Revive, Restore, Revitalize: An Eco-economic Methodology for Maasai Mara

The Maasai Mara in Kenya, renowned for its biodiversity, is witnessing ecosystem degradation and species endangerment due to intensified human activities. Addressing this, we introduce a dynamic system harmonizing ecological and human priorities. Our agent-based model replicates the Maasai Mara savanna ecosystem, incorporating 71 animal species, 10 human classifications, and 2 natural resource types. The model employs the metabolic rate-mass relationship for animal energy dynamics, logistic curves for animal growth, individual interactions for food web simulation, and human intervention impacts. Algorithms like fitness proportional selection and particle swarm mimic organism preferences for resources. To guide preservation activities, we formulated 21 management strategies encompassing tourism, transportation, taxation, environmental conservation, research, diplomacy, and poaching, employing a game-theoretic framework. Using the TOPSIS method, we prioritized four key developmental indicators: environmental health, research advancement, economic growth, and security. The interplay of 16 factors determines these indicators, each influenced by our policies to varying degrees. By evaluating the policies' repercussions, we aim to mitigate adverse animal-human interactions and equitably address human concerns. We classified the policy impacts into three categories: Environmental Preservation, Economic Prosperity, and Holistic Development. By applying these policy groupings to our ecosystem model, we tracked the effects on the intricate animal-human-resource dynamics. Utilizing the entropy weight method, we assessed the efficacy of these policy clusters over a decade, identifying the optimal blend emphasizing both environmental conservation and economic progression.Comment: 25 pages, 16 figure

Research on the impact of asteroid mining on global equity

In the future situation, aiming to seek more resources, human beings decided to march towards the mysterious and bright starry sky, which opened the era of great interstellar exploration. According to the Outer Space Treaty, any exploration of celestial bodies should be aimed at promoting global equality and for the benefit of all nations. Firstly, we defined global equity and set a Unified Equity Index (UEI) model to measure it. We merge the factors with greater correlation, and finally, get 6 elements, and then use the entropy method (TEM) to find the dispersion of these elements in different countries. Then use principal component analysis (PCA) to reduce the dimensionality of the dispersion, and then use the scandalized index to obtain the global equity. Secondly, we simulated a future with asteroid mining and evaluated its impact on Unified Equity Index (UEI). Then, we divided the mineable asteroids into three classes with different mining difficulties and values, identified 28 mining entities including private companies, national and international organizations. We considered changes in the asteroid classes, mining capabilities and mining scales to determine the changes in the value of minerals mined between 2025 and 2085. We convert mining output value into mineral transaction value through allocation matrix. Based on grey relational analysis (GRA). Finally, we presented three possible versions of the future of asteroid mining by changing the conditions. We propose two sets of corresponding policies for changes in future trends in global fairness with asteroid mining. We test the separate and combined effects of these policies and find that they are positive, strongly supporting the effectiveness of our model.Comment: 19 page

Automatic Segmentation of Sinkholes Using a Convolutional Neural Network

Sinkholes are the most abundant surface features in karst areas worldwide. Understanding sinkhole occurrences and characteristics is critical for studying karst aquifers and mitigating sinkhole-related hazards. Most sinkholes appear on the land surface as depressions or cover collapses and are commonly mapped from elevation data, such as digital elevation models (DEMs). Existing methods for identifying sinkholes from DEMs often require two steps: locating surface depressions and separating sinkholes from non-sinkhole depressions. In this study, we explored deep learning to directly identify sinkholes from DEM data and aerial imagery. A key contribution of our study is an evaluation of various ways of integrating these two types of raster data. We used an image segmentation model, U-Net, to locate sinkholes. We trained separate U-Net models based on four input images of elevation data: a DEM image, a slope image, a DEM gradient image, and a DEM-shaded relief image. Three normalization techniques (Global, Gaussian, and Instance) were applied to improve the model performance. Model results suggest that deep learning is a viable method to identify sinkholes directly from the images of elevation data. In particular, DEM gradient data provided the best input for U-net image segmentation models to locate sinkholes. The model using the DEM gradient image with Gaussian normalization achieved the best performance with a sinkhole intersection-over-union (IoU) of 45.38% on the unseen test set. Aerial images, however, were not useful in training deep learning models for sinkholes as the models using an aerial image as input achieved sinkhole IoUs below 3%