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The purpose of this paper is to present a novel image sensor technology for raised characters based on line structured‐light. It can convert raised character's three‐dimensional (3D) features into image's grayscale levels.

The measurement principle and mathematical model are described. An experimental device is established and system parameters are calibrated. A grayscale conversion algorithm is proposed to convert the distortion of laser stripe to the grayscale intensity of image. The article also introduces a four‐factor method to assess the image quality of characters.

Experimental results show that the method can get high‐contrast images of raised characters that are conventionally low‐contrast with the background. Besides, the method does not need complicated calibration and mass computation, which makes the system structure simple and increases the speed of image acquisition.

The paper presents a novel image acquisition method for raised characters.

The purpose of this paper is to examine the effect of application of a heat pipe in an aspect of hydrostatic thrust bearings on thermal balance and deformation and the role of this application in increasing the rotating speed of a workbench.

Numerical simulations of oil film temperature field, the temperature field and thermal deformation of the bearing’s workbench and base were performed by finite element analysis (FEA) software for both the traditional hydrostatic thrust bearings and the heat pipe ones.

Oil pad and workbench of the hydrostatic thrust bearings are fabricated with a heat pipe cooling structure, which can take away most of the heat generated by shearing of the oil film, control the temperature rise and thermal deformation of the hydrostatic thrust bearing effectively, avoid the dry friction phenomenon and finally improve the processing quality of equipment.

The heat pipe hydrostatic thrust bearings could control the temperature rise and thermal deformation of the hydrostatic thrust bearing effectively, avoid the dry friction phenomenon and improve the processing quality of equipment.

Online portfolio selection sequentially allocates wealth among a set of assets and aims to maximize the investor’s cumulative return in the long run. Various existing algorithms in the finance and accounting area adopt an indirect approach to exploit one asset characteristic through the channel of assets’ expected return and thus cannot fully leverage the power of various asset characteristics found in the literature. This study aims to propose new algorithms to overcome this issue to enhance investment performance.

We propose a parameterized portfolio selection (PPS) framework, which directly incorporates multiple asset characteristics into portfolio weights. This framework can update parameters timely based on final performance without intermediate steps and produce efficient portfolios. We further append L1 regularization to constrain the number of active asset characteristics. Solving the PPS formulation numerically, we design two online portfolio selection (OLPS) algorithms via gradient descent and alternating direction method of multipliers.

Empirical results on five real market datasets show that the proposed algorithms outperform the state of the arts in cumulative returns, Sharpe ratios, winning ratios, etc. Besides, short-term characteristics are more important than long-term characteristics, and the highest return category is the most important characteristic to improve portfolio performance.

The proposed PPS algorithms are new end-to-end online learning approaches, which directly optimize portfolios by asset characteristics. Such approaches thus differ from existing studies, which first predict returns and then optimize portfolios. This paper provides a new algorithmic framework for investors’ OLPS.

Construction enterprises increase their competitive advantage by joining the project ecosystem, but the dual nature of the enterprise’s niche has attracted attention, and existing research has gaps in understanding niche and ecosystem governance issues. This study aims to promote ecosystem compliance governance by exploring the impact of the niche of the key role of construction enterprises on compliance behavior.

First, the study proposes a model on the impact of the enterprise's niche on its compliance behavior and the mediating role of the enterprise's perception of sanctions and the moderating role of a shared vision for compliance of ecological partners within these relationships. Second, we used 205 samples, who were Chinese contractors with international construction project experience through a questionnaire survey. Third, the study conducted a hierarchical regression to test the hypothesis.

The results show that construction enterprises with a wider niche or with a higher degree of niche overlap are more inclined to performance compliance. Enterprises' perception of sanctions plays a part in mediating the enterprise's niche and compliance behavior. The relationship between enterprises' perception of sanctions and compliance behavior can be moderated by the shared vision for compliance of ecological partners. Given a low compliance shared vision of ecological partners, the impact of enterprise perception of sanctions on compliance behavior is positively stronger.

The findings provide valuable evidence upon which ecosystem governance needs to focus and leverage the role of key members, using the advantageous resources of key members as a fulcrum to leverage a larger governance scope. Construction enterprises should keep improving their niche and the shared vision for partners' compliance to promote the evolution and upgrading of cooperation to an ecosystem model that creates greater value.

This study provides new insights for future compliance governance in the project ecosystem by introducing the concept of niche and answering whether construction enterprises with a higher niche in the project ecosystem are more willing to implement compliance behavior.