The negative effect of primary sources on the environment and the exhaustion of fossil fuels brings a serious challenge to human society. To address these issues, renewable energy sources (RESs) are considered to be the power of the future, and wind energy is expected to be an important part of this evolution. However, the uncertainty of wind speed has posed significant hurdles to wind power development. Thanks to the tremendous breakthrough in Artificial Intelligence, a novel method to handle this problem is proposed. This paper illustrates a wind speed prediction scheme based on Long Short-Term Memory (LSTM) neural network and Self-Attention mechanism (SAM) with different forecast horizon. In addition to conducting point forecast, the proposed model combined with Quantile Regression is employed to implement interval forecast. The predicted results on two given datasets demonstrate that the proposed model outperforms three predictive benchmark models.

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Automatic medical image segmentation normally is a difficult task because medical images are complex in nature therefore many researchers have studied a lot of approaches to analyze patterns of images. In which, the crucial applications of deep learning in medicine are growing trends, especially Convolutional Neural Networks (CNNs) in the field of Computer Vision, yielding many remarkable results. In this paper, we propose a method to apply graph-based signal processing to CNNs architecture for medical image segmentation application. In particular, the processed architecture is based on the graph convolution to extract features in the image instead of the traditional convolution in DSP (Digital Signal Processing). The proposed solution is effective in learning neighboring links. We also introduce a back-propagation algorithm that optimizes the weights of the graph filter and finds the adjacency matrix that fits the training data. Then, the network model is applied on the dataset of medical images to help detect abnormal areas.

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A burgeoning literature on disaster evacuation is now a relatively well-established field, with a comprehensive set of theories and practices regarding human behavior, modeling techniques, and spatial analysis. However, knowledge is still lacking in the area of evacuation research concerning informal urban settlements (urban kampongs), in which the urban poor often reside in disaster-prone areas and in a maze-like spatial setting that can impede evacuation. This paper aims to develop a conceptual framework for the development of plans for informal settlement evacuation by systematically reviewing the theory, empirical evidence, and approaches to understanding evacuation route decision-making that apply in informal settlements. To achieve this, the authors reviewed 71 articles published between 1970 and 2021 and applied the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) technique. The conceptual framework described in this paper identifies a range of internal and external factors that each govern and determine evacuation route choice behavior. These factors encapsulate the individual characteristics of the informal urban settler, path risk elements, and path network configurations that compound the difficulties of evacuation observed in most informal settlements. This study also draws conclusions about the limitations of its framework and suggests some avenues for future research on emergency evacuation planning for informal urban settlements.

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Transformative capacity (TC) accommodates the changes to specific system functions to resolve disaster governance challenges, such as the unmet conditions existing between disaster risk reduction (DRR) and preparedness. This study sought to identify which local economic-driven and livelihood initiatives could be alternatives to DRR and become transformative activities for the Merapi volcano community in Indonesia. Using a qualitative approach, primary and secondary data were analyzed using four TC elements, focusing on three main community-based economic driven (CBED) activities; the tourism village, lava tours, and campsite management; the Merapi community could apply the TC framework to enable both DRR and livelihood enhancement. Even though, the results of these achievements remain somewhat unclear, the study shows that TC could accommodate change and that there were interdependencies and multidimensional approaches between each element.

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