Complex-valued Zhang neural network for online complex-valued time-varying matrix inversion

In this paper, a new complex-valued recurrent neural network (CVRNN) called complex-valued Zhang neural network (CVZNN) is proposed and simulated to solve the complex-valued time-varying matrix-inversion problems. Such a CVZNN model is designed based on a matrix-valued error function in the complex domain, and utilizes the complex-valued first-order time-derivative information of the complex-valued time-varying matrix for online inversion. Superior to the conventional complex-valued gradient-based neural network (CVGNN) and its related methods, the state matrix of the resultant CVZNN model can globally exponentially converge to the theoretical inverse of the complex-valued time-varying matrix in an error-free manner. Moreover, by exploiting the design parameter γ>1, superior convergence can be achieved for the CVZNN model to solve such complex-valued time-varying matrix inversion problems, as compared with the situation without design parameter γ involved (i.e., the situation with γ=1). Computer-simulation results substantiate the theoretical analysis and further demonstrate the efficacy of such a CVZNN model for online complex-valued time-varying matrix inversion.     

Dependence of temporal diffusion spectra on microstructural properties of biological tissues

The apparent diffusion coefficient (ADC) measured using magnetic resonance imaging methods provides information on microstructural properties of biological tissues, and thus has found applications as a useful biomarker for assessing changes such as those that occur in ischemic stroke and cancer. Conventional pulsed gradient spin echo methods are in widespread use and provide information on, for example, variations in cell density. The oscillating gradient spin echo (OGSE) method has the additional ability to probe diffusion behaviors more readily at short diffusion times, and the temporal diffusion spectrum obtained by the OGSE method provides a unique tool for characterizing tissues over different length scales, including structural features of intracellular spaces. It has previously been reported that several tissue properties can affect ADC measurements significantly, and the precise biophysical mechanisms that account for ADC changes in different situations are still unclear. Those factors may vary in importance depending on the time and length scale over which measurements are made. In the present work, a comprehensive numerical simulation is used to investigate the dependence of the temporal diffusion spectra measured by OGSE methods on different microstructural properties of biological tissues, including cell size, cell membrane permeability, intracellular volume fraction, intranucleus and intracytoplasm diffusion coefficients, nuclear size and T₂ relaxation times. Some unique characteristics of the OGSE method at relatively high frequencies are revealed. The results presented in the paper offer a framework for better understanding possible causes of diffusion changes and may be useful to assist the interpretation of diffusion data from OGSE measurements.     

Videographic Documentation of an Open Cyst Converting Into a Sulcus Vocalis

Study Design

Case study.

Discussion

The origin of sulcus vocalis is debatable, it may arise from a congenital or acquired cyst that ruptures leaving a sulcus attached to the deep layer of the lamina propria. This has been hypothesized but never shown before.

Conclusion

In this article, we present the first videographic documentation of an intracordal cyst that over time may have become a sulcus vocalis.     

甲酸Pt电极电化学氧化体系中时间自组织导致的可利用功耗失

在电极化学反应步骤为控制步骤的条件下,对甲酸阳极氧化体系中出现的电化学振荡的阈值及跨越阈值后状态演化的极限环路径进行了系统动力学计算.同时,根据非平衡电极过程的耗散-涨落理论,进一步对跨越振荡阈值前后的临界区与电化学震荡区的极化曲线及功耗谱进行了随机热力学分析计算.研究结果表明,跨越临界区后单位电化学反应功耗谱出现突降...     

典型“高山-绿洲-荒漠”生态区植被净初级生产力时空变化特征分析

“高山-绿洲-荒漠”作为干旱区独特的自然景观,其内部迥异的生态系统极易在全球变化影响下产生不同波动,而植被净初级生产力（NPP）作为评价生态环境质量的重要指标,对整体认识区域变化有重要意义。遥感影像的产生为大尺度、长时间的区域NPP估算提供了可能,通过土地利用类型数据划分的不同植物覆被下的最大光能利用效率也提高了NPP估算的精度。因此选取具有典型“高山-绿洲-荒漠”生态系统的叶尔羌河流域作为研究区,使用多年遥感影像数据、气象数据,选用基于光能利用率的CASA模型对其进行模拟并对各生态区NPP状况进行分析,得出以下结论：(1) 叶尔羌河流域NPP年均值在2000年之后整体呈波动上升状态,约有85.9%的区域呈上升趋势,在绿洲区域的水域以及居住地处NPP出现下降状况。(2) 流域内NPP变化与降水量相关性较强,其空间分布特征与NPP和温度的相关性表现出相反状态。(3) 叶尔羌河流域NPP在各生态区上表现出“绿洲最高,荒漠绿洲过渡带次之,高山以及荒漠最低”的状况,生态环境相对绿洲、荒漠绿洲过渡带更为脆弱的区域（荒漠、高山）NPP波动性更强。研究结果将为区域生态环境保护修复、应对气候变化、实现人类与自然协调发展、促进多民族共同繁荣提供科学支撑。     

Learning temporal nodes Bayesian networks

Temporal Nodes Bayesian Networks (TNBNs) are an alternative to Dynamic Bayesian Networks for temporal reasoning with much simpler and efficient models in some domains. TNBNs are composed of temporal nodes, temporal intervals, and probabilistic dependencies. However, methods for learning this type of models from data have not yet been developed. In this paper, we propose a learning algorithm to obtain the structure and temporal intervals for TNBNs from data. The method consists of three phases: (i) obtain an initial approximation of the intervals, (ii) obtain a structure using a standard algorithm and (iii) refine the intervals for each temporal node based on a clustering algorithm. We evaluated the method with synthetic data from three different TNBNs of different sizes. Our method obtains the best score using a combined measure of interval quality and prediction accuracy, and a competitive structural quality with lower running times, compared to other related algorithms. We also present a real world application of the algorithm with data obtained from a combined cycle power plant in order to diagnose temporal faults.     

On the Estimation of Nonlinearly Aggregated Mixed Models

The article proposes an iterative algorithm for the estimation of fixed and random effects of a nonlinearly aggregated mixed model. The latter arises when an additive Gaussian model is formulated at the disaggregate level on a nonlinear transformation of the responses, but information is available in aggregate form. The nonlinear transformation breaks the linearity of the aggregate model, yielding a nonlinear tight observational constraint. The algorithm rests upon the sequential linearization of the nonlinear aggregation constraint around proposals that are iteratively updated until convergence. Two alternative pseudo maximum likelihood methods are discussed and compared. As a byproduct we provide a solution to the problem of distributing the aggregate responses over the units of analysis, enforcing the nonlinear observational constraints. Illustrations referring to the temporal disaggregation problem are provided.     

The mass transfer process and the growth rate of NaCl crystal growth by evaporation based on temporal phase evaluation

The mass transfer process and the crystal growth rate have been proved to be very important in the study of crystal growth kinetics, which influence the crystal quality and morphological stability. In this paper, a new method based on temporal phase evaluation was presented to characterize the mass transfer process in situ and determine the crystal growth rate. The crystallization process of NaCl crystal growth by evaporation was monitored in situ by a Mach-Zehnder interferometer, and the absolute concentration evolution, the evaporation rate and the real-time supersaturation of solution were obtained using temporal phase analysis, which acted as a novel technique to extract phase variation along time axis recently. Based on the evaporation rate and the absolution concentration, a new method to calculate mass transfer flux during the crystal growth without the knowledge of the mass transfer coefficient was proposed, and then the crystal growth rate could also be retrieved under the hypothesis of cubic crystals. The results show that the crystal growth rate increases with the supersaturation linearly. It is in agreement with the diffusion theories, which presume that matter is deposited continuously on a crystal face at a rate proportional to the difference in concentration between the points of deposition and the bulk of solution. The method is applicable to the research of crystallization process based on evaporation or vapor diffusion of which the precise conditions of nucleation and supersaturation are usually unknown because of the complexity of the evaporation rate and crystal growth rate.     

基于函数拟合的玉米叶片反射光谱时间变化规律分析

为了探索玉米(Zea mays L.)叶片反射光谱时间变化规律,对21个叶位的玉米叶片进行了每天一次的反射光谱测量,获得了玉米活体叶片整个生命周期反射光谱数据1 261条。在此基础上采用光谱相关图方法,对400～960 nm的可见光、近红外波段的玉米叶片反射光谱进行分段拟合,获得了7个表征玉米叶片反射光谱时间变化规律的拟合参数,并对这7个参数的时间变化趋势采用二元二次多项式进行拟合。结果表明该方法对单片叶片反射光谱的拟合效果非常理想,其中98.7%的叶片反射光谱拟合复相关系数r大于0.99,80.9%的叶片反射光谱拟合均方根误差RMSE小于0.001 5。将所有数据的拟合结果与原始数据进行比较,复相关系数r为0.9978,均方根误差RMSE为0.010 5,拟合结果表明该方法较好地保持了反射光谱时间变化的趋势,能够充分利用叶片反射光谱的相关性,有效地表征玉米叶片反射光谱随时间变化的规律。     

Projected equation methods for approximate solution of large linear systems

We consider linear systems of equations and solution approximations derived by projection on a low-dimensional subspace. We propose stochastic iterative algorithms, based on simulation, which converge to the approximate solution and are suitable for very large-dimensional problems. The algorithms are extensions of recent approximate dynamic programming methods, known as temporal difference methods, which solve a projected form of Bellman’s equation by using simulation-based approximations to this equation, or by using a projected value iteration method.