Genetic consequences of chronic gamma irradiation on agro morphological traits in chili under hydrogel enhance media.

Induced mutation for the creation of desirable traits through chronic gamma irradiation provides an opportunity for the selection and development of new chili varieties. This study was conducted to assess the effects of different doses of chronic gamma irradiation on morpho-physiological traits in chili. Ten plants from each variety were exposed to different doses of chronic gamma irradiation for 277.02 h at three weeks after germination under gamma greenhouse facilities, with accumulative dose; 185.61Gy, 83.11Gy, 47.096Gy, 30.474Gy, 19.4Gy, 13.9Gy, 11.1Gy, 8.31Gy, 5.54Gy) and 2.77Gy respectively. Highly significant differences were observed among doses (Rings) of chronic gamma irradiation expressed in mean values for all investigated traits. Relatively moderate doses of chronic gamma irradiation represented by doses 47.096 Gy (Ring 4) and 19.40 Gy (Ring 6) resulted in significant stimulation for most of the studied characters. The highest heritability was recorded in days to flowering at 99.88 while the lowest was observed in fruit dry weight at 34.66 %. High genetic advance were recorded for most of the quantitative traits studied. In addition, a highly significant positive correlation was observed between total fruit per plant, total number of fruit per plant, plant height, fruit fresh weight, number of secondary branches, chlorophyll a, fruit dry weight, total chlorophyll content, stem diameter, fruit length and fruit girth. With increasing chronic gamma dose, mutagenic efficiency and efficacy generally increased. Induced variety of desirable features will considerably increase the chilli's amelioration through mutation breeding, leading to the development of improved varieties. The results of this research offer valuable information for the use of chronic gamma radiation in the mutations breeding of Capsicum annuum L., which will be advantageous for future breeding programs.

Chaotic Mapping Lion Optimization Algorithm-Based Node Localization Approach for Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) contain several small, autonomous sensor nodes (SNs) able to process, transfer, and wirelessly sense data. These networks find applications in various domains like environmental monitoring, industrial automation, healthcare, and surveillance. Node Localization (NL) is a major problem in WSNs, aiming to define the geographical positions of sensors correctly. Accurate localization is essential for distinct WSN applications comprising target tracking, environmental monitoring, and data routing. Therefore, this paper develops a Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA algorithm is to define the localization of unknown nodes based on the anchor nodes (ANs) as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept into the standard LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization error and transmission range with a minimum average localization error of 2.09%.

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks.

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.