The growing global freshwater scarcity urgently requires innovative wastewater treatment technologies. This study hypothesized that biomimicry-inspired automated machine learning (AML) could effectively manage wastewater variability through adaptive processing techniques. Utilizing decentralized swarm intelligence, specifically the Respected Parametric Insecta Swarm (RPIS), the system demonstrated robust adaptability to fluctuating influent conditions, maintaining stable effluent quality without centralized control. Bio-inspired oscillatory control algorithms maintained stability under dynamic influent scenarios, while adaptive sensor feedback enhanced real-time responsiveness. Machine learning (ML) methods inspired by biological morphological evolution accurately classified influent characteristics (F1 score of 0.91), optimizing resource allocation dynamically. Significant reductions were observed, with chemical consumption decreasing by approximately 11% and additional energy usage declining by 14%. Furthermore, bio-inspired membranes with selective permeability substantially reduced fouling, maintaining minimal fouling for up to 30 days. Polynomial chaos expansions efficiently approximated complex nonlinear interactions, reducing computational overhead by approximately 35% through parallel processing. Decentralized swarm algorithms allowed the rapid recalibration of system parameters, achieving stable pathogen removal and maintaining effluent turbidity near 3.2 NTU (Nephelometric Turbidity Units), with total suspended solids consistently below 8 mg/L. Integrating biomimicry with AML thus significantly advances sustainable wastewater reclamation practices, offering quantifiable improvements critical for resource-efficient water management.

Wastewater treatment is evolving rapidly with the advent of advanced deep-learning AI, graph-based, and physics-informed approaches. This study integrates graph neural networks, physics-informed neural networks, and multi-agent reinforcement learning within a hybrid digital-twin framework, evaluated on multi-scale real-world datasets. The developed models achieved over 90% dissolved organic carbon removal and reduced aeration energy by up to 22% while maintaining process stability. The results demonstrate that graph–physics synergies not only boost operational efficiency but also reveal critical trade-offs between energy savings and hydraulic performance. Our findings establish a new benchmark for resilient, low-carbon wastewater treatment, highlighting the transformative role of data-driven system design.

This study introduces Logarithmic Positional Partition Interval Encoding (LPPIE), a novel lossless compression methodology employing iterative logarithmic transformations to drastically reduce data size. While conventional dictionary-based algorithms rely on repeated sequences, LPPIE translates numeric data sequences into highly compact logarithmic representations. This achieves significant reduction in data size, especially on large integer datasets. Experimental comparisons with established compression methods—such as ZIP, Brotli, and Zstandard—demonstrate LPPIE’s exceptional effectiveness, attaining compression ratios nearly 13 times superior to established methods. However, these substantial storage savings come with elevated computational overhead due to LPPIE’s complex numerical operations. The method’s robustness across diverse datasets and minimal scalability limitations underscore its potential for specialized archival scenarios where data fidelity is paramount and processing latency is tolerable. Future enhancements, such as GPU-accelerated computations and hybrid entropy encoding integration, are proposed to further optimize performance and broaden LPPIE’s applicability. Overall, LPPIE offers a compelling alternative in lossless data compression, substantially redefining efficiency boundaries in high-volume numeric data storage.

Clean water security demands autonomous systems that sense, reason, and act at scale. We introduce AquaSurveil, a unified multi-agent platform coupling mobile robots, fixed IoT nodes, and privacy-preserving machine learning for continent-scale water health surveillance. The architecture blends Gaussian-process mapping with distributed particle filtering, multi-agent deep-reinforcement Voronoi coverage, GAN/LSTM anomaly detection, and sheaf-theoretic data fusion; components are tuned by Bayesian optimization and governed by Age-of-Information-aware power control. Evaluated on a 2.82-million-record dataset (1940–2023; five countries), AquaSurveil achieves up to 96% spatial-coverage efficiency, an ROC-AUC of 0.96 for anomaly detection, ≈95% state-estimation accuracy, and reduced energy consumption versus randomized patrols. These results demonstrate scalable, robust, and energy-aware water quality surveillance that unifies robotics, the IoT, and modern AI.

Coral reefs play an essential ecological role in maintaining marine water quality by naturally filtering contaminants. This study investigates the quantitative capability of coral reef ecosystems to reduce waterborne pollutants using biologically mediated processes. A systematic methodology, combining in situ observations, laboratory simulations, and analytical modeling, was adopted to determine the filtration efficiency of coral reefs. Remote sensing and photogrammetry characterized reef morphology, while microbial consortia transformations and coral polyp assimilation rates were quantified using biochemical assays. Results demonstrated significant nutrient uptake by coral polyps, particularly nitrogenous compounds, with higher removal efficiencies under stable salinity conditions. Temperature-induced stress was found to reduce polyp functionality. Enhanced sediment attenuation near reef structures improved coastal water transparency. The integration of vegetation buffers adjacent to reefs further augmented pollutant removal efficiency, with combined ecological strategies for effective pollution management.

Background: Epidemiologic and molecular studies report inverse, shared, and context-dependent associations between amyotrophic lateral sclerosis (ALS) and cancer. We evaluated population estimates, causal-genetic evidence, cross-disease transcriptomic patterns, model robustness, and patient-level transport. Methods: We integrated four published all-cancer estimates, a targeted Mendelianrandomization (MR) and phenome-wide evidence refresh, eleven public expression cohorts, covariateadjusted differential expression, sample-label permutation, gene-set analyses, nine machine-learning families with five fixed variants each, native MOFA+/SOFA and scVI/totalVI/MultiVI analyses, a 40-donor ALS cohort analyzed as donor-summed RNA-count pseudobulks with voom-limma precision weights to account for mean-variance dependence, and a five-patient paired lung-cancer positive control. Results: The mixed-time-origin population synthesis gave a Paule-Mandel ratio of 0.939 (modified Hartung-Knapp 95% CI 0.687-1.282; I 2 = 66.1%); the strict post-index estimate was 0.998 (95% CI 0.609-1.634). Published overall-cancer MR gave an odds ratio of 1.01 (95% CI 0.94-1.09). The nominal European discovery colorectal association, 0.84 (0.74-0.96), did not replicate: the larger European panel gave 0.99 (0.93-1.06) and the combined estimate was 0.92 (0.79-1.09). A five-instrument East Asian analysis gave 0.32 (0.15-0.69; P = 0.004), requiring independent replication. The four ALS-cancer gene correlations had permutation-adjusted q ≥ 0.262; fifteen ALS pathways replicated by rank enrichment. Graph neural networks (GNNs) ranked highest in the expanded gene-level benchmark (median AUC 0.645; five-variant range 0.638-0.649), versus approximately 0.54-0.61 across the other eight family medians. In 40 independent donors, the locked 103-gene score had AUC 0.604 (95% bootstrap CI 0.418-0.777; permutation P = 0.133), with acquisition-block AUCs of 0.273-0.290 and leave-one-donor-out AUCs of 0.348-0.414. Five classical lung-cancer classifiers ranked tumor above matched normal in all five held-out patients (exact P = 0.03125); the ALS-cancer score increased in three patients and decreased in two (P = 0.406). Conclusions: Population and overall-cancer genetic estimates centered near 1. Colorectal findings did not establish a replicated, general protective association. Molecular associations varied by tissue and ALS subtype. The GNN advantage concerned gene-level discrimination, not validated patient-level diagnosis or cancer-risk prediction.