Predicting the stress-strain behaviour of zeolite-cemented sand based on the unconfined compression test using GMDH type neural network

Stabilizing sand with cement is considered to be one of the most cost-effective and useful methods of in-situ soil improvement, and the effectiveness is often assessed using unconfined compressive tests. In certain cases, zeolite and cement blends have been used; however, even though this is a fundamental issue that affects the settlement response of a soil, very few attempts have been made to assess the stress-strain behaviour of the improved soil. Also, the majority of previous studies that predicted the unconfined compressive strength (UCS) of zeolite cemented sand did not examine the effect of the soil improvement variables and strain concurrently. Therefore, in this paper, an initiative is taken to predict the relationships for the stress-strain behaviour of cemented and zeolite-cemented sand. The analysis is based on using the unconfined compression test results and Group Method of Data Handling (GMDH) type Neural Network (NN). To achieve this end, 216 stress-strain diagrams resulting from unconfined compression tests for different cement and zeolite contents, relative densities, and curing times are collected and modelled via GMDH type NN. In order to increase the accuracy of the predictions, the parameters associated with successive stress and strain increments are considered. The results show that the suggested two and three hidden layer models appropriately characterise the stress-strain variations to produce accurate results. Moreover, the UCS values derived from this method are much more accurate than those provided in previous approaches. Moreover, the UCS values derived from this method are much more accurate than those provided in previous approaches which simply proposed the UCS values based on the content of the chemical binders, compaction, and/or curing time, not considering the relationship between stress and strain. Finally, GMDH models can be considered to be a powerful method to determine the mechanical properties of a soil including the stre

A randomized controlled clinical trial evaluating quality of life when using a simple acupressure protocol in women with primary dysmenorrhea

Objective: To evaluate a simple acupressure protocol in LIV3 and LI4 acupoints in women with primary dysmenorrhea. Methods: This paper reports a randomized, single blinded clinical trial. 90 young women with dysmenorrhea were recruited to three groups to receive 20 minutes acupressure every day in either LIV3 or LI4, or placebo points. Acupressure was timed five days before menstruation for three successive menstrual cycles. On menstruation, each participant completed the Wong Baker faces pain scale, and the quality of life short form -12 (QOL SF-12). Results: Intensity and duration of pain between the three groups in the second and third cycles during the intervention (p<0.05) differed significantly. Significant differences were seen in all domains of QOL except for mental health (p=0.4), general health (p=0.7) and mental subscale component (p=0.12) in the second cycle, and mental health (p=0.9), and mental subscale component (p=0.14) in the third cycle. Conclusion: Performing the simple acupressure protocol is an effective method to decrease the intensity and duration of dysmenorrhea, and improve the QOL. Key words: Dysmenorrhea, acupressure, quality of life Registration ID in IRCT: IRCT2016052428038N

Prediction of compressive and tensile strengths of zeolite-cemented sand using porosity and composition

The current paper aims to examine the effects of cement and zeolite contents on the strength of zeolite-cemented sand using the unconfined compressive strength (UCS) and splitting tensile strength (q(t)). First of all, the optimum content (i.e., the corresponding water content to the maximum UCS) was obtained from the response surface (RSM) and central composite design (CCD) methods. Then, unconfined compression and splitting tensile tests considering four distinct porosity percentages (TO related to D-rsand = 35, 50, 70 and 85% (Dr = relative density), five cement contents (2, 4, 6, 8. and 10%), and six different percentages of zeolite replacement (0, 10, 30, 50, 70 and 90%) were performed. Then, the amounts of the improved UCS and qt of the specimens as a result of the porosity, zeolite and cement were measured. The results indicated that the 30% replacement of cement with zeolite (Z) was found the optimum amount of replacement. The strength improvement rate of the optimum zeolite-cemented sand (Z = 30%) compared to the mere cemented sand (Z = 0%) increased with the increase in the cement content as well as increase in the porosity of the compacted mixture. Based on the results of the zeolite-cement-sand mixtures, it has been shown that the UCS and qt improved by increasing the cement content (C). Also, the power function is well-matched to fit (UCS and qt)-C. The active composition parameter (AC) participate in the chemical reaction was introduced, as the minimum amount of either CaO or Al2O3 + SiO2. Afterward, the UCS and qt were plotted against the porosity/active composition parameter (VAC), which is regarded as a controlling and key parameter of the UCS and qv Also, the experimental results and the parameter of eta(-1.79)AC(1.43) introduce an acceptable description of the mechanical strength. Finally, the q(t)/UCS relationship is unique for the zeolite-cemented sand studied, being independent of the eta/AC. (C) 2019 Elsevier Ltd. All rights reserved

Multiclass Semi-Supervised Boosting and Similarity Learning

In this paper, we consider the multiclass semi-supervised classification problem. A boosting algorithm is proposed to solve the multiclass problem directly. The proposed multiclass approach uses a new multiclass loss function, which includes two terms. The first term is the cost of the multiclass margin and the second term is a regularization term on unlabeled data. The regularization term is used to minimize the inconsistency between the pair wise similarity and the classifier predictions. It assigns the soft labels weighted with the similarity between unlabeled and labeled examples. We then derive a boosting algorithm, named CD-MSSBoost, from the proposed loss function using coordinate gradient descent. The derived algorithm is further used for learning optimal similarity function for a given data. Our experiments on a number of UCI datasets show that CD-MSSBoost outperforms the state-of-the-art methods to multiclass semi-supervised learning

Recent Progress of Triboelectric Nanogenerators for Biomedical Sensors: From Design to Application

Triboelectric nanogenerators (TENG) have gained prominence in recent years, and their structural design is crucial for improvement of energy harvesting performance and sensing. Wearable biosensors can receive information about human health without the need for external charging, with energy instead provided by collection and storage modules that can be integrated into the biosensors. However, the failure to design suitable components for sensing remains a significant challenge associated with biomedical sensors. Therefore, design of TENG structures based on the human body is a considerable challenge, as biomedical sensors, such as implantable and wearable self-powered sensors, have recently advanced. Following a brief introduction of the fundamentals of triboelectric nanogenerators, we describe implantable and wearable self-powered sensors powered by triboelectric nanogenerators. Moreover, we examine the constraints limiting the practical uses of self-powered devices