Double Vision Model Using Space-Time Function Control within Silicon Microring System

Silicon - This paper presents the use of space and time function applied simultaneously into the silicon microring system arrangement for double vision problem solving and enhancement. The eye...     

Correction to: Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks

Silicon - The original version of the article unfortunately contained an error.     

采用Butterworth传递函数设计二自由度纯滞后控制系统

采用Butterworth传递函数设计了纯滞后对象的状态反馈控制系统,针对纯滞后对象的状态反馈控制系统是单自由度的,提出一种采用Butterworth传递函数设计二自由度纯滞后控制系统的方法。用m阶模型逼近纯滞后因子,引入状态观测器,构成状态反馈子系统,再串入一个积分器,构成纯滞后过程的串级状态反馈控制系统,以n+1阶Butterworth传递函数为整个系统的目标函数,按状态反馈增益阵和状态观测器的协调优化设计状态反馈子系统。仿真结果表明了该方法设计的系统同时获得良好的给定值跟踪特性和干扰抑制特性。     

Correction to: Effect of Different Forms of Silicon on Growth of Spring Wheat Cultivated in Organic Farming System

Silicon - The original version of this article has been published with an error.     

Correction to: Non-destructive Testing of a Monocrystalline Silicon Solar Cell: Magnetic Field - Electrical Properties Correlation

Silicon - Due to an oversight, the author incorrectly labeled the axis in Fig. 5 of the published online version.     

An Arrhenius-Type Viscosity Function to Model Sintering Using the Skorohod–Olevsky Viscous Sintering Model Within a Finite-Element Code

The ease and ability to predict sintering shrinkage and densification with the Skorohod–Olevsky viscous sintering (SOVS) model within a finite-element (FE) code have been improved with the use of an Arrhenius-type viscosity function. The need for a better viscosity function was identified by evaluating SOVS model predictions made using a previously published polynomial viscosity function. Predictions made using the original, polynomial viscosity function do not accurately reflect experimentally observed sintering behavior. To more easily and better predict sintering behavior using FE simulations, a thermally activated viscosity function based on creep theory was used with the SOVS model. In comparison with the polynomial viscosity function, SOVS model predictions made using the Arrhenius-type viscosity function are more representative of experimentally observed viscosity and sintering behavior. Additionally, the effects of changes in heating rate on densification can easily be predicted with the Arrhenius-type viscosity function. Another attribute of the Arrhenius-type viscosity function is that it provides the potential to link different sintering models. For example, the apparent activation energy, Q , for densification used in the construction of the master sintering curve for a low-temperature cofire ceramic dielectric has been used as the apparent activation energy for material flow in the Arrhenius-type viscosity function to predict heating rate-dependent sintering behavior using the SOVS model.     

Correction to: Study of Structural,Electronic and Vibrational Properties of Porous Silicon with Different Porosity

Silicon - The original version of the article unfortunately contained an error.     

Control of a Batch Polymerization System Using Hybrid Neural Network ‐ First Principle Model

In this work, the utilization of neural network in hybrid with first principle models for modelling and control of a batch polymerization process was investigated. Following the steps of the methodology, hybrid neural network (HNN) forward models and HNN inverse model of the process were first developed and then the performance of the model in direct inverse control strategy and internal model control (IMC) strategy was investigated. For comparison purposes, the performance of conventional neural network and PID controller in control was compared with the proposed HNN. The results show that HNN is able to control perfectly for both set points tracking and disturbance rejection studies.     

Correction to: Direct and Residual Effects of Water Deficit Stress,Different Sources of Silicon and Plant-Growth Promoting Bacteria on Silicon Fractions in the Soil

Silicon - A Correction to this paper has been published: https://doi.org/10.1007/s12633-021-01161-w     

Correction to: A Micellar Liquid Chromatographic Method for the Determination of Azosemide in Solubilized System

In the original publication of the article, the corresponding author's name was incorrectly published. The corresponding author's name should appear as Ashok Kumar.     

Correction to: System of Operation of Ceramic Tools in the External Burnishing of Cylindrical Billets

Refractories and Industrial Ceramics - The Acknowledgement on page 105 should read The work was financially supported by the Ministry of Education and Science of the RF under a State Task for...     

Adaptive Generic Model Control of Multivariable Processes: Application to Semi-Batch Reactors with Different Relative Degrees

In this study, a multivariable Generic Model Control (GMC) approach is proposed based on input-output linear-in-parameters time series data-driven models. Adaptation of the model parameters is carried out at every sampling instant. For higher relative degree systems, two different definitions are used for output derivatives, yielding two versions of adaptive GMC for multivariable processes. The performance of the proposed control algorithms is illustrated by application to multivariable semi-batch reactors without and with coolant dynamics for control of temperature and one of the reactant concentrations. The study indicated that the adaptive GMC (AGMC) algorithms for higher relative degree multiple-input and multiple-output (MIMO) systems with a different relative degree have exhibited performance comparable to or better than the phenomenological model-based GMC with respect to both set point tracking and smooth input profiles, and also that the predictive version of AGMC (AGMC-II) has exhibited slightly lower integral square error (ISE) values compared to AGMC-I in case of multivariable semi-batch reactor with coolant dynamics.     

A Novel Model Using AAV9-Cre to Knockout Adult Leydig Cell Gene Expression Reveals a Physiological Role of Glucocorticoid Receptor Signalling in Leydig Cell Function

Glucocorticoids are steroids involved in key physiological processes such as development, metabolism, inflammatory and stress responses and are mostly used exogenously as medications to treat various inflammation-based conditions. They act via the glucocorticoid receptor (GR) expressed in most cells. Exogenous glucocorticoids can negatively impact the function of the Leydig cells in the testis, leading to decreased androgen production. However, endogenous glucocorticoids are produced by the adrenal and within the testis, but whether their action on GR in Leydig cells regulates steroidogenesis is unknown. This study aimed to define the role of endogenous GR signalling in adult Leydig cells. We developed and compared two models; an inducible Cre transgene driven by expression of the Cyp17a1 steroidogenic gene (Cyp17-iCre) that depletes GR during development and a viral vector-driven Cre (AAV9-Cre) to deplete GR in adulthood. The delivery of AAV9-Cre ablated GR in adult mouse Leydig cells depleted Leydig cell GR more efficiently than the Cyp17-iCre model. Importantly, adult depletion of GR in Leydig cells caused reduced expression of luteinising hormone receptor (Lhcgr) and of steroidogenic enzymes required for normal androgen production. These findings reveal that Leydig cell GR signalling plays a physiological role in the testis and highlight that a normal balance of glucocorticoid activity in the testis is important for steroidogenesis.     

Biofragments: An Approach towards Predicting Protein Function Using Biologically Related Fragments and its Application to Mycobacterium tuberculosis CYP126

We present a novel fragment‐based approach that tackles some of the challenges for chemical biology of predicting protein function. The general approach, which we have termed biofragments, comprises two key stages. First, a biologically relevant fragment library (biofragment library) can be designed and constructed from known sets of substrate‐like ligands for a protein class of interest. Second, the library can be screened for binding to a novel putative ligand‐binding protein from the same or similar class, and the characterization of hits provides insight into the basis of ligand recognition, selectivity, and function at the substrate level. As a proof‐of‐concept, we applied the biofragments approach to the functionally uncharacterized Mycobacterium tuberculosis (Mtb) cytochrome P450 isoform, CYP126. This led to the development of a tailored CYP biofragment library with notable 3D characteristics and a significantly higher screening hit rate (14 %) than standard drug‐like fragment libraries screened previously against Mtb CYP121 and 125 (4 % and 1 %, respectively). Biofragment hits were identified that make both substrate‐like type‐I and inhibitor‐like type‐II interactions with CYP126. A chemical‐fingerprint‐based substrate model was built from the hits and used to search a virtual TB metabolome, which led to the discovery that CYP126 has a strong preference for the recognition of aromatics and substrate‐like type‐I binding of chlorophenol moieties within the active site near the heme. Future catalytic analyses will be focused on assessing CYP126 for potential substrate oxidative dehalogenation.     

Sesame Oil Oxidation Control by Active and Smart Packaging System Using Wheat Gluten/Chlorophyll Film to Increase Shelf Life and Detecting Expiration Date

In this study, a smart biodegradable film based on wheat gluten modified with chlorophyll (WG/Ch) is presented. The effect of chlorophyll on the antioxidant activity and mechanical properties of WG/Ch film is studied. Scanning electron microscopy and Fourier‐transform infrared spectroscopy (FT‐IR) analyses are used to study the structure and chemical composition of gluten‐based films. The WG/Ch film is used in the packaging of sesame oil. The effect of WG/Ch film on increasing the shelf life and detecting expiration time of oil is studied. FT‐IR results show that weak interactions are created between gluten and chlorophyll. The antioxidant activity of the WG film is 60% and it increases to 85% by the addition of chlorophyll. The results show that with the increasing storage time of oil samples, oxidative indices are increased, but the WG/Ch film decreases the sesame oil oxidation significantly (almost in the all oxidative indices 50% decrease is observed). Practical Applications: The WG/Ch film is used in the packaging of sesame oil. The effect of WG/Ch film on the increasing of shelf life and detecting expiration time of oil is studied. By increasing storage time and oxidant, acidity, acid number, PA, and oil color change are increased, but the WG/Ch active film decreases the sesame oil oxidation significantly. Also, by examining the color properties of the active film, it is observed that with increasing storage time and oxidant percent, the color of the film is changed (from green to yellow), which can be used to estimate the oil expiration time. It should be mentioned that the film's color changes are visible to the naked eye.     

Transition from Batch to Continuous Operation in Bio‐Reactors: A Model Predictive Control Approach and Application

This work considers the problem of determining the transition of ethanol‐producing bio‐reactors from batch to continuous operation and subsequent control subject to constraints and performance considerations. To this end, a Lyapunov‐based non‐linear model predictive controller is utilized that stabilizes the bio‐reactor under continuous mode of operation. The key idea in the predictive controller is the formulation of appropriate stability constraints that allow an explicit characterization of the set of initial conditions from where feasibility of the optimization problem and hence closed‐loop stability is guaranteed. Additional constraints are incorporated in the predictive control design to expand on the set of initial conditions that can be stabilized by control designs that only require the value of the Lyapunov function to decay. Then, the explicit characterization of the set of stabilizable initial conditions is used in determining the appropriate time for which the reactor must be run in batch mode. Specifically, the predictive control approach is utilized in determining the appropriate batch length that achieves stabilizable values of the state variables at the end of the batch. Application of the proposed method to the ethanol production process using Zymomonas mobilis as the ethanol producing micro‐organism demonstrates the effectiveness of the proposed model predictive control strategy in stabilizing the bio‐reactor.     

RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences

Protein-Protein Interactions (PPIs) play essential roles in most cellular processes. Knowledge of PPIs is becoming increasingly more important, which has prompted the development of technologies that are capable of discovering large-scale PPIs. Although many high-throughput biological technologies have been proposed to detect PPIs, there are unavoidable shortcomings, including cost, time intensity, and inherently high false positive and false negative rates. For the sake of these reasons, in silico methods are attracting much attention due to their good performances in predicting PPIs. In this paper, we propose a novel computational method known as RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the AB feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We performed five-fold cross-validation experiments on yeast and Helicobacter pylori datasets, and achieved very high accuracies of 92.98% and 95.58% respectively, which is significantly better than previous works. In addition, we also obtained good prediction accuracies of 88.31%, 89.46%, 91.08%, 91.55%, and 94.81% on other five independent datasets C. elegans, M. musculus, H. sapiens, H. pylori, and E. coli for cross-species prediction. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-AB method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool. To facilitate extensive studies for future proteomics research, we developed a freely available web server called RVMAB-PPI in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/ppi_ab/.