The Service Quality Evaluation of Mobile Communication from Quality Improvement Perspective ----a case study on China telecom in Wuchang District Wuhan City

Based on SERVAUAL model, this paper brings in the entropy method to rank quality improvement (QI) priority for service attributes, and a service quality evaluation(SQE) model integrating competitive analyses has been structured to evaluate the mobile communication service quality (SQ) for Wuhan Branch of China Telecom(WBCT). The research shows that the QI priority of 22 service attributes has changed as adopts entropy method comparing with gap-based SERVQUAL. The service attributes that finally should be improved have changed from Q20(Various business charges reasonable) and Q22(Record customer complaints and improve) to Q21(provide customers all kinds of value-added services) and Q11(Staff serves with high efficiency)

Oscillation theorems for certain third order nonlinear delay dynamic equations on time scales

In this paper, we establish some new oscillation criteria for the third order nonlinear delay dynamic equations $$\left(b(t)\left([a(t)(x^\Delta(t))^{\alpha_1}]^\Delta\right)^{\alpha_2}\right)^\Delta+q(t)x^{\alpha_3}(\tau(t))=0$$ on a time scale $\mathbb{T}$, where $\alpha_i$ are ratios of positive odd integers, $i=1,\ 2,\ 3,$ $b,\ a$ and $q$ are positive real-valued rd-continuous functions defined on $\mathbb{T}$, and the so-called delay function $\tau:\mathbb{T}\rightarrow \mathbb{T}$ is a strictly increasing function such that $\tau(t)\leq t$ for $t\in \mathbb{T}$ and $\tau(t)\rightarrow\infty$ as $t\rightarrow\infty.$ By using the Riccati transformation technique and integral averaging technique, some new sufficient conditions which insure that every solution oscillates or tends to zero are established. Our results are new for third order nonlinear delay dynamic equations and complement the results established by Yu and Wang in J. Comput. Appl. Math., 2009, and Erbe, Peterson and Saker in J. Comput. Appl. Math., 2005. Some examples are given here to illustrate our main results

Query2GMM: Learning Representation with Gaussian Mixture Model for Reasoning over Knowledge Graphs

Logical query answering over Knowledge Graphs (KGs) is a fundamental yet complex task. A promising approach to achieve this is to embed queries and entities jointly into the same embedding space. Research along this line suggests that using multi-modal distribution to represent answer entities is more suitable than uni-modal distribution, as a single query may contain multiple disjoint answer subsets due to the compositional nature of multi-hop queries and the varying latent semantics of relations. However, existing methods based on multi-modal distribution roughly represent each subset without capturing its accurate cardinality, or even degenerate into uni-modal distribution learning during the reasoning process due to the lack of an effective similarity measure. To better model queries with diversified answers, we propose Query2GMM for answering logical queries over knowledge graphs. In Query2GMM, we present the GMM embedding to represent each query using a univariate Gaussian Mixture Model (GMM). Each subset of a query is encoded by its cardinality, semantic center and dispersion degree, allowing for precise representation of multiple subsets. Then we design specific neural networks for each operator to handle the inherent complexity that comes with multi-modal distribution while alleviating the cascading errors. Last, we define a new similarity measure to assess the relationships between an entity and a query's multi-answer subsets, enabling effective multi-modal distribution learning for reasoning. Comprehensive experimental results show that Query2GMM outperforms the best competitor by an absolute average of $5.5\%$. The source code is available at \url{https://anonymous.4open.science/r/Query2GMM-C42F}

IL-17A stimulates the production of inflammatory mediators via Erk1/2, p38 MAPK, PI3K/Akt, and NF-κB pathways in ARPE-19 cells

Purpose: To investigate the signaling pathways involved in interleukin (IL)-17A -mediated production of interleukin 8 (CXCL8), chemokine (C-C motif) ligand 2 (CCL2), and interleukin 6 (IL-6) by ARPE-19 cells, a spontaneously arisen cell line of retinal pigment epithelium (RPE).Methods: Flow cytometry analysis and western blot were used to detect the phosphorylation of extracellular signal-regulated kinases 1/2 (Erk1/2), p38 mitogen activated protein kinase (MAPK) and protein kinase B (PKB; Akt) in ARPE-19 cells stimulated with IL-17A. These cells were further pretreated with a series of kinase inhibitors and followed by incubation with IL-17A. CXCL8, CCL2, and IL-6 in the supernatant were quantified by enzyme-linked immunosorbent assay (ELISA).Results: Coculture of ARPE-19 cells with IL-17A resulted in significant increases in Erk1/2, p38 MAPK, and Akt phosphorylation. Inhibition of p38MAPK, phosphoinositide 3-kinase (PI3K)-Akt and nuclear factor-kappaB (NF-kappa B), with the inhibitors SB203580, LY294002 and pyrrolydine dithiocarbamate (PDTC) respectively, reduced IL-17 (100 ng/ml) mediated production of CXCL8, CCL2, and IL-6 in a concentration dependent manner. Inhibition of Erk1/2 with PD98059 decreased the expression of the tested three inflammatory mediators when using low doses of IL-17A (0-10 ng/ml) but not at higher concentrations.Conclusions: IL-17A-induced production of inflammatory mediators by ARPE-19 cells involves Erk1/2, p38MAPK, PI3K-Akt and NF-kappa B pathways

Charting new paradigms for CAR-T cell therapy beyond current Achilles heels

Chimeric antigen receptor-T (CAR-T) cell therapy has made remarkable strides in treating hematological malignancies. However, the widespread adoption of CAR-T cell therapy is hindered by several challenges. These include concerns about the long-term and complex manufacturing process, as well as efficacy factors such as tumor antigen escape, CAR-T cell exhaustion, and the immunosuppressive tumor microenvironment. Additionally, safety issues like the risk of secondary cancers post-treatment, on-target off-tumor toxicity, and immune effector responses triggered by CAR-T cells are significant considerations. To address these obstacles, researchers have explored various strategies, including allogeneic universal CAR-T cell development, infusion of non-activated quiescent T cells within a 24-hour period, and in vivo induction of CAR-T cells. This review comprehensively examines the clinical challenges of CAR-T cell therapy and outlines strategies to overcome them, aiming to chart pathways beyond its current Achilles heels

PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering

Recovering high quality surfaces from noisy point clouds, known as point cloud denoising, is a fundamental yet challenging problem in geometry processing. Most of the existing methods either directly denoise the noisy input or filter raw normals followed by updating point positions. Motivated by the essential interplay between point cloud denoising and normal filtering, we revisit point cloud denoising from a multitask perspective, and propose an end-to-end network, named PCDNF, to denoise point clouds via joint normal filtering. In particular, we introduce an auxiliary normal filtering task to help the overall network remove noise more effectively while preserving geometric features more accurately. In addition to the overall architecture, our network has two novel modules. On one hand, to improve noise removal performance, we design a shape-aware selector to construct the latent tangent space representation of the specific point by comprehensively considering the learned point and normal features and geometry priors. On the other hand, point features are more suitable for describing geometric details, and normal features are more conducive for representing geometric structures (e.g., sharp edges and corners). Combining point and normal features allows us to overcome their weaknesses. Thus, we design a feature refinement module to fuse point and normal features for better recovering geometric information. Extensive evaluations, comparisons, and ablation studies demonstrate that the proposed method outperforms state-of-the-arts for both point cloud denoising and normal filtering