Capitalisation of research and development investment and enterprise value: a study on the threshold effect based on level of financialisation

This study uses a mathematical model to explore how enterprises’ financialisation levels affect the role of research and development (R&D) investment capitalisation in enterprise value. We construct a mathematical model involving the financialisation level, capitalised R&D investment, and enterprise value. The sample comprises A-share listed companies that disclosed the capitalisation of R&D investment in the Shanghai and Shenzhen stock markets from 2014 to 2020. The results suggest that R&D investment capitalisation positively impacts enterprise value, especially in the current phase. With financialisation level as the threshold variable, R&D investment capitalisation has a double threshold effect on enterprise value in the current and next phases. Additionally, corporate financial investment behaviour has a timely impact on capitalised R&D investment but does not significantly impact enterprise value in a future phase. Enterprises evidently choose financial investment to enhance enterprise value by increasing capitalised R&D investment. These results can help enterprises formulate financial asset investment strategies and promote their development from virtual to real. The government should standardise enterprises’ financial investment behaviour, prevent excessive financialisation, and promote high-quality development of the real economy

Quantification and scenario analysis of CO2 emissions from the central heating supply system in China from 2006 to 2025

Policies associated with the central heating supply system affect the livelihoods of people in China. With the extensive consumption of energy for central heating, large quantities of CO2 emissions are produced each year. Coal-fired heating boiler plants are the primary source of emissions; however, thermal power plants are becoming much more prevalent, and gas-fired heating boiler plants remain uncommon. This study quantified the amount of CO2 emitted from the central heating supply system in China using a mass balance method with updated emission factors from the IPCC. Emissions increased from 189.04 Tg to 319.39 Tg between 2006 and 2015. From a spatial perspective, regions with larger central heating areas, durations and coverages produced more CO2 emissions. The central heating method depends on the level of electric power consumption, policies and regulations, and resource reserves at the local scale. Compared with the use of only coal-fired heating boiler plants to provide central heating, using thermal power plants and gas-fired heating boiler plants reduced CO2 emissions by 98.19 Tg in 2015 in China. A comparison of the CO2 emissions under various central heating scenarios showed that emissions will be 520.97 Tg, 308.79 Tg and 191.86 Tg for business as usual, positive and optimal scenarios through 2025, respectively. China has acknowledged the considerable potential for reducing central heating and will make efforts to pursue improved heating strategies in the future

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Scene text detection, an important step of scene text reading systems, has witnessed rapid development with convolutional neural networks. Nonetheless, two main challenges still exist and hamper its deployment to real-world applications. The first problem is the trade-off between speed and accuracy. The second one is to model the arbitrary-shaped text instance. Recently, some methods have been proposed to tackle arbitrary-shaped text detection, but they rarely take the speed of the entire pipeline into consideration, which may fall short in practical applications.In this paper, we propose an efficient and accurate arbitrary-shaped text detector, termed Pixel Aggregation Network (PAN), which is equipped with a low computational-cost segmentation head and a learnable post-processing. More specifically, the segmentation head is made up of Feature Pyramid Enhancement Module (FPEM) and Feature Fusion Module (FFM). FPEM is a cascadable U-shaped module, which can introduce multi-level information to guide the better segmentation. FFM can gather the features given by the FPEMs of different depths into a final feature for segmentation. The learnable post-processing is implemented by Pixel Aggregation (PA), which can precisely aggregate text pixels by predicted similarity vectors. Experiments on several standard benchmarks validate the superiority of the proposed PAN. It is worth noting that our method can achieve a competitive F-measure of 79.9% at 84.2 FPS on CTW1500.Comment: Accept by ICCV 201

A Bio-Inspired Method for the Constrained Shortest Path Problem

The constrained shortest path (CSP) problem has been widely used in transportation optimization, crew scheduling, network routing and so on. It is an open issue since it is a NP-hard problem. In this paper, we propose an innovative method which is based on the internal mechanism of the adaptive amoeba algorithm. The proposed method is divided into two parts. In the first part, we employ the original amoeba algorithm to solve the shortest path problem in directed networks. In the second part, we combine the Physarum algorithm with a bio-inspired rule to deal with the CSP. Finally, by comparing the results with other method using an examples in DCLC problem, we demonstrate the accuracy of the proposed method

Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial

On top of machine learning models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Toward this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. Two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage and the remaining useful life prediction of turbofan engines

Conclusions and Future Research Directions

In this book, we have discussed channel estimation for various situations in PLNC, including frequency flat fading environment, frequency selective fading environment, and time-selective fading environment. In each environment, we demonstrated how the channel estimation is different from the conventional point-to-point transmission as well as from the uni-directional relay network. The key idea is that the individual channel knowledge should be obtained at three nodes, i.e., the terminals and the relay, within one round of the data exchange. One may, of course, apply more complicated training process, say, training each channel link separately and share the information through feedback channels but such processing is not compatible with the overall structure of the data frame. Moreover, we developed channel estimation algorithms that fit the speciality of different environments, for example in frequency selective fading environment it is possible to remove the redundant estimates so that the overall training length could be reduced, while in time selective fading environment the individual BEM coefficient is estimated instead of the channel parameters

The Effect of Tong-Xie-Yao-Fang on Intestinal Mucosal Mast Cells in Postinfectious Irritable Bowel Syndrome Rats

Objective. To investigate the effects of Tong-Xie-Yao-Fang (TXYF) on intestinal mucosal mast cells in rats with postinfectious irritable bowel syndrome (PI-IBS). Design. PI-IBS rat models were established using a multistimulation paradigm. Then, rats were treated with TXYF intragastrically at doses of 2.5, 5.0, and 10.0 g·kg−1·d−1 for 14 days, respectively. Intestinal sensitivity was assessed based on abdominal withdrawal reflex (AWR) scores and fecal water content (FWC). Mast cell counts and the immunofluorescence of tryptase and c-Fos in intestinal mucosa were measured; and serum IL-1β, TNF-α, and histamine levels were determined. Results. AWR reactivity and FWC which were significantly increased could be observed in PI-IBS rats. Remarkably increased mast cell activation ratio in intestinal mucosa, together with increased serum TNF-α and histamine levels, could also be seen in PI-IBS rats; furthermore, PI-IBS-induced changes in mast cell activation and level of serum TNF-α and histamine could be reversed by TXYF treatment. Meanwhile, tryptase and c-Fos expression were also downregulated. Conclusion. TXYF improves PI-IBS symptoms by alleviating behavioral hyperalgesia and antidiarrhea, the underlying mechanism of which involves the inhibitory effects of TXYF on activating mucosal mast cells, downregulating tryptase and c-Fos expression, and reducing serum TNF-α and histamine levels

Analysis of Greenhouse Gases Emissions and Feasibility of Mitigations Options for Dairy Farms in China

Climate change has gained increased attention all over the world. Dairy farming is a significant contributor to the emission of greenhouse gas emissions that cause climate change. This has prompted international dairy sectors to develop plans to reduce the greenhouse gas emission from dairy farming. The Chinese dairy sector is also exploring how to contribute to that reduction, while responding to the growing domestic demand for dairy products. In this study, we collected and analyzed technical data including greenhouse gas emissions of a pilot group of 15 farms where a greenhouse gas reduction method will be tested in the future. Next to that, we held a survey among farmers and stakeholders about the feasibility of mitigation options to reduce greenhouse gases. This was to explore how farmers will respond to recommended mitigation options. The results show that farmers with different herd sizes and different types of stakeholders have different opinions about the feasibility and attractiveness of mitigation options. The results are useful insights to further prepare the application of the tailored made approach to come to dairy farms that combine efficiency with low green house gas emisions