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There are many factors that affect an airlift pump for dredging. In actual applications, if these factors cannot be fully considered, the performance of the airlift pump will be reduced. In view of this, based on the critical model of the water–solid two-phase flow and the gas holding model, a critical model of the gas–water–solid three-phase flow is proposed. The research results show that, compared with the two-phase flow, the critical water flow rate for lifting solids in the three-phase flow is always smaller. Therefore, it is necessary to consider the static chip retention force so that the critical model in the two-phase flow can be modified. It shows that the static chip retention effect is opposite to the “start” of the particle. Not only that, but also discussed are the critical conditions of solid particles or vertical lifting water from a practical point of view. The results show that solid particles will not affect the critical air flow rate of pumping water. However, the size and location of the solid particles affect the critical air flow rate.

Virtual world technologies provide new and immersive space for learning, training, and education. They are enabled by the content creation and content sharing function for allowing users to create and interoperate various learning objects. Unfortunately, virtual world content sharing based on persistent virtual world content storage, to the best of our knowledge, does not exist. In this paper, we address this problem by proposing a content sharing scheme based on Virtual Net, a virtual world persistency framework. For efficient content retrieval, three strategies have been proposed to reduce communication overhead and content load delay. By integrating these strategies, a virtual world content search and retrieval algorithm has been devised. The experiment results verify the effectiveness of the algorithm.

In the last decade, blockchain smart contracts emerged as an automated, decentralized, traceable, and immutable medium of value exchange. Nevertheless, existing blockchain smart contracts are not compatible with legal contracts. The automatic execution of a legal contract written in natural language is an open research question that can extend the blockchain ecosystem and inspire next-era business paradigms. In this paper, we propose an AI-assisted Smart Contract Generation (AIASCG) framework that allows contracting parties in heterogeneous contexts and different languages to collaboratively negotiate and draft the contract clauses. AIASCG provides a universal representation of contracts through the machine natural language (MNL) as the common understanding of the contract obligations. We compare the design of AIASCG with existing smart contract generation approaches to present its novelty. The main contribution of AIASCG is to address the issue in our previous proposed smart contract generation framework. For sentences written in natural language, existing framework requires editors to manually split sentences into words with semantic meaning. We propose an AI-based automatic word segmentation technique called Separation Inference (SpIn) to fulfill automatic split of the sentence. SpIn serves as the core component in AIASCG that accurately recommends the intermediate MNL outputs from a natural language sentence, tremendously reducing the manual effort in contract generation. SpIn is evaluated from a robustness and human satisfaction point of view to demonstrate its effectiveness. In the robustness evaluation, SpIn achieves state-of-the-art F1 scores and Recall of Out-of-Vocabulary (R_OOV) words on multiple word segmentation tasks. In addition, in the human evaluation, participants believe that 88.67% of sentences can be saved 80–100% of the time through automatic word segmentation.

In multilingual semantic representation, the interaction between humans and computers faces the challenge of understanding meaning or semantics, which causes ambiguity and inconsistency in heterogeneous information. This paper proposes a Machine Natural Language Parser (MParser) to address the semantic interoperability problem between users and computers. By leveraging a semantic input method for sharing common atomic concepts, MParser represents any simple English sentence as a bag of unique and universal concepts via case grammar of an explainable machine natural language. In addition, it provides a human and computer-readable and -understandable interaction concept to resolve the semantic shift problems and guarantees consistent information understanding among heterogeneous sentence-level contexts. To evaluate the annotator agreement of MParser outputs that generates a list of English sentences under a common multilingual word sense, three expert participants manually and semantically annotated 75 sentences (505 words in total) in English. In addition, 154 non-expert participants evaluated the sentences’ semantic expressiveness. The evaluation results demonstrate that the proposed MParser shows higher compatibility with human intuitions.

Virtual commerce applies immersive technology such as augmented reality and virtual reality into e-commerce to shift consumer perception from 2D product catalogs to 3D immersive virtual spaces. In virtual commerce, the alignment of application design paradigms and the factors influencing consumer behavior is paramount to promote purchase of products and services. The question of their relation needs to be answered, together with the possible improvement of application design. This paper used a systematic literature review approach to synthesize research on virtual commerce from both application design and consumer behavior research, considering the promotion of purchase in virtual commerce settings. Throughout the review, influential factors to purchase and preeminent design artifacts were identified. Then, the research gaps were discovered by mapping the design artifacts to the influential factors, which can inspire future research opportunities on the synergy of these two research directions. Moreover, the evolution of virtual commerce research along with multiple directions were discussed, including the suggestion of meta-commerce as a future trend.

Clinical practice calls for reliable diagnosis and optimized treatment. However, human errors in health care remain a severe issue even in industrialized countries. The application of clinical decision support systems (CDSS) casts light on this problem. However, given the great improvement in CDSS over the past several years, challenges to their wide-scale application are still present, including: 1) decision making of CDSS is complicated by the complexity of the data regarding human physiology and pathology, which could render the whole process more time-consuming by loading big data related to patients; and 2) information incompatibility among different health information systems (HIS) makes CDSS an information island, i.e., additional input work on patient information might be required, which would further increase the burden on clinicians. One popular strategy is the integration of CDSS in HIS to directly read electronic health records (EHRs) for analysis. However, gathering data from EHRs could constitute another problem, because EHR document standards are not unified. In addition, HIS could use different default clinical terminologies to define input data, which could cause additional misinterpretation. Several proposals have been published thus far to allow CDSS access to EHRs via the redefinition of data terminologies according to the standards used by the recipients of the data flow, but they mostly aim at specific versions of CDSS guidelines. This paper views these problems in a different way. Compared with conventional approaches, we suggest more fundamental changes; specifically, uniform and updatable clinical terminology and document syntax should be used by EHRs, HIS, and their integrated CDSS. Facilitated data exchange will increase the overall data loading efficacy, enabling CDSS to read more information for analysis at a given time. Furthermore, a proposed CDSS should be based on self-learning, which dynamically updates a knowledge model according to the data-stream-based upcoming data set. The experiment results show that our system increases the accuracy of the diagnosis and treatment strategy designs.

Virtual worlds have become global platforms connecting millions of people and containing various technologies. For example, No Man’s Sky (nomanssky.com), a cross-platform virtual world, can dynamically and automatically generate content with the progress of user adventure. AltspaceVR (altvr.com) is a social virtual reality platform supporting motion capture through Microsoft’s Kinect, eye tracking, and mixed reality extension. The changes in industrial investment, market revenue, user population, and consumption drive the evolution of virtual-world-related technologies (e.g., computing infrastructure and interaction devices), which turns into new design requirements and thus results in the requirement satisfaction problem in virtual world system architecture design. In this paper, we first study the new or evolving features of virtual worlds and emerging requirements of system development through market/industry trend analysis, including infrastructure mobility, content diversity, function interconnectivity, immersive environment, and intelligent agents. Based on the trend analysis, we propose a new design requirement space. We, then, discuss the requirement satisfaction of existing system architectures and highlight their limitations through a literature review. The feature-based requirement satisfaction comparison of existing system architectures sheds some light on the future virtual world system development to match the changing trends of the user market. At the end of this study, a new architecture from an ongoing research, called Virtual Net, is discussed, which can provide higher resource sufficiency, computing reliability, content persistency, and service credibility.

Healthcare information exchange is an important research topic, which can benefit both healthcare providers and patients. In healthcare data sharing, many cloud-based solutions have been proposed, but the trustworthiness of a third-party cloud service is questionable. Recently, blockchain has been introduced in healthcare record sharing, which does not rely on trusting a third party. However, existing approaches only focus on the records collected from medical examination. They are not efficient in sharing data streams continuously generated from sensors and other monitoring devices. Today, IoT devices have been widely deployed and sensors and mobile applications can monitor patients’ body conditions. The collected data are shared to laboratories and institutions for diagnosis and further study. Moreover, existing approaches are too rigid to efficiently support metadata change. In this paper, an efficient data-sharing scheme is proposed, called MedChain, which combines blockchain, digest chain, and structured P2P network techniques to overcome the above efficiency issues in the existing approaches for sharing both types of healthcare data. Based on MedChain, a session-based healthcare data-sharing scheme is devised, which brings flexibility in data sharing. The evaluation results show that MedChain can achieve higher efficiency and satisfy the security requirements in data sharing.