Debt Financing and Corporate Performance——Taking the case of China’s Listed Companies on Growth Enterprise Market

This article samples 293 listed companies on GEM (Growth Enterprise Market) of Shenzhen Stock Exchange, uses SPSS 17.0 to analyze their annual report data from 2009 to 2011. The results show that corporate performance will increase and then decrease along with the improvement of debt asset ratio, and achieve optimized performance at about 50%-60% of DAR (debt asset ratio); short-term borrowings have significant negative effects on corporate performance, bank debt, commercial credit and long-term borrowings have indistinctive positive effects on corporate performance, and other debt have marked positive effects on corporate performance. This research is helpful for governmental macro-control and SMEs themselves to build reasonable capital structure to improve their corporate performance

Simulation study on dust concentration distribution of single channel cavities construction

With the rapid development of underground space technology in China, the construction of tunnel caverns is increasing. Good air quality in the tunnel is not only necessary for labor safety, but also to create a good construction environment conditions, improve work efficiency, speed up the construction of an important guarantee. The complex construction in the caverns, resulting in a large number of dust will flow to other caves, not only affect the cavern, but also on the other cave caused secondary pollution, affecting its normal construction operations. However, the cavern group construction work, just rely on the traditional single tunnel construction ventilation technology is obviously unable to meet the dust group requirements. Therefore, the study on the distribution of dust in the construction of the caverns is of great significance to improve the dust control work

Two-axis-twisting spin squeezing by multi-pass quantum erasure

Many-body entangled states are key elements in quantum information science and quantum metrology. One important problem in establishing a high degree of many-body entanglement using optical techniques is the leakage of the system information via the light that creates such entanglement. We propose an all-optical interference-based approach to erase this information. Unwanted atom-light entanglement can be removed by destructive interference of three or more successive atom-light interactions, with only the desired effective atom-atom interaction left. This quantum erasure protocol allows implementation of Heisenberg-limited spin squeezing using coherent light and a cold or warm atomic ensemble. Calculations show that significant improvement in the squeezing exceeding 10 dB is obtained compared to previous methods, and substantial spin squeezing is attainable even under moderate experimental conditions. Our method enables the efficient creation of many-body entangled states with simple setups, and thus is promising for advancing technologies in quantum metrology and quantum information processing.Comment: 10 pages, 4 figures. We have improved the presentation and added a new section, in which we have generalized the scheme from a three-pass scheme to multi-pass schem

Detecting stealthy cyberattacks on adaptive cruise control vehicles: A machine learning approach

With the advent of vehicles equipped with advanced driver-assistance systems, such as adaptive cruise control (ACC) and other automated driving features, the potential for cyberattacks on these automated vehicles (AVs) has emerged. While overt attacks that force vehicles to collide may be easily identified, more insidious attacks, which only slightly alter driving behavior, can result in network-wide increases in congestion, fuel consumption, and even crash risk without being easily detected. To address the detection of such attacks, we first present a traffic model framework for three types of potential cyberattacks: malicious manipulation of vehicle control commands, false data injection attacks on sensor measurements, and denial-of-service (DoS) attacks. We then investigate the impacts of these attacks at both the individual vehicle (micro) and traffic flow (macro) levels. A novel generative adversarial network (GAN)-based anomaly detection model is proposed for real-time identification of such attacks using vehicle trajectory data. We provide numerical evidence {to demonstrate} the efficacy of our machine learning approach in detecting cyberattacks on ACC-equipped vehicles. The proposed method is compared against some recently proposed neural network models and observed to have higher accuracy in identifying anomalous driving behaviors of ACC vehicles

Updates of precision medicine in type 2 diabetes

Diabetes mellitus is prevalent worldwide and affects 1 in 10 adults. Despite the successful development of glucose-lowering drugs, such as glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 inhibitors recently, the proportion of patients achieving satisfactory glucose control has not risen as expected. The heterogeneity of diabetes determines that a one-size-fits-all strategy is not suitable for people with diabetes. Diabetes is undoubtedly more heterogeneous than the conventional subclassification, such as type 1, type 2, monogenic and gestational diabetes. The recent progress in genetics and epigenetics of diabetes has gradually unveiled the mechanisms underlying the heterogeneity of diabetes, and cluster analysis has shown promising results in the substratification of type 2 diabetes, which accounts for 95% of diabetic patients. More recently, the rapid development of sophisticated glucose monitoring and artificial intelligence technologies further enabled comprehensive consideration of the complex individual genetic and clinical information and might ultimately realize a precision diagnosis and treatment in diabetics