Search results

This study aims to examine the evolution of spatial–temporal patterns in China’s hotel industry from 1978 to 2018.

A database comprising over 140,000 hotels with more than 30 rooms was created. The exploratory spatial–temporal data analysis (ESTDA) method, based on space–time cube model, was used to explore and visualize the spatial–temporal pattern of hotels.

The Chinese hotel industry can be divided into two development stages, namely, a large hotel-dominant stage before 2000 and a small–medium-sized hotel-dominant stage after 2000. China’s prefecture-level cities were clustered into four tiers. The higher the tier, the earlier the city will initiate hotel development. The Chinese hotel industry has four continuous hotspots (the Yangtze River Delta, Pearl River Delta, Bohai Rim and Sichuan and Chongqing) and some temporary hotspots.

This study lacks quantitative investigation, which could show the underlying mechanism of the evolution of the Chinese hotel industry.

This study is the first to investigate China’s hotel evolution over 40 years by applying big data and the ESTDA method. The systematic and evolutionary exploration will enable hotel researchers to understand the spatial–temporal nature of hotel distribution better. Introducing the ESTDA method into tourism and hotel research also provides an additional tool to researchers. Hotel investors and operators, city and tourism planners and market regulators can learn from the evolution of location patterns to make better where and when decisions.

This paper aims to illustrate the logics that have shifted in the Chinese hotel industry since 1949 and discuss its implications for advance a better understanding of how and why the Chinese hotel industry has evolved into its present situation. The logic evolution and future trends in this market were also discussed.

As this research is aimed at answering the “how” and “why” aspects in the evolution of Chinese hotel industry, qualitative approach is applied to answer the questions.

This paper divided the history of contemporary Chinese hotel industry into three stages: 1949-1977, 1978-2001 and 2002 to the present. Hotel business in each period was dominated by state logic, profession logic and market/corporation logic, respectively.

The authors applied institutional logics perspective to explore how and why China hotel industry evolved in the past 70 years.

This paper aims to focus on achieving triple-shape memory effect (triple-SME) of a commercial poly (ethylene terephthalate) (PET) film with the thickness of 100 µm.

The thermal characteristics and microstructure of PET film were characterized by differential scanning calorimetry, thermogravimetric analysis and wide-angle X-ray diffraction analysis. The dual-shape memory effect (dual-SME) of the PET film was then systematically investigated, and based on that, triple-SME in thin PET film was achieved.

Investigation of the dual-SME in PET film revealed the difference between recovery temperature and programming temperature reduced with increasing programming temperature. An obvious intermediate shape shifting between the original and final programmed shape was observed during shape recovery in triple-shape memory behaviors.

Compared with dual-SME in polymer, relatively less work has been done on multi-SME in polymer, especially in thin polymer film. In this study, triple-SME in a PET film was investigated based on the results of dual-SME of the film. The main implication of the study is on how to achieve a watermark between the final programmed pattern and the original pattern, for the application of shape memory polymer in anti-counterfeiting label.

Dual- and triple-SMEs were achieved in a PET film that is only 100 µm in thickness, and the underlying mechanism for the difference between programming temperature and recovery temperature was discussed. For the novel application of triple-SME in anti-counterfeit label, the watermark during shape recovery in triple-SME can effectively prevent duplication.

This paper aims to propose a new keyword search method on graph data to improve the relevance of search results and reduce duplication of content nodes in the answer trees obtained by previous approaches based on distinct root semantics. The previous approaches are restricted to find answer trees having different root nodes and thus often generate a result consisting of answer trees with low relevance to the query or duplicate content nodes. The method allows limited redundancy in the root nodes of top-k answer trees to produce more effective query results.

A measure for redundancy in a set of answer trees regarding their root nodes is defined, and according to the metric, a set of answer trees with limited root redundancy is proposed for the result of a keyword query on graph data. For efficient query processing, an index on the useful paths in the graph using inverted lists and a hash map is suggested. Then, based on the path index, a top-k query processing algorithm is presented to find most relevant and diverse answer trees given a maximum amount of root redundancy allowed for a set of answer trees.

The results of experiments using real graph datasets show that the proposed approach can produce effective query answers which are more diverse in the content nodes and more relevant to the query than the previous approach based on distinct root semantics.

This paper first takes redundancy in the root nodes of answer trees into account to improve the relevance and content nodes redundancy of query results over the previous distinct root semantics. It can satisfy the users’ various information need on a large and complex graph data using a keyword-based query.

This paper studies a keyword search over graph-structured data used in various fields such as semantic web, linked open data and social networks. This study aims to propose an efficient keyword search algorithm on graph data to find top-k answers that are most relevant to the query and have diverse content nodes for the input keywords.

Based on an aggregative measure of diversity of an answer set, this study proposes an approach to searching the top-k diverse answers to a query on graph data, which finds a set of most relevant answer trees whose average dissimilarity should be no lower than a given threshold. This study defines a diversity constraint that must be satisfied for a subset of answer trees to be included in the solution. Then, an enumeration algorithm and a heuristic search algorithm are proposed to find an optimal solution efficiently based on the diversity constraint and an A* heuristic. This study also provides strategies for improving the performance of the heuristic search method.

The results of experiments using a real data set demonstrate that the proposed search algorithm can find top-k diverse and relevant answers to a query on large-scale graph data efficiently and outperforms the previous methods.

This study proposes a new keyword search method for graph data that finds an optimal solution with diverse and relevant answers to the query. It can provide users with query results that satisfy their various information needs on large graph data.

The paper aims to propose an effective method to process keyword-based queries over graph-structured databases which are widely used in various applications such as XML, semantic web, and social network services. To satisfy users' information need, it proposes an extended answer structure for keyword queries, inverted list indexes on keywords and nodes, and query processing algorithms exploiting the inverted lists. The study aims to provide more effective and relevant answers to a given query than the previous approaches in an efficient way.

A new relevance measure for nodes to a given keyword query is defined in the paper and according to the relevance metric, a new answer tree structure is proposed which has no constraint on the number of keyword nodes chosen for each query keyword. For efficient query processing, an inverted list-style index is suggested which pre-computes connectivity and relevance information on the nodes in the graph. Then, a query processing algorithm based on the pre-constructed inverted lists is designed, which aggregates list entries for each graph node relevant to given keywords and identifies top-k root nodes of answer trees most relevant to the given query. The basic search method is also enhanced by using extend inverted lists which store additional relevance information of the related entries in the lists in order to estimate the relevance score of a node more closely and to find top-k answers more efficiently.

Experiments with real datasets and various test queries were conducted for evaluating effectiveness and performance of the proposed methods in comparison with one of the previous approaches. The experimental results show that the proposed methods with an extended answer structure produce more effective top-k results than the compared previous method for most of the queries, especially for those with OR semantics. An extended inverted list and enhanced search algorithm are shown to achieve much improvement on the execution performance compared to the basic search method.

This paper proposes a new extended answer structure and query processing scheme for keyword queries on graph databases which can satisfy the users' information need represented by a keyword set having various semantics.

Fibre-reinforced additive manufacturing (FRAM) with short and continuous fibres yields light and stiff parts and thus increasing industry acceptance. High material anisotropy and specific manufacturing constraints shift the focus towards design for AM (DfAM), particularly on toolpath strategies. Assessing the design-property-processing relations of infill patterns is fundamental to establishing design guidelines for FRAM.

Subject to the DfAM factors performance, economy and manufacturability, the efficacy of two conventional infill patterns (grid and concentric) was compared with two custom strategies derived from the medial axis transformation (MAT) and guided by the principal stresses (MPS). The recorded stiffness and strength, the required CPU and print time, and the degree of path undulation and effective fibre utilisation (minimum printable fibre length) associated with each pattern, served as assessment indices for different case studies. Moreover, the influence of material anisotropy was examined, and a stiffness-alignment index was introduced to predict a pattern’s performance.

The highest stiffnesses and strengths were recorded for the MPS infill, emphasising the need for tailoring print paths rather than using fixed patterns. In contrast to the grid infill, the concentric infill offered short print times and reasonable utilisation of continuous fibres. The MAT-based infill yielded an excellent compromise between the three DfAM factors and experimentally resulted in the best performance.

This constitutes the first comprehensive investigation into infill patterns under DfAM consideration for FRAM, facilitating design and processing choices.

This paper aims to provide a better understanding of the challenges and potential solutions in Visual Simultaneous Localization and Mapping (SLAM), laying the foundation for its applications in autonomous navigation, intelligent driving and other related domains.

In analyzing the latest research, the review presents representative achievements, including methods to enhance efficiency, robustness and accuracy. Additionally, the review provides insights into the future development direction of Visual SLAM, emphasizing the importance of improving system robustness when dealing with dynamic environments. The research methodology of this review involves a literature review and data set analysis, enabling a comprehensive understanding of the current status and prospects in the field of Visual SLAM.

This review aims to comprehensively evaluate the latest advances and challenges in the field of Visual SLAM. By collecting and analyzing relevant research papers and classic data sets, it reveals the current issues faced by Visual SLAM in complex environments and proposes potential solutions. The review begins by introducing the fundamental principles and application areas of Visual SLAM, followed by an in-depth discussion of the challenges encountered when dealing with dynamic objects and complex environments. To enhance the performance of SLAM algorithms, researchers have made progress by integrating different sensor modalities, improving feature extraction and incorporating deep learning techniques, driving advancements in the field.

To the best of the authors’ knowledge, the originality of this review lies in its in-depth analysis of current research hotspots and predictions for future development, providing valuable references for researchers in this field.

The purpose of this study is to investigate the relationship between renewable energy consumption and economic growth, using the threshold variables of non-renewable energy consumption, urbanization level and per-capita income.

This study used a panel threshold regression model, on combined African countries and divided African countries into five regions (northern, western, central, southern and eastern Africa). The study used panel data from 54 African countries, from 1990 to 2018.

This study established a threshold interval where the significant negative impact of renewable energy consumption on the economic growth of combined African countries is different at each split asymmetric phase, meaning the relationship is negative and non-linear. Further, the study established the threshold effect of divided African countries into regions, revealed a negative effect of renewable energy consumption on economic growth and compared the differences of threshold effect and coefficient in the regions, which further highlight the varying resource and renewable energy development across African countries.

This study recommends strategies and investment priorities on energy transition, through optimizing renewable energy in Africa, hence aggressive investment in the renewable energy sector is highly encouraged especially for the oil-producing state to promote clean and sustainable energy.

The contribution of this study is the establishment of a non-linear panel threshold model to examine the asymmetric effect of renewable energy consumption on economic growth which to the best of the authors’ knowledge is pioneer research in Africa. Additionally, an in-depth analysis of renewable energy consumption’s effect on the economic growth of all the regions in Africa.

This paper employs nationwide, large-scale field survey data to provide the first empirical evidence on the impact of human educational capital on the acquisition of health entitlement among Chinese migrant workers. The findings of this study hold significant practical implications for the formulation of policies aimed at improving the health protection of migrant workers, as well as for socioeconomic policies during China's transitional period.

Using the IVProbit model, this research examines how human educational capital influences the attainment of health entitlement among migrant workers in China by analyzing the impact and mechanism of education on health entitlement. The study is based on the China Migrants Dynamic Survey (CMDS) data from 2018, which include 100,177 observations.

For migrant workers in China, higher levels of education have a significant positive effect on the acquisition of health entitlements, including medical insurance, health records and health education. The positive impact of human educational capital on health entitlements is more significant for non-provincial cities and young-generation migrant workers. The results also show that human educational capital can influence the acquisition of health entitlements through mediators such as financial status, social integration and health status.

This study represents the first empirical attempt to evaluate the influence of human educational capital on the access of migrant workers in China to health rights and interests. Additionally, the study develops a theoretical framework to examine how the impact of human educational capital varies across migrant workers with different characteristics and their access to health entitlements.