A PV system’s operation highly depends on weather conditions. In case of varying irradiances or load changes, there is a power mismatch between various modules of the PV array. This power mismatch causes instability in the output of the PV system and deteriorates the overall system efficiency. To overcome instability and lower efficiency problems, and to extract maximum power from the PV system, various maximum power point tracking (MPPT) techniques are employed. The success of these techniques depends on the identification of the actual operating conditions of the system. This article proposes a hybrid maximum power point tracking (MPPT) technique that is capable of efficiently differentiating between uniform irradiance, non-uniform irradiance, and load variations on the PV system. Based on the identified operating conditions, the proposed method uses modified perturb and observe (Modified P&O) to cope with uniform irradiance variations and chimp optimization algorithms (ChOA) for non-uniform conditions to track the oscillation free maximum power-point. The proposed method is implemented and verified using a 4×3 PV array model in MATLAB Simulink software. Different cases of uniformly changing irradiance and non-uniformly changing irradiance are applied to test the performance of the proposed hybrid technique. The load varying conditions are performed by applying a variable load resistor. The authenticity of the proposed hybrid technique is critically evaluated against the well-known and most widely used optimization techniques of modified perturb and observe (Modified P&O), particle swarm optimization (PSO), flower pollination algorithm (FPA), and grey wolf optimization (GWO). The results demonstrate the superiority of the proposed technique in oscillation-free tracking of global maximum power point (GMPP) in a minimum tracking time of 0.4 s and 0.15 s, and steady-state MPPT efficiency of 96.92% and 99.54% under uniform and non-uniform irradiance conditions, respectively.

The current study was conducted to demonstrate the genetic variability, gene flow and rate of migration in mosquito populations between rural and urban areas in Sialkot, Pakistan. The adult mosquitoes were collected with the help of sweep net and battery-operated aspirator, whereas the larvae were collected using standard dippers. DNA extraction was performed through TNE salt extraction method. Fourteen samples of mosquito populations for the selected seven species of three genera were studied using RAPD loci. Ten oligonucleotide decamer primers produced 92 polymorphic fragments ranging from 120 to 3000 base pairs. The data generated through RAPD markers were analyzed through POPGENE software. The UPGMA dendrogram demonstrated two distinct groups comprising of seven mosquito species of three genera' Culex, Anopheles and Aedes. All the species from both urban and rural areas showed genetic relatedness with the corresponding species. Aedes albopictus from urban areas was found more closely related to Ae. aegypti. Aedes species group originated from Ae. albopictus of rural areas. The genetic diversity observed in population from urban areas was G ST =0.113 (Nm=4.014) with heterozygosity of 0.3691; and the rural areas showed genetic variation of G ST =0.134 (Nm=2.62) with a total heterozygosity of 0.4019. The overall genetic variation among fourteen populations showed G ST =0.147 and rate of migration Nm=3.73. The genetic relatedness and Nm value showed low level of genetic variations in mosquito populations from rural and urban areas of Sialkot. Moreover, the genetic data show that mosquitoes are freely moving between rural and urban areas.

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In this paper, multi-objective evolutionary Pareto optimal design of Adaptive Neuro-Fuzzy Inference System (ANFIS) have been used for modeling of nonlinear systems using input-output data sets with probabilistic uncertainties. In this way, A Monte Carlo Simulation (MCS) is first performed to generate input-output data set using some probabilistic distributions.Multi-objective uniform-diversity genetic algorithms (MUGA) are then used for Pareto optimization of ANFIS networks. The important conflicting objectives of ANFIS networks that are considered in this work are, namely, the mean and variance of both Training Error (TE) and Prediction Error (PE) of such ANFIS models. It is shown that a robust ANFIS can be simply obtained using a criterion based on four values of means and variances of both TE and PE.The probabilistic evolved ANFIS model exhibits much more robustness to the uncertainties involved within the input-output data sets than that of the deterministic evolved ANFIS model. It is shown that ANFIS can be successfully applied for input-output data set with uncertainties so that a robust model can be compromisingly obtained from some non-dominated optimum ANFIS models