On four new mock theta functions

QiuXia Hu 대한수학회 2020 대한수학회보 Vol.57 No.2

In this paper, we first give some representations for four new mock theta functions defined by Andrews \cite{Andrews} and Bringmann, Hikami and Lovejoy \cite{BHL} using divisor sums. Then, some transformation and summation formulae for these functions and corresponding bilateral series are derived as special cases of $_2\psi_2$ series \[\sum_{n=-\infty}^\infty\frac{(a,c;q)_n}{(b,d;q)_n}z^n\] and Ramanujan's sum \[\sum_{n=-\infty}^\infty\frac{(a;q)_n}{(b;q)_n}z^n.\]

International Experience of “Integrating Kindergarten with Nursery Education” for Young Children and the Enlightenments - Focusing on Sweden and New Zealand

( Qiuxia Guo ) 한국유아교육학회 2023 정기학술대회 논문집 Vol.2023 No.2

Effects of probiotics on intestinal health in high-salt diet (HSD) treated mice

Qiuxia Dong,Tingting Liang,Xuemei Cheng,Henghong Zhong,Zixian Wang,Wanying Li,Qi Qi Pang,Jia-Le Song 한국식품영양과학회 2021 한국식품영양과학회 학술대회발표집 Vol.2021 No.10

To observe the effect of probiotics on intestinal health in a model of high-salt diet (HSD, 8%) treated mice for 49 days. Cinical (body weight, colon length and colon weight) and historical changes were evaluated. In addition, colon levels of interleukin-1β, IL-6, IL-18, IL-17A, tumor necrosis factor (TNF)-α were detected by enzyme-linked immunosorbent assay. Relative to the HSD model group, the mice in the probiotics treatment group increased in body weight, colon length, and the ratio of colon weight to length was significantly lower (P<0.05). The DAI index of mice in the intervention group was lower than that in the HSD model group (P<0.05). Histological observation also found that the intervention of probiotics is able to reduce the infiltration of inflammatory cells in colon tissue induced by HSD. The results of Masson staining suggest that the intervention of probiotics also reduces the occurrence of intestinal fibrosis in mice with colitis. Our results suggested that probiotics with an activity to improve intestinal health and reduced inflammation in HSD treated mice.

ON FOUR NEW MOCK THETA FUNCTIONS

Hu, QiuXia Korean Mathematical Society 2020 대한수학회보 Vol.57 No.2

In this paper, we first give some representations for four new mock theta functions defined by Andrews [1] and Bringmann, Hikami and Lovejoy [5] using divisor sums. Then, some transformation and summation formulae for these functions and corresponding bilateral series are derived as special cases of ₂𝜓₂ series $${\sum\limits_{n=-{{\infty}}}^{{\infty}}}{\frac{(a,c;q)_n}{(b,d;q)_n}}z^n$$ and Ramanujan's sum $${\sum\limits_{n=-{{\infty}}}^{{\infty}}}{\frac{(a;q)_n}{(b;q)_n}}z^n$$.

An SLA-oriented Multiparty Trust Negotiation Model based on HCPN in Cloud Environment

Chunzhi Wang,Qiuxia Chen,Hongwei Chen,Hui Xu 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.7

The negotiation on Service Level Agreement (SLA) in current complex cloud environment often involves the multilateral negotiation. Therefore, this paper presents an SLA-oriented multiparty trust negotiation model based on Hierarchical Colored Petri Net (HCPN) in cloud environment. This model mainly provides trust negotiation of SLA parameters such as credibility, reliability, availability and service price so on for Cloud Service Providers (CSPs) and Cloud Service Consumers (CSCs), thus making complex process description of multiparty trust negotiation on SLA in cloud environment simple and efficient. On the basis of the HCPN model on SLA, this paper puts forward a multi-objective optimization algorithm for SLA trust negotiation. Simulation results show that the algorithm not only improves efficiency of multilateral negotiation, but also avoids QoS negotiation falling into local optimization.

A CPN-based Trust Negotiation Model on Service Level Agreement in Cloud Environment

Hongwei Chen,Qiuxia Chen,Chunzhi Wang 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.2

The negotiation process of the Service Level Agreement (SLA) in cloud environment is an interaction process of access control rules and credentials. Due to the mass of credentials in cloud environment, the negotiation efficiency is not high. To address the problem, this paper proposes an Automated Trust Negotiation (ATN) model based on Colored Petri Net (CPN), which exists a legitimate occurrence sequence and a reachable state space graph. A Minimal Credential Path Searching (MCPS) algorithm is proposed to find a minimum credential disclosure set in the reachable marking graph. The results show that the algorithm can effectively improve the negotiation efficiency.

T2 Mapping with and without Fat-Suppression to Predict Treatment Response to Intravenous Glucocorticoid Therapy for Thyroid-Associated Ophthalmopathy

Zhai Linhan,Wang Qiuxia,Liu Ping,Luo Ban,Yuan Gang,Zhang Jing 대한영상의학회 2022 Korean Journal of Radiology Vol.23 No.6

Objective: To evaluate the performance of baseline clinical characteristics and pretherapeutic histogram parameters derived from T2 mapping of the extraocular muscles (EOMs) in the prediction of treatment response to intravenous glucocorticoid (IVGC) therapy for active and moderate-to-severe thyroid-associated ophthalmopathy (TAO) and to investigate the effect of fat-suppression (FS) in T2 mapping in this prediction. Materials and Methods: A total of 79 patients clinically diagnosed with active, moderate-to-severe TAO (47 female, 32 male; mean age ± standard deviation, 46.1 ± 10 years), including 43 patients with a total of 86 orbits in the responsive group and 36 patients with a total of 72 orbits in the unresponsive group, were enrolled. Baseline clinical characteristics and pretherapeutic histogram parameters derived from T2 mapping with FS (i.e., FS T2 mapping) or without FS (i.e., conventional T2 mapping) of EOMs were compared between the two groups. Independent predictors of treatment response to IVGC were identified using multivariable analysis. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performance of the prediction models. Differences between the models were examined using the DeLong test. Results: Compared to the unresponsive group, the responsive group had a shorter disease duration, lower kurtosis (FSkurtosis), lower standard deviation, larger 75th, 90th, and 95th (FS-95th) T2 relaxation times in FS mapping and lower kurtosis in conventional T2 mapping. Multivariable analysis revealed that disease duration, FS-95th percentile, and FS-kurtosis were independent predictors of treatment response. The combined model, integrating all identified predictors, had an optimized area under the ROC curve of 0.797, 88.4% sensitivity, and 62.5% specificity, which were significantly superior to those of the imaging model (p = 0.013). Conclusion: An integrated combination of disease duration, FS-95th percentile, and FS-kurtosis was a potential predictor of treatment response to IVGC in patients with active and moderate-to-severe TAO. FS T2 mapping was superior to conventional T2 mapping in terms of prediction.

FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information

( Jianli Zhao ),( Qinzhi Zhang ),( Qiuxia Sun ),( Huan Huo ),( Yu Xiao ),( Maoguo Gong ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.1

The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance.

Dynamic process: international diversification and innovation performance from emerging economies

Du Jian,Zheng Qiuxia,Chang Xiaoran 기술경영경제학회 2020 ASIAN JOURNAL OF TECHNOLOGY INNOVATION Vol.28 No.2

Drawing upon the transaction cost economics and organizational learning theory, this study examines how international diversification affects firm's innovation performance. With a longitudinal data set obtained from a sample of 73 Chinese firms in information communication equipment manufacturing industry, home appliances manufacturing industry, and low voltage apparatus and cable manufacturing industry, we find a curvilinear inverted S-shaped relationship between international diversification and innovation performance. It implies that emerging enterprises might gain increase at the expansion stage, experience a hard time at the conflict stage, and then reap again at the convergence phase.

MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System

( Jianli Zhao ),( Zhengbin Fu ),( Qiuxia Sun ),( Sheng Fang ),( Wenmin Wu ),( Yang Zhang ),( Wei Wang ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.5

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.