Simulating games using object-oriented methodology

In this report, we present a Bridge simulator and we discuss object-oriented analysis, design and programming. The design phase uses automated support to illustrate how we apply the concepts of object-oriented methodology to develop software--a Bridge simulator. The implementation of the Bridge simulator demonstrates the programming process by using an object-oriented language (C++). Important features of the Bridge simulator are the use of the object-oriented paradigm for design and the use of the X Window/Motif toolkits to construct a user interface for simulating the hidding and the playing of the game of Bridge. We conclude with the results of the Bridge simulator, discuss a research on computer Bridge and suggest avenues for further directions in which the project could be extended

Unified empirical likelihood ratio tests for functional concurrent linear models and the phase transition from sparse to dense functional data

We consider the problem of testing functional constraints in a class of functional concurrent linear models where both the predictors and the response are functional data measured at discrete time points. We propose test procedures based on the empirical likelihood with bias‐corrected estimating equations to conduct both pointwise and simultaneous inferences. The asymptotic distributions of the test statistics are derived under the null and local alternative hypotheses, where sparse and dense functional data are considered in a unified framework. We find a phase transition in the asymptotic null distributions and the orders of detectable alternatives from sparse to dense functional data. Specifically, the tests proposed can detect alternatives of √n‐order when the number of repeated measurements per curve is of an order larger than urn:x-wiley:13697412:media:rssb12246:rssb12246-math-0001 with n being the number of curves. The transition points urn:x-wiley:13697412:media:rssb12246:rssb12246-math-0002 for pointwise and simultaneous tests are different and both are smaller than the transition point in the estimation problem. Simulation studies and real data analyses are conducted to demonstrate the methods proposed

Robust estimation of heterogeneous treatment effects using electronic health record data

Estimation of heterogeneous treatment effects is an essential component of precision medicine. Model and algorithm-based methods have been developed within the causal inference framework to achieve valid estimation and inference. Existing methods such as the A-learner, R-learner, modified covariates method (with and without efficiency augmentation), inverse propensity score weighting, and augmented inverse propensity score weighting have been proposed mostly under the square error loss function. The performance of these methods in the presence of data irregularity and high dimensionality, such as that encountered in electronic health record (EHR) data analysis, has been less studied. In this research, we describe a general formulation that unifies many of the existing learners through a common score function. The new formulation allows the incorporation of least absolute deviation (LAD) regression and dimension reduction techniques to counter the challenges in EHR data analysis. We show that under a set of mild regularity conditions, the resultant estimator has an asymptotic normal distribution. Within this framework, we proposed two specific estimators for EHR analysis based on weighted LAD with penalties for sparsity and smoothness simultaneously. Our simulation studies show that the proposed methods are more robust to outliers under various circumstances. We use these methods to assess the blood pressure-lowering effects of two commonly used antihypertensive therapies

Research in nonlinearity of surface acoustic wave devices

Surface acoustic wave (SAW) devices are one of the indispensable components in the radio frequency (RF) front-end of mobile phones. With the development of mobile communication technology, the requirements for linear specification of devices are more and more strict. Nonlinear distortions of SAW devices have a serious influence on the application of mobile RF modules. To satisfy the strict requirement of linearity of communication system, it is necessary to understand the generation mechanism of nonlinearity and study the accurate modeling, appropriate measurement methods, and nonlinear response elimination technology. In this paper, we summarize the research progress on the nonlinearity of SAW devices in recent years from four aspects: the generation mechanism, simulation methods, measurement system, and suppression technology. The nonlinear harmonics with the nonlinear Mason equivalent circuit model are simulated. Furthermore, harmonics and intermodulation signals of SAW filters are tested by the authors. Thanks to these research studies, it is of great significance to the development of future RF front-end modules with high linear SAW devices

Capacitive and resistive response of humidity sensors based on graphene decorated by PMMA and silver nanoparticles

In this paper, we reported comparative study of the humidity characteristics of graphene/silver nanoparticles composite (Gr-AgNps) and graphene/silver nanoparticles/PMMA composite (Gr-AgNps-PMMA) based efficient humidity sensors. Aqueous solution of Gr-AgNps and Gr-AgNps-PMMA was drop casted over interdigitated copper electrodes with 50 μm gap embedded in the substrates in dust free environment. The band gap obtained from the UV-vis spectra for Gr-AgNps and Gr-AgNps-PMMA based humidity sensors was 4.7 and 4.1 eV respectively. The capacitive and resistive humidity response was studied using LCR meter (GW Instek817). Apparent increase in capacitance was observed (100-10,000 nF) with the increase in the humidity percentage (30-95%RH) at lower frequencies for both the sensors. Resistance of the sensors dropped to zero as the humidity level is increased from 30 to 95%RH in the chamber. The devices were tested for real time stability and for fast response/recovery time. Both the devices showed an excellent stability and response by recording their resistance and capacitance respectively. A lagging of RH decreasing response from RH increasing response was observed at 500 Hz frequency for both the sensors depicted from the hysteresis curve. The humidity response of Gr-AgNps was comparatively better than that of the Gr-AgNps-PMMA based humidity sensors

Robust estimation of heterogeneous treatment effects: an algorithm-based approach

Heterogeneous treatment effect estimation is an essential element in the practice of tailoring treatment to suit the characteristics of individual patients. Most existing methods are not sufficiently robust against data irregularities. To enhance the robustness of the existing methods, we recently put forward a general estimating equation that unifies many existing learners. But the performance of model-based learners depends heavily on the correctness of the underlying treatment effect model. This paper addresses this vulnerability by converting the treatment effect estimation to a weighted supervised learning problem. We combine the general estimating equation with supervised learning algorithms, such as the gradient boosting machine, random forest, and artificial neural network, with appropriate modifications. This extension retains the estimators’ robustness while enhancing their flexibility and scalability. Simulation shows that the algorithm-based estimation methods outperform their model-based counterparts in the presence of nonlinearity and non-additivity. We developed an R package, RCATE, for public access to the proposed methods. To illustrate the methods, we present a real data example to compare the blood pressure-lowering effects of two classes of antihypertensive agents

Highly Sensitive Love Mode Acoustic Wave Platform with SiO2 Wave-Guiding Layer and Gold Nanoparticles for Detection of Carcinoembryonic Antigens

A highly sensitive and precise Love wave mode surface acoustic wave (SAW) immunosensor based on an ST-cut 90°X quartz substrate and an SiO2 wave-guiding layer was developed to detect cancer-related biomarkers of carcinoembryonic antigens (CEAs). A delay line structure of the SAW device with a resonant frequency of 196 MHz was designed/fabricated, and its surface was functionalized through CEA antibody immobilization. The CEA antibodies were bound with gold nanoparticles and CEA antibodies to form a sandwich structure, which significantly amplified the mass loading effect and enhanced the maximum responses by 30 times. The center frequency of the Love wave immunosensor showed a linear response as a function of the CEA concentration in the range of 0.2–5 ng/mL. It showed a limit of detection of 0.2 ng/mL, and its coefficient of determination was 0.983. The sensor also showed minimal interference from nonspecific adsorptions, thus demonstrating its promise for point-of-care applications for cancer biomarkers

Inversion of thermal properties of lunar soil from penetration heat of projectile using a 2D axisymmetric model and optimized PSO algorithm

The thermophysical parameters of lunar soil can be inferred from the temperature field during the invasion process of reconnaissance projectile. This paper adopts a two-dimensional axisymmetric model to reconstruct the projectile invasion process. An optimized particle swarm optimization method is then used to retrieve the thermophysical parameters of lunar soil. When the reconnaissance projectile penetrates the lunar interior, it rubs against the lunar soil and generates heat, which diffuses between the projectile body and the lunar soil. The sensors inside the reconnaissance projectile measure the temperature variation of the projectile body to inverse the thermophysical parameters. This paper carried out physical modeling of the penetration process of reconnaissance projectile. A two-dimensional axisymmetric simulation model is constructed for the physical process, and the adaptive inertial weight particle swarm algorithm is adopted. The inversion experiment of lunar soil thermophysical parameters based on the simulation model shows that the inversion error is less than 10%, which verifies the feasibility of this method