Identification of heavy metal-contaminated Tegillarca granosa using laser-induced breakdown spectroscopy and linear regression for classification

Tegillarca granosa (T. granosa) is susceptible to heavy metals, which may pose a threat to consumer health. Thus, healthy and polluted T. granosa should be distinguished quickly. This study aimed to rapidly identify heavy metal pollution by using laser-induced breakdown spectroscopy (LIBS) coupled with linear regression classification (LRC). Five types of T. granosa were studied, namely, Cd-, Zn-, Pb-contaminated, mixed contaminated, and control samples. Threshold method was applied to extract the significant variables from LIBS spectra. Then, LRC was used to classify the different types of T. granosa. Other classification models and feature selection methods were used for comparison. LRC was the best model, achieving an accuracy of 90.67%. Results indicated that LIBS combined with LRC is effective and feasible for T. granosa heavy metal detection.     

Enhanced antibacterial activity of tetramethylguanidinium-conjugated linear polyethylenimine polymers

In the present study, varying amounts of tetramethylguanidinium moiety have been conjugated to linear polyethylenimine to obtain linear polyethylenimine-tmg (LPTG) polymers. Incorporation of hydrophobic and highly basic moiety in the polymeric backbone resulted in the significant improvement in the antibacterial activity which was confirmed by zone of inhibition and MIC assays. Further, the results of transmission electron microscopy and confocal studies revealed that the projected LPTG polymers possessed higher antibacterial activity than the native polymer. In addition, these modified polyethylenimine (PEI) polymers were capable of reducing auric chloride into stable gold nanoparticles. These polyamine-stabilized gold nanoparticles can be used in various biomedical applications.     

库水位变动下庄屋滑坡变形与稳定性分析

库水位变动是三峡库区滑坡等地质灾害频发的重要原因之一。为研究库水位变动对库岸涉水滑坡变形和稳定性的影响,以庄屋滑坡为例,结合GPS位移监测数据和数值模拟分析,探讨了该滑坡在库水升降过程中变形和稳定性的响应规律。结果表明,庄屋滑坡受库水影响滑坡后缘变形大于前缘,为推移式滑坡。水位变动过程中,滑坡渗流和稳定性变化具有动水压力型滑坡特点,即在水位上升时滑体内地下水浸润线呈内凹趋势,水位下降时滑体内地下水浸润线呈外凸趋势,且具有一定的滞后性;库水位上升时滑坡稳定性系数增大,水位下降时减小;当库水位保持在恒定水位时,稳定性系数也逐渐趋于稳定。     

Reference governors and maximal output admissible sets for linear periodic systems

ABSTRACT

In this paper, we consider the problem of constraint management in Linear Periodic (LP) systems using Reference Governors (RG). First, we introduce the periodic-invariant maximal output admissible sets for LP systems. We extend the earlier results in the literature to Lyapunov stable LP systems with output constraints, which arise in RG applications. We show that, while the invariant sets for these systems may not be finitely determined, a finitely-determined inner approximation, which is periodically invariant, can be obtained by constraint tightening. We then analyze the geometric and algebraic relationship between these sets and show that these sets are related via simple transformations, implying that it suffices to compute only one of them for real-time applications. This greatly reduces the memory burden of RG (or other similar constraint management strategies), at the expense of an increase in processing requirements. We present a thorough analysis of this trade-off. In the second part of this paper, we present two RG formulations, and discuss their properties and algorithms for their computation. Numerical simulations demonstrate the efficacy of the approach.     

基于修正幂律模型流体的柳叶形扶正器旋流规律及应用

为提高注水泥过程中水泥浆顶替效率，国内外愈来愈多地使用旋流扶正器。但是，目前旋流扶正器结构参数主要靠经验设计，缺乏理论基础也没有相关行业标准。针对依据文丘里效应设计的柳叶形旋流扶正器，基于修正幂律流体模型，构建了旋流扶正器旋流长度计算模型。通过数值模拟对旋流扶正器井下旋流规律进行研究，推导出了旋流扶正器在修正幂律流体条件下，周向平均速度和完全顶替的临界速度公式。基于旋流扶正器井下旋流规律，分析了井眼环空结构、流体性能、井下井壁附着力对水泥浆顶替效率的影响，优化了柳叶形旋流扶正器旋流角、扶正棱长度、扶正棱有效体积和扶正器高度等设计参数。对比分析了柳叶形与常规旋流扶正器在不同工况下的旋流长度，柳叶形扶正器的旋流长度比常规扇形平均提升了32%，比常规梯形扶正器平均提升了47%。柳叶形扶正器在新疆顺北油田顺北3井等20多井次超深井应用，应用井固井质量明显优于常规扇形的旋流扶正器。     

Robust predictive extended state observer for a class of nonlinear systems with time-varying input delay

ABSTRACT

This paper deals with asymptotic stabilisation of a class of nonlinear input-delayed systems via dynamic output feedback in the presence of disturbances. The proposed strategy has the structure of an observer-based control law, in which the observer estimates and predicts both the plant state and the external disturbance. A nominal delay value is assumed to be known and stability conditions in terms of linear matrix inequalities are derived for fast-varying delay uncertainties. Asymptotic stability is achieved if the disturbance or the time delay is constant. The controller design problem is also addressed and a numerical example with an unstable system is provided to illustrate the usefulness of the proposed strategy.     

A deep neural network based classifier for brain tumor diagnosis

Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset.     

Nonuniform linear array DOA estimation using EM criterion

In this letter, we address the problem of Direction of Arrival (DOA) estimation with nonuniform linear array in the context of sparse Bayesian learning (SBL) framework. The nonuniform array output is deemed as an incomplete-data observation, and a hypothetical uniform linear array output is treated as an unavailable complete-data observation. Then the Expectation-Maximization (EM) criterion is directly utilized to iteratively maximize the expected value of the complete-data log likelihood under the posterior distribution of the latent variable. The novelties of the proposed method lie in its capability of interpolating the actual received data to a virtual uniform linear array, therefore extending the achievable array aperture. Simulation results manifests the superiority of the proposed method over off-the-shelf algorithms, specially on circumstances such as low SNR, insufficient snapshots, and spatially adjacent sources.     

粉土界面恒刚度循环剪切试验研究

竖向循环荷载作用下桩土界面的作用机理是研究桩土摩擦疲劳的关键。针对循环荷载作用下桩-粉土界面的剪切性能,使用改进的剪切试验装置在恒刚度条件下进行循环剪切试验,研究循环次数、累积位移和法向刚度对其摩擦疲劳性能、循环后单调剪切性能的影响。试验结果表明,粉土在循环剪切过程中,法向应力和剪应力在初始10个循环内随循环数增加快速衰减,随着循环进行,逐渐趋于稳定;单次循环内在剪切位移方向变化时,土体呈现表现出剪缩-剪胀-剪缩交替现象,总体变形呈现剪缩的趋势;循环荷载作用下,粉土界面的法向应力和剪应力随法向刚度增大衰减速率增大,达到稳定的累积循环位移越小;粉土循环后的单调剪切、法向应力恢复的单调剪切的剪应力比小于首次单调剪切试验值,且法向应力恢复的循环后剪切试验的剪胀程度较小,表明循环剪切过程中界面处粉土颗粒棱角破碎,颗粒变得光滑。在对试验数据分析的基础上,提出了与累积位移、法向刚度和初始应力相关的无量纲累积位移,建立了法向应力和界面摩擦角随累积位移的衰减方程。