A Partition-Based Random Search Method for Multimodal Optimization

Practical optimization problems are often too complex to be formulated exactly. Knowing multiple good alternatives can help decision-makers easily switch solutions when needed, such as when faced with unforeseen constraints. A multimodal optimization task aims to find multiple global optima as well as high-quality local optima of an optimization problem. Evolutionary algorithms with niching techniques are commonly used for such problems, where a rough estimate of the optima number is required to determine the population size. In this paper, a partition-based random search method is proposed, in which the entire feasible domain is partitioned into smaller and smaller subregions iteratively. Promising regions are partitioned faster than unpromising regions, thus, promising areas will be exploited earlier than unpromising areas. All promising areas are exploited in parallel, which allows multiple good solutions to be found in a single run. The proposed method does not require prior knowledge about the optima number and it is not sensitive to the distance parameter. By cooperating with local search to refine the obtained solutions, the proposed method demonstrates good performance in many benchmark functions with multiple global optima. In addition, in problems with numerous local optima, high-quality local optima are captured earlier than low-quality local optima

Watershed Management, A Tool for Sustainable Safe Reuse Practice, Case Study: El-Salam Canal

In Egypt, drainage and irrigation network receives a complex mixture of industrial and domestic effluent. Therefore, water quality was subjected to rapid deterioration over the past decades. A need for using marginal quality water in agriculture for new expansion projects is becoming a great necessity. Good quality water is no longer available for new irrigation projects. One strategy to increase available water resources is to reuse agriculture drainage water for irrigation. Surface water of low quality along with limitation of current water resources was found to be the largest current environmental threat to the drainage reuse practice in Egypt. The detrimental effects of drainage water reuse can be minimized by adopting appropriate pollution sources management. Although domestic diffuse sources represent very small portion of the total discharge in drains, they contribute to a high percentage of organic load to the water system. Lack of investment and time required to execute proper wastewater treatment plants (WWTPs), become a constrain impeding the improvement in surface water quality. The proper water quality management system along with good planning for constructing, upgrading and upscaling of WWTPs within a certain watershed can positively improve the water quality at the mixing point with fresh water for reuse. In this study, a practical management tool based on watershed as one of the primer water system unit has been introduced. The tool works under GIS environment to help water managers and planners concerned in irrigation system to incorporate the reuse of drainage water to set best prioritization scenario of WWTPs implementation, upgrading or upscaling within the sub-watershed of El-Serw and Bahr-Hadous drains that feed El-Salam canal. The study is based on analyzing the transport and decay of pollutants expressed as BOD load through network analysis of drains network within El-Salam canal watershed as a case study. Keywords: Water quality management, Watershed, Drainage water reuse, GIS, Point source pollution (PSP), BOD. DOI: 10.7176/CER/11-4-06 Publication date:May 31st 2019

Estimativas da diversidade genética de acessos de Paspalum spp. com ouso de marcadores microssatélites.

Editores técnicos: João de Mendonça Naime, Caue Ribeiro, Maria Alice Martins, Elaine Cristina Paris, Paulino Ribeiro Villas Boas, Ladislau Marcelino Rabello

Coexpression and transcriptome analyses identify active apomixis-related genes in Paspalum notatum leaves.

RNA sequencing (RNA-seq) is the most effective method for simultaneously predicting new transcripts and identifying differentially expressed genes among distinct tissues, genotypes, abiotic conditions and developmental stages [1]. Conversely, considering the large amount of data generated from RNA-seq, new approaches that efficiently extract meaningful associations from highly multivariate datasets are needed [2]. Transcriptome coexpression studies can show how complex phenotypes depend on the activity of coordinated batteries of genes [3]. Therefore, the construction of coexpression networks based on gene expression data using correlation metrics provides valuable information regarding alterations in biological systems in response to differential gene expression patterns [2, 4]

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future