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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.

China has been going through a “museum boom” paralleling the domestic tourism boom since 2000; such growth changed the cultural landscape; museums became a vital characteristic of some Chinese cities for both residents and tourists. Encouraged by this growth, the more ambitious “All-for-one Museum (全域博物馆)” was proposed. The physical boundary between museums and living spaces is infinite ambiguity, challenging the idea of museums as “heterotopias.” This study aims to explore the musealization of urban spaces in the context of anthropology and museology, scrutinizing the cultural-political intentions and meanings of these developments, and seeks to ignite further investigation into the reconstruction of historical imaginaries for tourists and urban populations across related disciplines.

This paper examines two cases in Chinese metropolises, Beijing and Shanghai, to illustrate this development of musealization, that is, how the cities actively leverage museological values and methods to connect with their past. In the Beijing case, the authors explore how the local government is leading the effort to musealize the city; in the Shanghai case, they will see how tourists, especially dweller-tourists, navigate through a curated past story in the city and connect their own experience, memory and identity with the place.

The all-for-one museum creates a museal layer projected onto the bigger urban space, even though the authenticity of the “past” is challenged by the modernization development of the city. The authors also find out that for some tourists (especially dweller-tourists), an existential sense of authenticity plays a more significant role as they not only seek to sightsee the past of the city but also to take part in its creation.

This paper discusses two kinds of musealization in cosmopolitan cities of Beijing and Shanghai: top-down and bottom-up. It approaches questions about the musealization of urban spaces from the perspectives of anthropology and museology, and discusses musealization in the specific historical context of China’s modernization process.

This study aims to clarify the relationship of friction material type and brake disc temperature through braking experiment.

The braking performances of resin materials (RM), semimetallic materials (SM) and copper-based powder metallurgy materials (PM) friction blocks mating with forged steel brake disc were examined based on TM-I-type reduced-scale inertial braking dynamometer. The brake disc surface temperature was recorded by infrared thermal camera during braking.

Experimental results indicate that the thermal wear resistance of three friction materials differs with mental content, resulting in the deviation of pad-disc system contact state during braking, thus forming different temperature distribution on the brake disc surface. The peak temperature on the disc face of RM (190°C) is 36.6% and 45.4% lower than that of PM (300°C) and SM (348°C) at 160 km/h. The maximum radial temperature deviation of PM (35°C) is approximately three times than that of RM (12°C) and 40% higher than that of SM (25°C) at 50 km/h, whereas the maximum temperature deviation of SM (97°C) is six times than that of RM (16°C) and 31% higher than that of PM (74°C) at 160 km/h.

The effect of friction material type on the disc surface temperature distribution is revealed, which provides a meaningful reference for the design of brake friction pairs and choice of brake pad materials.

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.

Chatbots have been explored as a novel approach to enhancing consumer engagement by delivering more enjoyable, personalized services. This research aims to investigate the mechanism through which anthropomorphic elements of chatbots influence consumers' intentions to use the technology.

This research introduces five key concepts framed through the “computers-are-social-actors” (CASA) paradigm: form realism (FR), behavioral realism (BR), cognitive trust (CT), entertainment (EM) and chatbot usage intention (CUI). An online questionnaire garnered 280 responses from China and 207 responses from Indonesia. Data collection employed a combination of purposive and snowball sampling techniques. This research utilized structural equation modeling through the analysis of moment structures (AMOS) 27 software to test the hypotheses.

(1) FR positively predicts CT and EM, (2) FR negatively predicts CUI, (3) BR positively predicts CT and EM, (4) BR positively predicts CUI and (5) Both CT and EM mediate the relationship between FR and CUI, as well as between BR and CUI.

This research enriches the current literature on interactive marketing by exploring how the anthropomorphic features of chatbots enhance consumers' intentions to use such technology. It pioneers the exploration of CT and EM as mediating factors in the relationship between chatbot anthropomorphism and consumer behavioral intention. Moreover, this research makes a methodological contribution by developing and validating new measurement scales for measuring chatbot anthropomorphic elements.

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.

This study examines the key determinants of subscription intentions for ChatGPT Plus (paid version) in business settings, focusing on tasks such as system quality, information support, service quality, perceived intelligence, goal-congruent outcome and self-efficacy.

The study utilized a survey of office workers, analyzed through structural equation modeling, to explore these determinants.

The results demonstrate that system quality, service quality and perceived intelligence significantly influence satisfaction, while service quality and perceived intelligence also impact goal-congruent outcomes. Contrary to traditional models, satisfaction does not significantly correlate with usage. Instead, a significant relationship is observed between goal-congruent outcomes and usage. Self-efficacy emerges as a crucial predictor of subscription intentions, further underlined by the significant impact of usage on subscription intention.

The study’s focus on office workers and a single artificial intelligence (AI) chatbot type may limit generalizability. Its findings illuminate several avenues for future research, particularly in diversifying the context and demographics studied.

This research offers actionable insights for businesses and practitioners in the implementation of AI chatbots. It highlights the importance of enhancing system quality, personalization and user confidence to boost subscription intentions, thereby guiding strategies for user engagement and technology adoption.

This study pioneers in investigating subscription intentions towards AI chatbots, particularly ChatGPT, providing a novel framework that expands upon traditional user behavior theories.

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

As artificial intelligence technology empowers service robots, they increasingly communicate with consumers in a human-like manner. This study aims to investigate the effect of service robots’ different conversational styles (competent conversational style vs. cute conversational style) on consumer service acceptance and demonstrate the moderating role of consumers’ technology anxiety.

Based on anthropomorphism theory and social presence theory, the authors conducted two scenario-based experiments (restaurant scenario and hotel scenario) to investigate this issue.

The results indicate that service robots’ conversational styles impact consumers’ willingness to accept the use of service robots through perceived social presence and positive emotion. Moreover, consumers perceived social presence and positive emotion play a serial mechanism. In addition, the effect of competent conversational style on consumers perceived social presence is less effective than that of cute conversational style. Finally, the authors demonstrate the moderating role of consumer technology anxiety in the relationship between conversational styles and perceived social presence.

To provide consumers with a positive human–robot interaction experience at the service front line, managers need to make better use of the conversational styles of service robots by comprehensively considering the characteristics of consumer technology anxiety.

This research extends the literature on service robots by integrating consumer characteristics and robots’ conversational styles. These findings highlight the effectiveness of cute conversational style in alleviating consumer technology anxiety.

The rotor in screw motor is driven to rotate by highly pressure difference of drilling fluid (DF), while rotor drives drill bit to break rocks. DF works in the volume cavity (VC) which exists between the stator and rotor (SAR), these process realizes the conversion from hydraulic energy to mechanical energy finally. In order to assure seal performance and output power reliability of VC in common hypocycloid screw motor (CHSM), it’s essential to survey SAR end-face profile.

In this article, based on the internal and external cycloid method given for SAR end-face of φ172 7/8-head LZ type CHSM, the interference among SAR is established based on the meshing model through theoretical equilibrium method (TEM). Last, the reasonable design value of SAR interference in TEM is verified with the hydraulic parameters test results.

The profile optimization that top-root part of rotor end-face profiles is replaced by elliptical-circular arcs (ECA) makes the transition area of tooth-top and tooth-root connect smoother than before. The reasonable interference of SAR in TEM is almost 0.16mm~0.22mm to ensure better sealing performance. Through the hydraulic test, the interference positive fluctuation or the number of SAR head reduces increase (starting-pressure-drop) SPD while negative fluctuations by contraries. Meanwhile, DF penetration also decreases the revolution speed with the SAR interference decreases. The less SAR head revolution speed is always below the more with the constant driving power and DF hydraulic drop. Ultimately, decreasing in overall-efficiency occurs for larger fluctuation of interference or or less interference among SAR.

The line type optimization and analysis in TEM for CHSM improves the motor seal and output performance, also has important application values simultaneously.