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In managing the fish environment and underwater ecosystem, there's been a big shift from old-generational schoolings, hands-on methods to high-technologies, automatic ways of doing things, because of some clever computer technology, using a type of Artificial Intelligence smartness known as Convolutional Neural Networks, or CNNs in short. These smart systems help deal with the tricky task of keeping tabs on where fish stay out in deeper underwater, particularly when it's hard to see underwater and when the fish are all over the place. There are some pretty good tools and techniques already out there, like DPANet and a few others with names like PIFS-Net, EFS-Net, and MFAS-Net, which have been helping a bunch of researches and developments for underwater ecosystem. But Deeper water environment still needs a newer and better way to get the tasks done. So, to answer this major question, this hybrid architecture rolls out a new combination that takes the best bits of transformer architecture known as Swin Transformer and another mechanism known as ConvMixer, making it in a form of SwinUNet Architecture. This Swin Transformer is well defined at picking up on the spatial information from underwater deeper images in our case, which is a key point to be highlighted in really getting the fish's niche turf and enquiring their ecosystem. And when the underwater turns misty, the specialized implementation of ConvMixer modules incorporated with Swin Transformer increases its ability to sort out the fish from the rest of the underwater world. And this makes it to perform even more better compared with the existing state of arts model, even under Modern Underwater Machine Vision System conditions. This model even over takes to utilization of Few-Shot Learning model for training it in this lesser Underwater Images, with its capability of identifying the patterns and differentiations in both larger and smaller class objects in the given ground truth image. In this new approach, the paper combines the smart focusing methods with a way to blend different signals to better mIoU score, making it super useful for having a deeper understanding on fish-ecosystem and also their underwater ecosystem. While comparing it with the pre-existing state of arts model and the booming Image Segmentation models (YOLO v8, Pix 2 Pix GAN model, Auto Encoders, UNet), the hybrid approach outperformed the existing model results on this Semantic Segmentation for Underwater Imagery (SUIM) dataset, which is been disclosed in the further sections below.

Exploration of blend polymer membrane with desire performances is a highly essential research topic because it makes them as key component of the battery technologies. However, the blend membrane cannot simultaneously meet out the high electrical insulation and ion conductivity. Here, we report porous PVdF-HFP based polymer membrane blended with P123 polymer which is prepared by phase inversion technique and along with solution casting technique for comparison. Especially, the major improvements can be claimed with porous PVdF-HFP blend with P123 matrix by phase inversion process that could be mainly due to increasing the disorganize crystallinity of the PVdF-HFP, thereby enhancing the ionic conductivity of PVdF-HFP/P123 than the same prepared by traditional solution casting. More importantly, our studies reveals that, besides having an effect of crystallinity and high ionic conduction PVdF-HFP/P123 blend matrix yield homogeneous pores with higher pore density when prepared by phase inversion approach. We attribute this to remarkable enhancement in higher electrolyte uptake and higher cation transport in the blend membrane. This facilitates high ionic conductivity of 7.06 × 10−5 S/cm and electrochemical stability of 2.8 V,while the solution casting approach display low ionic conductivity of 1.63 × 10−6 S/cm and electrochemical stability of 2.2 V, respectively. A symmetric supercapacitor device is fabricated with the membrane prepared by phase inversion process, resulting in a high specific capacitance of 80 F/g at the current density of 0.25 A/g with 90.51% specific capacitance retention at 0.5 A/g. The energy density of 90.9 W/kg can be achieved at the power density of 0.375 W/kg.

Mangrove ecotourism is gaining immense popularity in the Gulf Cooperation Council (GCC) countries as a neoliberal conservation tool, and it has contributed significantly to the growth of the tourism sector in the region over the past two decades. However, there is no comprehensive review on the full extent of mangrove ecotourism activities and the contribution to mangrove conservation/restoration and economic growth in the region. A systematic literature review approach was used to examine the evolution of mangrove ecotourism in the GCC countries from 2010 to 2023. A total of 55 articles were retrieved from the Google and Google Scholar search engines, and the Scopus and Web of Science databases were incorporated. We synthesized the results and provided perspectives on the following: (1) the geographical and temporal distribution of studies in relation to mangrove extent, (2) key sites, attractions, and values for mangrove ecotourism activities, (3) the positive and negative impacts of mangrove ecotourism, and (4) existing mangrove conservation and restoration initiatives for the growth of mangrove ecotourism in the GCC countries. The findings underscore the significance of mangrove ecotourism in supporting economic development, protecting coastal ecosystems, and sustaining local livelihoods in the GCC countries. However, this study highlights the crucial need for sustainable coastal environmental management through integrated land use planning and zoning to address the negative impacts of anthropogenic pressures on mangrove ecosystems and ecotourism attractions. The use of remote sensing tools is invaluable in the monitoring of mangrove ecosystems and associated ecotourism impacts for informing evidence-based conservation and restoration management approaches. Thus, harnessing mangrove ecotourism opportunities can help the GCC countries with balancing economic growth, coastal environmental sustainability, and community well-being.