Iron diffusivity into superconducting YBa2Cu3O7−δ at oxygen-assisted sintering: structural,magnetic, and transport properties

Optimization of materials exhibiting high-temperature superconductivity for producing controllable nano-devices is crucial for industrial applications. Herein we report a comprehensive study of the diffusion process between YBa₂Cu₃O_7−δ (YBCO) and iron particles. Fe diffusion into the YBCO matrix could be fundamental for multilayer systems with YBCO/Fe-alloy interfaces. We have found that the orthorhombic YBCO structure adopts to 3 wt% Fe, while for higher Fe content, a formation of BaFeO_3−δ and iron oxides was observed. Complementary measurements confirmed the strong superconductivity suppression in YBCO-Fe materials containing more than 7 wt% Fe. The YBCO with diffused Fe material retain the unit cell orthorhombicity (max. 3 wt% Fe), and their superconducting properties follow the principle of critical scaling with different exponents (γ). The critical current density (J_c), pinning fields (H_P) exhibit γ = 1, the first critical field (H_c1) shows γ = 1/2, and critical temperature (T_c) demonstrates γ = 7/4.     

Deep blue/ultraviolet microcavity OLEDs based on solution-processed PVK:CBP blends

There is an increasing need to develop stable, high-intensity, efficient OLEDs in the deep blue and UV. Applications include blue pixels for displays and tunable narrow solid-state UV sources for sensing, diagnostics, and development of a wide band spectrometer-on-a-chip. With the aim of developing such OLEDs we demonstrate an array of deep blue to near UV tunable microcavity (μc) OLEDs (λ ∼373–469 nm) using, in a unique approach, a mixed emitting layer (EML) of poly(N-vinyl carbazole) (PVK) and 4,4′-bis(9-carbazolyl)-biphenyl (CBP), whose ITO-based devices show a broad electroluminescence (EL) in the wavelength range of interest. This 373–469 nm band expands the 493–640 nm range previously attained with μcOLEDs into the desired deep blue-to-near UV range. Moreover, the current work highlights interesting characteristics of the complexity of mixed EML emission in combinatorial 2-d μcOLED arrays of the structure 40 nm Ag/x  nm MoO_x/∼30 nm PVK:CBP (3:1 weight ratio)/y  nm 4,7-diphenyl-1,10-phenanthroline (BPhen)/1 nm LiF/100 nm Al, where x = 5, 10, 15, and 20 nm and y = 10, 15, 20, and 30 nm. In the short wavelength μc devices, only CBP emission was observed, while in the long wavelength μc devices the emission from both PVK and CBP was evident. To understand this behavior simulations based on the scattering matrix method, were performed. The source profile of the EML was extracted from the measured EL of ITO-based devices. The calculated μc spectra indeed indicated that in the thinner, short wavelength devices the emission is primarily from CBP; in the thicker devices both CBP and PVK contribute to the EL. This situation is due to the effect of the optical cavity length on the relative contributions of PVK and CBP EL through a change in the wavelength-dependent emission rate, which was not suggested previously. Structural analysis of the EML and the preceding MoO_x layer complemented the data analysis.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

Direct and quantitative understanding of the non-Ohmic contact resistance in organic and oxide thin-film transistors

We explore the device physics of thin film transistors (TFTs) with non-Ohmic contacts and develop a simple and fast method for evaluating the contact properties TFTs through output characteristics. Using one single output scan, the quantitative relationship between contact resistances and drain voltage were evaluated, revealing the property of interfacial injection at non-Ohmic contacts. This is demonstrated and validated in both TFT simulations and experiments employing inorganic and organic TFTs. The approach can be applied to general TFTs with arbitrary materials and configurations conveniently and enables faster and improved understanding of TFT operation and device physics.     

Integrated smart grid systems security threat model

The smart grid (SG) integrates the power grid and the Information and Communication Technology (ICT) with the aim of achieving more reliable and safe power transmission and distribution to the customers. Integrating the power grid with the ICT exposes the SG to systems security threats and vulnerabilities that could be compromised by malicious users and attackers. This paper presents a SG systems threats analysis and integrated SG Systems Security Threat Model (SSTM). The reference architecture of the SG, with its components and communication interfaces used to exchange the energy-related information, is integrated with the results of SG systems security threat analysis to produce a comprehensive, integrated SG SSTM. The SG SSTM in this paper helps better depict and understand the vulnerabilities exploited by attackers to compromise the components and communication links of the SG. The SG SSTM provides a reference of the systems security threats for industrial security practitioners, and can be used for design and implementation of SG systems security controls and countermeasures.     

面向球幕投影系统的几何校正方法

摘 要：用于飞行模拟的视景仿真系统，经常会以多台投影仪同步投影以得到较大范围的 视场角。当投影机斜对屏幕或者投影屏幕为曲面时，图像会发生几何失真。针对此问题，本文 提出了一种专门面向球幕投影系统的几何校正方法，并以一个三通道显示系统为实例，详述了 该方法的理论原理以及校正流程。实例结果表明，经几何校正后，各投影图像无几何畸变，通 道过渡处几何内容完全一致。该方法是一种纯软件方法，成本小而且操作简单，能够适应于不 同的投影场景。     

Cloud manufacturing ecosystem analysis and design

The manufacturing industry is facing the impact of a dynamic market and intensive competition. Many companies are looking for a new approach to improve their business activities in a collaborative business ecosystem with other stakeholders. Cloud computing enables the sharing of manufacturing resources and capabilities between different stakeholders to support business and physical production. The purpose of this research is to explore approaches moving towards a cloud manufacturing ecosystem and present possible implications for practice. To fulfil the research objectives, a multiple-case study was conducted within sheet metal manufacturing companies. Business and technology related requirements for cloud-based collaborative manufacturing portals were collected through interviews with industrial practitioners from the sheet metal manufacturing perspective. Based on analysis a prototype model of a CloudEcosystem was presented to demonstrate the essential features of the portals. This research found that there are three different portal types for cloud manufacturing ecosystems depending on the value chain configuration. Close to real-time information provided by cloud-based platforms can create manufacturing ecosystems where machine owners, product designers and customers may collaborate and compete simultaneously. The CloudEcosystem designed in this research can provide a practical tool and technical solution to help manufacturers think about moving towards cloud manufacturing ecosystems.