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Since their inception in late 2007, the Qualified Domestic Institutional Investor (QDII) funds, which help Chinese investors to invest in foreign capital markets, have experienced significant portfolio losses and persistent fund outflows. While these losses are large in absolute terms, QDII funds, on average, performed better than Chinese A-share funds, but slightly worse than a group of foreign mutual funds. Our study focuses on the QDII industry, and asks three interrelated questions: (1) why have there been large fund outflows from the industry? (2) what explains QDII funds’ poor performance? and (3) why have QDII funds been so heavily exposed to the Hong Kong market? Our empirical analysis shows that the persistent capital outflows were primarily a result of disappointing fund performance. This poor performance can, in turn, be explained by the deficiency of knowledge required of QDII fund managers to successfully invest in foreign capital markets and manage global portfolios. Finally, our study goes some way to explain the phenomenon of QDII funds’ large asset allocation in the Hong Kong market. This ‘Hong Kong bias’ is shown to be consistent with the well-documented ‘home bias’ behaviour in cross-border portfolio investment, but is greatly exacerbated by the lack of global investing experience of QDII managers.

The purpose of this study satisfied the need for rapid, sensitive and highly portable identification of an explosion gas. In our study, a battery-operated, low-cost and portable gas detection system consisting of a cataluminescence-based sensor array was developed for the detection and identification of explosion gas. This device shows how the discriminatory capacity of sensor arrays utilizing pattern recognition operate in environments.

A total of 25 sensor units, including common metal oxides and decorated materials, have been carefully selected as sensing elements of 5 × 5 sensor array. Dynamic and static analysis methods were utilized to characterize the performance of the explosion gas detection system to five kinds of explosion gases. The device collects images of chemical sensors before and after exposing to the target gas and then processes those images to extract the unique characteristic for each gas. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used to analyze the image patterns.

Our study demonstrated that the portable gas detection device shows promising perspective for the recognition and discrimination of explosion gas. It can be used for the olfactory system of robot made by integrating the electronic nose and computer together.

The device collects images of chemical sensors before and after exposing to the target gas and then processes those images to extract the unique characteristic for each gas. HCA and (PCA were used to analyze the image patterns. Our study demonstrated that the portable gas detection device shows promising perspective for the recognition and discrimination of explosion gas. It can be used for olfactory system of robot made by integrating the electronic nose and computer together.

The purpose of this paper is to develop a new recognition system for identifying advanced radar emitter signals (RES).

Initially, the framework of the new recognition system is outlined. Then, feature extraction using resemblance coefficient and wavelet packet decomposition, and feature selection based on quantum‐inspired genetic algorithm, and a classifier combining K‐means clustering, support vector machines and Mahalanobis distance are applied to actualize the recognition system. Finally, experiments are carried out on RES.

A valid recognition system with its framework and implementation is presented to solve the difficult problem of advanced RES recognition.

Initial investigation is made on modern RES recognition. Further work may be done on decreasing the error rates and enhancing recognition efficiency. Real signals instead of simulated signals can be applied.

The system developed here can be applied to electronic reconnaissance systems such as electronic support measures, electronic intelligence and radar warning receiver.

The paper presents a novel recognition system and its implementation for modern RES. Extensive experiments conducted on 155 RES with eight intra‐pulse modulations show the feasibility and validity of the introduced system.

The Chinese civilization is an important part of the history of mankind. The purpose of this paper is to show that there are project management lessons to be learned from Chinese history, including that relating to the management of the building process in ancient China.

Through a review of the literature, this paper discusses the key management and economic practices in the building process of ancient China and highlights these practices from an important document, the Yingzao Fashi or (“Treatise on Architectural Methods”), that was compared with the modern‐day project management framework.

This paper explains the official systems instituted for public projects; the management of labour, design and planning of construction works; quantity surveying practices; the use, control and recycling of building materials; and inspection of building elements in ancient China.

The study suggests that lessons in the principles of construction project management in ancient China bear many similarities with the nine areas of modern‐day project management body of knowledge relating to integration, scope, time, cost, quality, human resource, communications, risk, and procurement management. An area for future research would be to compare the Yingzao Fashi with modern‐day codes of practice for building works to determine which of its “ancient” provisions relating to quality management are still relevant today.

It was found that much emphasis was placed by the ancient Chinese on the quality aspects of prominent building projects. This is one facet from which modern‐day project managers and clients can draw lessons.

A new cutting tool is always well-defined and sharp at the onset of the metal cutting process and gradually losses these properties as the machining process advances. Similarly, at the beginning of the machining process, amplitude of tool vibrations is considerably low and it increases gradually and peaks at the end of the service period of the cutting tool while machining. It is significant to provide a corresponding real-time varying damping to control this chatter, which directly influences accuracy and quality of productivity. This paper aims to review the literature related to the application of smart fluid to control vibration in metal cutting and also focused on the challenges involved in the implementation of active control system during machining process.

Smart dampers, which are used as semi-active and active dampers in metal cutting, were reviewed and the research studies carried out in the field of the magnetorheological (MR) damper were concentrated. In smart materials, MR fluids possess some disadvantages because of their sedimentation of iron particles, leakage and slow response time. To overcome these drawbacks, new MR materials such as MR foam, MR elastomers, MR gels and MR plastomers have been recommended and suggested. This review intents to throw light into available literature which exclusively deals with controlling chatter in metal cutting with the help of MR damping methods.

Using an MR damper popularly known for its semi-active damping characteristics is very adaptable and flexible in controlling chatter by providing damping to real-time amplitudes of tool vibration. In the past, many researchers have attempted to implement MR damper in metal cutting to control vibration and were successful. Various methods with the help of MR fluid are illustrated.

A new cutting tool is always well-defined and sharp at the onset of metal cutting process and gradually losses these properties as the machining process advances. Similarly, at the beginning of the machining process, amplitude of tool vibrations is considerably low and it increases gradually and peaks at the end of service period of cutting tool while machining. Application of MR damper along with the working methodology in metal cutting is presented, challenges met are analyzed and a scope for development is reviewed.

This study provides corresponding real-time varying damping to control tool vibration which directly influences accuracy and quality of productivity. Using an MR damper popularly known for its semi-active damping characteristics is very adaptable and flexible in controlling chatter by providing damping to real-time amplitudes of tool vibration.

This study attempts to implement smart damper in metal cutting to control vibrations.

It is significant to provide corresponding real-time varying damping to control tool vibration which directly influences accuracy and quality of productivity.

Part quality inspection is playing a critical role in the metal additive manufacturing (AM) industry. It produces a part quality analysis report which can be adopted to further improve the overall part quality. However, the part quality inspection process puts heavy reliance on the engineer’s background and experience. This manual process suffers from both low efficiency and potential errors and, therefore, cannot meet the requirement of real-time detection. The purpose of this paper is to look into a deep neural network, Convolutional Neural Network (CNN), towards a robust method for online monitoring of AM parts.

The proposed online monitoring method relies on a deep CNN that takes a real metal AM part’s images as inputs and the part quality categories as network outputs. The authors validate the efficacy of the proposed methodology by recognizing the “beautiful-weld” category from material CoCrMo top surface images. The images of “beautiful-weld” parts that show even hatch lines and appropriate overlaps indicate a good quality of an AM part.

The classification accuracy of the developed method using limited information of a small local block of an image is 82 per cent. The classification accuracy using the full image and the ensemble of model outputs is 100 per cent.

A real-world data set of high resolution images of ASTM F75 I CoCrMo-based three-dimensional printed parts (Top surface images with magnification 63×) annotated with categories labels. Development of a CNN-based classification model for the supervised learning task of recognizing a “beautiful-weld” AM parts. The classification accuracy using the full image and the ensemble of model outputs is 100 per cent.

In Laser Power Bed Fusion (LPBF), the process and operating parameters influence the mechanical and geometrical characteristics of the manufactured parts. Therefore, the optimization and control of these parameters are mandatory to improve the quality of the produced parts. During manufacturing, the process parameters are usually constant whatever the part size or the built layer. With such settings, the manufacturing process may lead to an inhomogeneous thermal behavior and locally overheating areas, impacting the part quality. The aim of this study is to take advantage of an analytical thermal model to modulate the laser power upstream of manufacturing.

The approach takes place in two steps: the first step consists in calculating the preheating temperature at the considered point and the second one determines the power modulation of the laser to reach the desired temperature at this point.

Numerical investigations on several use cases show the effectiveness of the method to control the overheated areas and to homogenize the simulated temperature distribution.

The specificity of this model lies in its ability to directly calculate the amount of energy to be supplied without any iterative calculation. Furthermore, to be as close as possible to the technology used on LPBF machines, the kinematic behavior of the scanning head and the laser response time are also integrated into the calculation.

This paper aims to establish the microstructures and the process-structure relationships in duplex stainless steel powders consolidated by selective laser melting (SLM).

A priori data on energy density levels most appropriate to consolidation of duplex stainless steel powders through SLM served as the basis to converge on the laser settings. Experimental designs with varying laser power and scan speeds and test pieces generated allowed metallographic evaluations based on optical and scanning electron microscopy and electro backscatter diffraction analyses.

Duplex stainless steel powders are established for processing by SLM. However, the dynamic point heat source and associated transient thermal fields affect the microstructures to be predominantly ferritic, with grains elongated in the build direction. Austenite precipitated either at the grain boundaries or as Widmanstätten laths, whereas the crystallographic orientations and the grain growth are affected around the cavities. Considerable CrN precipitation is also evidenced.

Duplex stainless steels are relatively new candidates to be brought into the additive manufacturing realm. Considering the poor machinability and other difficulties, the overarching result indicating suitability of duplex powders by SLM is of considerable value to the industry. More significantly, the metallographic evaluation and results of the current research allowed further understanding of the material consolidation aspects and pave ways for fine tuning and establishment of the process-structure-property relationships for this important process-material combination.

WC-Co is a well-known material for conventional tooling but is not yet commercially available for additive manufacturing. Processing it by selective laser sintering (SLS) will pave the way for its commercialization and adoption.

It is intended to optimize process parameters (laser power, hatch spacing, scan speed) by fabricating a bigger part (minimum size of 10 mm diameter and 5 mm height). Microstructural analysis, EDX and hardness testing is used to study effects of process parameters. Optimized parameter is ascertained after fabricating 49 samples in preliminary experiment, 27 samples in pre-final experiment and 9 samples in final experiment.

Higher laser power gives rise to cracks and depletion of cobalt while higher scan speed increases porosity. Higher hatch spacing is responsible for delamination and displacement of parts. Optimized parameters are 270 W laser power, 500 mm/s scan speed, 0.04 mm layer thickness, 0.04 mm hatch spacing (resulting in energy density of 216 J/mm3) and 200°C powder bed temperature. A part comprising of small hole of 2 mm diameter, thin cylindrical pin of 0.5 mm diameter and thin wall of 2 mm width bent up to 30° angle to the base plate is fabricated. In order to calculate laser energy density, a new equation is introduced which takes into account both beam diameter and hatch spacing unlike old equation does. In order to calculate laser energy density, a new equation is formulated which takes into account both beam diameter and hatch spacing unlike old equation does. WC was not completely melted as intended giving rise to partial melting-type binding mechanism. This justified the name SLS for process in place of SLM (Selective Laser Melting).

Using all possible combination of parameters plus heating the part bed to maximum shows limitation of state-of-the-art commercial powder bed fusion machine for shaping hardmetal consisting of high amount of WC (83 wt. per cent).

The research shows that microfeatures could be fabricated using WC-Co which will herald renewed interest in investigating hardmetals using SLS for manufacturing complex hard tools, molds and wear-resistance parts.

This is the first time micro features are successfully fabricated using WC-Co without post-processing (infiltration, machining) and without the help of additional binding material (such as Cu, Ni, Fe).

This paper aims to present a comprehensive review of the laser engineered net shaping (LENS) process in an attempt to provide the reader with a deep understanding of the controllable and fixed build parameters of metallic parts. The authors discuss the effect and interplay between process parameters, including: laser power, scan speed and powder feed rate. Further, the authors show the interplay between process parameters is pivotal in achieving the desired microstructure, macrostructure, geometrical accuracy and mechanical properties.

In this manuscript, the authors review current research examining the process inputs and their influences on the final product when manufacturing with the LENS process. The authors also discuss how these parameters relate to important build aspects such as melt-pool dimensions, the volume of porosity and geometry accuracy.

The authors conclude that studies have greatly enriched the understanding of the LENS build process, however, much studies remains to be done. Importantly, the authors reveal that to date there are a number of detailed theoretical models that predict the end properties of deposition, however, much more study is necessary to allow for reasonable prediction of the build process for standard industrial parts, based on the synchronistic behavior of the input parameters.

This paper intends to raise questions about the possible research areas that could potentially promote the effectiveness of this LENS technology.