Fake News Detection with Heterogeneous Transformer

The dissemination of fake news on social networks has drawn public need for effective and efficient fake news detection methods. Generally, fake news on social networks is multi-modal and has various connections with other entities such as users and posts. The heterogeneity in both news content and the relationship with other entities in social networks brings challenges to designing a model that comprehensively captures the local multi-modal semantics of entities in social networks and the global structural representation of the propagation patterns, so as to classify fake news effectively and accurately. In this paper, we propose a novel Transformer-based model: HetTransformer to solve the fake news detection problem on social networks, which utilises the encoder-decoder structure of Transformer to capture the structural information of news propagation patterns. We first capture the local heterogeneous semantics of news, post, and user entities in social networks. Then, we apply Transformer to capture the global structural representation of the propagation patterns in social networks for fake news detection. Experiments on three real-world datasets demonstrate that our model is able to outperform the state-of-the-art baselines in fake news detection

Measuring pollutant emissions of cattle breeding and its spatial-temporal variation in China

The rapid development of animal husbandry has resulted in serious pollution issues in the livestock and poultry breeding industry, increasing the cost of environmental management. This issue is particularly prominent in China due to its rapid economic development, significant domestic consumption, and aggressive carbon neutrality targets. This study analyses pollution emissions and spatial-temporal variation in China's cattle breeding industry. Using an emission coefficient method and panel data of 31 Chinese provinces/municipalities between 2002 and 2017, we measure the total volume of pollutant emissions from China's cattle breeding industry and five major pollutants: chemical oxygen demand, total nitrogen, total phosphorus, copper, and zinc. We also analyse the dynamic variation of the spatial distribution. The results show that both the total emissions volume and emissions of the five major pollutants have decreased to different extents, among which chemical oxygen demand has decreased the fastest. Spatial divergence is strengthened as the heavy pollution areas have moved from the southeast to the northwest of the country. This study contributes to current research by its focus on the cattle breading industry and by our improvements to the pollutant emission measurement method

Research on Urban Rainfall Runoff Pollution Prediction Model Based on Feature Fusion

In this paper, a rainfall runoff pollution prediction method based on grey neural network algorithm is proposed in consideration of the current situation that the accuracy of research results related to rainfall runoff pollution prediction needs to be improved. Meanwhile, the characteristics of rainfall runoff pollution are analyzed from the perspectives of the main sources of rainfall runoff pollution, the types of rainfall runoff pollution, and the initial erosion. The neural network algorithm is optimized and trained according to the sample data to obtain the sample features; the sample data are predicted according to the extracted sample features, and the prediction model is generated by using the feature fusion technology for two groups of prediction results to generate the prediction model and realize the water drop prediction. The pollution concentration of runoff was obtained by the exponential function method. The experimental results show that the predicted values of discharge and pollution concentration are well fitted with the actual values, indicating that the proposed method has high accuracy and feasibility. Finally, from the viewpoint of non-engineering measures and engineering measures, the suggestions for treating runoff pollution and relevant supports for ecological environment protection are given

Effect of CaO on NOx Reduction by Selective Non-Catalytic Reduction under Variable Gas Compositions in a Simulated Cement Precalciner Atmosphere

High-concentration CaO particles and gas compositions have a significant influence on NOx reduction by selective non-catalytic reduction (SNCR) in cement precalciners. The effect of gas composition on NOx reduction by SNCR with NH3 was studied in a cement precalciner atmosphere with and without CaO at 700–1100 °C. It was found that CaO significantly lowers NOx reduction efficiency between 750 °C and 1000 °C, which is attributed to the catalytic oxidation of NH3 to NO. Although increasing NH3 concentration was advantageous to NOx reduction, the existence of CaO led to the opposite result at 750–900 °C. Adding H2O can suppress the negative effect of CaO on NOx reduction. Decreasing O2 content from 10% to 1% shifts the temperature range in which CaO has a significant effect from 750–1000 °C to 800–1050 °C. CO has a variety of influences on the CaO effect under different experimental conditions. The influences of NH3, H2O, O2, and CO on the effect of CaO can be attributed to the impacts of the gas compositions on gas-phase NH3 conversion, gas-solid catalytic NH3 oxidation, or both processes. A proposed pathway for the effect of gas compositions on NOx reduction in CaO-containing SNCR process was developed that well predicted the CaO-containing SNCR process

Pt-Au/MOx-CeO2 (M = Mn, Fe, Ti) Catalysts for the Co-Oxidation of CO and H2 at Room Temperature

A series of nanostructured Pt-Au/MOx-CeO2 (M = Mn, Fe, Ti) catalysts were prepared and their catalytic performance for the co-oxidation of carbon monoxide (CO) and hydrogen (H2) were evaluated at room temperature. The results showed that MOx promoted the CO oxidation of Pt-Au/CeO2, but only the TiO2 could enhance co-oxidation of CO and H2 over Pt-Au/CeO2. Related characterizations were conducted to clarify the promoting effect of MOx. Temperature-programmed reduction of hydrogen (H2-TPR) and X-ray photoelectron spectroscopy (XPS) results suggested that MOx could improve the charge transfer from Au sites to CeO2, resulting in a high concentration of Ce3+ and cationic Au species which benefits for the CO oxidation. In-situ diffuse reflectance infrared Fourier transform spectroscopy (In-situ DRIFTS) results indicated that TiO2 could facilitate the oxidation of H2 over the Pt-Au/TiO2-CeO2 catalyst

Catalytic Oxidation of NO over MnOx–CeO2 and MnOx–TiO2 Catalysts

A series of MnOx–CeO2 and MnOx–TiO2 catalysts were prepared by a homogeneous precipitation method and their catalytic activities for the NO oxidation in the absence or presence of SO2 were evaluated. Results show that the optimal molar ratio of Mn/Ce and Mn/Ti are 0.7 and 0.5, respectively. The MnOx–CeO2 catalyst exhibits higher catalytic activity and better resistance to SO2 poisoning than the MnOx–TiO2 catalyst. On the basis of Brunauer–Emmett–Teller (BET), X-ray diffraction (XRD), and scanning transmission electron microscope with mapping (STEM-mapping) analyses, it is seen that the MnOx–CeO2 catalyst possesses higher BET surface area and better dispersion of MnOx over the catalyst than MnOx–TiO2 catalyst. X-ray photoelectron spectroscopy (XPS) measurements reveal that MnOx–CeO2 catalyst provides the abundance of Mn3+ and more surface adsorbed oxygen, and SO2 might be preferentially adsorbed to the surface of CeO2 to form sulfate species, which provides a protection of MnOx active sites from being poisoned. In contrast, MnOx active sites over the MnOx–TiO2 catalyst are easily and quickly sulfated, leading to rapid deactivation of the catalyst for NO oxidation. Furthermore, temperature programmed desorption with NO and O2 (NO + O2-TPD) and in situ diffuse reflectance infrared transform spectroscopy (in situ DRIFTS) characterizations results show that the MnOx–CeO2 catalyst displays much stronger ability to adsorb NOx than the MnOx–TiO2 catalyst, especially after SO2 poisoning

The plasmonic enhancement in silicon nanocone hole solar cells with back located metal particles

A theoretical analysis of plasmonic enhancement in silicon nanocone hole (NCH) solar cells with back located periodic Ag particles is presented. The nanostructured Ag back particles can significantly enhance the absorption in the near-infrared spectrum. The ultimate efficiency of the optimized nanostructure can achieve 39%, which is 22% higher than that of NCH arrays without Ag particles. The angle-dependent ultimate efficiency is also investigated. The proposed nanostructure is expected to shed some light on the design and fabrication of high-efficiency solar cells