With the advancement of capsule endoscopy technology, the treatment of gastrointestinal (GI) tract diseases has entered a new era. Capsule robots enable the diagnosis and treatment of lesions in a painless and minimally invasive manner. Research on highly controllable Drug Delivery Systems (DDSs) using capsule endoscopy is significant for treating GI tract diseases. Despite the variety and complexity of DDS designs, most systems lack the capability for multi-target drug delivery and simultaneous carriage of multiple drugs. This paper proposes a dual-drug-chamber drug delivery capsule robot (DDCR) that utilizes a single Internal Permanent Magnet (IPM) for both drug delivery and propulsion. The design of the dual-drug-chamber primarily aims to carry one or two drugs simultaneously for treating one or multiple target sites. The proposed DDCR uses a balloon mechanism for drug containment. An external magnetic drive system activates a needle attached to a piston, which punctures the balloon and, thereby, releases the medication. This mechanism ensures rapid and effective coverage of lesions by the drugs. Based on both theoretical and experimental results regarding balloon drug loading and the control distance of the external permanent magnet (EPM) control distance, it was established that a drug load of 0.3 ml per chamber was most suitable for the design, and the feasibility of the dosing method was demonstrated. The size (29mm in length and ~13mm in diameter) of the DDCR was also found to be suitable for biological experiments, including multi-target drug delivery. By continuously adjusting the distance between the DDCR and the EPM in porcine small intestine samples, the optimal driving distance and drug delivery distance were found to be 80mm and 140mm.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this article proposes a value entropy model that links the operating state of the system with the efficiency of value creation, which helps to clarify the performance of the service ecosystem from the perspective of multi-dimensional integration. In addition, a computational experiment system is established to verify the effectiveness of value entropy model, which stimulates the competitive evolution process of two service ecosystems with different strategies. The result shows that our model can provide new ideas for the analysis of service ecosystem evolution, and can also provide decision support for the optimization of operation strategy.

Trans-boundary has become not only a trend but also an important requirement and feature of Modern Service Industry. In Internet era, crossover service not only changes people's daily life, but also has a profound impact on the business model of traditional industry. However, because of the complexity and convergence of crossover service, it is difficult to analyze and identify whether crossover services are valuable and how to achieve them. In order to solve the problems, the analysis framework of crossover service is proposed from the perspective of value creation and realization, which includes four main parts: value assessment, data analysis, capability analysis, and execution path. Based on the proposed method, the New Retail in China is given as a case study to clarify the implementation process of crossover service. Furthermore, JD (self-employed mode) and Alibaba (platform mode) are taken as examples to explain how to migrate from different e-commerce models to the New Retail model. By introducing computational experiment, the performance of different execution paths can be evaluated. The comparison of experimental results with the reality shows that the proposed method can provide a roadmap for the implementation of crossover service.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. As shown in Fig.1 , the value creation of service ecosystem consists of three elements: Input, Output, and Operation. Input means customers’ value demands, which drives the constant evolution of service ecosystem. Output means the value created by the service ecosystem in a certain period of time. Operation means the value creation ability of service ecosystem.

Internet business can be divided into two categories: pure online business and Online to Offline (O2O) business. Currently, the recommendation technology for online business is maturing, such as news, movies, products, and so forth. However, traditional recommendation technology can easily cause the overcrowding at some O2O services because of the big data traps. In the end, the users’ experience with the O2O service recommendation is useless or very poor because they have to wait for a long time and can't enjoy the service immediately. Hence, how to improve the performance of O2O service recommendation has become a vital problem. To solve the problem, this paper proposes a research framework based on the continuous feedback learning mechanism between cyber layer and social layer. Then, the continuous feedback ideas are implemented in the design of the O2O service recommendation strategy step by step. Furthermore, the computational experiment system is constructed to perform performance analysis of these service strategies. The results show that our research framework is conductive to help O2O service recommendation to escape the big-data traps and to improve user experience.

With the development of cloud manufacturing technology, Manufacturing Service Ecosystem (MSE) is emerging as a typical complex cyber-social system. On the one hand, service strategy (cyber layer) drives the evolution of manufacturing community (social layer); on the other hand, the initial conditions of manufacturing community (social layer) affect the performance of service strategy. In order to promote the evolution of MSE in the expected direction, it is necessary to clarify the loop feedback mechanism between heterogeneous networks. However, how to analyze and intervene in the possible evolution directions of MSE has become a serious challenge in the field. In order to face this challenge, this paper proposes a parallel system theory-based research framework to study the evolution and controlling of MSE. First, the corresponding digital system of MSE is constructed from the perspective of supply and demand matching. Second, the specific computational experiment is executed to present the effect of different service strategies (cyber layer) and different initial conditions (social layer) on the evolution of MSE. Furthermore, the comparison of experiment results with real data verifies the credibility of the proposed approach. It demonstrates that our approach can provide a new way for analyzing the complexity of MSE.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. To meet the customized user demands, service ecosystems begins to emerge with the formation of various IT services collaboration network. However, service ecosystem is a complex social-technology system with the characteristics of natural ecosystems, economic systems and complex networks. Hence, how to realize the multi-dimensional evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this paper proposes a value entropy model to analyze the performance of service ecosystem, which is conducive to integrate evaluation indicators of different dimensions. In addition, a computational experiment system is constructed to verify the effectiveness of value entropy model. The result shows that our model can provide new means and ideas for the analysis of service ecosystem.

As a new form of manufacturing industry in the Internet era, social manufacturing has its inherent “social-cyber” complexity: the source of manufacturing service is social, and such sociality aggravates the diversity, uncertainty, and dynamics of service supply. This poses new challenges to the service matching between supply-side and demand-side. In order to meet this challenge, it is necessary to conduct a complexity analysis of social manufacturing. Traditional researches mainly rely on data statistics and macro analysis, in which there are difficulties in clearly identifying the links between various impact factors and macro evolution phenomena. In order to change such a situation, this paper proposes a modeling framework of social manufacturing from the aspect of social learning evolution (SLE), including individual evolution model, organizational learning model, and social learning model. Based on the SLE framework, the corresponding computational experiment system is built to analyze the complexity of social manufacturing. The performance of several evolution mechanisms in social manufacturing is simulated and compared as a case study to present the application of SLE framework. The results demonstrate that our method has a substantial promise.

O2O (online to offline) service recommendation is a typical context-aware service application, which needs to provide the most suitable services to customers in time according to their user profile and current context. By means of composing various data sources, different O2O service recommendation strategies can be customized, which may lead to great performance difference. Incorrect or non-real-time service recommendation would not work well and even cause negatives consequences. As a result, how to evaluate the performance of different O2O service recommendation strategies and select the most suitable one has become a key problem in the field. Due to the diversity and the variability of context events, as well as the economic, legal, and ethical impact, it is difficult or even impossible for traditional methods to realize comprehensive evaluation of various service strategies. Based on the background, this paper proposes a computational experiment-based evaluation method of O2O service recommendation strategies, which mainly consists of three parts: customization of O2O service strategies, modeling of experiment system, and execution of experiment evaluation. As a case study, the method was applied to Food O2O service. Three kinds of service strategies were compared respectively under two different market environments. Experiment results show that the proposed evaluation method is effective.

Transboundary has become not only a trend but also an important requirement and feature of modern service industry development. In the Internet era, transboundary service not only changed people's daily life, but also had a profound impact on the business model of traditional industry. China has proposed the “Internet+” development plan, hoping to use the Internet for the transformation of traditional industries, including retail, manufacturing, catering, agriculture, finance, and health care. However, the transboundary impact of Internet on the operating model of traditional industry is decided by a combination of a variety of factors. Currently, the appropriate assessment model and analysis methods are absent in this field. Based on this, the impact evaluation method of Internet (service bridge) is proposed from the prospect of supply and demand matching, which includes three main parts: the capability model of supply side, the characteristic model of demand side, and the service bridge model. Based on the proposed method, the corresponding computational experiment system is built to evaluate the impact of Internet mode in different industries. Finally, this paper verifies the method with actual cases, and compares the transboundary impact of Internet in different daily consumption industries (online to offline in beauty service and take-out food service industry). The results showed that the “service bridge” method can introduce a new idea for the transboundary impact evaluation of Internet, which can provide some decision support to the reconstruction of demand value chain in some traditional industries.

In the framework of Industry 4.0, collaborative manufacturing across different supply chains is one of the most important business models. In order to avoid the uneven distribution of service requirements among service providers (i.e., non-equalization phenomenon), a lot of service strategies with different characteristics can be taken as candidate solutions to adjust the matching between service providers and service consumers. Based on the background, how to identify the application conditions of various service strategies in complex environment has become a serious challenge in the field. To solve this problem, the computational experiment-based evaluation method is proposed in this paper, including customization of service strategy, construction of experiment system, and experiment analysis of service strategy. In this paper, three possible service strategies are built to deal with the non-equalization phenomenon, i.e., non-equalization strategy, equalization strategy, collaborative equalization strategy. Experiment results show that: collaborative equalization strategy can effectively enhance the service utilization rate and reduce the completion time in short supply environment; equalization strategy is the optimal one in oversupply market environment. This case study can show that the proposed method is feasible and the result is satisfactory.

Most of the face recognition algorithms were proposed based on training numerous still examples of face images to accommodate different face variations, such as pose and illumination variations. However, it is not practical to collect lots of face images under different variations for each subject in a real authentication system. In this paper, we propose a novel face recognition system with only one single image for each individual in the training dataset. The proposed face recognition system applies the 3D face model reconstructed from the single face to synthesize different views for effectively training, thus leading to robustness against poses variations. The proposed system integrates the temporal face recognition results from the video in a probabilistic framework to make reliable decision when enough evidence is accumulated. In addition, it rejects imposters with the notion of locally linear embedding. The experiment results on FG-Net video database are shown to validate the effectiveness and reliability of the proposed algorithm.