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This paper aims to investigate the friction noise properties of M50 matrix curved microporous channel composites filled with solid lubricant Sn-Ag-Cu (MS).

Pure M50 (MA) and MS are prepared by selective laser melting and vacuum-pressure infiltration technology. The tribological and friction noise properties of MA and MS are tested through dry sliding friction and then the influential mechanism of surface wear sate on friction noise is investigated by analyzing the variation law of noise signals and the worn surface characteristics of MS.

Experimental results show that the friction noise sound pressure level of MS is only 75.6 dB, and it mainly consists of low-frequency noise. The Sn-Ag-Cu improves the surface wear state, which reduces self-excited vibration of the interface caused by fluctuation of friction force, leading to the decrease of friction noise.

This investigation is meaningful to improve the tribological property and suppress the friction noise of M50 bearing steel.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

This paper aims to save time spent on manufacturing the data set and make the intelligent grasping system easy to deploy into a practical industrial environment. Due to the accuracy and robustness of the convolutional neural network, the success rate of the gripping operation reached a high level.

The proposed system comprises two diverse kinds of convolutional neuron network (CNN) algorithms used in different stages and a binocular eye-in-hand system on the end effector, which detects the position and orientation of workpiece. Both algorithms are trained by the data sets containing images and annotations, which are generated automatically by the proposed method.

The approach can be successfully applied to standard position-controlled robots common in the industry. The algorithm performs excellently in terms of elapsed time. Procession of a 256 × 256 image spends less than 0.1 s without relying on high-performance GPUs. The approach is validated in a series of grasping experiments. This method frees workers from monotonous work and improves factory productivity.

The authors propose a novel neural network whose performance is tested to be excellent. Moreover, experimental results demonstrate that the proposed second level is extraordinary robust subject to environmental variations. The data sets are generated automatically which saves time spent on manufacturing the data set and makes the intelligent grasping system easy to deploy into a practical industrial environment. Due to the accuracy and robustness of the convolutional neural network, the success rate of the gripping operation reached a high level.

Employee Stock Options are the most widely used incentive compensation tool, and prior research has shown their advantages. However, research among different peer groups, different time frames, different research methodologies, and the constantly changing public opinion prevents unanimous agreements on the various benefits of Employee Stock Options. In this paper we apply a number of research hypotheses tested in recent US studies to a European sample of EuroStoxx 50 companies. Due to the globalisation, the similar accounting regulations and the IT and telecommunications revolu tions, Europe and the United States have grown closer together than ever before and are expected to display similar business practices. This assessment should be especially relevant for the large European companies, which mostly have a dual listing in the United States and are therefore essentially forced to manage according to American practices. How ever, the results differ significantly from the existing US research, providing insufficient grounds to accept previous findings for European companies.

Recent wireless communication and electronics technology has enabled the development of low‐cost, low‐power, and multi‐functional sensor nodes. However, the fact that sensor nodes are severely energy‐constrained has been an issue and many energy‐efficient routing protocols have been proposed to resolve it. Cluster‐based routing protocol is one of them. To achieve longer lifetime, some cluster‐based routing protocols use information on GPS‐based location of each sensor node. However, because of high cost, not all sensor nodes can be GPS‐enabled. The purpose of this paper is to propose a simple dynamic clustering approach to achieve energy efficiency for wireless sensor networks (WSN).

Instead of using location information of each sensor node, this approach utilizes information of remaining energy of each sensor node and changes in the number of cluster head nodes dependent on the number of sensor nodes alive. Performance results are presented and compared with some related protocols.

The simulations described in the paper show that both residual energy of each sensor node and changing cluster head nodes depending on the number of sensor nodes alive are very critical factors to obtain performance enhancement in terms of lifetime and data transmission. Especially, in some special environment, the proposal has better performance than GPS‐enabled protocol.

The paper is of value in proposing a simple dynamic clustering approach to achieve energy efficiency for WSN.

Presence of innumerable sensors, complex deduction of contexts from sensor data, and reusability of contextual information impose the requirement of middleware for context aware computing. Smart applications, hosted in myriad devices (e.g. PDA, mobile, PCs), acquire different contexts from the middleware and act intelligently based on the available contexts in a context‐aware computing environment. As the system grows larger, scalable delivery of contexts from the middleware to numerous context‐aware applications will be inevitable. However, pure unicast based or pure broadcast‐based dissemination cannot provide high scalability as well as low‐average latency. The purpose of this paper is to present a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems.

The proposed scheme is based on hybrid data dissemination technique where the most frequently requested data (e.g. HOT contexts) are delivered through multicast and the rest (e.g. COLD contexts) are delivered through unicast to reduce network traffic. The paper dynamically prioritizes and classifies the HOT and COLD context data depending on the number of requests and longest waiting time. Moreover, the division of bandwidth between the delivery of HOT and COLD contexts reduces average latency. Polling traffic is decreased by incorporating leasing mechanism. Extensive simulation is conducted to evaluate the proposed scheme.

The mechanism dynamically prioritizes and classifies the hot and cold context data depending on the request rate and longest waiting time. The solution addresses the push popularity problem that occurs in the passive as the passive clients access data without sending explicit requests. The leasing mechanism is incorporated to reduce the periodical requests (polling) for better performance.

The paper is of value in presenting a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems and also in presenting implementation details of a prototype that is developed using Jini framework and Java reliable multicast service (JRMS) library.

The purpose of this paper is to describe a speech and character combined recognition engine (SCCRE) developed for working on personal digital assistants (PDAs) or on mobile devices. Also, the architecture of a distributed recognition system for providing a more convenient user interface is discussed.

In SCCRE, feature extraction for speech and for character is carried out separately, but the recognition is performed in an engine. The client recognition engine essentially employs a continuous hidden Markov model (CHMM) structure and this CHMM structure consists of variable parameter topology in order to minimize the number of model parameters and to reduce recognition time. This model also adopts the proposed successive state and mixture splitting (SSMS) method for generating context independent model. SSMS optimizes the number of mixtures through splitting in mixture domain and the number of states through splitting in time domain.

The recognition results show that the developed engine can reduce the total number of Gaussian up to 40 per cent compared with the fixed parameter models at the same recognition performance when applied to speech recognition for mobile devices. It shows that SSMS can reduce the size of memory for models to 65 per cent and that for processing to 82 per cent. Moreover, the recognition time decreases 17 per cent with the SMS model while maintaining the recognition rate.

The proposed system will be very useful for many on‐line multimodal interfaces such as PDAs and mobile applications.

Recently the advances in wireless communication technology and the popularity of portable computers have rendered mobile computing environments from which mobile users with battery‐operated palmtops can access the information via wireless channels, without space and time restriction. In mobile computing environments, mobile users cache the data items to use the bandwidth efficiently and improve the response time of mobile transactions. If the data items cached in mobile users are updated at the server, the server broadcasts an invalidation report for maintaining the cache consistency of mobile users. However, this method has an obstacle that does not guarantee the serializable execution of mobile transactions. The purpose of this paper is to propose the four types of reports for mobile transaction (FTR‐MT) method for ensuring the serializable execution of mobile transactions.

The paper describes the FTR‐MT method, which is composed of four types of algorithms, e.g. group report composition algorithm, immediate commit decision algorithm, cache consistency algorithm, and disconnection cache consistency algorithm. FTR‐MT method for improving the response time of mobile transactions makes a commit decision by using the four types of reports.

With the FTR‐MT method, mobile users can make a commit decision by using the four types of reports. The response time of mobile transactions can be reduced. Furthermore, the FTR‐MT method can improve the cache efficiency in the case that the disconnection of mobile users is longer than the broadcast interval of the window report.

This paper proposes a new method for guaranteeing the serializable execution of mobile transactions, called FTR‐MT, using four types of reports. Also, it can prevent the entire cache dropping, even though the disconnection of a mobile host is longer than the broadcast interval of a window report. Through the analytical model, this method is felt to be superior to other methods, in terms of the average response time and the commit rate of mobile transactions, and bandwidth usage.

Context‐aware mobile computing extends the horizons of the conventional computing model to a ubiquitous computing environment that serves users at anytime, anywhere. To achieve this, mobile applications need to adapt their behaviors to the changing context. The purpose of this paper is to present a generalized adaptive middleware infrastructure for context‐aware computing.

Owing to the vague nature of context and uncertainty in context aggregation for making adaptation decisions, the paper proposes a fuzzy‐based service adaptation model (FSAM) to improve the generality and effectiveness of service adaptation using fuzzy theory.

By the means of fuzzification of the context and measuring the fitness degree between the current context and the predefined optimal context, FSAM selects the most suitable policy to adopt for the most appropriate service. The paper evaluates the middleware together with the FSAM inference engine by using a Campus Assistant application.

The paper is of value in presenting a generalized adaptive middleware infrastructure for context‐aware computing and also comparing the performance of the fuzzy‐based solution with a conventional threshold‐based approach for context‐aware adaptation.

The purpose of this paper is to examine empirically how managers establish ethical guanxi (interpersonal relationships) with their business partners to prevent potential ethical incidences in Chinese and Western business contexts.

The present study is guided by a qualitative, abductive approach and draws on in-depth interviews with ten senior managers in five urban New Zealand organisations.

The results point out that guanxi (interpersonal relationships) purely working through renqing (reciprocity) is not sustainable, because it perpetuates a never-ending cycle of favours, once exchanging favours stopped or disappeared, then business relationships dwindled. To establish an ethical guanxi model, the authors found that xinyong (trust) is the foundation, and its enlargement stimulates lijie (empathy) that transfers pure business relationships to a genuine friendship which enhances ethical decision-making. They also posit that once managers are embodied with lijie, then they will have the virtue of ren to behave like junzi (ideal Confucian ethical person) whose business actions tend to be intrinsically guided by a sense of obligation to do something right that will work for diverse stakeholders’ interests, for the prosperity of organisations and society.

This study suggests that managers should take Confucian virtues of xinyong (trust) and lijie (empathy), because they will trigger ren (humanity) as an intrinsic value. In this way, it is more likely for them to become junzi (ideal Confucian ethical person) whose business actions are intrinsically guided by a sense of obligation to do something right that benefits various stakeholders, organisations and society.

This study contributes to the extant literature on preventing ethical incidents of guanxi (interpersonal relationships) by drawing a framework of ethical guanxi, which is built on Confucian virtues of xinyong (trust), lijie (empathy) and ren (humanity). Further, this paper strongly suggests that companies should educate their staff to become more humane to make moral decisions in daily management practice.