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Porous concrete is a mixture of open‐graded coarse aggregate, water and cement. It is also occasionally referred to as no‐fines concrete or pervious concrete. Due to its high infiltration capacity, it is viewed as an environmentally sustainable paving material for use in urban drainage systems since it can lead to reduced flooding and to the possibilities of stormwater harvesting and reuse. However, the high porosity is due in the main part to the lack of fine aggregate particles used in the manufacture of porous concrete. The purpose of this paper is to present a numerical method to understand more fully the structural properties of porous concrete. This method will provide a useful tool for engineers to design with confidence higher strength porous concrete systems.

In the method, porous concrete is modelled using a discrete element method (DEM). The mechanical behaviour of a porous concrete sample subjected to compressive and tensile forces is estimated using two‐dimensional Particle Flow Code (PFC2D).

Three numerical examples are given to verify the model. A comprehensive set of micro‐parameters particularly suitable for porous concrete is proposed. The accuracy and effectiveness of simulation are confirmed by comparison with experimental results and empirical equations.

The experimental investigations for porous concrete described in this paper have been designed and conducted by the authors. In addition, the type of two dimensional PFC analysis presented has rarely been used to model porous concrete strength characteristics and from the results presented in this paper, this analysis technique has good potential for predicting its mechanical properties.

The Romance of the Three Kingdoms (RTK) is probably the most popular classical novel in Chinese literature. Of more than 1,000 characters in this extensive, three‐volume novel, Kong Ming in RTK is widely seen by the Chinese as the most remarkable strategist. This paper attempts to develop a cognitive schema of Kong Ming on strategy.

First, the paper reviews broadly the traditionally, four most popular novels within the Chinese classical literature: The Romance of the Three Kingdoms, Water Margin, Journey to the West and A Dream of the Red Mansions. After emphasizing the role of the RTK in influencing Chinese strategic thinking, the paper utilizes the methods of textual analyses in research. Focusing on a specific episode of global strategy making and taking (Chapter 38), the methodology is explained.

From textual analysis, the paper presents as its finding a schematically linear, cognitive structure of the strategy process. It also presents the central role of the “Counselor of Strategy” in Chinese strategy making process. From this research, a Kong Ming‐grounded, cognitive model of strategy process is developed. If the RTK (more than say, any MBA program) continues to influence Chinese strategy processes, then fast strategic decision making (as exemplified in our analyses) may be one of the pivotal reasons for the equally fast rise of the Chinese economy in our global economic landscape.

Kong Ming (or Zhuge Liang) had been hailed by the Chinese with such lofty epithets such as the “Divine Strategist”. His exploits during the declining Han dynasty and emergence of the Three Kingdoms of Wei, Shu and Wu (220‐280 CE) are still cited by the Chinese people in their daily conversations on strategy. In this paper, we draw lessons for CEOs through a Kong Ming‐grounded model for strategy.

The latest developments in Data Science and in advanced Scientometrics set a very challenging context for the analysis and the understanding of human behavior toward the design of value adding library services and sophisticated information systems. The purpose of this paper is to present an innovative research that integrates the creation and the consumption of scientific knowledge across regions. From a human behavior point of view, this is significant since it provides an advanced decision-making layer for bringing together researchers from all over the world.

More specific in this paper, the authors analyze the production and consumption of scientific knowledge across the regions in an important field of sustainable and renewable energy – using publications and citations data indexed in Scopus. As a case study, the authors select the USA a major producer of scientific publications in the field. At first, the authors identify the topics produced by the USA. Further topics produced by the scientific communities outside the USA that consume the knowledge produced by the USA are identified. The authors generate topics by employing the proposed topic model with distance matrix – an extension of classic latent Dirichlet allocation model.

The results show that research topics produced by the USA are consumed in different international contexts, interestingly. Consuming the knowledge produced by the USA, Chinese scientific community heavily produces topics related to biomass – to produce renewable energy. In contrast, Japanese scientific community produces topics related to fuel cell – used for the production of hybrid and electronic vehicles. Whereas the Taiwanese scientific community shows remarkable competency in solar cells. Among the European nations, while the German scientific community produces topics related to photovoltaic, the French scientific community covers topics related to Energy Storage and Green Chemistry. The authors believe that such analyses may be helpful in establishing more effective multi-national research collaborations by understating the actual consumption of produced knowledge.

Overall, the study provides a new dimension to comprehensively understand production and consumption of knowledge using scientific literature. From a human behavior analysis view in the context of sophisticated library systems, this is a significant contribution.

The use of advanced Data Mining and computing methods for deriving critical insights for the use of scientific knowledge is a bold action toward the global knowledge society vision.

The purpose of this paper is to semantically annotate the content of digital images with the use of Semantic Web technologies and thus facilitate retrieval, integration and knowledge discovery.

After a review and comparison of the existing semantic annotation models for images and a deep analysis of the characteristics of the content of images, a multi-dimensional and hierarchical general semantic annotation framework for digital images was proposed. On this basis, taking histories images, advertising images and biomedical images as examples, by integrating the characteristics of images in these specific domains with related domain knowledge, the general semantic annotation framework for digital images was customized to form a domain annotation ontology for the images in a specific domain. The application of semantic annotation of digital images, such as semantic retrieval, visual analysis and semantic reuse, were also explored.

The results showed that the semantic annotation framework for digital images constructed in this paper provided a solution for the semantic organization of the content of images. On this basis, deep knowledge services such as semantic retrieval, visual analysis can be provided.

The semantic annotation framework for digital images can reveal the fine-grained semantics in a multi-dimensional and hierarchical way, which can thus meet the demand for enrichment and retrieval of digital images.

This paper aims to semantically linking scientific research events implied by scientific and technical literature to support information analysis and information service applications. Literature research is an important method to acquire scientific and technical information which is important for research, development and innovation of science and technology. It is difficult but urgently required to acquire accurate, timely, rapid, short and comprehensive information from the large-scale and fast-growing literature, especially in the big data era. Existing literature-based information retrieval systems focus on basic data organization, and they are far from meeting the needs of information analytics. It becomes urgent to organize and analyze scientific research events related to scientific and technical literature for forecasting development trend of science and technology.

Scientific literature such as a paper or a patent is represented as a scientific research event, which contains elements including when, where, who, what, how and why. Metadata of literature is used to formulate scientific research events that are implied in introduction and related work sections of literature. Named entities and research objects such as methods, materials and algorithms can be extracted from texts of literature by using text analysis. The authors semantically link scientific research events, entities and objects, and then, they construct the event space for supporting scientific and technical information analysis.

This paper represents scientific literature as events, which are coarse-grained units comparing with entities and relations in current information organizations. Events and semantic relations among them together formulate a semantic link network, which could support event-centric information browsing, search and recommendation.

The proposed model is a theoretical model, and it needs to verify the efficiency in further experimental application research. The evaluation and applications of semantic link network of scientific research events are further research issues.

This paper regards scientific literature as scientific research events and proposes an approach to semantically link events into a network with multiple-typed entities and relations. According to the needs of scientific and technical information analysis, scientific research events are organized into event cubes which are distributed in a three-dimensioned space for easy-to-understand and information visualization.

This study aims to build the intellectual structure of knowledge sharing (KS) research by objectively and systematically capturing and sketching the content of research papers published in the KS research area.

This study uses a systematic literature review for data collection, and content analysis and bibliometric techniques of citation and co-citation analysis for data analyses and interpretation.

Based on the study, the intellectual structure of KS research consisting of five themes has emerged. The five themes identified are: models, frameworks and understanding for KS, the behaviour-oriented perspective of KS, technology-oriented perspective of KS, KS barriers and KS and firm’s performance.

This study has used published literature extracted from selected journals using the web of science database. More journals and databases may be included in future studies.

This study will give future researchers a comprehensive understanding of KS discipline and serve as a quick reference and resource for those interested in KS research. It identifies major areas of KS for the practitioners to enable them to focus and apply various organizational interventions to derive a competitive advantage. The identified themes in the intellectual structure of KS will also provide a holistic view and give multiple perspectives to practitioners so that they can better manage KS in their organizations.

This is among early studies aiming to extract the intellectual structure of KS in the broad area of knowledge management research.

This paper presents a generic simulation model to determine the equipment mix (quay, yard and intra-terminal transfer) for a Container Terminal Logistics Operations System (CTLOS). The simulation model for the CTLOS, a typical type of discrete event dynamic system (DEDS), consists of three sub-models: ship queue, loading-unloading operations and yard-gate operations. The simulation model is empirically applied to phase 1 of the Yangshan Deep Water Port in Shanghai. This study considers different scenarios in terms of container throughput levels, equipment utilization rates, and operational bottlenecks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions.

This paper aims to exploit shape memory polymer (SMP) composite as multifunctional coatings for protecting substrates from surface wear and bacterial. The efficiency of added nano or micro-sized particles in enhancing the properties of SMP was investigated. This study also attempts to use a low-cost and effective spraying approach to fabricate the coatings. The coatings are expected to have good conformability with the substrate and deliver multi-functional performance, such as wrinkle free, wear resistance, thermal stability and antimicrobial property.

High-performance SMP composite coatings or thin films were fabricated by a home-made continuous spray-deposition system. The morphologies of the coatings were studied using the scanning electron microscope and the transmission electron microscope. The abrasion properties were evaluated by Taber Abraser test, and thermo-gravimetric analysis was carried out to investigate the thermal properties of prepared composites. The antimicrobial property was determined by the inhibition zone method using E. coli. The thermally responsive shape memory effect of the resulting composites was also characterized.

The morphology analysis indicated that the nanoclay was distributed on the surface of the coating which resulted in a significant improvement of the wear property. The wear resistance of the coatings with nanoclay was improved as much as 40 per cent compared with that of the control sample. The thermo-gravimetric analysis revealed that the weight loss rate of composites with nanoclay was dropped over 40 per cent. The SMP coating with zinc oxide (ZnO) showed excellent antimicrobial effect. The shape recovery effect of SMP/nanoclay and SMP/ZnO composites can be triggered by external heating and the composites can reach a full shape recovery within 60 s.

This study proposed a continuous spray-deposition fabrication of SMP composite coatings, which provides a new avenue to prepare novel multi-functional coatings with low cost.

Most studies have emphasized on the sole property of SMP composites. Herein, a novel SMP composite coating which could deliver multi-functionality such as wrinkle free, wear resistance, thermal stability and antimicrobial property was proposed.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

The main objective of the present study is to determine the role of citation-based metrics (PageRank and HITS’ authority and hub scores) and non-citation metrics (Goodreads readers, reviews and ratings, textbook edition counts) in predicting educational ranks of textbooks.

The rankings of 1869 academic textbooks of various disciplines indexed in Scopus were extracted from the Open Syllabus Project (OSP) and compared with normalized counts of Scopus citations, scores of PageRank, authority and hub (HITS) in Scopus book-to-book citation network, Goodreads ratings and reviews, review sentiment scores and WorldCat book editions.

Prediction of the educational rank of scholarly syllabus books ranged from 32% in technology to 68% in philosophy, psychology and religion. WorldCat editions in social sciences, medicine and technology, Goodreads ratings in humanities, and book-citation-network authority scores in law and political science accounted for the strongest predictions of the educational score. Thus, each indicator of editions, Goodreads ratings, and book citation authority score alone can be used to show the rank of the academic textbooks, and if used in combination, they will help explain the educational uptake of books even better.

This is the first study examining the role of citation indicators, Goodreads readers, reviews and ratings in predicting the OSP rank of academic books.