Leveraging scenario differences for cross-task generalization in water plant transfer machine learning models.

Machine learning (ML) models are increasingly deployed in urban water systems to optimize operations, enhance efficiency, and curb resource consumption amid growing sustainability demands. Yet, their transferability across plants is hampered by scenario differences-variations in environmental factors, protocols, and data distributions-that erode performance and necessitate energy-intensive retraining. While existing strategies focus on minimizing these differences via domain adaptation or fine-tuning, none exploit them as inherent prior knowledge for improved generalization.

Machine Learning-Driven Dynamic Measurement of Environmental Indicators in Multiple Scenes and Multiple Disturbances.

Digital city water management systems require extensive data sensing for various environmental indicators, yet measurement accuracy often falls short under diverse extreme conditions. This study proposes a chemical oxygen demand (COD) measurement method based on ultraviolet-visible spectrum analysis and machine learning (ML), taking into account the removal of interferences, including temperature, pH, turbidity, common anions and cations, as well as COD composition and different water environments. The data collected from the river and wastewater were processed through principal component analysis, and random forest (RF) performed the best among the multiclass models with a mean absolute percentage error (MAPE) of only 6.

Flexible Carbon Source Regulation for Mitigating Greenhouse Gas Emissions in Full-Scale Wastewater Treatment.

Achieving carbon neutrality in wastewater treatment plants (WWTPs) by 2060 requires effective strategies to mitigate greenhouse gas (GHG) emissions. This study explores the potential of flexible carbon source regulation to reduce GHG emissions while improving the nutrient removal efficiency under varying influent conditions. A plant-wide model was developed, calibrated with one year of hourly monitoring data, to quantify GHG emissions in a full-scale WWTP.

Data-driven differentiable model for dynamic prediction and control in wastewater treatment.

Wastewater treatment plants, while critical for environmental protection, face mounting challenges in operational efficiency and sustainability due to increasing urbanization and stricter environmental standards. In this study, we introduce an innovative continuous-time neural framework based on Neural Ordinary Differential Equations (Neural ODEs) to enhance the modeling of sewage treatment processes. Addressing the dual challenges of operational efficiency and sustainable development in urban wastewater treatment plants (WWTPs), our methodology marks a significant departure from traditional approaches by implementing a continuous-time neural framework that captures the inherent dynamics of wastewater treatment processes while reducing computational demands by 95 % compared to discrete-time models.

Deciphering and Mitigating of Dynamic Greenhouse Gas Emission in Urban Drainage Systems with Knowledge-Infused Graph Neural Network.

Deciphering and mitigating dynamic greenhouse gas (GHG) emissions under environmental fluctuation in urban drainage systems (UDGSs) is challenging due to the absence of a high-prediction model that accurately quantifies the contributions of biological production pathways. Here we infused biological production pathways into the graph neural network (GNN) model architecture, developing ecological knowledge-infused GNN (EcoGNN-GHG) models to evaluate methane (CH) and nitrous oxide (NO) production in sewers and wastewater treatment plants (WWTPs). The EcoGNN-GHG model demonstrated high predictive accuracy, achieving an of 0.

Pushing the Limits of Capacitively Coupled Contactless Conductivity Detection for Capillary Electrophoresis.

Capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-CD) has proven to be an efficient technique for the separation and detection of charged inorganic, organic, and biochemical analytes. It offers several advantages, including cost-effectiveness, nanoliter injection volume, short analysis time, good separation efficiency, suitability for miniaturization, and portability. However, the routine determination of common inorganic cations (NH, K, Na, Ca, Mg, and Li) and inorganic anions (F, Cl, Br, NO, NO, PO, and SO) in water quality monitoring typically exhibits limits of detection of about 0.

Correction: A compact and high-performance setup of capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-CD).

Correction for 'A compact and high-performance setup of capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-CD)' by Lin Li , , 2024, https://doi.org/10.1039/d4an00354c.

A compact and high-performance setup of capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-CD).

Capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-CD) has the advantages of high throughput (simultaneous detection of multiple ions), high separation efficiency (higher than 10 theoretical plates) and rapid analysis capability (less than 5 min for common inorganic ions). A compact CE-CD system is ideal for water quality control and on-site analysis. It is suitable not only for common cations ( Na, K, Li, NH, Ca, .

Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer.

Intelligent control of wastewater treatment plants (WWTPs) has the potential to reduce energy consumption and greenhouse gas emissions significantly. Machine learning (ML) provides a promising solution to handle the increasing amount and complexity of generated data. However, relationships between the features of wastewater datasets are generally inconspicuous, which hinders the application of artificial intelligence (AI) in WWTPs intelligent control.

Pollen aggregation by viscin threads in Rhododendron varies with pollinator.

Pollen grains can be dispersed singly or variously aggregated in groups. Whether the evolution of pollen aggregation is driven by the pollinator remains unexplored. We hypothesize that an extensive pollen aggregation is favored under a scarcity of pollinators.

Evidence for passerine bird pollination in species.

When insect activity is limited at low temperature, birds may be comparatively more important pollinators than insects for flowering plants. It has been thought that many large-flowered species are pollinated by local birds in the Himalayan regions because most of these species flower in spring at high elevation with cool atmospheric temperature. However, experimental evidence for the role of bird pollination in this hyperdiverse genus remains scarce.

Development and characterization of microsatellite loci for the pseudometallophyte Commelina communis (Commelinaceae).

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Premise Of The Study: Microsatellite primers were developed for the pseudometallophyte Commelina communis (Commelinaceae), an important pioneer plant for phytoremediation of copper-contaminated soil. Two wild populations collected from metalliferous and nonmetalliferous sites were used to assess the polymorphism at each locus. •

Methods And Results: Based on the Fast Isolation by AFLP of Sequences COntaining repeats (FIASCO) method, a total of 34 pairs of simple sequence repeat (SSR) markers were designed.