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With the rapid development of materials informatics and the Semantic Web, the semantic-driven solution has emerged to improve traditional query technology, which is hard to discover implicit knowledge from materials data. However, it is a nontrivial thing for materials scientists to construct a semantic query, and the query results are usually presented in RDF/XML format which is not convenient for users to understand. This paper aims to propose an approach to construct semantic query and visualize the query results for metallic materials domain.

The authors design a query builder to generate SPARQL query statements automatically based on domain ontology and query conditions inputted by users. Moreover, a semantic visualization model is defined based on the materials science tetrahedron to support the visualization of query results in an intuitive, dynamic and interactive way.

Based on the Semantic Web technology, the authors design an automatic semantic query builder to help domain experts write the normative semantic query statements quickly and simply, as well as a prototype (named MatViz) is developed to visually show query results, which could help experts discover implicit knowledge from materials data. Moreover, the experiments demonstrate that the proposed system in this paper can rapidly and effectively return visualized query results over the metallic materials data set.

This paper mainly discusses an approach to support semantic query and visualization of metallic materials data. The implementation of MatViz will be a meaningful work for the research of metal materials data integration.

Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.

This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.

The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.

This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.

With the increasing spread of ontologies in various domains, units have gradually become an essential part of ontologies and units ontologies have been developed to offer a better expression ability for the practical usage. From the perspectives of architecture, comparison and reuse, the purpose of this paper is to provide a comprehensive survey on four mainstream units ontologies: quantity-unit-dimension-type, quantities, units, dimensions and values, ontology of units of measure and units ontology (UO) of the open biomedical ontologies, in order to address well the state of the art and the reuse strategies of the UO.

An architecture of units ontologies is presented, in which the relations between key factors (i.e. units of measure, quantity and dimension) are discussed. The criteria for comparing units ontologies are developed from the perspectives of organizational structure, pattern design and application scenario. Then, the authors compare four typical units ontologies based on the proposed comparison criteria. Furthermore, how to reuse these units ontologies is discussed in materials science domain by utilizing two reuse strategies of partial reference and complete reference.

Units ontologies have attracted high attention in the scientific domain. Based on the comparison of four popular units ontologies, this paper finds that different units ontologies have different design features from the perspectives of basis structure, units conversion and axioms design; a UO is better to be applied to the application areas that satisfy its design features; and many challenges remain to be done in the future research of the UO.

This paper makes an extensive review on units ontologies, by defining the comparison criteria and discussing the reuse strategies in the materials domain. Based on this investigation, guidelines are summarized for the selection and reuse of units ontologies.

This study aims to fabricate and manipulate ensemble spin of negative nitrogen-vacancy (NV⁻) centres optimally for future solid atomic magnetometers/gyroscope. Parameters for sample preparation most related to magnetometers/gyroscope are, in particular, the concentration and homogeneity of the NV⁻ centres, the parameters’ microwave antenna of resonance frequency and the strength of the microwave on NV⁻ centres. Besides, the abundance of other impurities such as neutral NV centres (NV⁰) and substitutional nitrogen in the lattice also plays a critical role in magnetic sensing.

The authors succeeded in fabricating the assembly of NV centres in diamond and they determined its concentration of (2-3) × 10¹⁶ cm⁻³ with irradiation followed by annealing under a high temperature condition. They explored a novel magnetic resonance approach to detect the weak magnetic fields that takes advantage of the solid-state electron ensemble spin of NV⁻ centres in diamond. In particular, the authors set up a magnetic sensor on the basis of the assembly of NV centres. They succeeded in fabricating the assembly of NV centres in diamond and determined its concentration. They also clarified the magnetic field intensity measured at different positions along the antenna with different lengths, and they found the optimal position where the signal of the magnetic field reaches the maximum.

The authors mainly reported preparation, initialization, manipulation and measurement of the ensemble spin of the NV centres in diamond using optical excitation and microwave radiation methods with variation of the external magnetic field. They determined the optimal parameters of irradiation and annealing to generate the ensemble NV centres, and a concentration of NV⁻ centres as high as 10¹⁶ cm⁻³ in diamond was obtained. In addition, they found that sensitivity of the magnetometer using this method can reach as low as 5.22 µT/Hz currently.

This research can shed light on the development of an atomic magnetometer and a gyroscope on the basis of the ensemble spin of NV centres in diamond.

High concentration spin of NV⁻ in diamond is one of the advantages compared with that of the atomic vapor cells, because it can obtain a higher concentration. When increasing the spin concentration, the spin signal is easy to detect, and macro-atomic spin magnetometer become possible. This research is the first step for solid atomic magnetometers with high spin density and high sensitivity potentially with further optimization. It has a wide range of applications from fundamental physics tests, sensor applications and navigation to detection of NMR signals.

As has been pointed out, in this research, the authors mainly worked on fabricating NV⁻ centres with high concentration (10¹⁵-10¹⁶ cm⁻³) in diamond by using optimal irradiation and annealing processes, and they quantitatively defined the NV⁻ concentration, which is important for the design of higher concentration processes in the magnetometer and gyroscope. Until now, few groups can directly define the NV⁻ concentration. Besides, the authors optimized the microwave antenna parameters experimentally and explored the dependence between the splitting of the magnetic resonance and the magnetic fields, which dictated the minimum detectable magnetic field.

In spite of the vehicle, magnetic field interference can be reduced by some measures and techniques in ammunition design and manufacturing stage, the corruption of the vehicle magnetic field can still reach hundreds to thousands of nanoteslas. Besides, the magnetic field that the ferromagnetic materials generate in response to the strong magnetic field in the vicinity of the body. So, a real-time and accurate vehicle magnetic field calibration method is needed to improve the real-time measurement accuracy of the geomagnetic field for spinning projectiles.

Unlike the past two-step calibration method, the algorithm uses a linear model to calibrate the magnetic measurement error in real-time. In the method, the elliptical model of magnetometer measurement is established to convert the coefficients of hard and soft iron errors into the parameters of the elliptic equation. Then, the parameters are estimated by recursive least square estimator in real-time. Finally, the initial conditions for the estimator are established using prior knowledge method or static calibration method.

Studies show the proposed algorithm has remarkable estimation accuracy and robustness and it realizes calibration the magnetic measurement error in real-time. A turntable experiments indicate that the post-calibration residuals approximate the measurement noise of the magnetometer and the roll accuracy is better than 1°. The algorithm is restricted to biaxial magnetometers’ calibration in real-time as expressed in this paper. It, however, should be possible to broaden this method’s applicability to triaxial magnetometers' calibration in real-time.

Unlike the past two-step calibration method, the algorithm uses a linear model to calibrate the magnetic measurement error in real-time and the calculation is small. Besides, it does not take up storage space. The proposed algorithm has remarkable estimation accuracy and robustness and it realizes calibration the magnetic measurement error in real time. The algorithm is restricted to biaxial magnetometers’ calibration in real-time as expressed in this paper. It, however, should be possible to broaden this method’s applicability to triaxial magnetometers’ calibration in real-time.

The purpose of this study is to explore mitigation measures for cash flow interruption during the epidemic and provide decision support to ensure the regular operation and robustness of the supply chain (SC).

Considering the scenarios of production capacity and demand disruption during the epidemic, the authors adopt system dynamics (SD) to construct a three-echelon SC financial system consisting of a core manufacturer, a capital-constrained retailer and the customer. In different interruption scenarios, through the decision adjustments of stakeholders, the differences in performance are compared to explore solutions for SC robust optimization.

The results show that partial credit guarantee (PCG) could solve cash flow interruption and maintain the regular operation of the SC. During epidemic, with the product price increases, the revenue of stakeholders and the robustness are generally negatively correlated. But when the manufacturer's production capacity is fully interrupted, increasing product price is the right decision for the retailer and could simultaneously promote performance and robustness.

This paper primarily focuses on the PCG under the cash flow interruption caused by epidemics. The authors adopt the supply chain finance (SCF) theory and SD method to supplement and expand existing research on interruption management of SC. It is a pioneering study to explore the robustness of the SC financial system under disruptions.

With the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG.

The authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user’s question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method.

The designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches.

Although the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user’s information query based on the emerging KG.

Under the background of rapid urbanization, all kinds of urban water problems have gradually come into being: local flooding frequently happens, water environment is deteriorated, water-supply is in tension, etc. Meanwhile, with rapid development of higher education in China, campus area and scale are gradually expanding, but traditional campus construction has many drawbacks. In order to promote sponge campus planning and construction of universities in hilly areas and provide demonstration windows for sponge city construction, based on deficiencies of campus construction of Hunan City University in the aspect of water resource utilization, we used ArcGis spatial analysis method, simulation method and comparative analysis method on Storm Water Management Model (SWMM) to establish sponge campus construction indexes, content system and optimal design strategies with objectives of campus water safety, water environment and water resource utilization. Results indicate that: difference between sponge campus planning and traditional campus planning mainly lies in rainfall management. We combed the design process of sponge campus planning in hilly areas from the perspective of rainfall management, and simulated the process of sponge facilities controlling the rainfall in the campus via computer model to verify reasonability of sponge facility planning and select the optimal planning and construction plan. This study has defined design process of sponge campus planning in hilly areas to a certain degree and provided a research basis for sponge campus planning and construction of universities, setting up a typical example and driving effects on solving urban local flooding problem and rainfall resource utilization in hilly areas.

This article outlines the challenges faced by the next Chief Executive of the Hong Kong Special Administrative Region (HKSAR) in 2022 – the need to address economic problems resulting from Hong Kong’s slow growth; its inability to restructure its economy to broaden job opportunities and improve upward mobility for young people; and the government’s belated attempt to deploy innovation and technology.

This article is based on the author’s in-depth analysis of the current situation and insights on the challenges faced by the next Chief Executive.

Tensions are inherent in the concept of “One Country, Two Systems”. Back in November 2012, Deputy Director of the Hong Kong and Macao Office Zhang Xiaoming already reminded Hong Kong of the need to manage well three sets of relationships: (1) maintaining the “One Country” principle while preserving the SARs’ “separate systems”; (2) upholding Central Authority while preserving the SARs’ “high degree of autonomy”; and (3) unleashing the economic potential of mainland China while raising the competitiveness of the SARs. These three sets of relationships represent three fundamental difficulties in implementing “One Country, Two Systems”. However, Hong Kong kept ignoring Beijing’s advice and failed to resolve the tension between the mainland and Hong Kong SAR, culminating in the riotous events of 2019, which morphed into a dangerous, anti-China insurgency.

The next Chief Executive needs to mediate between the constitutional requirements of the Central Authority while preserving Hong Kong SAR’s “high degree of autonomy”, its unique character and lifestyle. He or she also needs to deal with continuity and change. Hong Kong cannot stand still, and should not allow itself to be lulled by the “50 years no change” mantra into overlooking the need to move with the times. Much reform needs to be implemented by the next Chief Executive to give people hope, faith in “One Country, Two Systems” and true love of the country.

The integration and retrieval of the vast data have attracted sufficient attention, thus the W3C workgroup releases R2RML to standardize the transformation from relational data to semantic-aware data. However, it only provides a data transform mechanism to resource description framework (RDF). The generation of mapping alignments still needs manual work or other algorithms. Therefore, the purpose of this paper is to propose a domain-oriented automatic mapping method and an application of the R2RML standard.

In this paper, materials science is focussed to show an example of domain-oriented mapping. source field concept and M3B2 (Metal Materials Mapping Background Base) knowledge bases are established to support the auto-recommending algorithm. As for the generation of RDF files, the idea is to generate the triples and the links, respectively. The links of the triples follow the object-subject relationship, and the links of the object properties can be achieved by the range individuals and the trail path.

Consequently based on the previous work, the authors proposed Engine for Metal Materials Mapping Background Base (EM3B2), a semantic integration engine for materials science. EM3B2 not only offers friendly graphical interfaces, but also provides auto-recommending mapping based on materials knowledge to enable users to avoid vast manually work. The experimental result indicates that EM3B2 supplies accurate mapping. Moreover, the running time of E3MB2 is also competitive as classical methods.

This paper proposed EM3B2 semantic integration engine, which contributes to the relational database-to-RDF mapping by the application of W3C R2RML standard and the domain-oriented mapping.