Photocontrolled multiple-state photochromic benzo[b]phosphole thieno[3,2-b]phosphole-containing alkynylgold(I) complex via selective light irradiation

Photochromic materials have drawn growing attention because using light as a stimulus has been regarded as a convenient and environmental-friendly way to control properties of smart materials. While photoresponsive systems that are capable of showing multiple-state photochromism are attractive, the development of materials with such capabilities has remained a challenging task. Here we show that a benzo[b]phosphole thieno[3,2‑b]phosphole-containing alkynylgold(I) complex features multiple photoinduced color changes, in which the gold(I) metal center plays an important role in separating two photoactive units that leads to the suppression of intramolecular quenching processes of the excited states. More importantly, the exclusive photochemical reactivity of the thieno[3,2‑b]phosphole moiety of the gold(I) complex can be initiated upon photoirradiation of visible light. Stepwise photochromism of the gold(I) complex has been made possible, offering an effective strategy for the construction of multiple-state photochromic materials with multiple photocontrolled states to enhance the storage capacity of potential optical memory devices.

Multiferroicity in Aurivillius Based Bi4Ti3O12 Ceramics: An Overview, Future Prospective and Comparison with Ferrites

The growing modern society demands more new generation devices to fulfil their requirements. This has forced the scientific community to develop multifunctional smart devices. Aurivillius based Bi4Ti3O12 ceramics are one of the leading families of oxide materials, which attract immense attention due to their electrical, ferroelectric, optical, and dielectric properties. These materials have gained special attention due to their numerous device applications such as magnetic recording, sensors, read head technology, spintronic devices, switching devices, data storage devices and multiple state memory devices etc. Multiferroic are the materials in which two or more than two ferroic orders exist simultaneously. This chapter focuses on the possibility of existence of multiferroic behaviour in Aurivillius based compounds specially Bi4Ti3O12. Firstly, we have discussed the basics of multiferroics and their types and the magnetoelectric effect. The effect of different dopants in originating the multiferroism in Bi4Ti3O12 have been reviewed and discuss in detail. At the end comparison of multiferroic and ferrite materials and their future perspective have been discussed.

Spending More on the Poor? A Comprehensive Summary of State-Specific Responses to School Finance Reforms from 1990–2014

Sixty-seven school finance reforms (SFRs), a combination of court-ordered and legislative reforms, have taken place since 1990; however, there is little empirical evidence on the heterogeneity of SFR effects. In this study, we estimate the effects of SFRs on revenues and expenditures between 1990 and 2014 for 26 states. We find that, on average, per pupil spending increased, especially in low-income districts relative to high-income districts. However, underlying these average effect estimates, the distribution of state-level effect sizes ranges from negative to positive—there is substantial heterogeneity. When predicting SFR impacts, we find that multiple state-level SFRs, union strength, and some funding formula components are positively associated with SFR effect sizes in low-income districts. We also show that, on average, states without SFRs adopted funding formula components and increased K-12 state revenues similarly to states with SFRs.

Smartphone-based food recognition system using multiple deep CNN models

People with blindness or low vision utilize mobile assistive tools for various applications such as object recognition, text recognition, etc. Most of the available applications are focused on recognizing generic objects. And they have not addressed the recognition of food dishes and fruit varieties. In this paper, we propose a smartphone-based system for recognizing the food dishes as well as fruits for children with visual impairments. The Smartphone application utilizes a trained deep CNN model for recognizing the food item from the real-time images. Furthermore, we develop a new deep convolutional neural network (CNN) model for food recognition using the fusion of two CNN architectures. The new deep CNN model is developed using the ensemble learning approach. The deep CNN food recognition model is trained on a customized food recognition dataset.The customized food recognition dataset consists of 29 varieties of food dishes and fruits. Moreover, we analyze the performance of multiple state of art deep CNN models for food recognition using the transfer learning approach. The ensemble model performed better than state of art CNN models and achieved a food recognition accuracy of 95.55 % in the customized food dataset. In addition to that, the proposed deep CNN model is evaluated in two publicly available food datasets to display its efficacy for food recognition tasks.

Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems

This paper presents a novel diagnostic framework for distributed power systems that is based on using generative adversarial networks for generating artificial knockoffs in the power grid. The proposed framework makes use of the raw data measurements including voltage, frequency, and phase-angle that are collected from each bus in the cyber-physical power systems. The collected measurements are firstly fed into a feature selection module, where multiple state-of-the-art techniques have been used to extract the most informative features from the initial set of available features. The selected features are inputs to a knockoff generation module, where the generative adversarial networks are employed to generate the corresponding knockoffs of the selected features. The generated knockoffs are then fed into a classification module, in which two different classification models are used for the sake of fault diagnosis. Multiple experiments have been designed to investigate the effect of noise, fault resistance value, and sampling rate on the performance of the proposed framework. The effectiveness of the proposed framework is validated through a comprehensive study on the IEEE 118-bus system.