Performance Assessment of Natural Pozzolan Roller Compacted Concrete Pavements

Concrete pavement is cost effective and beneficial because of its sustainability and durability. The maintenance and renovation periods for such pavement compared to other pavements are relatively long; however, a significant issue with pavements, especially roller compacted concrete pavements (RCCP), is salt scaling which occurs due to saline solutions such as deicer salts. In the present work, the performance of RCC containing a natural pozzolan called Trass, as a supple- mentary cementitious material, and an air-entraining agent for salt scaling was investigated. Mechanical and durability tests were performed on specimens containing a water to binder ratio of 0.32, with and without Trass, and an air-entraining agent. It was concluded that, Trass could not improve the compressive and tensile strengths, however, the permeability was improved. Moreover, the amount of mass loss due to salt scaling was not decreased. In all concrete mixtures, using a suitable amount of an air-entraining agent to maintain a total air content of 4.5–5% was found to be necessary for producing RCC containing Trass

Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets

This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ), expectation maximisation (EM), K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet

ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

In this paper, a novel swarm intelligent algorithm is proposed called ant nesting algorithm (ANA). The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. Although the algorithm is inspired by the swarming behavior of ants, it does not have any algorithmic similarity with the ant colony optimization (ACO) algorithm. It is worth mentioning that ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change (e.g., step or velocity). ANA computes the rate of change differently as it uses previous, current solutions, fitness values during the optimization process to generate weights by utilizing the Pythagorean theorem. These weights drive the search agents during the exploration and exploitation phases. The ANA algorithm is benchmarked on 26 well-known test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), five modified versions of PSO, whale optimization algorithm (WOA), salp swarm algorithm (SSA), and fitness dependent optimizer (FDO). ANA outperformances these prominent metaheuristic algorithms on several test cases and provides quite competitive results. Finally, the algorithm is employed for optimizing two well-known real-world engineering problems: antenna array design and frequency-modulated synthesis. The results on the engineering case studies demonstrate the proposed algorithm’s capability in optimizing real-world problems

Genetic stability of in vitro multiplied Phalaenopsis gigantea protocorm-like bodies as affected by chitosan

Chitosan is a carbohydrate polymer derivative of chitin which presents in shell of crustaceans. This biopolymer is a non toxic and environmentally friendly, considered as a plant growth stimulator in some plant species. The present study investigates the effects of chitosan and media types on multiplication and genetic stability of Phalaenopsis gigantea protocorm-like bodies (PLBs). PLBs were inoculated in liquid New Dogashima Medium (NDM) and Vacin and Went (VW) supplemented with various concentrations of chitosan (0, 5, 10, 15, 20 and 25 mg/L). The highest PLB multiplication was observed on VW and NDM supplemented with 10 mg/L chitosan with mean number of PLBs 177 and 147, respectively. Chitosan promoted the formation of juvenile leaves and the highest number was observed in NDM supplemented with 20 mg/L chitosan with mean number of 66 leaves after 8 weeks of culture. Genetic stability was assessed among mother plant and secondary PLBs after 2, 4, 6, and 8 weeks of culture in liquid media. 8 out of 10 ISSR markers produced a total of 275 clear and reproducible bands with mean of 6.9 bands per primer. The secondary PLBs produced during sub-culturing process of chitosan treated liquid culture were genetically uniform and similar to mother plant

Risk factors for paclitaxel-induced peripheral neuropathy in patients with breast cancer

Abstract Background Paclitaxel induced peripheral neuropathy (PIPN) is a major debilitating side effect of paclitaxel in patients with breast cancer with no fully known mechanisms. The aim of the study was to find out the possible risk factors for PIPN. Methods Eligible patients with node positive breast cancer undergoing chemotherapy with paclitaxel were assessed. They belonged to an initial randomized controlled trial in which the effectiveness of omega-3 fatty acids in preventing and reducing severity of PIPN was evaluated (protocol ID: NCT01049295). Reduced total neuropathy score (r-TNS) was used for measuring PIPN. All analyses were performed adjusting for intervention effect. The association between age, BMI, BSA, pathological grade, molecular biomarkers and PIPN was evaluated. Results Fifty-seven patients with breast cancer were investigated. Age was significantly associated with risk of PIPN (RR:1.50, P value = .024). Body mass index and BSA had significant association with severity of PIPN (B:1.28, P = .025; and B: 3.88, P = .010 respectively). Also, BSA showed a significant association with the risk of PIPN (RR: 2.28, P = .035; B: 3.88, P = .035). Incidence and severity of PIPN were much more pronounced in progesterone receptor positive (PR+) patients (RR:1.88, P = .015 and B:1.54, P = .012). Multivariate analysis showed that age and the status of PR+ were independent risk factor for incidence and the status of PR+ was the only independent risk factor for severity of PIPN. Conclusion Age, BSA and the status of PR+, should be considered as the risk factors for PIPN before commencement of chemotherapy with paclitaxel in patients with breast cancer. Older patients, those with greater BSA and PR+ patients may need closer follow up and more medical attention due to greater incidence and severity of PIPN

MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm

The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search ability, maintain the balance between exploration and exploitation, and prevent the original MFO’s premature convergence during the optimization process. Furthermore, the MTV-MFO algorithm uses the knowledge of inferior moths preserved in two archives to prevent premature convergence and avoid local optima. The performance of the MTV-MFO algorithm was evaluated using 29 benchmark problems taken from the CEC 2018 competition on real parameter optimization. The gained results were compared with eight metaheuristic algorithms. The comparison of results shows that the MTV-MFO algorithm is able to provide competitive and superior results to the compared algorithms in terms of accuracy and convergence rate. Moreover, a statistical analysis of the MTV-MFO algorithm and other compared algorithms was conducted, and the effectiveness of our proposed algorithm was also demonstrated experimentally

A Cost-Efficient-Based Cooperative Allocation of Mining Devices and Renewable Resources Enhancing Blockchain Architecture

The impressive furtherance of communication technologies has exhorted industrial companies to link-up these developments with their own abilities with the target of efficiency enhancement through smart supervision and control. With this in mind, the blockchain platform is a prospective solution for merging communication technologies and industrial infrastructures, but there are several challenges. Such obstacles should be addressed to effectively adopt this technology. One of the most recent challenges relative to adopting blockchain technology is the energy consumption of miners. Thus, providing an accurate approach that addresses the underlying cause of the problem will carry weight in the future. This work addresses managing the energy consumption of miners by using the advantage of distributed generation resources (DGRs). Along the same vein, it appears that achieving the optimal solution requires executing the modified reconfirmation of DGRs and miners (indeed, mining pool systems) in the smart grid. In order to perform this task, this article utilizes the Intelligent Priority Selection (IPS) method since this method is up to snuff for corporative allocation. In order to find practical solutions for this problem, the uncertainty is also modeled as a credible index highly correlated with the load and generation. All in all, it can be said that the outcome of this research study can help researchers in the field of enhancement of social welfare by using the proposed technology

Particle Swarm Optimization: A Comprehensive Survey

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided

Dynamic Butterfly Optimization Algorithm for Feature Selection

Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimization algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimization algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimization process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics