Fly ash was utilized as raw material for the preparation of spherical SiO2 (SS), which was subsequently ammonified using APTES (H2NCH2CH2CH2Si(OC2H5)3) to obtain aminated spherical SiO2 (SSN). The physicochemical properties of SS and SSN were systematically characterized. Notably, SS exhibited a remarkable specific surface area and pore volume, enabling it to accommodate abundant nitrogen-containing groups. These functional groups served as crucial active sorption sites, significantly enhancing the sorption capacity of SiO2 for Pb2+ and Cu2+ ions. Thus, the removal efficiency was above 99.9% when using dosages of 4 and 6 g/L SSN in solutions containing 200 mg/L of Pb2+ and Cu2+, respectively. Additionally, SSN showed a higher theoretical maximum sorption capacity for Pb2+ and Cu2+ ions, as determined by the Langmuir isotherm model, with values of 185.2 mg/g and 86.2 mg/g, respectively. These results surpass those reported in previous studies on adsorbents derived from fly ash. The chemical reactions that occurred between the aqueous cations and nitrogen-containing groups were identified as the pivotal factors governing the sorption of Pb2+ and Cu2+. This study presents a practical approach to fly ash utilization, along with the effective removal of Pb2+ and Cu2+ from water.
This paper presented an improved thermal network model for temperature rise rapid prediction of high-power linear ultrasonic motor (HPLUM) in high vacuum. The particularity of the heat conduction of piezoelectric materials is considered in the presented thermal circuit elements. The overall losses including mechanical damping losses and the three internal losses in piezoelectric elements, as well as the impact and friction losses occurring in the contact interface between the stator and the mover were calculated analytically. Then the calculated losses were used as the thermal sources inputted to the developed thermal network to predict the transient and steady-state temperature rise of the motor in high vacuum. Finally, the FEM-based temperature field analysis was conducted to validate the developed rapid thermal prediction model of HPLUM under high vacuum condition.
During the ignition process of a solid rocket motor, the pressure changes dramatically and the ignition process is very complex as it includes multiple reactions. Successful completion of the ignition process is essential for the proper operation of solid rocket motors. However, the measurement of pressure becomes extremely challenging due to several issues such as the enormity and high cost of conducting tests on solid rocket motors. Therefore, it needs to be investigated using numerical calculations and other methods. Currently, the fundamental theories concerning the ignition process have not been fully developed. In addition, numerical simulations require significant simplifications. To address these issues, this study proposes a solid rocket motor pressure prediction method based on bidirectional long short-term memory (CBiLSTM) combined with adaptive Gaussian noise (AGN). The method utilizes experimental pressure data and simulated pressure data as inputs for co-training to predict pressure data under new operating conditions. By comparison, the AGN-CBiLSTM method has a higher prediction accuracy with a percentage error of 3.27% between the predicted and actual data. This method provides an effective way to evaluate the performance of solid rocket motors and has a wide range of applications in the aerospace field.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
This paper presents a new method called depth from defocus (DFD) to obtain the image depth from a single still image. The traditional approaches always depend on the local features which are insufficient for estimation or need multiple images that cause a large amount of computation. The reverse heat equation is applied to get the defocused image. Then we use confidence interval to segment the defocused image and obtain a hierarchical image with guided image filter. The method need only a single image so it overcomes the massive computation and enhances the computation effect. The result shows that the DFD method is validate and efficient.
Text image translation (TIT) aims to translate the source texts embedded in the image to target translations, which has a wide range of applications and thus has important research value. However, current studies on TIT are confronted with two main bottlenecks: 1) this task lacks a publicly available TIT dataset, 2) dominant models are constructed in a cascaded manner, which tends to suffer from the error propagation of optical character recognition (OCR). In this work, we first annotate a Chinese-English TIT dataset named OCRMT30K, providing convenience for subsequent studies. Then, we propose a TIT model with a multimodal codebook, which is able to associate the image with relevant texts, providing useful supplementary information for translation. Moreover, we present a multi-stage training framework involving text machine translation, image-text alignment, and TIT tasks, which fully exploits additional bilingual texts, OCR dataset and our OCRMT30K dataset to train our model. Extensive experiments and in-depth analyses strongly demonstrate the effectiveness of our proposed model and training framework.
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