Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations

It is challenging but important to predict the collaborations between different entities which in academia, for example, would enable finding evaluating trends of scientific research collaboration and the provision of decision support for policy formulation and incentive measures. In this paper, we propose an attention-based Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) model to predict the collaborations between different research affiliations, which takes both the influence of research articles and time (year) relationships into consideration. The experimental results show that the proposed model outperforms the competitive Support Vector Machine (SVM), CNN and LSTM methods. It significantly improves the prediction precision by a minimum of 3.23 percent points and up to 10.80 percent points when compared with the mentioned competitive methods, while in terms of the F1-score, the performance is improved by 13.48, 4.85 and 4.24 percent points, respectively.This work was supported in part by the Humanities and Social Science Research Project of the Ministry of Education in China under Grant 17YJCZH262 and Grant 18YJAZH136, in part by the National Natural Science Foundation of China under Grant 61303167, Grant 61702306, Grant 61433012, Grant U1435215, and Grant 71772107, in part by the Natural Science Foundation of Shandong Province under Grant ZR2018BF013 and Grant ZR2017BF015, in part by the Innovative Research Foundation of Qingdao under Grant 18-2-2-41-jch, in part by the Key Project of Industrial Transformation and Upgrading in China under Grant TC170A5SW, and in part by the Scientific Research Foundation of SDUST for Innovative Team under Grant 2015TDJH102

Recovery of REEs from leaching liquor of ion-adsorbed-type rare earths ores using ionic liquid based on cooking oil

Ionic liquid (IL) based on tetraheptylammonium cooking oil (TC) was used for the first time to recovery rare earth elements (REEs) from leaching liquor of ion-adsorbed-type rare earths ores. Firstly, extraction performances of tetraheptylammonium oleate (TO) and tetraheptylammonium linoleate (TL) type ILs were investigated. It was shown that these ILs had good extraction performances, such as short equilibrium time less than 1 min, good extraction capacity of 0.225 mol/L and high stripping efficiency of 99%. Additionally, REEs extraction occurred in the solutions with high acidity (pH = 0.24) because the cation (C7H15)(4)N+ of TO and TL, could form a strong amine salt with hydrogen ion to decrease acidity of the feed solution. Also, an ion association mechanism of REEs extraction was proposed by FT-IR spectra, H-1 NMR investigation and slope analysis method. Secondly, TC was synthesized in water bath by mixing tetraheptylammonium bromide with saponified peanut oil. Then, comparative study of commercial extractants showed that TC had the highest REEs recovery yield of 100% from the leaching liquor. The sustainability, biocompatibility and non-toxicity, as well as simple synthesis route and well extraction performances make these cooking oil based ILs promising for REEs recovery from the leaching liquor

Proceedings of the International Symposium on Electrical Insulating Materials

Baseload power generation plants like thermal, coal, nuclear etc. contribute to around 30-35 % of the total power demand. Electric power grids depend on these power plants to provide reliable electric power supply with good power quality to the end consumer. To ensure reliable power generation, critical components of these plants need to be monitored continuously. In the distribution side of power system networks, medium voltage switchgear is being considered as the vital component to be looked at for industrial applications. In the past research studies on MV switchgear have given enough insight into various kinds of failures associated with it. This paper focuses on Gas insulted Switchgear (GIS) failure causes, failure phenomena, sensing techniques and signal processing tools for online diagnosis

Rheological, Thermal, and Degradation Properties of PLA/PPG Blends

The work presented herein focuses on simulating the compounding process via a torque rheometer, as well as the relationship between the melt viscosity and the polymer molecular weight (MW). We aim to predict the plasticization of polylactic acid (PLA) using polypropylene glycol (PPG) with different MWs. The rheological properties of the PLA/PPG composites containing PPG with different MWs were systematically studied by capillary rheometry and torque rheometry. The initial degradation of PLA/PPG composites during melt processing was monitored in real time. The results indicate that PPG can significantly reduce the melt viscosity of PLA/PPG composites, leading to obvious pseudoplastic fluid behavior. The lower the MW of PPG, the lower the viscosity of the PLA/PPG composite. The addition of PPG was favorable for the degradation of PLA during processing, and the degradation degree of the composite materials increased as the MW of PPG was decreased

Comprehensive investigation of a building integrated crossed compound parabolic concentrator photovoltaic window system: Thermal, optical and electrical performance

Integrating PV solar cells with concentrators into window systems can not only generate electricity for a building, but also has the potential to enhance the thermal resistance of the building's windows without a significant sacrifice of light transmittance for passive daylight. A novel photovoltaic window system, crossed compound parabolic concentrator photovoltaic window, has been recently studied for its electrical properties. However, its thermal and optical performance, including factors such as the overall heat transfer coefficient (U-value) and total optical transmittance when integrated into a building, have not been studied yet. These window factors are crucial for predicting its impact on a building's energy efficiency and indoor comfort. Therefore, this paper aims to investigate the U-value of this window system under various temperature scenarios and the optical transmittance of the window under different incident angles. The thermal conductance was assessed through numerical simulations using a computational fluid dynamics model, which was validated by experimental measurements conducted in a large climate chamber. The optical transmittance was investigated using a validated 3D ray-tracing model, and the total optical transmittance and electricity generation were calculated for typical sunny days in winter and summer under London's climate conditions. Additionally, new configurations were designed to explore the impact of the pitch between adjacent optics on the thermal conductance and optical transmittance of the window. The results showed that the window with a structure of Dx = Dy = 5 mm (where Dx and Dy represent the horizontal and vertical pitches between two adjacent solar optics) has the lowest U-value (2.566 W/m2·K). This U-value is slightly lower than that of the original window (2.575 W/m2·K). The original window with a structure of Dx = Dy = 1.77 mm produces the highest power output. Specifically, it generates 499.25 Wh/m2 on a typical sunny day in winter and 162.73 Wh/m2 on a typical sunny day in summer. However, it exhibits the lowest transmittance (14.6 % on a typical sunny day in winter and 25.2 % on a typical sunny day in summer, respectively), indicating that it is more suitable for buildings with a higher window-to-wall ratio to ensure an adequate amount of natural light. For buildings with a lower window-to-wall ratio, the CCPC-PV window should be designed with a larger horizontal pitch, such as 15 mm and 30 mm, to meet indoor illuminance requirements while also providing enhanced thermal performance and additional power output

Effects of Substitute Substrate, Water, and Fertilizer Management on the Growth of Potted Chrysanthemums

The chrysanthemum is a perennial herbaceous flower with a long history of cultivation dating back over 3000 years. The potted chrysanthemum is a significant type and is widely used in landscaping. Expensive substrate costs, complicated management of water and fertilizer, and uneven product quality currently plague the potted chrysanthemum industry. This study systematically investigated the growth status of potted chrysanthemums under different substrates, water, and fertilizer ratios and established a simplified cultivation system for potted chrysanthemums. The substitute substrate experiment demonstrated that coir: moss peat: perlite: pine needle mulch = 2:4:2:2 is the most suitable substitute substrate. Research on fertilizer ratios found that chrysanthemums’ best growth and flowering characteristics were achieved with nitrogen, phosphorus, and potassium concentrations of 336 mg/L, 93 mg/L, and 273 mg/L, respectively. A comprehensive, simplified cultivation system was established when utilizing T4 substitute substrate (2:4:2:2 ratios of coir, moss peat, perlite, and pine needle mulch), 40% water capacity, and F9 fertilizer (336 mg/L nitrogen, 93 mg/L phosphorus, and 273 mg/L potassium). This study comprehensively and systematically explored the cultivation and maintenance schemes in the production of potted chrysanthemums and built a light, simple, and efficient production technology system of potted chrysanthemums in the open field suitable for the climatic characteristics of northern China, which provides feasible technical specifications and a theoretical basis for the refinement and large-scale management of potted chrysanthemums. This experiment lays the foundations for cost reduction and efficiency in the potted chrysanthemum industry