What Factors Will Determine Users’ Knowledge Payment Decision? An Theoretical and Empirical Research

With the increase of peoples’ eagerness for higher quality knowledge, paid Q&A is becoming a new tendency. However, what factors are helpful to drive potential users’ payment decisions remains unknown. In this paper, we investigated the effects of expert attributes and reputation on users’ payment decisions made on an online Q&A platform in China. We developed auto-parsing crawlers to collect online observational data and used the negative binomial panel regression method to estimate the effects of expert attributes and reputation on users’ payment decision. The results show that expert attributes such as the number of paid questions, the number of times that answers are approved, whether the expert has a personal home page, whether the expert mentions his/her area of expertise, the number of followers, score of expert answers have significant effects, whereas the times that the expert shared knowledge free and whether the expert has a real name certification do not influence users’ willingness to pay for an answer. The results help experts on paid Q&A platforms to improve their performance, perfect their personal information, and enhance users’ trust, so as to promote the development of knowledge sharing economy

Impacts of Live Chat on Refund Intention: Evidence from an Online Labor Market

Live chat plays a significant role in online labor markets, which mitigates the information asymmetry caused by the highly customized nature of service products. This study examines the impacts of live chat on refund intention in online labor markets and how these impacts are moderated by business familiarity. We collect unique archived data from a leading online labor market in Asia and hypothesize that reply speed has a negative effect on refund intention while both politeness intensity and sentiment intensity have a U-shaped effect on refund intention. In addition, these effects are proposed to be weakened by business familiarity formed by previous transaction experience. The study not only offers theoretical contributions to the online labor market literature by providing empirical insights on the impact of live chat on refund intention but also yields managerial implications for service providers and platform operators

Towards load-bearing biomedical titanium-based alloys: From essential requirements to future developments

The use of biomedical metallic materials in research and clinical applications has been an important focus and a significant area of interest, primarily owing to their role in enhancing human health and extending human lifespan. This article, particularly on titanium-based alloys, explores exceptional properties that can address bone health issues amid the growing challenges posed by an aging population. Although stainless steel, magnesium-based alloys, cobalt-based alloys, and other metallic materials are commonly employed in medical applications, limitations such as toxic elements, high elastic modulus, and rapid degradation rates limit their widespread biomedical applications. Therefore, titanium-based alloys have emerged as top-performing materials, gradually replacing their counterparts in various applications. This article extensively examines and highlights titanium-based alloys, along with an in-depth discussion of currently utilized metallic biomedical materials and their inherent limitations. To begin with, the essential requirements for load-bearing biomaterials are introduced. Then, the biomedical metallic materials are summarized and compared. Afterward, the microstructure, properties, and preparations of titanium-based alloys are explored. Furthermore, various surface modification methods are discussed to enhance biocompatibility, wear resistance, and corrosion resistance. Finally, the article proposes the development path for titanium-based alloys in conjunction with additive manufacturing and the novel alloy nitinol

The Verification of Rail Thermal Stress Measurement System

Continuous Welded Rail (CWR) is widely used in modern railways. With the absence of the expansion joints, CWR cannot expansion freely when the temperature changes, which could cause buckling in hot weather or breakage in cold weather. Therefore, rail thermal stress measuring system plays an important role in the safe operation of railways. This paper designed a thermal stress measurement system based on the acoustoelastic effect of the ultrasonic guided wave. A large-scale rail testbed was built to simulate the thermal stress in the rail track, and to establish the relationship of time-delay of guided wave and thermal stress. After laboratory testing, the system was installed in several railway lines in China for field tests. The results showed that the system was stable and accurate in stress measurement. The performance and potentials of the system were discussed

Application analysis of ai technology combined with spiral CT scanning in early lung cancer screening

At present, the incidence and fatality rate of lung cancer in China rank first among all malignant tumors. Despite the continuous development and improvement of China's medical level, the overall 5-year survival rate of lung cancer patients is still lower than 20% and is staged. A number of studies have confirmed that early diagnosis and treatment of early stage lung cancer is of great significance to improve the prognosis of patients. In recent years, artificial intelligence technology has gradually begun to be applied in oncology. ai is used in cancer screening, clinical diagnosis, radiation therapy (image acquisition, at-risk organ segmentation, image calibration and delivery) and other aspects of rapid development. However, whether medical ai can be socialized depends on the public's attitude and acceptance to a certain extent. However, at present, there are few studies on the diagnosis of early lung cancer by AI technology combined with SCT scanning. In view of this, this study applied the combined method in early lung cancer screening, aiming to find a safe and efficient screening mode and provide a reference for clinical diagnosis and treatment.Comment: This article was accepted by Frontiers in Computing and Intelligent Systems https://drpress.org/ojs/index.php/fcis/article/view/15781. arXiv admin note: text overlap with arXiv:nlin/0508031 by other author

Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation

The process of transforming input images into corresponding textual explanations stands as a crucial and complex endeavor within the domains of computer vision and natural language processing. In this paper, we propose an innovative ensemble approach that harnesses the capabilities of Contrastive Language-Image Pretraining models

Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method

In the realm of patent document analysis, assessing semantic similarity between phrases presents a significant challenge, notably amplifying the inherent complexities of Cooperative Patent Classification (CPC) research. Firstly, this study addresses these challenges, recognizing early CPC work while acknowledging past struggles with language barriers and document intricacy. Secondly, it underscores the persisting difficulties of CPC research. To overcome these challenges and bolster the CPC system, This paper presents two key innovations. Firstly, it introduces an ensemble approach that incorporates four BERT-related models, enhancing semantic similarity accuracy through weighted averaging. Secondly, a novel text preprocessing method tailored for patent documents is introduced, featuring a distinctive input structure with token scoring that aids in capturing semantic relationships during CPC context training, utilizing BCELoss. Our experimental findings conclusively establish the effectiveness of both our Ensemble Model and novel text processing strategies when deployed on the U.S. Patent Phrase to Phrase Matching dataset.Comment: It accepted by The 6th International Conference on Machine Learning and Machine Intelligence (MLMI 2023