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The purpose of this paper is to investigate whether the Boardroom heterogeneity affects IPO underpricing for entrepreneurial firms, where Boardroom heterogeneity was classified in terms of functional background, educational background, age and length of tenure.

A national research design was conducted using data collected from 355 firms listed on China’s Growth Enterprise Market from its start in 2009 to 2012.

The author found that IPO underpricing has a significant negative correlation with functional heterogeneity, a positive correlation with educational heterogeneity, a significant negative correlation with age heterogeneity, but it does not show significant correlation with heterogeneity in tenure. Board heterogeneity affects IPO underpricing of entrepreneurial firms partially, which means functional, educational and age heterogeneity conveys signals to potential investors regarding a firm’s quality.

More entrepreneurial firms in more years for data and long-term performance research design in future research would be required for further understanding of the relationships among the variables in this study.

This paper suggests that IPO firms may make use of such an influencing mechanism to determine the issue price or to control the IPO underpricing by showing the Boardroom heterogeneity.

This paper revealed the influence of the characteristics of board members of such firms on IPO underpricing, which is rare in recent studies comparing to the study for the top management team; also this study provides empirical support for such effect.

The purpose of this paper is to present the Switched Inductor Z-Source Inverter (SLZSI) topology for three-phase on-line uninterruptible power supply (UPS) by employing third harmonic injected maximum constant boost pulse width modulation (PWM) control. Conventional UPS consists of step-up transformer or boost chopper along with voltage source inverter (VSI) which reduces the efficiency and increases energy conversion cost. The proposed three-phase UPS by using SLZSI has the voltage boost capability through shoot through zero state which is not available in traditional VSI and current source inverter.

Performance of three-phase on-line UPS based on ZLZSI by using third harmonic injected maximum constant boost PWM control is analyzed and evaluated in MATLAB/Simulink software and the results are compared with Z-source inverter (ZSI) fed UPS. Experimental results are presented for the validation of the simulation and theoretical analysis.

The output voltages, currents, THD values, voltage stress and efficiencies for different loading condition are determined and compared with the theoretical values and UPS with ZSI. The experimental results validate the theoretical and simulation results.

Compared with the traditional ZSI, the SLZSI provides high-voltage boost inversion ability with a very short shoot through zero state. This proposed UPS by using SLZSI increases the efficiency with less number of components, reduces the harmonics, increases the voltage gain and reduces the voltage stress.

Cloud manufacturing platform has a high degree of openness, with a large variety of users having different needs. Designers on such platforms exhibit great differences in their knowledge abilities and knowledge needs, necessitating the cloud platform to provide personalized knowledge recommendation. To satisfy the personalized knowledge needs of the designers in product design tasks and other manufacturing tasks on a cloud manufacturing platform and provide them with high-quality knowledge resources, a knowledge recommendation method based on designers’ knowledge ability is proposed. The proposed method, with appropriate adjustments, can also be used for personalized knowledge recommendation to other personnel or institutions in cloud manufacturing platforms.

A knowledge recommendation method model is developed. The method consists of three stages. First, a designer knowledge system is constructed based on customer reviews in historical tasks, and designer knowledge ability and knowledge demand degree are quantitatively evaluated by synthesizing customer reviews and expert evaluations. Subsequently, the design knowledge domain ontology is constructed, and knowledge resources and tasks are modeled based on the ontology. Finally, the semantic similarity between tasks and knowledge resources and the knowledge demand degree of designers are integrated to calculate the knowledge recommendation coefficient, which realizes the personalized knowledge recommendation of designers.

Two design tasks of a 3D printing cloud platform are taken as examples to verify the feasibility and effectiveness of the proposed method. Compared with other methods, it is proved that the method proposed in this paper can obtain more knowledge resources that meet the needs of designers and tasks.

The method proposed in this paper is important for the expansion of data applications of the cloud manufacturing platform and for enriching the knowledge recommendation method. The proposed method has two innovations. First, both designer needs and task needs are considered in knowledge recommendation. Compared with most of the existing methods, which only consider one factor, this method is more comprehensive. Second, the designer’s knowledge ability model is constructed by using customer reviews on the cloud manufacturing platform. This overcomes the defect of low accuracy of the interest model in existing methods and makes full use of the big data of the cloud manufacturing platform.