A Coupled FEM-BEM Approach for the Solution of the Free-Boundary Axi-Symmetric Plasma Equilibrium Problem

In this paper we present a coupled Finite Element Method – Boundary Element Method (FEM-BEM) approach for the solution of the free-boundary axi-symmetric plasma equilibrium problem. The proposed method, obtained from an improvement of the Hagenow-Lackner coupling method, allows to efficiently model the equilibrium problem in unbounded domains by discretizing only the plasma region; the external conductors can be modelled either as 2D or 3D models, according to the problem of interest. The paper explores different iterative methods for the solution of the nonlinear Grad-Shafranov equation, such as Picard, Newton-Raphson and Newton-Krylov, in order to provide a robust and reliable tool, able to handle large-scale problems (e.g. high resolution equilibria). This method has been implemented in the FRIDA code (FRee-boundary Integro-Differential Axisimmetric – https://github. om/matteobonotto/ FRIDA), together with a suitable Adaptive Integration Technique (AIT) for the computation of the source term. FRIDA has been successfully tested and validated against experimental data from RFX-mod device, and numerical equilibria of an ITER-like device.     

Stature estimation for Saudi men based on different combinations of upper limb part dimensions

Estimating stature based on body/limb parts can help define the characteristics of unidentified bodies. The most studied upper limb part is the hand, although few studies have examined whether stature can be estimated using fingers plus other hand dimensions. Moreover, there is paucity in anthropometric studies that determined whether bilateral whole limb parts (e.g., arms, forearms, and hands) are related to stature among the living subjects.This prospective cross-sectional study aimed to evaluate the relationship between different upper limb measurements and the stature of Saudi men. Furthermore, I assessed whether upper limb asymmetry was present, and developed regression models to estimate stature based on different available measurements. Stature and 13 upper limb parameters were measured for 100 right-handed Saudi men who were 18 to 24 years old.All measurements were positively correlated with stature (P < .001), and the best single predictor was the bilateral ulnar length. Asymmetry was more pronounced in the hand measurements. A multiparameter model provided reasonable predictive accuracy (±3.77–5.68 cm) and was more accurate than single-parameter models. Inclusion of the right-side fingers improved the model''s accuracy.This study developed potential models for estimating stature during the identification of bodies of Saudi men.     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.     

Temperature-controlled power modulation compensates for heterogeneous nanoparticle distributions: a computational optimization analysis for magnetic hyperthermia

Purpose: To study, with computational models, the utility of power modulation to reduce tissue temperature heterogeneity for variable nanoparticle distributions in magnetic nanoparticle hyperthermia.

Methods: Tumour and surrounding tissue were modeled by elliptical two- and three-dimensional computational phantoms having six different nanoparticle distributions. Nanoparticles were modeled as point heat sources having amplitude-dependent loss power. The total number of nanoparticles was fixed, and their spatial distribution and heat output were varied. Heat transfer was computed by solving the Pennes’ bioheat equation using finite element methods (FEM) with temperature-dependent blood perfusion. Local temperature was regulated using a proportional-integral-derivative (PID) controller. Tissue temperature, thermal dose and tissue damage were calculated. The required minimum thermal dose delivered to the tumor was kept constant, and heating power was adjusted for comparison of both the heating methods.

Results: Modulated power heating produced lower and more homogeneous temperature distributions than did constant power heating for all studied nanoparticle distributions. For a concentrated nanoparticle distribution, located off-center within the tumor, the maximum temperatures inside the tumor were 16% lower for modulated power heating when compared to constant power heating. This resulted in less damage to surrounding normal tissue. Modulated power heating reached target thermal doses up to nine-fold more rapidly when compared to constant power heating.

Conclusions: Controlling the temperature at the tumor-healthy tissue boundary by modulating the heating power of magnetic nanoparticles demonstrably compensates for a variable nanoparticle distribution to deliver effective treatment.     

2009-2019年西安市肺结核流行特征及治疗转归分析

目的 了解2009-2019年西安市肺结核的流行特征及治疗转归情况，为完善西安市肺结核防治策略提供依据。方法 收集2009-2019年《传染病信息管理系统》中登记的肺结核患者病案信息数据，通过描述性研究方法对肺结核流行病学特征和治疗转归情况进行分析。结果 2009-2019年间西安市肺结核年平均发病率为49.06/10万。郊县、郊区、城区肺结核年平均发病率分别为54.13/10万、47.46/10万和46.19/10万；男性发病率是女性的1.85倍。职业分布排在前5位的为农民（47.48%），家政、家务及待业（14.65%），离退人员（9.55%），学生（8.63%）和工人（5.23%）。利福平敏感或耐药性未知患者的成功治疗率平均为98.03%，利福平耐药患者治疗成功率为51.47%。结论 西安市近年来肺结核发病率和治疗率呈增长趋势，利福平耐药患者治疗成功率较低，要加强对重点人群的健康促进工作，加大肺结核发现力度及患者治疗管理工作。     

A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales

Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are easy to implement, natural for nonlinear problems, and have shown great potential to overcome the curse of dimensionality. In this work, we utilize the multi-scale DNN-based algorithm (MscaleDNN) proposed by Liu, Cai and Xu (2020) to solve multi-scale elliptic problems with possible nonlinearity, for example, the p-Laplacian problem. We improve the MscaleDNN algorithm by a smooth and localized activation function. Several numerical examples of multi-scale elliptic problems with separable or non-separable scales in low-dimensional and high-dimensional Euclidean spaces are used to demonstrate the effectiveness and accuracy of the MscaleDNN numerical scheme.