Water scarcity presents a critical challenge to global sustainability, exacerbated by population growth, climate change, and environmental pollution. In this context, graywater reuse has emerged as a promising solution, offering substantial water savings with significant potential for agricultural applications. However, efficient treatment methods are essential to ensure safe reuse, as contaminants vary depending on the source. This study introduces a cyclic graywater treatment system that integrates both mechanic…

A unitary model of complex systems dynamics is proposed, assuming that these systems can be assimilated to a multifractal mathematical object. Then, if we describe these dynamics in the framework of The Scale Relativity Theory, through continuous and non-differentiable curves (fractal or multifractal curves), possible holographic-type behaviors can be obtained. In this context, the Schrödinger and Madelung multifractal scenarios align through the generation of patterns, symmetries of the normalized velocity relati…

Assimilating complex systems to multifractal-type objects reveals continuous and non-differentiable curve dynamics, aligning with the Multifractal Theory of Motion. Two scenarios, a Schrödinger-type and a Madelung-type multifractal scenario, are possible in this setting. If the Madelung scenario employs maximized information entropy for a distribution density, then Newtonian and oscillator-type forces can be determined. In the presence of these forces and a matter background, we analyze the two-body problem. The o…

Background/Objectives: Synaptic plasticity is fundamental to learning and memory, yet classical models such as Hebbian learning and spike-timing-dependent plasticity often overlook the distributed and wave-like nature of neural activity. We present a computational framework grounded in Scale Relativity Theory (SRT), which describes neural propagation along fractal geodesics in a non-differentiable space-time. The objective is to link nonlinear wave dynamics with the emergence of structured memory representations i…

Background: Dental caries arise from a multifactorial interplay between microbial dysbiosis, host immune responses, and enamel degradation visible on radiographs. Deep learning excels in image-based caries detection; however, integrative analyses that combine radiographic, microbiome, and transcriptomic data remain rare because public cohorts are seldom aligned. Objective: To determine whether three independent deep-learning pipelines—radiographic segmentation, microbiome regression, and transcriptome regression—c…