DNA Isolation and Optimization of ISSR-PCR Reaction System in Oryza sativa L.

Inter simple sequence repeats (ISSRs) have been utilized widely for molecular markers in analyzing the genetic diversity and phylogenetic and regions in the genome flanked by microsatellite sequences. PCR amplification of these regions using a single primer yields multiple amplification products that can be used as a dominant multilocus marker system for the study of genetic variation in various organisms. For this study provides, DNA isolation, adjusting in six factors (Buffer, MgCl2, dNTPs, ISSR primers, Template DNA and Taq polymerase) at six levels, and optimization of PCR temperature for the ISSR reaction was 60-45 °C, primers screening on indica rice (Oryza sativa). In this research, simple method of DNA isolation by using seedling. The objective of the present investigation was to assess the optimizations and quantification. Has been shown that stalk enhanced the maximum value of genomic. The results show that 100 ISSR primers were examined as well as, 56 ISSR primers was productively amplified. Optimum components for PCR reactions were 5.0 μl of 5X PCR Buffer, 1.5 μl of 25mM MgCl2, 1 μl of 10 mM dNTP, 1 μl of 10 Μm ISSR primers, 2 μl Template DNA, and 0.1 μl of 5 units/ml Taq polymerase. Based on this study, has brought out some information on the relationship between these ISSR primers will be applied further for molecular profiling as well as response evaluation in rice varietie

β-Glucan-Mediated Alleviation of NaCl Stress in Ocimum basilicum L. in Relation to the Response of Antioxidant Enzymes and Assessment DNA Marker

Salinity is one of the most important abiotic stresses which can negatively affect the plant metabolic processes in the world. This can impact the plant production, either for economic or sustenance benefits. The salinity stress can cause many physiological and biochemical changes in the plants. β-glucans are important polysaccharides, which are present in the cell walls of various cereal grains. They protect the plant responses and occur in plant suspensions. In this study, the researchers attempted to investigate various physiological mechanisms and determine the role of the β-glucans in the NaCl-mediated stress conditions on the Ocimum basilicum L. seedlings. For this purpose, they carried out an experiment for assessing various shoot and root parameters along with the antioxidant enzyme activities, proline levels and the ISSR markers. When the seedlings were exposed to the NaCl stress conditions, they showed a significant decrease in the growth parameters and an increase in the antioxidant and proline levels compared to the control seedlings grown under normal saline conditions. On the other hand, the β-glucantreated seeds, when grown under the saline stress conditions, showed better growth parameters as well as high antioxidant enzyme activities and proline levels, compared to the control and NaCl-treated plants. Furthermore, a PCR analysis was carried out using the ISSR-marker technology, which could help in evaluating the DNA fingerprints and genetic variations in the plants. The results indicated that the exogenous application of the β-glucans could protect the antioxidant enzyme activities and protect the plants against the salinity stresses, without affecting the DNA-markers without affecting the genetic variations and could be a better choice for use in DNA-markers

Using DNA Fingerprinting to Detect the Genetic Relationships in Acacia by Inter-Simple Sequence Repeat Markers

The objective of this study was to complete the molecular evaluation of five Acacia species including first by determining the genetic diversity of the plants using the polymerase chain reaction (PCR)-based inter-simple sequence repeat (ISSR) method. This investigation was carried out to assess fingerprint and thus genetic variations among the Acacia species. The ISSR method was used to determine DNA fingerprints for Acacia spp. Eight primers were used, with all primers delivering amplification products. Our data show a total of 71 bands of 70 bp to 2,200 bp were amplified, of which 0.77 demonstrated an average polymorphism information content per primer. Among the eight primers tested, the mean annealing temperature was 48°C and average polymorphism information content was between 0.36 and 0.84. The ISSR primers for the five species of Acacia showed four main groups, with a higher level of similarity between these species. These results indicated ISSR markers provide an efficient alternate for identification via DNA fingerprinting of the genetic relationships in Acacia. PCR-based ISSR represents a powerful method that can provide practical information for the development of molecular markers, molecular cytogenetic techniques, and DNA Fingerprinting for application in an Acacia spp breeding program

A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA

In this study, an improved artificial intelligence algorithms augmented Internet of Things (IoT)-based maximum power point tracking (MPPT) for photovoltaic (PV) system has been proposed. This will facilitate preventive maintenance, fault detection, and historical analysis of the plant in addition to real-time monitoring. Further, the simulation results validate the improved performance of the suggested method. To demonstrate the superiority of the proposed MPPT algorithm over current methods, such as cuckoo search algorithms and the incremental conductance approach, a performance comparison is offered. The outcomes demonstrate the suggested algorithm’s capability to track the Global Maximum Power Point (GMPP) with quicker convergence and less power oscillations than before. The results clearly show that the artificial intelligence algorithm-based MPPT is capable of tracking the GMPP with an average efficiency of 88%, and an average tracking time of 0.029 s, proving both its viability and effectiveness

A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA

In this study, an improved artificial intelligence algorithms augmented Internet of Things (IoT)-based maximum power point tracking (MPPT) for photovoltaic (PV) system has been proposed. This will facilitate preventive maintenance, fault detection, and historical analysis of the plant in addition to real-time monitoring. Further, the simulation results validate the improved performance of the suggested method. To demonstrate the superiority of the proposed MPPT algorithm over current methods, such as cuckoo search algorithms and the incremental conductance approach, a performance comparison is offered. The outcomes demonstrate the suggested algorithm’s capability to track the Global Maximum Power Point (GMPP) with quicker convergence and less power oscillations than before. The results clearly show that the artificial intelligence algorithm-based MPPT is capable of tracking the GMPP with an average efficiency of 88%, and an average tracking time of 0.029 s, proving both its viability and effectiveness

A novel economic dispatch in the stand-alone system using improved butterfly optimization algorithm

Distributed renewable energy systems are now widely installed in many buildings, transforming the buildings into ‘electricity prosumers'. Additionally, managing shared energy usage and trade in smart buildings continues to be a significant difficulty. The main goal of solving such problems is to flatten the aggregate power consumption-generation curve and increase the local direct power trading among the participants as much as possible. This study provides a coordinated smart building energy-sharing concept for smart neighborhood buildings integrated with renewable energy sources and energy storage devices within the building itself. This neighborhood energy management model's primary objective is to reduce the total power cost of all customers of smart buildings in the neighborhood by increasing the use of locally produced renewable energy. In the first stage, a group of optimum consumption schedules for each HEMS is calculated by an Improved Butterfly Optimization Algorithm (IBOA). A neighborhood energy management system (NEMS) is established in the second stage based on a consensus algorithm. A group of four smart buildings is used as a test system to evaluate the effectiveness of the suggested neighborhood smart building energy management model. These buildings have varying load profiles and levels of integration of renewable energy. In this paper, the proposed framework is evaluated by comparing it with the Grey Wolf optimization (GWO) algorithm and W/O scheduling cases. With applying GWO, the total electricity cost, peak load, PAR, and waiting time are improved with 3873.723 cents, 21.6005 (kW), 7.162225 (kW), and 87 s respectively for ToU pricing and 11217.57 (cents), 18.0425(kW), 5.984825 (kW), and 98 s respectively for CPP tariff. However, using the IBOA Improves the total electricity cost, peak load, PAR, and waiting time by 3850.61 (cents), 20.1245 (kW), 6.7922 (kW), and 53 s respectively, for ToU and 10595.8 (cents), 17.6765(kW), 5.83255(kW), and 74 s for CPP tariff. Also, it is noted that the run time is improved using GWO and IBOA by 13% and 47%, respectively, for ToU and 2% and 26% for CPP. However, the number of iterations required to obtain the optimal solution is reduced using the GWO and IBOA by 60% and 81% for ToU and 55% and 80% for CPP tariffs. The results show significant improvements obtained by applying just intelligent programming and management