实验用熔炉中同步进行结晶测试

在先前的文献中描述了一种空气冷却的测试管,该测试管拥有一系列的插座和插头（在结晶过程中使用的转换插座）,可以安装在实验室箱式熔炉中。该装置的用途是调节物质在坩埚槽中结晶过程的晶面和结晶速率。在本文中我们讨论一种改进型的内部冷却器——锯齿状的空气冷却管（结晶架）。这种改进型的冷却器可以简单地搭建和操作,可以延长和弯曲成为各种形状,可以用于坩埚和熔炉管中。该冷却器可以通过简单的调控对不同结晶参数和物质同步进行结晶测试,利用实验用熔炉就可以达到从一系列新型的具有层状晶体结构的化合物中得到晶体的目的。     

On-capillary ion-exchange preconcentration of inorganic anions in open-tubular capillary electrochromatogrpahy with elution using transient-isotachophoretic gradient. 2. Characterization of the isotachophoretic gradient

Diffuse transient-isotachophoretic boundaries can be used as an elution gradient of increasing eluotropic strength to elute inorganic anions that have been preconcentrated on an open-tubular ion-exchange stationary phase prior to electrophoretic separation. The generation and characteristics of these gradients for elution after preconcentration have been investigated. The gradients are generated by placing a low-mobility, weak ion-exchange competing anion in the capillary (weak electrolyte, WE), and a high-mobility, strong ion-exchange competing anion in the electrolyte vials (strong electrolyte, SE). Application of voltage establishes a diffuse boundary with the composition changing from the weak anion to the strong anion. Comparison of elution gradients generated with different electrolyte systems was accomplished by comparing the eluotropic strength (a function of eluent concentration, ion-exchange selectivity coefficient, and charge) and the shape of the profile as it changes from WE to SE. The ion-exchange selectivity coefficient of the SE competing anion is important in establishing a sharp change in elution strength. A large difference in mobility between the WE and SE competing anions gives an SE with a higher final eluotropic strength, but the slope of the gradient is shallower. This results in a reduction in the efficiency of analyte focusing. To ensure maximum focusing efficiency, the WE and SE electrolytes should be selected such that the SE has the highest possible eluotropic strength for a given concentration of WE. The SE competing anion should also have a sufficiently low electrophoretic mobility to ensure focusing for the maximum number of analytes, and the mobility difference between the WE and SE competing anions should be as small as possible.     

1D Physical Non-Quasi Static BJT Circuit Model Based on the Equivalent Transmission Line Analysis

We describe a novel physically based non-quasi-static (NQS) bipolar junction transistor (BJT) model derived from theoretical analysis of the equivalent transmission line representing the one-dimensional minority carrier transport trough silicon quasi-neutral regions (QNRs). The NQS BJT model holds for arbitrarily doped BJTs operating at all injection levels. It also incorporates most important high-injection effects (Webster effect, Kirk effect, Early effect) and heavy-doping effects (position-dependant mobility, life-time, and band-gap narrowing). Novel NQS BJT model is compact since it allows the DC, AC and transient circuit analysis to be performed with single BJT model representation. In addition, it includes for the first time the influence of momentum relaxation time term appearing in DD equations. The advantages of the proposed NQS BJT model over the standard quasi-static Gummel-Poon model are demonstrated with simulations of Si BJT and SiGe HBT devices as well as with stability analysis of bipolar differential amplifier.     

Electrically polarized ion-exchange beds in ion chromatography: eluent generation and recycling

This paper describes how electrically polarized ion-exchange beds pumped with water can produce electrolyte of steady and controllable concentration. Such devices make it possible to use water as the pumped phase in ion chromatography (IC), thus avoiding off-line eluent preparation. Control of the electrical current flowing through the devices allows precise control of the concentration of eluent that they deliver. This provides a new way of performing gradient and isocratic elutions. Using water as the carrier and two small beds of resin, one as a generator the other as a suppressor, and periodically reversing their roles through automatically switched valves, we have developed a form of continuous IC that involves little intervention by the user. The paper presents the principles of the new method and examples of its use in anion analysis.     

Stel Component Analysis: Joint Segmentation, Modeling and Recognition of Objects Classes

Models that captures the common structure of an object class have appeared few years ago in the literature (Jojic and Caspi in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 212?C219, 2004; Winn and Jojic in Proceedings of International Conference on Computer Vision (ICCV), pp. 756?C763, 2005); they are often referred as ??stel models.?? Their main characteristic is to segment objects in clear, often semantic, parts as a consequence of the modeling constraint which forces the regions belonging to a single segment to have a tight distribution over local measurements, such as color or texture. This self-similarity within a region in a single image is typical of many meaningful image parts, even when across different images of similar objects, the corresponding parts may not have similar local measurements. Moreover, the segmentation itself is expected to be consistent within a class, although still flexible. These models have been applied mostly to segmentation scenarios. In this paper, we extent those ideas (1) proposing to capture correlations that exist in structural elements of an image class due to global effects, (2) exploiting the segmentations to capture feature co-occurrences and (3) allowing the use of multiple, eventually sparse, observation of different nature. In this way we obtain richer models more suitable to recognition tasks. We accomplish these requirements using a novel approach we dubbed stel component analysis. Experimental results show the flexibility of the model as it can deal successfully with image/video segmentation and object recognition where, in particular, it can be used as an alternative of, or in conjunction with, bag-of-features and related classifiers, where stel inference provides a meaningful spatial partition of features.     

Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes

Fog computing provides quality of service for cloud infrastructure. As the data computation intensifies, edge computing becomes difficult. Therefore, mobile fog computing is used for reducing traffic and the time for data computation in the network. In previous studies, software-defined networking (SDN) and network functions virtualization (NFV) were used separately in edge computing. Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance, reliability, and scalability. SDN/NFV is still in development. The traditional Internet of things (IoT) data analysis system is only based on a linear and time-variant system that needs an IoT data system with a high-precision model. This paper proposes a combined architecture of SDN and NFV on an edge node server for IoT devices to reduce the computational complexity in cloud-based fog computing. SDN provides a generalization structure of the forwarding plane, which is separated from the control plane. Meanwhile, NFV concentrates on virtualization by combining the forwarding model with virtual network functions (VNFs) as a single or chain of VNFs, which leads to interoperability and consistency. The orchestrator layer in the proposed software-defined NFV is responsible for handling real-time tasks by using an edge node server through the SDN controller via four actions: task creation, modification, operation, and completion. Our proposed architecture is simulated on the EstiNet simulator, and total time delay, reliability, and satisfaction are used as evaluation parameters. The simulation results are compared with the results of existing architectures, such as software-defined unified virtual monitoring function and ASTP, to analyze the performance of the proposed architecture. The analysis results indicate that our proposed architecture achieves better performance in terms of total time delay (1800 s for 200 IoT devices), reliability (90%), and satisfaction (90%).     

Protecting Data Mobility in Cloud Networks Using Metadata Security

At present, health care applications, government services, and banking applications use big data with cloud storage to process and implement data. Data mobility in cloud environments uses protection protocols and algorithms to secure sensitive user data. Sometimes, data may have highly sensitive information, leading users to consider using big data and cloud processing regardless of whether they are secured are not. Threats to sensitive data in cloud systems produce high risks, and existing security methods do not provide enough security to sensitive user data in cloud and big data environments. At present, several security solutions support cloud systems. Some of them include Hadoop Distributed File System (HDFS) baseline Kerberos security, socket layer-based HDFS security, and hybrid security systems, which have time complexity in providing security interactions. Thus, mobile data security algorithms are necessary in cloud environments to avoid time risks in providing security. In our study, we propose a data mobility and security (DMoS) algorithm to provide security of data mobility in cloud environments. By analyzing metadata, data are classified as secured and open data based on their importance. Secured data are sensitive user data, whereas open data are open to the public. On the basis of data classification, secured data are applied to the DMoS algorithm to achieve high security in HDFS. The proposed approach is compared with the time complexity of three existing algorithms, and results are evaluated.     

Structural and electrical properties of sintered zinc-titanate ceramics

The aim of this work was an investigation of structural and electrical properties of sintered zinc-titanate ceramics obtained by mechanical activation. Mixtures of ZnO and TiO₂ were mechanically activated in a planetary ball mill up to 90 min and sintered isothermally in air for 120 min at 1100 °C. The phase composition in the ZnO–TiO₂ system after milling and sintering was analyzed using the XRD method. Microstructure analyses were performed using SEM. The results of electric resistivity, capacitance and loss tangent of the sintered samples were obtained. The existence of zinc-titanate as a dielectric was proved (?_r = 12.5, Q = 386.1, tgδ = 0.0026, ρ = 1.02 Ωm).     

A physics-based electrical model of a magnetically sensitive AlGaN/GaN HEMT

Journal of Computational Electronics - The electrical model of a magnetically sensitive two-drain contact AlGaN/GaN HEMT (MagHEMT) is described for the first time and implemented in...     

Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity. The user’s access over the internet creates massive data processing over the internet. Big data require an intelligent feature selection model by addressing huge varieties of data. Traditional feature selection techniques are only applicable to simple data mining. Intelligent techniques are needed in big data processing and machine learning for an efficient classification. Major feature selection algorithms read the input features as they are. Then, the features are preprocessed and classified. Here, an algorithm does not consider the relatedness. During feature selection, all features are misread as outputs. Accordingly, a less optimal solution is achieved. In our proposed research, we focus on the feature selection by using supervised learning techniques called grey wolf optimization (GWO) with decomposed random differential grouping (DrnDG-GWO). First, decomposition of features into subsets based on relatedness in variables is performed. Random differential grouping is performed using a fitness value of two variables. Now, every subset is regarded as a population in GWO techniques. The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research. Once the features are optimized, we classify using advanced kNN process for accurate data classification. The result of DrnDG-GWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm. The accuracy and time complexity of the proposed algorithm are 98% and 5 s, which are better than the existing techniques.