Predictor‐based tracking for neuromuscular electrical stimulation

We present a new tracking controller for neuromuscular electrical stimulation (NMES), which is an emerging technology that artificially stimulates skeletal muscles to help restore functionality to human limbs. The novelty of our work is that we prove that the tracking error globally asymptotically and locally exponentially converges to zero for any positive input delay, coupled with our ability to satisfy a state constraint imposed by the physical system. Also, our controller only requires sampled measurements of the states instead of continuous measurements and allows perturbed sampling schedules, which can be important for practical purposes. Our work is based on a new method for constructing predictor maps for a large class of time‐varying systems, which is of independent interest. Copyright © 2014 John Wiley & Sons, Ltd.     

Elucidating the Catalytic Subunit Composition of Distinct Proteasome Subtypes: A Crosslinking Approach Employing Bifunctional Activity‐Based Probes

In addition to two well‐recognized proteasome subtypes—constitutive proteasomes and immunoproteasomes—mounting evidence also suggests the existence of intermediate proteasome subtypes containing unconventional mixtures of catalytic subunits. Although they appear to play unique biological roles, the lack of practical methods for detecting distinct proteasome subtypes has limited functional investigations. Here, we report the development of activity‐based probes that crosslink two catalytic subunits within intact proteasome complexes. Identification of the crosslinked subunit pairs provides direct evidence of the catalytic subunit composition of proteasomes. Using these probes, we found that U266 multiple myeloma cells contain intermediate proteasomes comprising both β1i and β2, but not β1 and β2i, consistent with previous findings with other cell types. Our bifunctional probes can be utilized in functional investigations of distinct proteasome subtypes in various biological settings.     

Modeling and Multiresponse Optimization of the Mechanical Properties of Roselle Fiber-Reinforced Vinyl Ester Composite

In this study, the roselle fiber-reinforced vinyl ester composites were prepared based on Taguchi’s L₂₇ experimental design using hand lay-up technique. A gray-based Taguchi technique was used to optimize the process parameters with mechanical properties (multiple performance characteristics). The results also show that the fiber content is the most significant process parameter which greatly affects the mechanical properties. It was proved that the multiple performance characteristics of the plant-based natural fiber-reinforced polymer composites can be effectively improved by this method. The proposed response surface mathematical models to predict mechanical properties of composite were found statistically valid.     

Calculating the medial axis of a CAD model by multi-CPU based parallel computation

Computational efficiency is still a great challenge for the generation of the Medial Axis (MA) for complicated CAD models. Current research mainly focuses on CPU-based MA generation methods. However, most of the methods emphasize using a single CPU. The highly-efficient methods based on parallel computing are still missing. In this study, a parallel method based on multi-CPU is proposed for the efficient MA generation of CAD models using distance dilation. By dividing the whole model into several parts for which MAs are calculated in parallel and then combined, computational efficiency can be greatly improved in theory and the computation time can be reduced nearly K times if K CPUs are used. Firstly, an adaptive division method is proposed to divide the voxelized model into blocks which have nearly the same number of voxels to balance the computational burden. Secondly, the local Euclidean Distance Transform (EDT) is calculated for each block based on the existing distance dilation method. Thirdly, the complete inter-dilation method is proposed to compute the influence between different blocks to get a global EDT for each block. Finally, each block generates a sub-MA separately and then all the generated MAs are combined to obtain the final MA. The last three processes can be efficiently conducted in parallel by using multiple CPUs. Several groups of experiments are conducted which demonstrate the good performance of the proposed methods in terms of efficiency.     

Questioning numerical integration methods for microsphere (and microplane) constitutive equations

In the last few years, more and more complex microsphere models have been proposed to predict the mechanical response of various polymers. Similarly than for microplane models, they consist in deriving a one-dimensional force vs. stretch equation and to integrate it over the unit sphere to obtain a three-dimensional constitutive equation. In this context, the focus of authors is laid on the physics of the one-dimensional relationship, but in most of the case the influence of the integration method on the prediction is not investigated.Here we compare three numerical integration schemes: a classical Gaussian scheme, a method based on a regular geometric meshing of the sphere, and an approach based on spherical harmonics. Depending on the method, the number of integration points may vary from 4 to 983,040! Considering simple quantities, i.e. principal (large) strain invariants, it is shown that the integration method must be carefully chosen. Depending on the quantities retained to described the one-dimensional equation and the required error, the performances of the three methods are discussed. Consequences on stress–strain prediction are illustrated with a directional version of the classical Mooney–Rivlin hyperelastic model. Finally, the paper closes with some advices for the development of new microsphere constitutive equations.     

Using constraints and their value for optimization of large ODE systems

We provide analytical tools to facilitate a rigorous assessment of the quality and value of the fit of a complex model to data. We use this to provide approaches to model fitting, parameter estimation, the design of optimization functions and experimental optimization. This is in the context where multiple constraints are used to select or optimize a large model defined by differential equations. We illustrate the approach using models of circadian clocks and the NF-κB signalling system.     

Multiple characteristic model‐based golden‐section adaptive control: Stability and optimization

The characteristic model‐based golden‐section adaptive control (CM‐GSAC) law has been developed for over 20 years in China with a broad range of applications in various fields. However, quite a few theoretical problems remain open despite its satisfying performance in practice. This paper revisits the stability of the CM‐GSAC from its very beginning and explores the underlying implications of the so‐called golden‐section parameter l₂≈0.618. The closed‐loop system, which consists of the CM and the GSAC, is a discrete time‐varying system, and its stability is discussed from three perspectives. First, attentions have been paid to select the optimal controller coefficients such that the closed‐loop system exhibits the best transient performance in the worst case. Second, efforts are made to improve the robustness in the presence of parameter estimation errors, which provide another choice when designing the adaptive controller. Finally, by measuring the slowly time‐varying nature in an explicit inequality form, a bridge is built between the instantaneous stability and the time‐varying stability. In order to relax the constraints on the parameter bounds of the CM, the GSAC is further extended to multiple CMs, which shows more satisfying tracking performance than that of the traditional multiple model adaptive control method. Copyright © 2014 John Wiley & Sons, Ltd.     

SiC单晶片切割力建模与自适应控制

Si C单晶因优良的物理和机械性能而大量用于大功率器件和IC行业。但由于材料的高硬度和高脆性,使其加工过程变得很困难。为此,分析了Si C单晶片切割过程,建立切割过程模型,通过F检验法进行系统阶次辨识,采用遗忘因子递推最小二乘算法在线估计模型参数,建立进给量与切割力的差分方程,设计基于最小方差自校正的切割力控制器,并进行实验验证。结果表明:控制器能够很好的跟踪不同信号,具有良好的鲁棒性,提高了Si C单晶片的加工效率和表面质量。     

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

From organ-on-a-chip towards body-on-a-chip

Organ-on-a-chip technology aims to reproduce the key physiological features of human organs and tissues, even complex actions of multi-organ interaction. While organ-on-a-chips at single-organ level has made notable achievement during the last decade, multi-organ-on-a-chips, which manifests unique advantages, has started gathering attention only recently. In this viewpoint, we discuss the current status of organ-on-a-chip technology, with a specific emphasis on multi-organ-on-a-chip. Key technological advances contributing to the maturation of the field, and challenges that need to be addressed before wider adoption in relevant fields are discussed. We will share our perspectives on how the multi-organ-on-a-chip can improve the drug development process.