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Plant secondary metabolites as defenses : a review

Jusuf Herlina, Elveny Marischa, Azizova Feruza, Shichiyakh Rustem A., Kulikov Dmitriy, Al-Taee Muataz M., Atiyah Karrar K., Jalil Abduladheem T., Aravindhan Surendar

Journal of Water and Land Development

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2022

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no. 55

206--211

EN

Plant secondary metabolites have a variety of functions, including mediating relationships between organisms, responding to environmental challenges, and protecting plants against infections, pests, and herbivores. In a similar way, through controlling plant metabolism, plant microbiomes take part in many of the aforementioned processes indirectly or directly. Researchers have discovered that plants may affect their microbiome by secreting a variety of metabolites, and that the microbiome could likewise affect the metabolome of the host plant. Pesticides are agrochemicals that are employed to safeguard humans and plants from numerous illnesses in urban green zones, public health initiatives, and agricultural fields. The careless use of chemical pesticides is destroying our ecology. As a result, it is necessary to investigate environmentally benign alternatives to pathogen management, such as plant-based metabolites. According to literature, plant metabolites have been shown to have the ability to battle plant pathogens. Phenolics, flavonoids, and alkaloids are a few of the secondary metabolites of plants that have been covered in this study.

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Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach

Syah Rahmad, Elveny Marischa, Soerjati Enni, Guerrero John William Grimaldo, Jowad Rawya Read, Suksatan Wanich, Aravindhan Surendar, Voronkova Olga Yuryevna, Mavaluru Dinesh

Foundations of Computing and Decision Sciences

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2022

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Vol. 47, No. 2

177--192

EN

Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of the model is to locate an appropriate number of facilities among potential locations and allocate customers. The model is presented as a multi-objective nonlinear mixed-integer programming model. The objective functions include the summation of the customer and the waiting time in the system and the waiting time in the system and minimizing the maximum possibility of unemployment in the facility. To solve the model, the technique of accurate solution of the epsilon constraint method is used for multi-objective optimization, and Pareto solutions of the problem will be calculated. Moreover, the sensitivity analysis of the problem is performed, and the results demonstrate sensitivity to customer demand rate. Based on the results obtained, it can be concluded that the proposed model is able to greatly summate the customer and the waiting time in the system and reduce the maximum probability of unemployment at several levels of all facilities. The model can also be further developed by choosing vehicles for each customer.

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Development of an Adaptive Genetic Algorithm to Optimize the Problem of Unequal Facility Location

Budi Hendrik Setia, Elveny Marischa, Zhuravlev Pavel, Jalil Abduladheem Turki, Al-Janabi Samaher, Alkaim Ayad F., Saleh Marwan Mahmood, Shichiyakh Rustem Adamovich, Sutarto

Foundations of Computing and Decision Sciences

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2022

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Vol. 47, No. 2

111--125

EN

The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents an efficient method for optimizing the problem of unequal facility layouts. In this method, the genetic algorithm is improved and developed into an adaptive genetic algorithm. In this algorithm, the mutation operator is applied only when the similarity of chromosomes in each population reaches a certain level. This intelligence prevents jumps in situations where they are not needed and reduces computational time. In order to measure the performance of the proposed algorithm, its performance is compared with the performance of conventional genetic algorithms and refrigeration simulators. Computational results show that the adaptive genetic algorithm is able to achieve higher-quality solutions.

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A New Model for Scheduling Operations in Modern Agricultural Processes

Noer Zulhery, Elveny Marischa, Jalil Abduladheem Turki, Iswanto A. Heri, Al-Janabi Samaher, Alkaim Ayad F., Mullagulova Gulnara, Nikolaeva Natalia, Shichiyakh Rustem Adamovich

Foundations of Computing and Decision Sciences

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2022

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Vol. 47, No. 2

151--161

EN

In recent years, the increase in population and the decrease in agricultural lands and water shortages have caused many problems for agriculture and farmers. That is why scheduling is so important for farmers. Therefore, the implementation of an optimal schedule will lead to better use of agricultural land, reduce water consumption in agriculture, increase efficiency and quality of agricultural products. In this research, a scheduling problem for harvesting agricultural products has been investigated. InPaper this problem, there are n number of agricultural lands that in each land m agricultural operations are performed by a number of machines that have different characteristics. This problem is modeled as a scheduling problem in a flexible workshop flow environment that aims to minimize the maximum completion time of agricultural land. The problem is solved by programming an integer linear number using Gams software. The results show that the proposed mathematical model is only capable of solving small and medium-sized problems, and due to the Hard-NP nature of the problem, large-scale software is not able to achieve the optimal solution.