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Wireless network localization technology is very popular in recent years and has attracted worldwide attention. The purpose of this paper is to improve the localization accuracy of ultra-wideband (UWB) with lower localization error taking into consideration the special real environment with the closed long and narrow space.

The principle of multidimensional scaling (MDS), particle swarm optimization (PSO) and Taylor series expansion algorithm (Taylor-D) were introduced. A novel positioning algorithm, MDS-PSO-Taylor was proposed to minimize the localization error. MDS-PSO algorithm provided a more accurate preliminary coordinate by applying the PSO algorithm so that the Taylor-D was used for further enhancing the localization accuracy.

Experimental results manifested that the proposed algorithm, providing small localization error value and higher positioning accuracy, can effectively reduce errors and achieve better performance in terms of the considerable improvement of localization accuracy.

The presented study with the real environment test attempts to demonstrate the proposed algorithm is hopeful to be applied to the underground environment for in the future.

This paper aims to improve shearer positioning accuracy. Shearer positioning using an inertial navigation system (INS) is a highly useful technology; however, positioning accuracy is seriously hindered by INS attitude error, particularly heading drift.

A shearer positioning model with double-INS based on extended Kalman filter was proposed. The constant distance between two INSs (INS 1 and INS 2) was selected as the observation vector. Allan variance was used to identify the noise type of the vertical-axis gyroscope, and the stochastic process of heading drift for two INSs was obtained and divided into incongruous drift and concurrent drift.

Simulation was then carried out to determine the optimal arrangement of the two INSs. For incongruous drift, the optimal arrangement satisfied the condition that the line connecting INS 1 and INS 2 was perpendicular to the shearer lateral axis (in the shearer coordinate frame) and parallel to the east-north plane (in the east-north-up coordinate frame). Under optimal arrangement, the positioning accuracy increased against the distance between INS 1 and INS 2. For concurrent drift, the double-INS positioning model had no effect. Under the circumstances, the number of INSs should be increased so that the uncertainty of INS drift was reflected as much as possible.

A new double-INS positioning model was proposed with the constant distance between the two INSs. The optimal arrangement for double-INS was obtained.

Chatbots are increasingly engaged in service marketing. Some academics and managers think using anthropomorphism chatbots will improve positive attitudes and behaviors in the customer journey, but at a high degree of anthropomorphism, consumers may experience negative emotions such as fear and disgust due to the feeling that the robots resemble humans too much, which is known as the uncanny valley effect. Therefore, the authors aim to explore whether chatbot anthropomorphism will promote or limit the development of the customer journey and explore the moderating factors and the antecedent factors affecting consumers' perceptions of chatbot anthropomorphism.

The authors collected 72,782 unique data points from 42 articles and 82 samples using a meta-analysis. Based on the stimuli-organism-response (SOR) model, the impact of anthropomorphic chatbots on the consumer journey was discussed.

The authors’ findings show that chatbot anthropomorphism positively impacts the customer journey but not their negative attitudes. Further moderator analysis reveals that the impact depends on service result, chatbot gender and sample source. The chatbot anthropomorphism is significantly influenced by social presence cues, emotional message cues and mixed cues.

This research contributes to the chatbot anthropomorphism literature and offers guidance for managers on whether and how to enhance chatbot anthropomorphism to facilitate the customer journey and improve service sustainability.

Criminal elements in today's technology‐driven society are using every means available at their disposal to launder the proceeds from their illegal activities. While many anti‐money laundering (AML) solutions have been in place for some time within the financial community, they face the challenge to adapt to the ever‐changing risk and methods in relation to money laundering (ML). This research seeks to focus on ML control and prevention, which aim to automate the monitoring and diagnosing of ML schemes in order to report suspicious activities to banks.

The research adopted the technology of intelligent agents to provide a more adaptive, flexible, and knowledge‐based solution for AML.

Based on the analysis of monitoring, diagnosing, and reporting of ML activities occurring in electronic transactions, several types of intelligent agents are proposed and a multi‐agent framework is presented for AML. Furthermore, business knowledge such as business rules and strategies are extracted from AML practice, and applied to the design of individual agents to make them act autonomously and collaboratively to fulfil the goal of ML detection.

The proposed multi‐agent framework is a stand‐alone system, which can be integrated by banks to combat ML. Although it is a uni‐bank framework at present, it can be extended to multi‐bank application in the future.

The research explores the approach of applying an intelligent agent for knowledge‐based AML in an electronic transaction environment for banks. By separating business logic from the business model, such a business‐rules approach can enhance the flexibility and adaptability of the agent‐based AML system.

The selection of purchasing channels by wine consumers indirectly affects buying experience and satisfaction, therefore, it is of great practical significance to study consumers' preference on channel selection. The purpose of this study is to investigate the current state of consumer selection for purchasing channel and the corresponding influencing factors.

A total of 2,976 valid questionnaires were collected by convenience sampling from 34 provinces, municipalities and autonomous regions of China in 2020 via the Internet, yielding a response rate of 82.2%. A categorical statistical approach was used to understand consumer's selection for each channel. Besides, binary logistic regression model was used to analyze the factors affecting consumers' channel selection.

The results show that Chinese wine consumers' main purchasing channels are as follows: supermarket/mall, wine specialty stores, comprehensive e-business flagship stores, comprehensive e-business individual stores, restaurants and short video and live streaming platforms. Estimation results showed that among the 12 influencing factors in 4 categories, consumers' education and some other factors significantly influenced consumers' decision on wine purchasing channels.

Limited by time and experimental conditions, this study did not analyze the trend of wine consumers' purchasing channel selection. Future work would concentrate on multi-year data and conduct longitudinal comparative analysis.

This study innovatively subdivides the currently popular wine sales channels in Chinese market and conducts research related to consumer channel selection. The results of the study can provide reference for wine producers and distributors to update their strategic layout and also help various channels to understand the characteristics of their customer groups for targeted marketing.

The majority of existing studies about named entity recognition (NER) concentrate on the prediction enhancement of deep neural network (DNN)-based models themselves, but the issues about the scarcity of training corpus and the difficulty of annotation quality control are not fully solved, especially for Chinese ancient corpora. Therefore, designing a new integrated solution for Chinese historical NER, including automatic entity extraction and man-machine cooperative annotation, is quite valuable for improving the effectiveness of Chinese historical NER and fostering the development of low-resource information extraction.

The research provides a systematic approach for Chinese historical NER with a three-stage framework. In addition to the stage of basic preprocessing, the authors create, retrain and yield a high-performance NER model only using limited labeled resources during the stage of augmented deep active learning (ADAL), which entails three steps—DNN-based NER modeling, hybrid pool-based sampling (HPS) based on the active learning (AL), and NER-oriented data augmentation (DA). ADAL is thought to have the capacity to maintain the performance of DNN as high as possible under the few-shot constraint. Then, to realize machine-aided quality control in crowdsourcing settings, the authors design a stage of globally-optimized automatic label consolidation (GALC). The core of GALC is a newly-designed label consolidation model called simulated annealing-based automatic label aggregation (“SA-ALC”), which incorporates the factors of worker reliability and global label estimation. The model can assure the annotation quality of those data from a crowdsourcing annotation system.

Extensive experiments on two types of Chinese classical historical datasets show that the authors’ solution can effectively reduce the corpus dependency of a DNN-based NER model and alleviate the problem of label quality. Moreover, the results also show the superior performance of the authors’ pipeline approaches (i.e. HPS + DA and SA-ALC) compared to equivalent baselines in each stage.

The study sheds new light on the automatic extraction of Chinese historical entities in an all-technological-process integration. The solution is helpful to effectively reducing the annotation cost and controlling the labeling quality for the NER task. It can be further applied to similar tasks of information extraction and other low-resource fields in theoretical and practical ways.

The purpose of this paper is to provide a micro historical account of the work of a key Chinese educational reformer, Tao Xingzhi (1891–1946), who transformed educational ideas from John Dewey to effect social and cultural change in 1920s–1940s China.

This paper examines English and Chinese language sources, including Tao’s poetry, to present a fresh analysis of Tao’s epistemological life history. It draws upon transnational historical approaches to chart the multidirectional circulation of progressive education philosophies around the globe. It also explores some conceptual dimensions of Chinese historical thinking and historiographical strategies.

Tao Xingzhi engaged in critical intercultural knowledge exchange in implementing educational reforms in China. He blended and critiqued Chinese and Deweyian educational philosophies to create unique educational reform, which involved reversing some of Dewey’s approaches as well as adapting others.

This paper foregrounds Tao Xingzhi’s agency in transforming some of Dewey’s ideas in the Chinese context and challenges studies that adopt an “impact-response” approach to Tao’s contribution, which suggest a one-way flow of knowledge from a “modern” West to a “traditional” China. It brings hitherto unexplored Chinese language sources to an English-speaking audience, particularly Tao’s poetry, to gain new historical insights into Tao’s educational reforms. It contributes to transnational understandings of the multidirectional flows of knowledge about Progressive educational philosophies around the world.

The purpose of this study is to explore consumer behaviour in the context of online fashion renting by applying the theory of reasoned action (TRA) to online fashion rental services and comparing how consumers perceive online fashion renting in terms of its ecological importance and compatibility with the cultures of the USA and China.

Quantitative research was conducted, and 646 data sets were collected from a research firm within three weeks. Prior to launching the main survey, back-and-forth translation processes were engaged for the Chinese survey. To test the proposed hypotheses, structural equation modelling (SEM) and PROCESS models were employed.

The results suggested that both online fashion renting attitudes and social norms positively influenced online fashion renting intentions. Meanwhile, online fashion renting attitudes were positively impacted by perceived compatibility and perceived ecological importance. The moderating effect of national difference was also examined. Specifically, the positive effect of perceived compatibility on online fashion renting attitudes was stronger in US consumers than in Chinese consumers.

Although the findings of this study are valuable, comparing online fashion renting services in East Asian regions can be further explored, or comparing the USA with European countries may yield different results.

This study is one of the few to investigate how consumers engage in and perceive online fashion renting by comparing cultural differences between the USA and China. These two countries were selected because they are the leading countries in online shopping behaviour.

This study is part of a larger research project which aims to investigate whether sentiments in online reviews on children’s books would represent significant factors which are useful for selecting the right books for children. This paper aims to examine whether positive, negative or neutral attitude would be directly associated with the overall ratings of books.

The study investigates subjectivity and polarity of online reviews on children’s books such as neutral, positive or negative sentiment. For the investigation of a statistical association between the sentiment values and the rating scores, this study performs correlation analysis. For a clear explanation of the factors affecting the relationships between the sentiment value and the rating score, this study uses the concept-level sentiment analysis of online reviews.

The findings of this study demonstrate that there is a weak or low correlation between the sentiment value and the rating score of a book and they are hardly related for most books. The results of this study also uncover key contributing factors that affected the correlations between two variables and made the relationship weak.

This study increases awareness of the implications of online reviews as user-generated contents for complementing the existing controlled vocabulary.

This study contributes to improving library catalogs by using latent topics extracted from online reviews which provide additional access points for assisting in the selection of books.

Although several studies have conducted on online reviews in the domain of business, no research appears to exist on the sentiment analysis of online reviews about children’s books. This study attempts to address the potential and challenges associated with using online reviews to help find the right books for children.

The purpose of this paper is to investigate the friction and wear mechanisms in copper-based self-lubricating composites with MoS₂ as the lubricating phase, which provides a theoretical basis for subsequent research on high-performance copper-based self-lubricating materials.

Friction tests were performed at a speed of 100 r/min, a load of 10 N, a friction radius of 5 mm and a sliding speed of 30 min. Friction experiments were carried out at RT-500°C. The phase composition of the samples was characterized by X-ray diffraction of Cu Ka radiation, and the microstructure, morphology and elemental distribution were characterized by scanning electron microscopy and energy dispersive spectroscopy. Reactants and valences formed during the wear process were analyzed by X-ray photoelectron spectroscopy.

The addition of MoS₂ can effectively improve friction-reducing and anti-wear action of the matrix, which is beneficial to form a lubricating film on the sliding track. After analyzing different changing mechanism of the sliding tracks, the oxides and sulfides of MoS₂, MoO₂, Cu₂O, CuO and Ni(OH)₂ were detected to form a synergetic lubricating film on the sliding track, which is responsible for the excellent tribological properties from room to elevated temperature.

For self-lubrication Cu–Sn–Ni–MoS₂ material in engineering field, there are still few available references on high-temperature application.

This paper provides a theoretical basis for the following research on copper-based self-lubricating materials with high performance.

With this statement, the authors hereby certify that the manuscript is the results of their own effort and ability. They have indicated all quotes, citations and references. Furthermore, the authors have not submitted any essay, paper or thesis with similar content elsewhere. No conflict of interest exits in the submission of this manuscript.