With green hydrogen projected to supply over 60% of global hydrogen production by 2050, large-scale alkaline electrolyzers (>5 MW, 1000 Nm3/h) require highly efficient and scalable power conversion. MCSCs, featuring single-stage buck-type operation and low-harmonic output, outperform conventional two-stage VSC-based solutions. However, under carrier phase-shifted pulse width modulation (PWM) with large module counts, conventional zero-vector substitution schemes may result in divergent current behavior between submodules (SMs), posing risks of current imbalance and increased switching frequency. To address this critical challenge, a Global Zero-Vector Substitution (GZVS) method is proposed, which extends zero-vector substitution from a single SM to a pair of SMs selected based on current sorting and comparison results, thereby ensuring current convergence for both current polarities. Experimental results on a MCSC prototype demonstrate the effectiveness and scalability of the proposed strategy. It enables stable operation with 18 SMs while ensuring current balancing. An DC output current of 19kA/6.65MW is achieved with less than 3% total harmonic distortion (THD) on the AC side, paving the way for the industrial deployment of next-generation green hydrogen production systems.

Contemporarily, in light of the intelligent transportation systems (ITS) sector, the tendency can be observed that the solution of the multi-objective cyber-physical optimization problems with imperfect information takes an increasingly weighted role. In the present scientific work, the authors want to take these developments into account by introducing an innovative cyber-physical architectural design and corresponding the two-stage heuristic computing approach. It is utilized in synergy with the MCSA11Multi-tier Cyber-physical System Architecture and DCEx architectural principles for the workflow scheduling of Monte-Carlo simulation, which is based on the intelligent and sustainable route-order dispatching process model. Factors such as emissions, transport costs, risks, and the individual weighting of orders are reflected in the model. In particular, the authors define a stochastic ILP-based22Integer Linear Programming monte-carlo workflow model. They further propose two-stage scheduling heuristic with 6-HEFT DAG relaxation as first stage and apply state-of-the-art techniques as a part of SCIP framework to solve 2nd 1–0 ILP-based stage; evaluate the performance of the scheduling approach. The authors obtain preliminary results of the second stage behavior using a realistic heterogeneous computing scenario and corresponding constraint structures within MACS simulator engine33Modular Architecture for Complex Computing Systems Analysis. The results from the experiments illustrate moderate complexity of the approach. Scalability of the model looks promising for the applicability in various industry-related scenarios and corresponding computing environments.

Summary The rapid development of data science and artificial intelligence provides new methods and tools for exploring semantics, knowledge, and grids—the evolving interconnection environment. Different streams of research including Internet of Things, cloud computing, edge‐computing, semantics modeling, and cyber‐physical society are converging to support an intelligent interconnection environment. It is an important issue to create the method for ensuring the sustainable development of this interconnection environment. Big data and machine learning provide an empirical model for resolving this issue that is different from the method based on semantics and knowledge.

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph‐based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph‐based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less‐biased ranking than previous methods. MutualRank provides a unified model that involves both intra‐ and inter‐network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well‐known universities and two well‐known textbooks. The experimental results show that MutualRank greatly outperforms the state‐of‐the‐art competitors, including PageRank, HITS, CoRank, Future Rank, and P‐Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.

Summary The precise prediction of bus routes or the arrival time of buses for a traveler can enhance the quality of bus service. However, many social factors influence people's preferences for taking buses. These social factors may include heavy traffic cost, traffic congestion, poor air quality and so forth. Existing prediction techniques rarely consider social sensing when predicting the bus arrival time. Accordingly, this paper proposes a social sensing enhanced service for predicting bus routes, which integrates sensing ability and social networks to understand and measure the influence between social events and vehicle velocity. We focus on the analysis of two different attributions: PT service quality attributions PEAs and road condition attributions PRCAs. Both of them synthesize the social sensing in their evaluation of bus routes. PEA represents individual preferences and PRCA represents physical factors that significantly influence vehicle velocity. Bus relevant social events were further categorized into PEA events or PRCA events. PEAs of buses were scored according to the tendency of bus conditions reflected in social events. Furthermore, an artificial neural network prediction model is established to estimate the bus travel time. Copyright © 2015 John Wiley & Sons, Ltd.

Summary As smart phones with GPS become popular, more and more textual documents with geographical locations are published on the Web. Keyword‐based location services like vehicle navigation, tour planning, nearby object querying, and location pattern discovering of spatial objects are becoming popular and important. However, processing both text and geographical locations brings more challenges to information‐retrieval techniques. In this paper, we focus on the problem of finding textual topics of clusters containing spatial objects with text descriptions. The key is how to combine clustering techniques with topic‐retrieval models to integrate both geo‐location information and text information. We investigated methods that combine clustering methods with the Latent Dirichlet Allocation model to discover topics of clusters of documents with geo‐locations. Six different methods of combination are investigated, each having outputs with different meanings, which can be further leveraged to answer different types of queries over spatial documents. Experiments are conducted on both synthetic and real data. The results show that the combination of the probabilistic topic model with clustering algorithms is an efficient and effective way to discover meaningful clusters in different facets and levels of documents with textual and geographical information. Copyright © 2015 John Wiley & Sons, Ltd.

It is important to help researchers find valuable scientific papers from a large literature collection containing information of authors, papers and venues. Graph-based algorithms have been proposed to rank papers based on networks formed by citation and co-author relationships. This paper proposes a new graph-based ranking framework MutualRank that integrates mutual reinforcement relationships among networks of papers, researchers and venues to achieve a more synthetic, accurate and fair ranking result than previous graph-based methods. MutualRank leverages the network structure information among papers, authors, and their venues available from a literature collection dataset and sets up a unified mutual reinforcement model that involves both intra- and inter-network information for ranking papers, authors and venues simultaneously. To evaluate, we collect a set of recommended papers from websites of graduate-level computational linguistics courses of 15 top universities as the benchmark and apply different methods to estimate paper importance. The results show that MutualRank greatly outperforms the competitors including Pag-eRank, HITS and CoRank in ranking papers as well as researchers. The experimental results also demonstrate that venues ranked by MutualRank are reasonable.

Semantics and knowledge are keys to realize interconnection environments (Grids). Many researchers are making effort to realize large-scale dynamic networking environment involving both physical network infrastructures and human beings. A basic issue is to support efficient representation, management and sharing of various resources in large-scale environment. It requires fundamental research on the models to understand the characteristics and the evolving principles of the semantic and knowledge networking environment. Scalable platform solutions in the future networking information system should be envisioned. The International Conference on Semantics, Knowledge and Grids (SKG) has been focusing on these topics for five years. The fifth SKG conference (SKG2009, http://www.knowledgegrid.net/SKG2009) successfully drew researchers from different areas to discuss and explore the possible technical breakthroughs towards the future interconnection environment. Nine representative papers are selected to demonstrate the latest progress in models, methods, platforms and visions on future interconnection environments. Heterogeneous resource management on large-scale networking environment is always a challenge. Nakanishi1 et al. 1 proposed a knowledge presentation framework for users to browse related heterogeneous knowledge information on the Knowledge Grid 2. A semantic network method is proposed to represent the related resources in the knowledge base. A dynamic browser is implemented to help users browse knowledge and their related information in a more user-friendly and direct way. Comito and Talia 3 studied a schema-mapping method of XML data on the P2P network where highly dynamic nodes publish heterogeneous data sources for sharing and integration. A path-to-path schema-mapping method is proposed to reformulate queries and distribute queries among peers to form the query results. The proposed framework can be viewed as a feasible solution for a typical scenario on such large-scale networking environments as the Web. Zhou et al. 4 proposed an assessment method for evaluating the adaptability of services on the heterogeneous environments where adaptors may not be available. The proposed method is based on graph search and can evaluate whether a requestor and a provider of a service can be matched without using intermediate adaptor. The degree of matching can be measured and the condition of matching can be identified when the matching is possible. Automatically discovering and mapping ontologies on the network is a key problem for building intelligent Knowledge Grid. Mao et al. 5 used a machine-learning technique to address the ontology-mapping problem. The ontology-mapping problem is mapped onto a binary classification problem that many traditional machine-learning methods can apply. To make the classification more generic, the authors selected a set of features that are independent to specific instances and domains. The features can be applied to different domains without using instance training. This advantage can be promising for implementing semantic search/retrieval on large-scale Semantic Web data set. Classification and link are two basic means that connect various resources in future interconnection environment 6. Discovering semantic links is important for intelligently sharing information. Zhuge and Zhang 7 proposed a rule-based semantic link discovery method on a semantic link network formed by document contents. The proposed method supports many important features including probabilistic relational reasoning, semantic link network evolution and rule evolution. It can be applied to the semantics mining of text contents. It can be also applied to other networked semantic data such as the Semantic Web since the method incorporates rules to guide the semantic link discovery. Finding the basic features of the semantic link network operations is important for humans to understand the formation and the evolution principles of the networked Cyber Physical Society. Zhuge and Xu 8 investigated the dynamic effects as well as the basic operation set and its completeness of the Cyber Physical Socio Semantic Link Network (CPSocio-SLN), which extends previous SLN research. An efficient CPSocio-SLN storage and maintenance model is proposed based on the dynamic characteristics of the operations. Chen and Zhang 9 proposed a P2P platform to support efficient querying of heterogeneous resources on a distributed network platform. A ring P2P network protocol is proposed to support efficient range queries and multi-dimensional queries on the resource space model (RSM) that is deployed on a large-scale distributed network 10. Web of Things and Cyber-Physical Systems (CPS) are ideas of future Internet that integrates both the cyber space and the physical world to form a new networking platform. Dillon et al. 11 present a framework of Web of Things for developing CPS over the Internet. A layered infrastructure is proposed as a feasible solution that provides a platform integrating Web and CPS. Beyond the CPS will be a more harmonic networking environment where humans should be involved. Cyber Physical Society is such a vision. Zhuge proposed early the notion of coordinating cyber space, natural physical space, mental space and society to develop a harmoniously evolved future interconnection environment 6, 12-14. Shi and Zhuge 15 discussed the ecology of the future Cyber Physical Society where human beings play a crucial role in the running and evolution of the whole system. Essential issues are discussed on the evolution features, interactions between human beings and systems, and the future architectures. Cyber Physical Society opened up a new vision that deserves more extensive researches. This special issue presents the latest advances and achievements in Semantics, Knowledge and Grids. It covers a set of important issues and directions that need extensive investigations. Readers can find both the detailed technical solutions and the keen technical visions that guide the future researches. We believe that the special issue will provide relevant communities with many inspirations.

Abstract When people realized that relational databases fall short in supporting advanced applications including multimedia data management due to the limited modeling power of the relational data model, researchers went ahead with devising semantic, object‐oriented data models in the 1980s (until early 1990s). While the later commercial development of database systems has led to the so‐called object‐relational databases since late 1990s, such a marriage of the two does not actually solve the problems encountered by multimedia data management. In this paper, we present a new semantic multimedia database model based on an extension to the traditional ANSI/SPARC three‐level architecture, attempting to cater for the unique requirements of multimedia data management. Various facilities of this semantic multimedia database model are described and discussed. A number of applications have been developed based on this new model, and in this paper we shall describe some of these including recipe modeling and graph mining for retrieval. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract This paper proposes a framework consisting of a semantic object model and a scalable platform for supporting effective publishing and sharing of massive knowledge on Web 2.0. The platform integrates the P2P computing techniques to provide users with flexible tools to manage personalized resources and services. The semantic object model provides an object‐oriented semantic description language and a semantic object query language for publishing and querying resources. Semantic indexing mechanisms are used to support efficient semantic query processing. The semantic object model enables users to define and manipulate semantics with more explicit object semantics and can be well adapted to the P2P platform to support scalable and extensible management on semantics‐rich Web resources. Copyright © 2008 John Wiley & Sons, Ltd.

This paper presents a general virtual ring method to design and analyze small-world structured P2P networks on the base topologies embedded in ID spaces with distance metric. Its basic idea is to abstract a virtual ring from the base topology according to the distance metric, then build small-world long links in the virtual ring and map the links back onto the real network to construct the small-world routing tables for achieving logarithmic greedy routing efficiency. Four properties are proposed to characterize the base topologies that can be turned into small-world by the virtual ring method. The virtual ring method is applied to the base topologies of d-torus with Manhattan distance, high dimensional d-torus base topologies, and other base topologies including the unbalanced d-torus and the ring topology with tree distance. Theoretical analysis and simulation experiments demonstrate the efficiency and the resilience of the proposed overlays.

This paper presents Harmonic Ring (HRing), a structured peer-to-peer (P2P) overlay where long links are built along the ring with decreasing probabilities coinciding with the Harmonic Series. HRing constructs routing tables based on the distance between node positions instead of node IDs in order to eliminate the effect of node ID distribution on the long link distribution and load balance. It supports leave-and-rejoin load balance without incurring uneven long link distribution. In addition, node IDs can be any form, like number, string, address, and date, without the prerequisite of uniform distribution, so they can preserve the semantics and range locality of data objects. HRing supports multidimensional range queries. Each node is expected to have O(ln(n)) long links. The construction of O(ln(n)) long links for a node costs (O(ln(n)) messages. Routing queries achieve O(ln(n)) hops. Analyses and simulations demonstrate the efficiency of query routing and the effectiveness of the long link construction method.

This paper presents a structured P2P overlay SCAN that augments CAN overlay with long links based on Kleinberg's small-world model in a d-dimensional Cartesian space. The construction of long links does not require the estimate of network size. Queries in multi-dimensional data space can achieve O(log n) hops by equipping each node with O(log n) long links and O(d)short links.

This paper presents SRing, a structured non DHT P2P overlay that efficiently supports exact and range queries on multiple attribute values. In SRing, all attribute values are interpreted as strings formed by a base alphabet and are published in the lexicographical order. Two virtual rings are built: N-ring is built in a skip-list way for range partition and queries; D-ring is built in a small-world way for the construction of N-ring. A leave-and-join based load balancing method is used to balance range overload in the network with heterogeneous nodes.

Abstract The fundamental challenge confronting peer‐to‐peer (P2P) resource‐sharing networks is to provide efficient and scalable search services in a large‐scale, open and dynamic environment. However, current search mechanisms are not efficient or scalable enough with the expansion of the systems. To improve search efficiency, this paper proposes the trust‐based probabilistic search mechanism, called the Preferential Walk (P‐Walk). Every peer adjusts its neighbors' rank continuously according to their performance in previous searches. The basic principle of the P‐Walk is that neighbors with higher ranks in the question domain have higher probabilities of being queried. Extensive simulation results show that the P‐Walk can significantly improve the success rate of the query and the response time whilst also effectively reducing traffic volume and controlling malicious behavior. Furthermore, we measure the rank distributions of the peers and find their power‐law‐like trends based on a Gnutella‐like network. Implications are drawn from the analysis of the rank distributions. The P‐Walk mechanism works effectively in a P2P view model that provides appropriate views of the P2P network for queries of different types. Copyright © 2006 John Wiley & Sons, Ltd.

A semantic link peer‐to‐peer (P2P) network specifies and manages semantic relationships between peers' data schemas and can be used as the semantic layer of a scalable Knowledge Grid. The proposed approach consists of an automatic semantic link discovery method, a tool for building and maintaining P2P semantic link networks (P2PSLNs), a semantic‐based peer similarity measurement for efficient query routing, and the schema mapping algorithms for query reformulation and heterogeneous data integration. The proposed approach has three important aspects. First, it uses semantic links to enrich the relationships between peers' data schemas. Second, it considers not only nodes but also the XML structure in measuring the similarity between schemas to efficiently and accurately forward queries to relevant peers. Third, it copes with semantic and structural heterogeneity and data inconsistency so that peers can exchange and translate heterogeneous information within a uniform view.

The knowledge grid needs to operate with a scalable platform to provide large-scale intelligent services. A key function of such a platform is to efficiently support various complex queries in a dynamic large-scale network environment. This paper proposes a platform to support index-based path queries by incorporating a semantic overlay with an underlying structured P2P network that provides object location and management services. Various distributed indexing structures can be dynamically formed by publishing, semantic objects as indexing nodes. Queries are forwarded along the chains of semantic object pointers to search for objects. We investigate the deployment of a scalable distributed trie index for broadcast queries on key strings, propose a decentralized load balancing method for solving the problem of uneven load distribution incurred by heterogeneity of loads and node capacities and by the distributed trie index, and give an approach for improving the availability of the semantic overlay and its trie index. Experiments demonstrate the scalability of the proposed platform.