UNet and its variations achieve state-of-the-art performances in medical image segmentation. In end-to-end learning, the training with high-resolution medical images achieves higher accuracy for medical image segmentation. However, the network depth, a massive number of parameters, and low receptive fields are issues in developing deep architecture. Moreover, the lack of multi-scale contextual information degrades the segmentation performance due to the different sizes and shapes of regions of interest. The extraction and aggregation of multi-scale features play an important role in improving medical image segmentation performance. This paper introduces the MH UNet, a multi-scale hierarchical-based architecture for medical image segmentation that addresses the challenges of heterogeneous organ segmentation. To reduce the training parameters and increase efficient gradient flow, we implement densely connected blocks. Residual-Inception blocks are used to obtain full contextual information. A hierarchical block is introduced between the encoder-decoder for acquiring and merging features to extract multi-scale information in the proposed architecture. We implement and validate our proposed architecture on four challenging MICCAI datasets. Our proposed approach achieves state-of-the-art performance on the BraTS 2018, 2019, and 2020 Magnetic Resonance Imaging (MRI) validation datasets. Our approach is 14.05 times lighter than the best method of BraTS 2018. In the meantime, our proposed approach has 2.2 times fewer training parameters than the top 3D approach on the ISLES 2018 Computed Tomographic Perfusion (CTP) testing dataset. MH UNet is available at https://github.com/parvezamu/MHUnet .

In today’s transportation sector, the growing number of electric vehicles (EVs) is progressively replacing petroleum-fueled vehicles, which are also expected to minimize greenhouse gas emissions. The main problem with EVs is the requirement of charging energy, which is fed through distribution network, while simultaneously feeding already connected load. The EV’s integration with the distribution network will overload the network due to EV’s charging load, which will eventually trip the power system protection. In this context, it is necessary to minimize power losses, improve voltage profile with sustainable power supply network. Therefore, optimal placement of EVs is required in the distribution network. Conventionally, researchers have used DGs to minimize power losses and improve voltage profile. In this paper, authors analyzed the effect of EV’s integration with simultaneous placement of distributed generations (DGs). The integration of EVs with higher penetration of DGs is cumbersome due to higher power losses and voltage variances that are outside allowable boundaries. The optimal placement of EVs into the distribution system with higher penetration of DGs is proposed in this paper using a battle royale optimization (BRO) algorithm. Since it is a new problem on CIGRE network which is not discussed before. Hence, the authors compared results with three most famous algorithms namely genetic algorithm (GA), particle swarm optimization (PSO), and accelerated PSO (APSO). The optimization problem is developed as a multi-objective function while decreasing active and reactive power losses, and minimising maximum voltage deviation index. The studied distribution network is the CIGRE 14-bus medium voltage (MV) distribution network. Three case studies are taken in which EVs are integrated in two scenarios with optimally sized and located DGs systems in the CIGRE distribution network using MATLAB. Case-1 includes simple network with DGs integration. Case-2 includes EVs only in the simple network, and case-3 finally includes EVs and DGs for optimal placement with minimum losses. The placement of the EVs results in a decrease in power losses and voltage deviation indices. The bus voltages of case-2, on the other hand, stay unchanged when the EVs are integrated. Case 3 with BRO showed the large reduction in power losses owing to the addition of EVs to the distribution network with DGs (from 19.98 kW, and 19.89 kVar to 2.54 kW, and 3.35 kVar).