid flow in heterogeneous media using the numerical manifold method (NMM) and the Lagrange multiplier method (LMM) for modeling boundary constraints. The advantages of NMM include meshing efficiency with fixed mathematical grids (covers), the convenience of increasing the approximation precision, and the high integration precision provided by simplex integration. In this discontinuous approach, the elements intersected by material interfaces are divided into different elements and linked together using the LMM. We derive and compare different forms of LMMs and arrive at a new LMM that is efficient in terms of not requiring additional Lagrange multiplier topology, yet stringently derived by physical principles, and accurate in numerical performance. To demonstrate the accuracy and efficiency of the NMM with the developed LMM for boundary constraints, we simulate a number of verification and demonstration examples, involving a Dirichlet boundary condition and dense and intersected material interfaces. Last, we applied the developed model for modeling fluid flow in heterogeneous media with several material zones containing a fault and an opening. We show that the developed discontinuous approach is very suitable for modeling fluid flow in strongly heterogeneous media with good accuracy for large material contrasts, complex Dirichlet boundary conditions, or complexly intersected material interfaces. Copyright © 2015 John Wiley & Sons, Ltd.
Allograft corneal ring segment for keratoconus management: Istanbul nomogram clinical results

Purpose To evaluate the clinical feasibility and visual outcomes of allograft corneal ring segment implantation for the treatment of keratoconus. Methods This case series, included forty-four eyes of 32 patients with a 6-month follow-up. All cases were treated according to the Istanbul nomogram. In the Istanbul Nomogram, corneal tunnels of 4 × 7.5 mm diameters are created at depth of 200 μm and implanted with sterile allograft corneal rings (KeraNaturalTM, Lions VisionGift, Portland, OR, USA) at the cone location. Uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), spherical equivalent (SE) and keratometric values were compared preoperatively versus postoperatively. Results There was significant improvement in UDVA, CDVA, SE and topographic keratometric values. The mean preoperative CDVA (Snellen, decimal) increased from 0.29 ± 0.20, to 0.56 ± 0.26 (P < 0.001), at the last visit. There was no statistically significant difference between preoperative and postoperative thinnest pachymetry values (P = 0.509). No major complications or adverse event were observed during and after the operation. Conclusions The results of this pilot study show that sterile allograft corneal ring segments may be safe, effective and enhance the visual performance of keratoconus patients. Larger clinical studies are needed to demonstrate the effectiveness and safety with long term follow-up.
Investigation of the correlation between the bulk and surface sensing performance in plasmonic crystals

In the investigation of the sensing performance based on localized surface plasmon resonance (LSPR), the bulk sensitivity, which is the spectral shift per refractive index unit upon the change of the surrounding index to spatial infinite, is often measured and considered as the indication of the sensing ability. To the contrary, in biosensing applications only the refractive index in a confined region close to the metal nanostructure surface is altered due to the attachment of the recognition and target molecules. The correlation between the bulk and surface sensitivity is nevertheless ambiguous, especially in strongly coupled plasmonic systems, and rarely discussed in the literature. In this paper, we examine the bulk and surface sensing performance of periodic Au nanodisk arrays on quartz substrates. By means of diffractive coupling of LSPR in the periodic arrays, the bulk sensitivity and the figure of merit (FoM, as high as ~30) could be varied by engineering the coupling strength and adjusting the size of the Au nanodisk and the array pitch. The surface sensing performance is also explored by sequential atomic layer deposition of Al2O3, and the electric field decay from the Au nanodisk surface could thus be extracted using an exponential function. It is demonstrated that in these substrates the surface and bulk sensitivity have an opposite dependence on the coupling strength. In spite of the high bulk sensitivity and FoM in systems with strong coupling, a low surface sensitivity is demonstrated due to the large electric field decay length. Therefore, detailed and careful design of the coupling strength and the surface electric field to match the size of the target biomolecule is very critical for the best sensing performance. Moreover, we provide a more reliable biosensor design protocol based on the surface sensing performance.