Reliability estimation of reinforced slopes to prioritize maintenance actions

Geosynthetics are extensively utilized to improve the stability of geotechnical structures and slopes in urban areas. Among all existing geosynthetics, geotextiles are widely used to reinforce unstable slopes due to their capabilities in facilitating reinforcement and drainage. To reduce settlement and increase the bearing capacity and slope stability, the classical use of geotextiles in embankments has been suggested. However, several catastrophic events have been reported, including failures in slopes in the absence of geotextiles. Many researchers have studied the stability of geotextile-reinforced slopes (GRSs) by employing different methods (analytical models, numerical simulation, etc.). The presence of source-to-source uncertainty in the gathered data increases the complexity of evaluating the failure risk in GRSs since the uncertainty varies among them. Consequently, developing a sound methodology is necessary to alleviate the risk complexity. Our study sought to develop an advanced risk-based maintenance (RBM) methodology for prioritizing maintenance operations by addressing fluctuations that accompany event data. For this purpose, a hierarchical Bayesian approach (HBA) was applied to estimate the failure probabilities of GRSs. Using Markov chain Monte Carlo simulations of likelihood function and prior distribution, the HBA can incorporate the aforementioned uncertainties. The proposed method can be exploited by urban designers, asset managers, and policymakers to predict the mean time to failures, thus directly avoiding unnecessary maintenance and safety consequences. To demonstrate the application of the proposed methodology, the performance of nine reinforced slopes was considered. The results indicate that the average failure probability of the system in an hour is 2.8 ≥ 105 during its lifespan, which shows that the proposed evaluation method is more realistic than the traditional methods

Metformin pretreatment enhanced learning and memory in cerebral forebrain ischaemia: the role of the AMPK/BDNF/P70SK signalling pathway

Context Metformin induced AMP-activated protein kinase (AMPK) and protected neurons in cerebral ischaemia. Objective This study examined pretreatment with metformin and activation of AMPK in molecular and behavioral levels associated with memory. Materials and methods Rats were pretreated with metformin (200 mg/kg) for 2 weeks and 4-vessels occlusion global cerebral ischaemia was induced. Three days after ischaemia, memory improvement was done by passive avoidance task and neurological scores were evaluated. The amount of Brain-Derived Neurotropic Factor (BDNF) and phosphorylated and total P70S6 kinase (P70S6K) were measured. Results Pretreatment with metformin (met) in the met + ischaemia/reperfusion (I/R) group reduced latency time for enter to dark chamber compared with the sham group (p < 0.001) and increased latency time compared with the I/R group (p < 0.001). Injection of Compound C (CC) (as an AMPK inhibitor) concomitant with metformin reduced latency time in I/R rats compared with the I/R + met group (p < 0.05). Neurological scores were reduced in met treated rats compared with the sham group. Pretreatment with metformin in I/R animals reduced levels of pro-BDNF compared with the I/R group (p < 0.001) but increased that compared with the sham group (p < 0.001). The level of pro-BDNF decreased in the met + CC + I/R group compared with the met + I/R group (p < 0.01). Pretreatment with metformin in I/R animals significantly increased P70S6K compared with the I/R group (p < 0.001). Conclusion Short-term memory in ischaemic rats treated with metformin increased step-through latency; sensory-motor evaluation was applied and a group of ischaemia rats that were pretreated with metformin showed high levels of BDNF, P70S6K that seemed to be due to increasing AMPK. © 2016 Informa UK Limited, trading as Taylor & Francis Group

Adaptive access and rate control of CSMA for energy, rate and delay optimization

In this article, we present a cross-layer adaptive algorithm that dynamically maximizes the average utility function. A per stage utility function is defined for each link of a carrier sense multiple access-based wireless network as a weighted concave function of energy consumption, smoothed rate, and smoothed queue size. Hence, by selecting weights we can control the trade-off among them. Using dynamic programming, the utility function is maximized by dynamically adapting channel access, modulation, and coding according to the queue size and quality of the time-varying channel. We show that the optimal transmission policy has a threshold structure versus the channel state where the optimal decision is to transmit when the wireless channel state is better than a threshold. We also provide a queue management scheme where arrival rate is controlled based on the link state. Numerical results show characteristics of the proposed adaptation scheme and highlight the trade-off among energy consumption, smoothed data rate, and link delay.This study was supported in part by the Spanish Government, Ministerio de Ciencia e Innovación (MICINN), under projects COMONSENS (CSD2008-00010, CONSOLIDER-INGENIO 2010 program) and COSIMA (TEC2010-19545-C04-03), in part by Iran Telecommunication Research Center under contract 6947/500, and in part by Iran National Science Foundation under grant number 87041174. This study was completed while M. Khodaian was at CEIT and TECNUN (University of Navarra)