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In this study, we propose and develop an opensource groupware system called NetIsle. NetIsle is a general purpose groupware system for uniform open groups that integrate a number of tools for online collaboration to ensure fast information exchange and sharing, increase the productivity of working groups, and reduce maintenance and administration costs. The main technologies used for the construction of the system are peer‐to‐peer (P2P) and push, which are best fitted to those principles and beliefs we build our system upon.

A cyber world (CW) is a digitized world created on cyberspaces inside computers interconnected by networks including the Internet. Following ubiquitous computers, sensors, e‐tags, networks, information, services, etc., is a road towards a smart world (SW) created on both cyberspaces and real spaces. It is mainly characterized by ubiquitous intelligence or computational intelligence pervasion in the physical world filled with smart things. In recent years, many novel and imaginative researches have been conducted to try and experiment a variety of smart things including characteristic smart objects and specific smart spaces or environments as well as smart systems. The next research phase to emerge, we believe, is to coordinate these diverse smart objects and integrate these isolated smart spaces together into a higher level of spaces known as smart hyperspace or hyper‐environments, and eventually create the smart world. In this paper, we discuss the potential trends and related challenges toward the smart world and ubiquitous intelligence from smart things to smart spaces and then to smart hyperspaces. Likewise, we show our efforts in developing a smart hyperspace of ubiquitous care for kids, called UbicKids.

The purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed.

This paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge–Kutta discrete method to reduce the solving complexity.

Extensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory.

A novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.

The purpose of this paper is to explore the evolvement of investors’ behavior in stock market dynamically on the basis of non-cooperative strategy applied by investors in complex networks.

Using modeling and simulation research method, this study designs and conducts a mathematical modeling and its simulation experiment of financial market behavior according to research’s basic norms of complex system theory and methods. Thus the authors acquire needed and credible experimental data.

The conclusions drawn in this paper are as follows. The dynamical evolution of investors’ trading behavior is not only affected by the stock market network structure, but also by the risk dominance degree of certain behavior. The dynamics equilibrium of trading behavior’s evolvement is directly influenced by the risk dominance degree of certain behavior, connectivity degree and the heterogeneity of the stock market networks.

This paper focuses on the dynamical evolvement of investors’ behavior on the basis of the hypothesis that common investors prefer to mimic their network neighbors’ behavior through different analysis by the strategy of anti-coordination game in complex network. While the investors’ preference and the beliefs among them are not easy to quantify, that is deterministic or stochastic as the environment changes, and is heterogeneous definitely. Thus, these limitations should be broken through in the future research.

This paper aims to address the dynamical evolvement of investors’ behavior in stock market networks on the principle of non-cooperative represented by anti-coordination game in networks for the first time, considering that investors prefer to mimic their network neighbors’ behavior through different analysis by the strategy of differential choosing in every time step. The methodology designed and used in this study is a pioneering and exploratory experiment.

The purpose of this study is to examine the current state of online sustainability reporting in the Hong Kong higher education sector from a variety of perspectives.

The multiple case study was the research method for this research. Content analysis of the university website, as well as interview with university management, personnel in the sustainability office, academics and students, were adopted for data collection. An assessment framework for sustainability reporting was developed as an instrument for content analysis, based on G4 guidelines and prior literature.

The authors observe that most Hong Kong public universities have a specific webpage for sustainability reporting on the website, which consists of an amount of sustainability-related information in terms of general aspects, specific aspects and stakeholder participation. Moreover, they notice that Hong Kong public universities attach importance to sustainability and its reporting, and they would like to communicate their sustainability activities to various stakeholders and the community where they operate.

This paper indicates some practical implications for academics, university management, policymakers and sustainability education amongst universities in Hong Kong and beyond.

This paper argues that universities, as a central place to foster talents and future leaders, should operate in a sustainable manner at the first place and then report their sustainability-related activities taking full advantage of the current information and communication technologies, so as to discharge accountability to various stakeholders and meet the expectations of the community where they operate.

This paper uses a multiple case study approach to explore the status of online sustainability reporting at Hong Kong universities, which contributes to limited research in the area, particularly for online sustainability reporting in the higher education institutions, and makes some contributions to the extant literature.

Sensorless passive lead-through programming (LTP) is a promising physical human-robot interaction technology that enables manual trajectory demonstrations based on gravity and friction compensation. The major difficulty lies in static friction compensation during LTP start-up. Instead of static friction compensation, conventional methods only compensate for Coulomb friction after the joint velocity exceeds a threshold. Therefore, conventional start-up external torques must overcome static friction. When the static friction is considerable, it is difficult for conventional LTP to start up and make small movements. This paper aims to decrease the start-up external torque and improve the small movement performance.

This paper reveals a novel usage of a high-gain position-loop in industrial robot applications aimed at sensitively detecting external torque during start-up. Then, the static friction is partly compensated by Coulomb friction to facilitate start-up. In addition, a detailed transition method between the proposed start-up and conventional passive LTP is proposed based on a finite state machine.

Experiments are implemented on the ROKAE XB4 robot to verify the effectiveness of the proposed external torque detection. Compared with the conventional LTP method, the proposed LTP method significantly decreases the start-up external torque and facilitates small movements.

This paper proposes and verifies a novel start-up method of sensorless LTP based on a start-up external torque detection and a transition method between start-up and conventional LTP. This research improves the LTP start-up performance, especially for industrial robots with large static friction.

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.

The purpose of this study is to examine the impact of social media affordances, specifically social engagement and social endorsement, on knowledge contribution in online Q&A communities. Building on self-determination theory, this research seeks to tackle the issue of under-provision of knowledge in these communities.

The study employs a sample collected from a popular social Q&A community in China and uses linear panel data models along with multiple robustness checks to test the research model.

The findings reveal that both social engagement and social endorsement have a positive effect on users' knowledge contribution to the online Q&A community. However, the impact of social engagement is mitigated by social endorsement.

This paper makes a valuable contribution to the field by filling the research gap on the role of social engagement behaviors and their interaction with social endorsement in online Q&A communities. The results provide insights into how social media affordances can be leveraged to enhance knowledge contribution in these communities.

This study aims to deem the new policy – talk for environmental protection – promoted in the second half of 2014 to be the exogenous event and adopts PSM and DID to verify whether and how the central government’s mechanism of supervision of environmental enforcement improves firm environmental performance and reveals the micro effect and working mechanism of the supervision of environmental enforcement.

The researchers first select reasonable control groups for target districts by means of PSM, then apply DID to compare corporations in the treatment group with those in the control group for the change of environmental performance before and after the talk for environmental protection, so as to evaluate the micro-level effect of such talks on corporate environmental performance; after that, the research examines the working mechanism of such talks on corporate environmental performance; then, it goes a step further to find out the environmental impact of such talks on corporations of different natures of property right.

It is found from the research that the talk for environmental protection will effectively improve the environmental performance of corporations in the target districts, and the improvement of environmental performance in state-owned corporations in the target districts will be more evident. However, such improvements, to a certain extent, are achieved by reducing the output value, and corporations do not increase environmental investments from a long-term perspective.

First, the targets of the talk for environmental protection are mainly principals of municipal governments, but the research expands the scope to the whole province due to the small sample at the municipal level. Despite evidences showing that such a pressure of supervision impacts the whole province, the results obtained based on the data at the municipal level will be accurate. Second, the research selects a relatively short research period. Third, due to the limited data on corporate environmental performance in China, the research selects only listed companies from key monitored and controlled firms by state.

First, for the central government, environmental policy making is not the end of its job; it shall also supervise local governments’ work at environmental governance and properly handle its relationship with local governments. Second, for the local governments, in the course of implementing environmental policies, they should not only strengthen law enforcement but keep the continuity of law enforcement to avoid moving law enforcement. Third, in the long run, corporations must start from the source of production to enhance environmental governance and make cleaner production, so as to keep boosting corporate competitiveness and their ability of fighting risks.

First, the research innovatively provides empirical evidence about the effect of China’s supervision of environmental enforcement. Previous studies on this topic are mostly theoretical discussions only, while this research makes the talk for environmental protection the exogenous event about the supervision of law enforcement and achieves breakthroughs in empirical studies of administrative enforcement supervision. Second, the research pushes the studies on the implementation effect of environmental policies from a medium level to a micro level. Third, the research achieves some breakthroughs in the data for measuring corporate environmental performance.

This paper aims to design an algorithm which is used to deal with non-linear discrete systems with constraints under the lower computation burden. As a result, we solve the non-holonomic vehicle tracking problem with the lower computational load and the convergence performance.

A fusion event-triggered model predictive control version is developed in this paper. The authors designed a shrinking prediction strategy.

The fusion event-triggered model predictive control scheme combines the strong points of event triggered and self-triggered methods. As the practical state approaches the terminal set, the computational complexity of optimal control problem (OCP) decreases.

The proposed strategy has proven to stabilize the system and also guarantee a reproducible solution for the OCP. Also, it is proved to be effected by the performance of the simulation results.