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The aim of this paper is to examine the effect of information disclosure by unlisted bond issuers on the stock price informativeness of listed firms in the same industry.

This paper takes advantage of information disclosure during the bond issuance and examines the spillover effect of unlisted bond issuers' information disclosure on listed firms in the stock market. The sample is composed of A-share firms listed on the Shanghai and Shenzhen stock exchanges from 2007 to 2018. All the data are obtained from the China Stock Market and Accounting Research and WIND databases. The impact of bond market information disclosure on price informativeness of listed firms in the same industry is identified through multivariate regression analyses.

Empirical results show that price informativeness of listed firms has a significantly positive association with the information disclosure of same-industry unlisted bond issuers. Further analyses show that the above finding is more significant when information disclosure of bond issuers is a more important channel for acquiring industry information (i.e. when industry is more concentrated, when economic uncertainty is high, and when industry information is less transparent) and understanding the industry competitive landscape (i.e. when bond issuers are relatively large, when bond issuers and listed firms have more direct product competition, when bond issuance firms are large-scale state-owned business groups), and when there are more cross-market information intermediaries (i.e. more cross-market institutional investors and more sell-side analysts). This paper indicates that information disclosure of bond issuers has a positive spillover effect on the stock market.

The novelty of the research is that the authors examine industry information spillover from unlisted firms to listed firms leveraging on unlisted firms' information disclosure in bond markets.

Fierce competition in the crowdfunding market has resulted in high failure rates. Owing to their dedication and efforts, many founders have relaunched failed campaigns as a second attempt. Despite the need for a better understanding, the success of campaign relaunches has not been well-researched. To fill this research gap, this study first theorizes how founders’ learning may enhance their competencies and influence investors’ attribution of entrepreneurial failure. The study then empirically documents the extent and conditions under which such learning efforts impact campaign relaunch performance.

This study examines 5,798 Kickstarter-relaunched campaigns. The founders’ learning efforts are empirically captured by key changes in campaign design that deviate from past business practices. Word movers’ distances and perceptual hashing algorithms (pHash) are used separately to measure differences in campaign textual descriptions and pictorial designs.

Differences in textual descriptions and pictorial designs during campaign failure–relaunch are positively associated with campaign relaunch success. The impacts are further amplified when the previous failures are more severe.

This study is one of the first to examine the success of a campaign relaunch after an initial failure. This study contributes to a better understanding of founders’ learning in crowdfunding contexts and provides insights into the strategies founders can adopt to reap performance benefits.

The purpose of this paper is to reveal the mechanism of nitrite (NO_2⁻) for the surface passivation of carbon steels in acidic environments through investigating the influences of 0.01 mol/L NaNO₂ addition on the corrosion and passivation behaviors of Q235 carbon steel in acidic phosphate buffer (APB) solutions (pH 2 to 6).

The electrochemical techniques including open circle potential evolution, potentiodynamic polarization, electrochemical impedance spectroscopy and cyclic voltammetry were applied.

In APB solutions without NO_2⁻, the Q235 steel presented the electrochemical behaviors of activation (A), activation-passivation-transpassivation and self-passivation-transpassivation at pH 2 to 4, pH 5 and pH 6, respectively; the corrosion rate decreased with the up of pH value, and the surface passivation occurred in the pH 5 and pH 6 solutions only: the anodic passivation at pH 5 and the spontaneous passivation at pH 6.

In APB solutions without NO_2⁻, the corrosion rate decreased with the up of pH value, and the surface passivation occurred in the pH 5 and pH 6 solutions only: the anodic passivation at pH 5 and the spontaneous passivation at pH 6. With the addition of 0.01 mol/L NaNO₂, into APB solutions, the variation of corrosion rate showed the same rule, but the surface passivation occurred over the whole acidic pH range, including the anodic passivation at pH 2 to 4 and the spontaneous passivation at pH 5 to 6.

The purpose of this paper is to understand whether firms are driven by external pressure or intrinsic value to conduct green management; this study examines the effects of coercive pressure and ethical responsibility on cross-functional green strategy alignment (GSA) and green process coordination (GPC), and in turn, market and environmental performance.

Based on data from 206 Chinese manufacturers, this study empirically tests the proposed relationships using structural equation modeling.

The results highlight the role of coercive pressure in promoting both GSA and GPC that represent functional green efforts at both strategic and operational levels, indicating firms’ critical concern of obtaining external legitimacy from stakeholders. Ethical responsibility as an intrinsic value promotes GPC that demands joint working from different functions at the operational level. Besides, the authors find that GSA improves market and environmental performance, whereas GPC only enhances environmental performance.

This study adds to the knowledge of the drivers of cross-functional green management from external pressure and intrinsic value perspectives. The findings are also fruitful for practitioners and policymakers.

The purpose of this paper is to establish a roughness prediction model of micro-milling Inconel718 with high precision.

A prediction model of micro-milling surface roughness of Inconel718 is established by SVM (support vector machine) in this paper. Three cutting parameters are involved in the model (spindle speed, cutting depth and feed speed). Experiments are carried out to verify the accuracy of the model.

The results show that the built SVM prediction model has high prediction accuracy and can predict the surface roughness value and variation law of micro-milling Inconel718.

Inconel718 with high strength and high hardness under high temperature is the suitable material for manufacturing micro parts which need a high strength at high temperature. Surface roughness is an important performance indication for micro-milling processing. Establishing a roughness prediction model with high precision is helpful to select the cutting parameters for micro-milling Inconel718.

The built SVM prediction model of micro-milling surface roughness of Inconel718 is verified by experiment for the first time. The test results show that the surface roughness prediction model can be used to predict the surface roughness during micro-milling Inconel718, and to provide a reference for selection of cutting parameters of micro-milling Inconel718.

This study aims to explore the infrared imaging effect of fabrics coated with phase change material microcapsules (PCM-MCs), which are prepared by the initiation of ultraviolet (UV) light.

PCM-MCs were prepared by UV polymerization using paraffin (PA) as core material, polymethyl methacrylate as wall material and ferric chloride as photoinitiator. The effects of emulsifier dosage and emulsification temperature on the properties of PA emulsion were investigated. Scanning electron microscopy, particle size analysis, infrared spectroscopy, differential scanning calorimetry and infrared imaging test were used to characterize the properties of microcapsules.

The PCM-MCs with good morphology and particle size were prepared with 25 cm of the distance between light source and the liquid. The average particle size was 1.066 µm and the latent heat of phase transition was 19.96 J/g. After 100 accelerated thermal cycles, the latent heat only decreased by 1.8%. It had good heat storage stability and thermal stability. The fabric coated by the microcapsules exhibited a variable temperature hysteresis effect when placed in the sun, and presented a color close to the infrared images of the human palm under the external environment temperature close to the human body temperature.

The PCM-MCs prepared based on UV light initiation showed good thermal properties and its coated fabrics had an infrared decoy effect below the temperature of the human body.

This study explored the application of microcapsules in textiles.

The microcapsules had a certain application potential in infrared decoy effect.

Laos joined ASEAN in 1997 and has practiced the Common Effective Preferential Tariff (CEPT) scheme under ASEAN Free Trade Area (AFTA) since 1998. To estimate the effects of AFTA on international trade in Laos, this paper has built a gravity model to analyze the specific effects of AFTA on aggregate international trade volume, import and export in Laos by using bilateral trade data between Laos and 29 partner countries during 2000 and 2012. Binary variables and the size of economy of AFTA are chosen to be the two variables reflecting the effect of AFTA on international trade of Laos. Results indicate that AFTA has positive effects on aggregate international trade volume and import while negative effects on export in Laos.

Background subtraction is a particularly popular foreground detection method, whose background model can be updated by using input images. However, foreground object cannot be detected accurately if the background model is broken. In order to improve the performance of foreground detection in human-robot interaction (HRI), the purpose of this paper is to propose a new background subtraction method based on image parameters, which helps to improve the robustness of the existing background subtraction method.

The proposed method evaluates the image and foreground results according to the image parameters representing the change features of the image. It ignores the image that is similar to the first image and the previous image in image sequence, filters the image that may break the background model and detects the abnormal background model. The method also helps to rebuild the background model when the model is broken.

Experimental results of typical interaction scenes validate that the proposed method helps to reduce the broken probability of background model and improve the robustness of background subtraction.

Different threshold values of image parameters may affect the results in different environments. Future researches should focus on the automatic selection of parameters’ threshold values according to the interaction scene.

A useful method for foreground detection in HRI.

This paper proposes a method which employs image parameters to improve the robustness of the background subtraction for foreground detection in HRI.

An optimal solution method based on 2-norm is proposed in this study to solve the inverse kinematics multiple-solution problem caused by a high redundancy. The current research also presents a motion optimization based on the 2-Norm of high-redundant mobile humanoid robots, in which a kinematic model is designed through the entire modeling.

The current study designs a highly redundant humanoid mobile robot with a differential mobile platform. The high-redundancy mobile humanoid robot consists of three modular parts (differential driving platform with two degrees of freedom (DOF), namely, left and right arms with seven DOF, respectively) and has total of 14 DOFs. Given the high redundancy of humanoid mobile robot, a kinematic model is designed through the entire modeling and an optimal solution extraction method based on 2-norm is proposed to solve the inverse kinematics multiple solutions problem. That is, the 2-norm of the angle difference before and after rotation is used as the shortest stroke index to select the optimal solution. The optimal solution of the inverse kinematics equation in the step is obtained by solving the minimum value of the objective function of a step. Through the step-by-step cycle in the entire tracking process, the kinematic optimization of the highly redundant humanoid robot in the entire tracking process is realized.

Compared with the before and after motion optimizations based on the 2-norm algorithm of the robot, its motion after optimization shows minimal fluctuation, improved smoothness, limited energy consumption and short path during the entire mobile tracking and operating process.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

Motion optimization based on the 2-norm of a highly redundant humanoid mobile robot with the entire modeling is performed on the basis of the entire modeling. This motion optimization can make the highly redundant humanoid mobile robot’s motion path considerably short, minimize energy loss and shorten time. These researches provide a theoretical basis for the follow-up research of the service robot, including tracking and operating target, etc. Finally, the motion optimization algorithm is verified by the tracking and operating behaviors of the robot and an example.