Analysis on Evolution Model of Zombie Company under the Absence of Bank Data

In view of the fact that the data of bank loaning is difficult to be collected, this paper innovatively explores the evolution model of zombie companies by text analysis based on the researches of previous papers at home and abroad based on the financial data of zombie companies. Through the relevant researches on 27 zombies collected, the common characteristics of zombies are found out by the grounded theory. According to the relevant models of enterprise life cycle theory, the evolution model of zombie companies is drawn up, and the corresponding feedback loop of system dynamics causality in each link is further found out, so as to explore the evolutionary rules and the reasons of zombie companies, which is helpful for government to further research on zombies, and favorable for the efficient allocation of market resources and the further rapid development of social economy

Exploring semantic information in disease: Simple Data Augmentation Techniques for Chinese Disease Normalization

The disease is a core concept in the medical field, and the task of normalizing disease names is the basis of all disease-related tasks. However, due to the multi-axis and multi-grain nature of disease names, incorrect information is often injected and harms the performance when using general text data augmentation techniques. To address the above problem, we propose a set of data augmentation techniques that work together as an augmented training task for disease normalization. Our data augmentation methods are based on both the clinical disease corpus and standard disease corpus derived from ICD-10 coding. Extensive experiments are conducted to show the effectiveness of our proposed methods. The results demonstrate that our methods can have up to 3\% performance gain compared to non-augmented counterparts, and they can work even better on smaller datasets

Which Factors Determine User’s First and Repeat Online Music Listening Respectively? Music Itself, User Itself, or Online Feedback

In the era of Web 2.0, does online feedback mainly dominant online users’ buying behavior, or are user’s own preference and product quality still important? Previous studies paid more attention to the influence of online feedback on users’ online buying behavior, however this paper focuses on how users’ own factors, product quality related factors and online feedback factors together influence a user’s buying behavior, and also how does this effect change as time goes by. Taking online music as our research industry and using the data from Last.fm website, this research shows that users’ preference and product quality are still the two most dominate factors influencing users’ online music listening, while online feedback plays an important role on users’ first listening. It is also found that the different influences of crowds and friends

Weighted Composition Operators on Some Weighted Spaces in the Unit Ball

Let Bn be the unit ball of Cn, H(Bn) the space of all holomorphic functions in Bn. Let u∈H(Bn) and α be a holomorphic self-map of Bn. For f∈H(Bn), the weigthed composition operator uCα is defined by (uCαf)(z)=u(z)f(α(z)),z∈Bn. The boundedness and compactness of the weighted composition operator on some weighted spaces on the unit ball are studied in this paper

Periodic Boundary Value Problems for Second-Order Functional Differential Equations

This note considers a periodic boundary value problem for a second-order functional differential equation. We extend the concept of lower and upper solutions and obtain the existence of extreme solutions by using upper and lower solution method.</p

Design and Implementation of Ontology-Based Query Expansion for Information Retrieval

Abstract. In Information Retrieval (IR), the user&apos;s input query conditions usually are not detailed enough, so the satisfactory query results can not be brought back. Query expansion of IR can help to solve this problem. However, the common query expansion in IR cannot get steady retrieval results. In this paper, we propose and implement query expansion method which combines domain ontology with the frequent of terms. Ontology is used to describe domain knowledge; logic reasoner and the frequency of terms are used to choose fitting expansion words. By this way, higher recall and precise can be gotten as user&apos; query results. Experimental results show that compared with the results of common query expansion, the method described in this paper can get statistically significant improvement in recall and precise combination. Keywords: Search engine, Ontology, Web ontology language (OWL), Knowledge management, Enterprise search l. INTRODCTION In information retrieval (IR), even the best system has a limited recall. Users may miss many important documents which they really need usually. There are two fundamental reasons for this problem. The first one is word mismatch, which means that concepts (or key words) of user queries are often different from the words of the resource documents although these words have similar meanings. Another is that users submit short queries which are not detailed enough for IR, so the bad search performance ensues. Query expansion (QE) can effectively alleviate the problem by adding additional terms which have similar meaning to the original query. In this study, we proposed a new expansion method which is based on domain ontology and frequency of keyword occurrence in resource documents to filter expansion words. It achieves better performance in both precision and recall

Would You Accept Doctor ChatGPT: An Empirical Study Based on the UTAUT Model

Since its introduction, ChatGPT has generated significant interest in many areas in just a few months, including healthcare. Recently, researchers have claimed that it has passed the U.S. medical licensure examination (Kung et. al, 2022). However, few empirical studies have investigated whether ChatGPT would be valued and accepted by public in the healthcare context. To understand the public\u27s willingness to accept Artificial Intelligence-Generated Content (AIGC) applications like ChatGPT in the healthcare sector, this study proposes a model of factors affecting the user acceptance of ChatGPT for healthcare purposes integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Trust Theory, the Perceived Risk Theory, and the Perceived Illness Theory. We will analyze the data collected from questionnaires using Structural Equation Modeling (SEM). The findings of this study will provide insights into the factors affecting the user acceptance of ChatGPT for healthcare services

An approach for medical event detection in Chinese clinical notes of electronic health records

Abstract Background Medical event detection in narrative clinical notes of electronic health records (EHRs) is a task designed for reading text and extracting information. Most of the previous work of medical event detection treats the task as extracting concepts at word granularity, which omits the overall structural information of the clinical notes. In this work, we treat each clinical note as a sequence of short sentences and propose an end-to-end deep neural network framework. Methods We redefined the task as a sequence labelling task at short sentence granularity, and proposed a novel tag system correspondingly. The dataset were derived from a third-level grade-A hospital, consisting of 2000 annotated clinical notes according to our proposed tag system. The proposed end-to-end deep neural network framework consists of a feature extractor and a sequence labeller, and we explored different implementations respectively. We additionally proposed a smoothed Viterbi decoder as sequence labeller without additional parameter training, which can be a good alternative to conditional random field (CRF) when computing resources are limited. Results Our sequence labelling models were compared to four baselines which treat the task as text classification of short sentences. Experimental results showed that our approach significantly outperforms the baselines. The best result was obtained by using the convolutional neural networks (CNNs) feature extractor and the sequential CRF sequence labeller, achieving an accuracy of 92.6%. Our proposed smoothed Viterbi decoder achieved a comparable accuracy of 90.07% with reduced training parameters, and brought more balanced performance across all categories, which means better generalization ability. Conclusions Evaluated on our annotated dataset, the comparison results demonstrated the effectiveness of our approach for medical event detection in Chinese clinical notes of EHRs. The best feature extractor is the CNNs feature extractor, and the best sequence labeller is the sequential CRF decoder. And it was empirically verified that our proposed smoothed Viterbi decoder could bring better generalization ability while achieving comparable performance to the sequential CRF decoder

Mussel-Inspired Polydopamine Coated Iron Oxide Nanoparticles for Biomedical Application

Mussel-inspired polydopamine (PDA) coated iron oxide nanoparticles have served as a feasible, robust, and functional platform for various biomedical applications. However, there is scarcely a systemic paper reviewed about such functionalising nanomaterials to date. In this review, the synthesis of iron oxide nanoparticles, the mechanism of dopamine self-oxidation, the interaction between iron oxide and dopamine, and the functionality and the safety assessment of dopamine modified iron oxide nanoparticles as well as the biomedical application of such nanoparticles are discussed. To enlighten the future research, the opportunities and the limitations of functionalising iron oxide nanoparticles coated with PDA are also analyzed

Synthesis of Superparamagnetic Iron Oxide Nanoparticles Modified with MPEG-PEI via Photochemistry as New MRI Contrast Agent

Novel method for synthesis of superparamagnetic iron oxide nanoparticles (SPIONs) coated with polyethylenimine (PEI) and modified with poly(ethylene glycol) methyl ether (MPEG), MPEG-PEI-SPIONs, was developed. PEI-SPIONs were successfully prepared in aqueous system via photochemistry, and their surface was modified with poly(ethylene glycol) methyl ether (MPEG). The so-obtained MPEG-PEI-SPIONs had a uniform hydrodynamic particle size of 34 nm. The successful coating of MPEG-PEI on the SPIONs was ascertained from FT-IR analysis, and the PEI and MPEG fractions in MPEG-PEI-SPIONs were calculated to account for 31% and 12%, respectively. Magnetic measurement revealed that the saturated magnetization of MPEG-PEI-SPIONs reached 46 emu/g and the nanoparticles showed the characteristic of being superparamagnetic. The stability experiment revealed that the MPEG-PEI modification improved the nanoparticles stability greatly. T2 relaxation measurements showed that MPEG-PEI-SPIONs show similar R2 value to the PEI-SPIONs. The T2-weighted magnetic resonance imaging (MRI) of MPEG-PEI-SPIONs showed that the magnetic resonance signal was enhanced significantly with increasing nanoparticle concentration in water. These results indicated that the MPEG-PEI-SPIONs had great potential for application in MRI