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The accurate prediction of total phosphorus (TP) is crucial for the early detection of water quality eutrophication. However, predicting TP concentrations among canal sites is challenging due to their complex spatiotemporal dependencies. To address this issue, this study proposes a GAT-Informer prediction method based on spatiotemporal correlations to predict TP concentrations in the Beijing–Hangzhou Grand Canal Basin in Changzhou City. The method begins by creating feature sequences for each site based on the time lag relationship of total phosphorus concentration between sites. It then constructs spatiotemporal graph data by combining the real river distance between sites and the correlation of feature sequences. Next, spatial features are extracted by fusing node features using the graph attention (GAT) module. The study employs the Informer network, which uses a sparse attention mechanism to extract temporal features efficiently for simulating and predicting total phosphorus data of the sites. The model was evaluated using R2, MAE, and RMSE, with the experimental results yielding values of 0.9619, 0.1489%, and 0.1999%, respectively. The GAT-Informer model exhibits enhanced robustness and superior predictive accuracy in comparison to traditional water quality prediction models. [ABSTRACT FROM AUTHOR]

The chemical oxygen demand (COD) emissions from the pulp and paper industry in China have declined continuously since the tighter COD discharge standards were implemented in 2008. Using firm-level data from 2003 to 2013, we investigate how China's pulp and paper firms would respond to the tighter COD discharge standards with a difference-in-differences design. We find that the tighter COD discharge standards have significant causal effects to induce the pulp and paper firms to reduce their COD emissions primarily through adopting cleaner production. However, the effectiveness of the tighter COD discharge standards depends on monitoring and enforcement activities as well as other regulations accompanying with the tighter COD discharge standards, all of which have become strengthened since China's 11th Five-Year-Plan (FYP, 2006–2010). While strengthened environmental regulations since the 11th FYP are from the top leadership of the Chinese government in order to deal with the increasing environmental degradation, the underlying driving force of the strengthened environmental regulations is from the household demand for better environmental quality as income increases. This study enriches literature not only on firms' responses to environmental regulations in developing countries but also on environmental regulations and technology adoption as well as environmental enforcement and compliance. [ABSTRACT FROM AUTHOR]

Author(s): Wen Xue [1, 9]; Sidi Chen [1, 9]; Hao Yin [1, 9]; Tuomas Tammela [1]; Thales Papagiannakopoulos [1]; Nikhil S. Joshi [1]; Wenxin Cai [1]; Gillian Yang [1]; Roderick [...]

Infrared thermography is an advanced technology that utilizes infrared detectors to sense the infrared thermal radiation emitted from the human body' s surface, converting it into electrical signals. These signals are then processed by computers to generate planar temperature color images, providing an intuitive display of the body surface temperature distribution. Rheumatic diseases, as prevalent conditions that significantly impact the quality of life of millions of people worldwide, pose significant challenges in diagnosis and assessment. Traditional diagnostic and evaluation methods, while possessing certain clinical value, exhibit non-negligible limitations. With the advancements and increasing popularity of infrared thermography technology, combined with the progress in medical image recognition and artificial intelligence algorithms, this technology has demonstrated increasingly prominent advantages in the field of rheumatology. By employing this non-invasive and highly efficient technique for analyzing superficial tissue thermal radiation distribution, early and precise diagnosis of rheumatic diseases, as well as dynamic monitoring of disease progression, becomes feasible. This technological advancement enhances the accuracy and efficiency of rheumatic disease diagnosis, thereby alleviating the burden on healthcare systems and optimizing the allocation of medical resources. Furthermore, infrared thermography introduces new vitality into the diagnosis and treatment monitoring of rheumatic diseases. It enables clinicians to detect subtle changes in body surface temperature that may indicate underlying inflammatory or metabolic processes associated with rheumatic conditions. This capability facilitates early intervention and personalized treatment strategies, ultimately contributing to improved patient outcomes and satisfaction. The integration of infrared thermography with advanced image processing algorithms and artificial intelligence further amplifies its potential, enabling automated analysis and interpretation of thermal images, thus reducing the dependence on manual interpretation and enhancing the reproducibility and reliability of diagnostic results. In conclusion, infrared thermography represents as a promising tool in the management of rheumatic diseases, offering a non-invasive, cost-effective, and efficient means for early diagnosis, monitoring, and therapeutic evaluation of diseases. As technology continues to evolve, the application of infrared thermography is expected to advance further in rheumatic diseases.