Unexpected weakened formation of disinfection byproducts and enhanced production of halates by cupric oxide during chlorination of peptide-bound aspartic acid.

Under the condition that the residual chlorine is guaranteed, the biofilm still thrives in drinking water distribution systems through secreting a large number of extracellular polymeric substances (EPS), in which protein components are the primary precursor of disinfection byproducts (DBPs), mostly in the form of combined amino acids. The aim of this study is to investigate the action of CuO on the formation of halates (XO3-, ClO3- and BrO3-) and DBPs (trihalomethanes, THMs; haloacetonitriles, HANs) with aspartic acid tetrapeptide (TAsp) as protein surrogate. The presence of CuO promoted the self-decay rather than TAsp-induced decay of oxidants, resulting in an increase in XO3- yield and a decrease in DBPs yield. It was CuO-induced weaker production of cyanoacetic acid and 3-oxopropanoic acid that induced the decreased yields of HANs and THMs, respectively. The FTIR and Raman spectra indicate a weak complexation between CuO and TAsp. Given this, the CuO-HOX/OX- complexes were inferred to be reactive to HOX/OX- but less reactive to TAsp. The study helps to better understand the formation of XO3- and DBPs during the chlorination of EPS, and propose precise control strategies when biofilm boosts in water pipes.

Multi-Scale Attention Network for Building Extraction from High-Resolution Remote Sensing Images.

The precise building extraction from high-resolution remote sensing images holds significant application for urban planning, resource management, and environmental conservation. In recent years, deep neural networks (DNNs) have garnered substantial attention for their adeptness in learning and extracting features, becoming integral to building extraction methodologies and yielding noteworthy performance outcomes. Nonetheless, prevailing DNN-based models for building extraction often overlook spatial information during the feature extraction phase. Additionally, many existing models employ a simplistic and direct approach in the feature fusion stage, potentially leading to spurious target detection and the amplification of internal noise. To address these concerns, we present a multi-scale attention network (MSANet) tailored for building extraction from high-resolution remote sensing images. In our approach, we initially extracted multi-scale building feature information, leveraging the multi-scale channel attention mechanism and multi-scale spatial attention mechanism. Subsequently, we employed adaptive hierarchical weighting processes on the extracted building features. Concurrently, we introduced a gating mechanism to facilitate the effective fusion of multi-scale features. The efficacy of the proposed MSANet was evaluated using the WHU aerial image dataset and the WHU satellite image dataset. The experimental results demonstrate compelling performance metrics, with the F1 scores registering at 93.76% and 77.64% on the WHU aerial imagery dataset and WHU satellite dataset II, respectively. Furthermore, the intersection over union (IoU) values stood at 88.25% and 63.46%, surpassing benchmarks set by DeepLabV3 and GSMC.