6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

Effects of Different Modification Methods on the Properties of Sisal Fibers

In this paper, sisal fibers were modified by different methods such as alkali treatment, γ-aminopropyltriethoxysilane treatment, tetraethylorthosilicate (TEOS) sol-gel treatment, TEOS, and silane combination treatment. The effects of different modification methods on the properties of sisal fibers were studied by thermogravimetric analysis (TGA), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and universal material testing machine. The results show that the combination treatment fiber has the best heat resistance and the most obvious improvement in tensile properties, the tensile strength and modulus increase by 31.58% and 15.82%, respectively

Differences between CEUS LI-RADS and CECT LI-RADS in the diagnosis of focal liver lesions in patients at risk for HCC

Abstract Objectives To compare the inter-modality consistency and diagnostic performances of the contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) and contrast-enhanced computed tomography (CECT) LI-RADS in patients at risk for hepatocellular carcinoma (HCC), so as to help clinicians to select a more appropriate modality to follow the focal liver lesions (FLLs). Methods This retrospective study included untreated 277 FLLs from 247 patients who underwent both CEUS and CECT within 1 month. The ultrasound contrast medium used was SonoVue. FLL categories were independently assigned by two ultrasound physicians and two radiologists using CEUS LI-RADS v2017 and CECT LI-RADS v2018, respectively. The diagnostic performances of CEUS and CECT LI-RADS were evaluated using sensitivity, specificity, positive predictive value (PPV), and negative predictive value. Cohen’s Kappa was employed to evaluate the concordance of the LI-RADS category. Results The inter-modality consistency for CEUS and CECT LI-RADS was 0.31 (p < 0.001). HCC was more frequently observed in CECT LR-3 and LR-4 hepatic lesions than in CEUS (7.3% vs. 19.5%, p < 0.001). The specificity and PPV of CEUS and CECT LR-5 for the diagnosis of HCC were 89.5%, 95.0%, and 82.5%, 94.4%, respectively. The sensitivity of CEUS LR-5 + LR-M for the diagnosis of hepatic malignancies was higher than that of CECT (93.7% vs. 82.7%, p < 0.001). The specificity and PPV of CEUS LR-M for the diagnosis of non-HCC malignancies were lower than those of CECT (59.7% vs. 95.5%, p < 0.001; 23.4% vs. 70.3%, p < 0.001). Conclusions The inter-modality consistency between the CEUS and CECT LI-RADS categories is fair. CEUS LI-RADS was more sensitive than CECT LI-RADS in terms of identifying hepatic malignancies, but weaker in terms of separating HCC from non-HCC malignancies

Association between TLR4 (+896A/G and +1196C/T) Polymorphisms and Gastric Cancer Risk: An Updated Meta-Analysis

<div><p>Background</p><p>Toll-like receptor 4 (TLR4) is a receptor of lipopolysaccharide in the signaling transduction of gastric epithelial cell. It plays a pivotal role in activation of innate immunity and pathogen recognition and thus acts as a modulator in the development and progression of gastric cancer. Growing studies explored the association of polymorphisms in TLR4 with susceptibility to gastric cancer, but the results have remained controversial and conflicting. To investigate the effect of two selected TLR4 (+896A/G and +1196C/T) polymorphisms on gastric cancer, we performed a meta-analysis.</p><p>Methods</p><p>A comprehensive search was conducted to identify all eligible case-control publications investigating the association between TLR4 polymorphisms and gastric cancer risk. Odds ratios (OR) and corresponding 95% confidence intervals (CI) were used to assess such association.</p><p>Results</p><p>Up to March 26 2014, 10 published case-control studies from PubMed and EMBase were available, involving a total of 1888 gastric cancer patients and 3433 control subjects. In the overall meta-analyses, a significantly increased gastric cancer risk was detected in TLR4 +896A/G polymorphism (heterozygous model, AG vs. AA: OR = 1.67, 95% CI, 1.39–2.01; additive model, G vs. A: OR = 1.64, 95% CI, 1.37–1.95) and TLR4 +1196C/T polymorphism (heterozygous model, CT vs. CC: OR = 1.42, 95% CI, 1.11–1.81; additive model, T vs. C: OR = 1.36, 95% CI, 1.08–1.72), similar results were obtained in the subgroup analyses of Caucasian, whereas no associations were detected in any genetic models of non-Caucasian.</p><p>Conclusions</p><p>The overall results suggest that TLR4 polymorphisms (+896A/G and +1196C/T) may be associated with a significantly increased gastric cancer risk in Caucasian.</p></div

The distribution of the TLR4 +896A/G and +1196C/T polymorphisms in gastric cancer.

<p>HWE: Hardy-Weinberg Equilibrium.</p><p>The distribution of the TLR4 +896A/G and +1196C/T polymorphisms in gastric cancer.</p

Egger's linear regression plots for publication bias test.

<p>A. AG vs. AA; B. G vs. A; C. CT vs. CC; D. T vs. C.</p

Forest plots showing the association of the TLR4 +896A/G and +1196C/T polymorphisms with risk of gastric cancer.

<p>A. AG vs. AA; B. G vs. A; C. CT vs. CC; D. T vs. C.</p

Characteristics of studies included in the meta-analysis.

<p>HB: hospital based, PB: population based, PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism.</p><p>Characteristics of studies included in the meta-analysis.</p

Forest plots showing the association of the TLR4 +896A/G polymorphism with H. Pylori infection in gastric cancer patients.

<p>A. AG vs. AA, B. G vs. A.</p

The results of Q-test in all genetic models for TLR4 +896A/G and +1196C/T polymorphisms in gastric cancer.

<p>The results of Q-test in all genetic models for TLR4 +896A/G and +1196C/T polymorphisms in gastric cancer.</p