Purpose -This paper investigates the determinants of capital structure using a cross-section sample of 1481 non-financial firms listed on the Chinese stock exchanges in 2011. Design/methodology/approach -Employing four leverage measures (total leverage and long-term leverage in terms of both book value and market value, respectively), this study examines the effects of factors with proven influences on capital structure in literature, along with industry effect and ownership effect. Findings -We find that large firms favour debt financing while profitable firms rely more on internal capital accumulation. Intangibility and business risk increase the level of debt financing but tax has little impact on capital structure. We also observe strong industrial effect and ownership effect. Real estate firms borrow considerably more and firms from utility and manufacturing industries use more long-term debt despite compared with commercial firms. On the other hand, firms with state ownership tend to borrow more, while firms with foreign ownership choose more equity financing. Research limitations -The study uses cross-section data to avoid any potential time effects, which allows us to focus on our main research question -to identify the determinants of capital structure for Chinese firms. Future research may gain more insights using panel data and considering other factors such as crisis and financial reforms. Practical implications -These results may provide important implications to investors in making investment decision and to firms in making financing decisions. Originality/value -this paper uses by far the largest and latest cross-section sample from the Chinese stock markets, offering a more complete picture of the financing behaviours in the Chinese firms, with known characters and the impact of ownerships.

We report a nonenzymatic wearable sensor for electrochemical analysis of perspiration glucose. Multipotential steps are applied on a Au electrode, including a high negative pretreatment potential step for proton reduction which produces a localized alkaline condition, a moderate potential step for electrocatalytic oxidation of glucose under the alkaline condition, and a positive potential step to clean and reactivate the electrode surface for the next detection. Fluorocarbon-based materials were coated on the Au electrode for improving the selectivity and robustness of the sensor. A fully integrated wristband is developed for continuous real-time monitoring of perspiration glucose during physical activities, and uploading the test result to a smartphone app via Bluetooth.

Minimal path techniques can efficiently extract geometrically curve-like structures by finding the path with minimal accumulated cost between two given endpoints. Though having found wide practical applications (e.g., line identification, crack detection, and vascular centerline extraction), minimal path techniques suffer from some notable problems. The first one is that they require setting two endpoints for each line to be extracted (endpoint problem). The second one is that the connection might fail when the geodesic distance between the two points is much shorter than the desirable minimal path (shortcut problem). In addition, when connecting two distant points, the minimal path connection might become inefficient as the accumulated cost increases over the propagation and results in leakage into some non-feature regions near the starting point (accumulation problem). To address these problems, this paper proposes an approach termed minimal path propagation with backtracking. We found that the information in the process of backtracking from reached points can be well utilized to overcome the above problems and improve the extraction performance. The whole algorithm is robust to parameter setting and allows a coarse setting of the starting point. Extensive experiments with both simulated and realistic data are performed to validate the performance of the proposed method.

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We investigate the impact of China's economic policy uncertainty (EPU) on the time series variation of Chinese stock market expected returns. Using the news‐based measure of EPU, we find that EPU predicts negatively future stock market return at various horizons. This negative relation between economic policy uncertainty and expected future return remains significant as we control for a number of economic and market uncertainty variables or conduct out‐of‐sample tests. Our findings are consistent with behavioural asset pricing models, in which high uncertainty amplifies behavioural biases and generates speculative mis‐pricing under short‐sales constraint.

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The Construction and demolition (C and D) waste generation is a critical issue for the construction industry, which negatively affects the economy, environment, and society. This study estimates the penalty-cost based on the produced C&D wastes in steel and concrete skeleton projects. Field survey and the BOQ data were collected from five concrete and four steel skeleton projects. The difference of materials used and wastes generated between concrete and steel skeleton projects were evaluated statistically (ANOVA and Welch and Brown-Forsythe). A financial analysis was implemented for estimating the penalty cost. The study outcomes demonstrate that the amount of waste that construction managers estimated is significantly lower than the actual amount generated. Furthermore, 0.055% of the total project cost of a penalty was estimated based on the waste produced at construction sites. In the end, the estimated penalty was validated by comparing it with the six recent completed projects. The penalty calculated in this study could save the project cost and reduce the C&D waste. As a result, imposing the estimated cost as a penalty would force construction managers to think thoroughly about the generated C&D waste problems. This study also has a novelty and will add to the body of knowledge by using penalty-cost quantification model to save project-cost of construction material-based-waste, and it can be further explored by adopting more quality data and engaging different construction materials.