Single probes and resonant four-wave-mixing enabling novel correlative light electron microscopy workflow

Correlative light electron microscopy prefers single probes with stable performance in both optical and electron microscopy. Now researchers have shown how to harness gold nanoparticles featuring exceptional photostability and four-wave-mixing nonlinearity to realize a new correlation imaging approach

A Three Phase Interleaved Boost Converter with L & C Voltage Extension Mechanism

In this paper a high step-up dc-dc voltage converter is proposed. The proposed converter employs coupled inductors and voltage extension capacitors for obtaining a high voltage gain. The coupled inductors and extension capacitors are merged in traditional interleaved boost converters to get the additional advantage of high step-up voltage conversion ratio and reduced voltage stress on switches along with existing features of interleaved boost converters. The main operating principle of the proposed converter is discussed and the key principle waveforms and equations are analysed. A simulation in PSIM is carried out for the proposed converter as well as traditional interleaved boost converter for the same parameters which shows that the proposed converter has better performance as compared to the traditional interleaved boost converter. Finally, an experiment is carried on a 32 W, 20 V input, 160 V output prototype in the laboratory for experimental validation of the proposed converter. Important future directions have also been given for future research on the proposed topology

A New Semantic-Based Tool Detection Method for Robots

Home helper robots have become more acceptable due to their excellent image recognition ability. However, some common household tools remain challenging to recognize, classify, and use by robots. We designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (Mask-R-CNN). This method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. It provides accurate recognition of the functional components of tools. The method is compared with existing algorithms on the dataset UMD Part Affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. The proposed method helps the robot understand and use household tools better than traditional object detection algorithms

Bank Loan Covenants and Accrual Quality

We examine whether financial covenants in loan contracts motivate banks to monitor borrowers’ financial reporting practices and result in a higher quality of reported accruals. We document that, relative to loans without financial covenants, loans with financial covenants lead to a significant improvement in accrual quality measured by the extent to which accruals can be mapped into cash flows. The effect of loan covenants on accrual quality is stronger when external monitoring by non-bank stakeholders (i.e., institutional investors and financial analysts) is weaker. Furthermore, initiations of bank loans with financial covenants are related to subsequent improvements in analysts’ information environment. The evidence supports the view that bank monitoring improves accounting quality