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Since composite structures with soft-material matrix do not have adequate pullout resistance with flat-type reinforcements such as fibers, there are increasing cases where reinforcements with passive resistance are used in conjunction. So, loopformed polyethylene fibers were used to reinforce soil against shear loading. Afterward, shear behavior of both fiber and loop-formed fiber-reinforced soil composite samples was modeled by using ''force-equilibrium method'' and ''slippage theory'' of short fiber composites. The proposed model indicated that both fiber parameters and ambient conditions determine shear strength of a fiber-soil composite. In the next step, a set of laboratory direct shear tests was performed on different samples including neat soil, loop-formed fiber and fiber-reinforced treatments. Thus, it was found that the performance of polyethylene looped fibers in shear strength improvement of soil composite is more than that of the ordinary polyethylene fibers. In addition, a novel apparatus based on fiber pullout test was designed to determine the interfacial shear stress between fiber and soil. Finally, an artificial neural network technique and least square method were used to calculate ''fiber reinforcing amplitude'' and ''slippage ratio'', as input parameters required for the model. Consequently, both the proposed models and established artificial neural network adequately predicted shear behavior of loop-formed fiber and fiber reinforced soil composite.

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Background Alzheimer disease (AD) is a neurological disorder with brain network dysfunction. Investigation of the brain network functional connectivity (FC) alterations using resting‐state functional MRI (rs‐fMRI) can provide valuable information about the brain network pattern in early AD diagnosis. Purpose To quantitatively assess FC patterns of resting‐state brain networks and graph theory metrics (GTMs) to identify potential features for differentiation of amnestic mild cognitive impairment (aMCI) and late‐onset AD from normal. Study Type Prospective. Subjects A total of 14 normal, 16 aMCI, and 13 late‐onset AD. Field Strength/Sequence A 3.0 T; rs‐fMRI: single‐shot 2D‐EPI and T1‐weighted structure: MPRAGE. Assessment By applying bivariate correlation coefficient and Fisher transformation on the time series of predefined ROIs' pairs, correlation coefficient matrixes and ROI‐to‐ROI connectivity (RRC) were extracted. By thresholding the RRC matrix (with a threshold of 0.15), a graph adjacency matrix was created to compute GTMs. Statistical Tests Region of interest (ROI)‐based analysis: parametric multivariable statistical analysis (PMSA) with a false discovery rate using (FDR)‐corrected P < 0.05 cluster‐level threshold together with posthoc uncorrected P < 0.05 connection‐level threshold. Graph‐theory analysis (GTA): P‐FDR‐corrected < 0.05. One‐way ANOVA and Chi‐square tests were used to compare clinical characteristics. Results PMSA differentiated AD from normal, with a significant decrease in FC of default mode, salience, dorsal attention, frontoparietal, language, visual, and cerebellar networks. Furthermore, significant increase in overall FC of visual and language networks was observed in aMCI compared to normal. GTA revealed a significant decrease in global‐efficiency (28.05 < 45), local‐efficiency (22.98 < 24.05), and betweenness‐centrality (14.60 < 17.39) for AD against normal. Moreover, a significant increase in local‐efficiency (33.46 > 24.05) and clustering‐coefficient (25 > 20.18) were found in aMCI compared to normal. Data Conclusion This study demonstrated resting‐state FC potential as an indicator to differentiate AD, aMCI, and normal. GTA revealed brain integration and breakdown by providing concise and comprehensible statistics. Evidence Level 1 Technical Efficacy Stage 2