Microbial Fuel Cells (MFCs) offer a promising pathway for sustainable energy generation by converting organic matter into electricity through microbial processes. A key factor influencing MFC performance is the anode structure, where design and material properties play a crucial role. Existing predictive models struggle to capture the complex geometric dependencies necessary to optimize these structures. To solve this problem, we propose KANGURA: Kolmogorov-Arnold Network-Based Geometry-Aware Learning with Unified Representation Attention. KANGURA introduces a new approach to three-dimensional (3D) machine learning modeling. It formulates prediction as a function decomposition problem, where Kolmogorov-Arnold Network (KAN)- based representation learning reconstructs geometric relationships without a conventional multi- layer perceptron (MLP). To refine spatial understanding, geometry-disentangled representation learning separates structural variations into interpretable components, while unified attention mechanisms dynamically enhance critical geometric regions. Experimental results demonstrate that KANGURA outperforms over 15 state-of-the-art (SOTA) models on the ModelNet40 benchmark dataset, achieving 92.7% accuracy, and excels in a real-world MFC anode structure problem with 97% accuracy. This establishes KANGURA as a robust framework for 3D geometric modeling, unlocking new possibilities for optimizing complex structures in advanced manufacturing and quality-driven engineering applications.

Depending on the robot's work, sustainability could look different from machine to machine. In general, making a more sustainable robot starts with ethically sourced recycled or sustainable materials, functioning as energy efficiently as possible. Then the robot has to be repairable if broken and recyclable when it's time to retire. While some sensors or computer chips might not currently be recyclable or reusable, those pieces wouldn't make up a large percentage of the machine. Some definitions of sustainability include the robot's function.

The Seinfeld idiom, "worlds are colliding," is probably the best description of work in the age of Corona. Pre-pandemic, it was easy to departmentalize one's professional life from one's home existence. Clearly, my dishpan hands have hindered my writing schedule. Thank goodness for the robots in my life, scrubbing and vacuuming my floors; if only they could power themselves with the crumbs they suck up. The World Bank estimates that 3.5 million tons of solid waste is produced by humans everyday, with America accounting for more than 250 million tons a year or over 4 pounds of trash per citizen.

Microbial fuel cells (MFCs) perform wastewater treatment and electricity production through the conversion of organic matter using microorganisms. For practical applications, it has been suggested that greater efficiency can be achieved by arranging multiple MFC units into physical stacks in a cascade with feedstock flowing sequentially between units. In this paper, we investigate the use of computational intelligence to physically explore and optimise (potentially) heterogeneous MFC designs in a cascade, i.e. without simulation. Conductive structures are 3-D printed and inserted into the anodic chamber of each MFC unit, augmenting a carbon fibre veil anode and affecting the hydrodynamics, including the feedstock volume and hydraulic retention time, as well as providing unique habitats for microbial colonisation. We show that it is possible to use design mining to identify new conductive inserts that increase both the cascade power output and power density.