Experimental evaluation of tensile performance of aluminate cement composite reinforced with weft knitted fabrics as a function of curing temperature

Cement composites (CC) are among the composites most widely used in the construction industry, such as a durable waterproof and fire-resistant concrete layer, slope protection, and application in retaining wall structures. The use of 3D fabric embedded in the cement media can improve the mechanical properties of the composites. The use of calcium aluminate cement (CAC) can accelerate the production process of the CC and further contribute to improving the mechanical properties of the cement media. The purpose of this study is to promote the use of these cementitious composites by deepening the knowledge of their tensile properties and investigating the factors that may affect them. Therefore, 270 specimens (three types of stitch structure, two directions of the fabric, three water temperature values, five curing ages, with three repetitions) were made, and the tensile properties, absorbed energy, and the inversion effects were evaluated. The results showed that the curing conditions of the reinforced cementitious composite in water with temperature values of 7, 23, and 50 °C affect the tensile behavior. The tensile strength of the CCs cured in water with a temperature of 23 °C had the highest tensile strength, while 7 and 50 °C produced a lower tensile strength. The inversion effect has been observed in CC at 23 °C between 7 and 28 days, while this effect has not occurred in other curing temperature values. By examining three commercial types of stitches in fabrics and the performance of the reinforced cementitious composites in the warp direction, it was found that the structure of the “Tuck Stitch” has higher tensile strength and absorbed energy compared to “Knit stitch” and “Miss Stitch”. The tensile strength and fracture energy of the CC reinforced with “Tuck Stitch” fabric in the warp direction, by curing in 23 °C water for 7 days, were found to be 2.81 MPa and 1.65 × 103 KJ/m3, respectively. These results may be helpful in selecting the design and curing parameters for the purposes of maximizing the tensile properties of textile CAC composites

Assessment of vase life and postharvest quality of cut rose (Rosa hybrida cv. Angelina) flowers by application of cumin (Cuminum cyminum L.) essential oil and 8-hydroxyquinoline sulfate

Natural preservatives such as herbal essential oils have potential ability for extending postharvest vase life of cut flowers. In this study, application effect of cumin (Cuminum cyminum L.) essential oil and 8-hydroxyquinoline sulfate on vase life and postharvest quality of cut rose (Rosa hybrida cv. Angelina) flowers were investigated. A factorial experiment with three levels of each in different time after harvesting was conducted. Results showed that usage of different level of cumin essential oil and hydroxyquinoline sulfate had significant effects on rose attributes at the level of 0.05. The results showed that the interaction effect of cumin essential oil and hydroxyquinoline sulfate in measuring time was significant (P<0.05) on all of parameters except for anthocyanin content in rose petals in a way that the highest amount for measured traits was obtained with treatment of 150 mg L-¹ cumin essential oil and 400 mg L-¹ 8-hydroxyquinoline sulfate

A new approach to calculate the gluon polarization

We derive the Leading-Order master equation to extract the polarized gluon distribution G(x;Q^2) = x \deltag(x;Q^2) from polarized proton structure function, g1p(x;Q^2). By using a Laplace-transform technique, we solve the master equation and derive the polarized gluon distribution inside the proton. The test of accuracy which are based on our calculations with two different methods confirms that we achieve to the correct solution for the polarized gluon distribution. We show that accurate experimental knowledge of g1p(x;Q^2) in a region of Bjorken x and Q^2, is all that is needed to determine the polarized gluon distribution in that region. Therefore, to determine the gluon polarization \deltag /g,we only need to have accurate experimental data on un-polarized and polarized structure functions (F2p (x;Q^2) and g1p(x;Q^2)).Comment: 12 pages, 5 figure

Feature Selection via Chaotic Antlion Optimization

Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used.This work was partially supported by the IPROCOM Marie Curie initial training network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grants agreement No. 316555, and by the Romanian National Authority for Scientific Research, CNDIUEFISCDI, project number PN-II-PT-PCCA-2011-3.2- 0917. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Volcano eruption algorithm for solving optimization problems

This is an accepted manuscript of an article published by Springer in Neural Computing and Applications on 30/06/2020, available online at https://doi.org/10.1007/s00521-020-05124-x The accepted version of the publication may differ from the final published version.Meta-heuristic algorithms have been proposed to solve several optimization problems in different research areas due to their unique attractive features. Traditionally, heuristic approaches are designed separately for discrete and continuous problems. This paper leverages the meta-heuristic algorithm for solving NP-hard problems in both continuous and discrete optimization fields, such as nonlinear and multi-level programming problems through extensive simulations of volcano eruption process. In particular, a new optimization solution named Volcano Eruption Algorithm (VEA) proposed in this paper, which is inspired from the nature of volcano eruption. The feasibility and efficiency of the algorithm are evaluated using numerical results obtained through several test problems reported in the state-of-theart literature. Based on the solutions and number of required iterations, we observed that the proposed meta-heuristic algorithm performs remarkably well to solve NP-hard problem. Furthermore, the proposed algorithm is applied to solve some large-size benchmarking LP and Internet of Vehicles (IoV) problems efficiently

A novel dynamic framework to detect DDoS in SDN using metaheuristic clustering

© 2019 John Wiley & Sons, Ltd. Security is a crucial factor in the continuously evolving programmable networks. With the emergence of programmable networking terminals, the need to protect the networks has become mandatory. Software-defined networks (SDNs) provide programmable switches, thereby isolating the data plane from the control plane. Many security algorithms have been proposed to protect the network; however, they have failed to protect SDNs from attacks such as distributed denial of service (DDoS), jamming, and man-in-the-middle attacks. In this article, we only address the DDoS attack that prevails in SDNs. Isolation of the control plane from the data plane increases the probability of an attack on the data plane. Therefore, a framework that can handle the dynamic traffic and can protect the network from DDoS attacks is required. Our proposed whale optimization algorithm–based clustering for DDoS detection (WOA-DD) avoids the DDoS attacks using a metaheuristic approach by clustering the attack requests. We evaluated this algorithm for robustness in comparison with several existing solutions and found it to be safe under several conditions. The proposed attack request clustering is explored to check its feasibility with various machine learning approaches and found to be stable with the prevailing mechanisms. Analysis of the algorithm under varied conditions reveals that WOA-DD is robust, stable, and efficient against DDoS attacks

Fitness Varying Gravitational Constant in GSA

Gravitational Search Algorithm (GSA) is a recent metaheuristic algorithm inspired by Newton's law of gravity and law of motion. In this search process, position change is based on the calculation of step size which depends upon a constant namely, Gravitational Constant (G). G is an exponentially decreasing function throughout the search process. Further, inspite of having different masses, the value of G remains same for each agent, which may cause inappropriate step size of agents for the next move, and thus leads the swarm towards stagnation or sometimes skipping the true optima. To overcome stagnation, we first propose a gravitational constant having different scaling characteristics for different phase of the search process. Secondly, a dynamic behavior is introduced in this proposed gravitational constant which varies according to the fitness of the agents. Due to this behavior, the gravitational constant will be different for every agent based on its fitness and thus will help in controlling the acceleration and step sizes of the agents which further improve exploration and exploitation of the solution search space. The proposed strategy is tested over 23 well-known classical benchmark functions and 11 shifted and biased benchmark functions. Various statistical analyses and a comparative study with original GSA, Chaos-based GSA (CGSA), Bio-geography Based Optimization (BBO) and DBBO has been carried out

Withanolides-Induced Breast Cancer Cell Death Is Correlated with Their Ability to Inhibit Heat Protein 90

Withanolides are a large group of steroidal lactones found in Solanaceae plants that exhibit potential anticancer activities. We have previously demonstrated that a withanolide, tubocapsenolide A, induced cycle arrest and apoptosis in human breast cancer cells, which was associated with the inhibition of heat shock protein 90 (Hsp90). To investigate whether other withanolides are also capable of inhibiting Hsp90 and to analyze the structure-activity relationships, nine withanolides with different structural properties were tested in human breast cancer cells MDA-MB-231 and MCF-7 in the present study. Our data show that the 2,3-unsaturated double bond-containing withanolides inhibited Hsp90 function, as evidenced by selective depletion of Hsp90 client proteins and induction of Hsp70. The inhibitory effect of the withanolides on Hsp90 chaperone activity was further confirmed using in vivo heat shock luciferase activity recovery assays. Importantly, Hsp90 inhibition by the withanolides was correlated with their ability to induce cancer cell death. In addition, the withanolides reduced constitutive NF-κB activation by depleting IκB kinase complex (IKK) through inhibition of Hsp90. In estrogen receptor (ER)-positive MCF-7 cells, the withanolides also reduced the expression of ER, and this may be partly due to Hsp90 inhibition. Taken together, our results suggest that Hsp90 inhibition is a general feature of cytotoxic withanolides and plays an important role in their anticancer activity