AN EMPIRICAL ANALYSIS OF CORPORATE DEFAULT RISK DURING THE FINANCIAL CRISIS OF 2007-2009

This paper investigates the sharp increase in corporate bankruptcies during the financial crisis of 2007-2009. The primary question explored is whether firms faced industry-level shocks to corporate bankruptcy risk in a way that can be understood through proximity to the housing and credit markets. Much of the research on bankruptcy risk to date has focused exclusively on idiosyncratic firm characteristics such as liquidity, leverage, and profitability. This paper uses a multi-period logit model based on panel data and introduces a crisis dummy variable in interaction terms to distinguish between the crisis period (3Q 2007 to 2Q 2009) and a pre-crisis period (3Q 2005 to 2Q 2007). The logit evidence suggests that the increase in corporate bankruptcy risk was driven largely by industry-level shocks in a way that was not observed prior to the crisis. Industries with greater proximity to the credit and housing markets (e.g., Finance, Insurance, and Real Estate and Construction) appeared to be disproportionately affected. In addition, I find that firms in those industries became more sensitive to changes in liquidity, leverage, and profitability

Visible-Light-Induced Catalytic Selective Halogenation with Photocatalyst

Halide moieties are essential structures of compounds in organic chemistry due to their popularity and wide applications in many fields such as natural compounds, agrochemicals, and pharmaceuticals. Thus, many methods have been developed to introduce halides into various organic molecules. Recently, visible-light-driven reactions have emerged as useful methods of organic synthesis. Particularly, halogenation strategies using visible light have significantly improved the reaction efficiency and reduced toxicity, as well as promoted reactions under mild conditions. In this review, we have summarized recent studies in visible-light-mediated halogenation (chlorination, bromination, and iodination) with photocatalysts

Synthesis of Tin Oxide Nanoparticle Film by Cathodic Electrodeposition

Three-dimensional SnO 2 nanoparticle films were deposited onto a copper substrate by cathodic electrodeposition in a nitric acid solution. A new formation mechanism for SnO 2 films is proposed based on the oxidation of Sn2+ ion to Sn4+ ion by NO+ ion and the hydrolysis of Sn4+. The particle size of SnO 2 was controlled by deposition potential. The SnO 2 showed excellent charge capacity (729 mAh/g) at a 0.2 C rate and high rate capability (460 mAh/g) at a 5 C rate. Copyright © 2012 American Scientific Publishers.1

Deep Learning-Based Real-Time Traffic Sign Recognition System for Urban Environments

A traffic sign recognition system is crucial for safely operating an autonomous driving car and efficiently managing road facilities. Recent studies on traffic sign recognition tasks show significant advances in terms of accuracy on several benchmarks. However, they lack performance evaluation in driving cars in diverse road environments. In this study, we develop a traffic sign recognition framework for a vehicle to evaluate and compare deep learning-based object detection and tracking models for practical validation. We collect a large-scale highway image set using a camera-installed vehicle for training models, and evaluate the model inference during a test drive in terms of accuracy and processing time. In addition, we propose a novel categorization method for urban road scenes with possible scenarios. The experimental results show that the YOLOv5 detector and strongSORT tracking model result in better performance than other models in terms of accuracy and processing time. Furthermore, we provide an extensive discussion on possible obstacles in traffic sign recognition tasks to facilitate future research through numerous experiments for each road condition

Deep Learning-Based Real-Time Traffic Sign Recognition System for Urban Environments

A traffic sign recognition system is crucial for safely operating an autonomous driving car and efficiently managing road facilities. Recent studies on traffic sign recognition tasks show significant advances in terms of accuracy on several benchmarks. However, they lack performance evaluation in driving cars in diverse road environments. In this study, we develop a traffic sign recognition framework for a vehicle to evaluate and compare deep learning-based object detection and tracking models for practical validation. We collect a large-scale highway image set using a camera-installed vehicle for training models, and evaluate the model inference during a test drive in terms of accuracy and processing time. In addition, we propose a novel categorization method for urban road scenes with possible scenarios. The experimental results show that the YOLOv5 detector and strongSORT tracking model result in better performance than other models in terms of accuracy and processing time. Furthermore, we provide an extensive discussion on possible obstacles in traffic sign recognition tasks to facilitate future research through numerous experiments for each road condition

Modelling one-dimensional reactive transport of toxic contaminants in natural rivers

Solute transport models are widely employed to the predict spatio-temporal fate of contaminants in rivers. However, most previous studies have assumed that contaminants spilled are unreactive; they disregard the inherent reactivity of contaminants in water. The Reactive Solute Transport Model (RSTM) (Gooseff et al., 2005) includes lumped decay terms to reflect the decayability of the chemicals, but its applicability was still poor due to dependence on an optimization method. The purpose of this study is to perform reactive transport modelling considering chemicals' reactivities for sorption, volatilization, and biodegradation. To this end, we manipulated the governing equations of the RSTM and suggested theoretical and empirical methods of estimating the key parameters of the reaction terms. The results showed, for example, that benzene lost 57.7% of its primary mass after being transported 4.54 km downstream due to its high volatility. Also, the arrival time of toluene was delayed by 10.4% due to adsorption.N