This paper presents an improved SOC estimation method for lithium ion batteries in Electric Vehicles using Bayesian optimized feedforward network. This innovative bayesian optimized neural network method attempts to minimize a scalar objective function by extracting hyperpa-rameters (hidden neurons in both layers) using a surrogate model. Furthemore, the hyperparameters are built and data samples are trained and validated. The performance of the proposed deep learning neural network is evaluated. Two reasonable size data samples are ex-tracted from Panasonic 18650PF Li-ion Mendeley datasets that are used for training and valida-tion. RNN and LSTM neural network algorithms offer the common core property of retaining past information and/or hidden states for better SOC estimation. However, the feature of this pro-posed method is the inclusion of Bayesian optimization that chooses optimal double layer hidden neurons. Analysis of results shows that Bayesian optimized feedforward algorithm with average MAPE (0.20%) is the lowest and is the best selection compared with average MAPE for other five deep learning algorithms. In the last quarter of fuel gauge, where fuel anxiety is severe, feed-forward with Bayesian Optimization algorithm is still the best selection (with MAPE of 0.64%).

Although microgrids facilitate the increased penetration of distributed generations (DGs) and improve the security of power supplies, they have some issues that need to be better understood and addressed before realising the full potential of microgrids. This paper presents a comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids. The discussion in this paper presented with a view to establishing microgrids as distinct from the existing distribution systems. This is accomplished by, firstly, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones. Also, the definitions, classifications and characteristics of microgrids are summarised to provide a sound basis for novice researchers to undertake ongoing research on microgrids.

Cryopreservation has been used extensively for cattle in Bangladesh, albeit no study was conducted on the cryopreservation techniques of buffalo. This study compares two freezing methods and the effects of diluters on the semen quality of buffalo. In the first freezing protocol, semen was frozen in two-step: from 37 °C to 5 °C for 30 minutes in a BLRI-developed equilibration chamber and from 5 oC to -120 oC in a Styrofoam box using liquid nitrogen vapor from different distances (0.5, 1.5, 1.6, 2 and 3 inches). At the same time semen was frozen in a programmable freezer in three steps. The semen samples were then evaluated for motility and morphological quality by CASA. In another experiment, the efficacy of one locally developed diluter-Tris-fructose-egg yolk-based (TFE) and three commercial diluters (Andromed, Triladyl and Steridyl) were evaluated. The highest number of motile sperms (62.67±1.12; P< 0.01) and progressive motility (38.97±1.10; P < 0.001) was observed at 1.6 inches above liquid nitrogen. There was no significant difference in overall motility, progressive and slow motility between semen cryopreserved in the cheap nitrogen vapor technique (57.49±5.67, 38.70± 4.04 and 3.83± 0.63, respectively) and expensive automated technique (65.94±4.65, 45.54 ± 3.64 and 2.43± 0.36, respectively). The highest recovery rate and conception rate were observed in semen diluted with TFE (82.4% and 80%, respectively). Hence, the cryopreservation technique using nitrogen vapor and TFE diluent is cost-effective and suitable for freezing buffalo semen that would produce superior semen for artificial insemination.

The research identified norovirus genotypes that circulated among Gambian children less than five years of age with diarrhoea during 2007-2012. The study highlights the importance of continuous surveillance for detecting the emergence of new norovirus genotypes.