This study introduces a novel flood simulation framework, termed WeightedCA, which extends the CA2D model by incorporating weight-based transition rules for simulating flooding over surface terrains. The model is quasi-physical, leveraging simple shallow-water equations and a weighting system to expedite simulation processes. Through the application of this model to the Kerala state in India, known for its heavy monsoon rainfall and predominantly sea-level terrain, we demonstrate the model's capability in accurately predicting flood patterns. The WeightedCA model utilizes a Digital Elevation Model (DEM) as input and processes it through five distinct layers, including ground level, water columns, slope field, flow directions, and previous intercellular volume, to simulate water flow and accumulation dynamically. Transition rules are defined to update water heights in each cell, considering the mass conservation principle and flow directions based on the least resistance path. Additionally, the study explores flow accumulation matrices as a heuristic for large-scale water flow predictions, alongside metrics such as total mass, average flow rate, and fraction of cells flooded to evaluate the model's performance. A rainfall model simulating 30 days of Poisson-distributed rainfall patterns further complements the flood simulation. The case study of Kerala, with its complex topography and monsoon climate, provides a challenging yet insightful application for the WeightedCA model, showcasing its potential in flood risk management and planning. The results indicate the model's effectiveness in identifying flood-prone areas and evaluating flood mitigation strategies, albeit with limitations in resolution and predictive precision for large-scale applications. Future work will focus on refining the model through smaller catchment simulations and advanced rainfall models for improved accuracy in flood prediction and management strategies.






A Weighted Cellular Automata for Flood Modelling

Posidonia oceanica is a protected endemic seagrass of Mediterranean sea that fosters biodiversity, stores carbon, releases oxygen, and provides habitat to numerous sea organisms. Leveraging augmented research, we collected a comprehensive dataset of 174 features compiled from diverse data sources. Through machine learning analysis, we discovered the existence of a robust correlation between the exact location of \P. oceanica and water biogeochemical properties. The model's feature importance, showed that carbon-related variables as net biomass production and downward surface mass flux of carbon dioxide have their values altered in the areas with P. oceanica, which in turn can be used for indirect location of P. oceanica meadows. The study provides the evidence of the plant's ability to exert a global impact on the environment and underscores the crucial role of this plant in sea ecosystems, emphasizing the need for its conservation and management.

Machine Learning Reveals Large-scale Impact of Posidonia Oceanica on Mediterranean Sea Water

[IMPORTED NODE]Automerge

[IMPORTED NODE]Sunshine and Mood: A Comprehensive Analysis of the Impact of Sunlight Exposure on Psychological Well-being

Thesis submitted for completion of a master's of science in physics from Lehigh University. Project supervised by Dr. Timm Wrase.

A Comparative Analysis of Theories of Quantum Gravity and Their Respective Approaches to the Information Paradox

Microbial community dynamics are dictated by both abiotic environmental conditions and biotic interactions. These communities consist of individual microorganisms across the continuum of phylogenetic diversity, ranging from coexisting members of different domains of life and phyla to multiple strains with only a handful of single nucleotide variants. Ecological forces act on a shifting template of population-level diversity that is shaped by evolutionary processes. However, understanding the ecological and evolutionary forces contributing to microbial community interactions and overall ecosystem function is difficult to interrogate for complex, naturally occurring microbial communities. Here, we use two time series of lab-scale engineered enrichment microbial communities simulating phosphorus removal to explore signatures of microbial diversity at multiple phylogenetic scales. We characterized microbial community dynamics and diversity over the course of reactor start-up and long-term dynamics including periods of eubiosis and dysbiosis as informed by the intended ecosystem function of phosphorus removal. We then compared these signatures to lineages from full-scale WWTPs performing phosphorus removal. We found that enriched lineages in lab-scale bioreactors harbor less intra-population diversity than lineages from the full-scale WWTP overall. Our work establishes a foundation for using engineered enrichment microbial communities as a semi-complex model system for addressing the fundamental ecological and evolutionary processes necessary for developing stable microbiome based biotechnologies.

Signatures of Microbial Diversity at Multiple Scales of Resolution within Engineered Enrichment Communities

Marine microorganisms are central to global ecological and biogeochemical systems, with their intricate interactions shaping community dynamics. While meta-omics datasets have revolutionized marine microbial ecology, they often provide fragmented insights, underscoring the need for advanced integrative modeling frameworks. In this review, we highlight the potential that community Genome-Scale Metabolic Models (cGEMs), in combination with meta-omics and environmental datasets, offer in advancing marine microbial ecology. We explore three key applications: quantifying marine ecosystem services, guiding bioremediation strategies for environmental challenges, and enhancing climate and biogeochemical models. Furthermore, we propose novel indices derived from cGEMs to assess microbial contributions to ecosystem functions, potentially informing economic valuation strategies for marine conservation. This interdisciplinary approach paves the way for innovative strategies in biotechnology, environmental restoration, and the development of nature-aligned economic systems, ultimately contributing to the preservation and sustainable use of marine ecosystems.