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The electromagnetic transient overvoltage in the substation is an important factor that affects the life of insulation equipment. For evaluate the economy and reliability of the insulation design of substation equipment, it is necessary to study and analyze the actual measured overvoltage waveforms in the substation. In this paper, firstly, the 3018 pieces of overvoltage data monitored in the 220 kV substation from 2018 to 2019 are preprocessed to obtain the required overvoltage waveform. Next, by analyzing and classifying the overvoltage data, four typical overvoltage waveforms suffered by the insulation equipment in the substation are extracted. Combined with the IEC60060 standard, the time parameters of the overvoltage waveform are selected and defined: wave-front time (Tf), half-value time (Thalf), main oscillation frequency (f), and attenuation constant (α). Further, statistical analysis is performed on the parameters of each waveform. The distribution law, distribution characteristics and distribution range of the parameters are obtained. Finally, the parameters of four typical overvoltage waveforms are summarized and compared, the cumulative distribution functions are obtained. This results can guide and optimize the insulation design of the insulation equipment in the substation, reduce design errors. It provide data support for the economy and reliability of insulation design.

At present, the course recommendation model of graph collaborative filtering mainly uses bipartite graph modeling to obtain user-course cooperative relationship. However, the bipartite graph lacks the acquisition of user-user and course-course relationship information. In addition, due to the inherent defects of graph convolution, multi-layer graph convolution will cause overfitting problems. Moreover, the existing graph contrastive learning methods to solve the sparsity of recommendation data simply divide nodes into positive and negative pairs, without taking into account that users who have chosen the same course in the recommendation are similar. In contrastive learning, the feature similarity distance of these users should be different.To solve these problems, a course recommendation model based on layer dropout graph differential contrastive learning(DGDCL) is proposed. Specifically, a hybrid graph convolution network of fusion graph and hypergraph is used to obtain both low-order and high-order information. Then, using the layer dropout method to alleviate overfitting in neural network, the multi-layer feature embeddings of graph nodes are randomly dropout. Finally, two different layer drops are used to generate the contrastive views to reduce the additional noise and computational overhead of generating the contrastive views. The prior similarity of users and courses is used to adjust the calculation of the contrastive loss function, and differentiated contrastive learning of graph nodes is realized to make the contrastive learning more suitable for the recommendation model. The experimental results of XuetangX and MOOCCube datasets show that the proposed model is better than the existing model.

AbstractContext The Xihuang pill (XHP) is a traditional Chinese medicine formulation that has been historically used in the prevention and treatment of proliferative breast diseases. However, there is a lack of guidelines that offer recommendations for its clinical use.Objective The task force from the Chinese Guangdong Pharmaceutical Association aims to develop evidence-based guidelines for XHP to prevent and treat proliferative breast diseases.Methods We searched six Chinese and English electronic databases, including the China National Knowledge Infrastructure, the Chinese Scientific Journal Database, the Wanfang Medical Database, PubMed, and Embase, up to November 1, 2022. Publications (case reports, clinical observation, clinical trials, reviews) on using XHP to treat proliferative breast diseases were manually searched. The search terms were Xihuang pill, hyperplasia of the mammary gland, breast lump, and mastalgia. The writing team developed recommendations based on the best available evidence.Results Treatment should be customized based on syndrome identification. We recommend using XHP for the prevention and treatment of breast hyperplasia disease when a patient presents the following syndromes: concurrent blood stasis syndrome, concurrent phlegm-stasis syndrome, and concurrent liver fire syndrome. Safety indicators, including blood analysis and liver and kidney function monitoring, should be performed regularly during treatment.Conclusions Current clinical evidence suggests that XHP can be used as a standalone treatment or in conjunction with other medications to prevent and manage breast hyperplasia diseases. More randomized controlled studies are warranted to establish high-quality evidence of its use.

With the increasingly competitive job market, the employment issue for college graduates has received more and more attention. Predicting graduation development can help students understand their suitable graduation development, thus easing the pressure of finding employment after graduation. However, existing research must look into the issue of imbalance and long-tail distribution in student graduation development. This paper proposes a novel hypergraph contrastive learning model based on imbalanced sampling (IS-HGCL) that enables us to address this problem. First, construct a hypergraph using students’ school performance and social behavior. Then, our proposed imbalanced sampling strategy is applied to optimize the hypergraph structure and alleviate the imbalance issue. A self-updating hypergraph neural network is designed to optimize hyperedge representation and alleviate the long-tail distribution issue to enhance the hypergraph representation further. Finally, the structural consistency between the two optimized hypergraphs is maximized via node-level contrastive learning. Experiments on a real-world campus dataset demonstrate the superiority of the IS-HGCL model.

Graphitic carbon nitride (g-C3N4) is a metal-free photocatalyst with visible light response. However, the disadvantages limit its application in a wider range, such as its small specific surface areas, fewer active sites, narrow visible light absorption range and high photogenic carrier recombination. In this paper, NaOH was used as activator for alkaline activation of g-C3N4. The phase composition, micromorphology, surface chemical state and optical properties of g-C3N4 after activation were tested. The photocatalytic performance of g-C3N4 over organic dyes was also tested. The results showed that Na+ entered the interlayers of g-C3N4, expanding the spaces between layers. The specific surface area and pore volume of powder were increased. The active sites were increased. The band gap was decreased, and the photogenic carrier recombination was reduced. Alkaline activated g-C3N4 had better adsorption and degradation performance over rhodamine B and methyl orange than inactivated g-C3N4. Therefore, the alkaline activated g-C3N4 promotes its further application in the field of wastewater treatment. This work sheds light on the material modification through a simple method with the aim to efficiently use solar energy.

Bubble columns represent an extreme case of gas-liquid two-phase flow, where net liquid velocity is zero and the gas simply bubbles up through the liquid. The bubble column-type gas-liquid metal two-phase flow always appears in an accident scenario for pool-type lead-bismuth eutectic cooled fast breeder reactors. To accurately predict the void fraction for the accident evaluation and design of a reactor system, nine existing drift-flux type constitutive correlations are evaluated with a nitrogen-liquid heavy metal two-phase flow test. Few correlations give a relatively good prediction and the basic assumption in the distribution parameter calculation is not applicable for a bubble column. To solve this problem, analysis based on Clark’s theoretical model is carried out. The results show that the distribution parameter assumes very high values at a low Re number. As the Froude number increases, the distribution parameter tends to decrease. At lower void factions, the distribution parameter is also assumed to be at high values. This indicates that the pipe size, flow rate, and void fraction can all influence the distribution parameter. Considering the quantitative laws of these influence factors obtained by theoretical analysis and fitting the data of Ariyoshi’s test, a new correlation for a bubble column-type gas-LBE two-phase flow is developed and evaluated. The results of the statistical analysis show that the new correlation gives the best prediction for gas-LBE two-phase flow in the void fraction range of 0.018–0.313.

Abstract Doxorubicin (DOX) induced myocardial toxicity may limit its therapeutic use in clinic. Psoralen (PSO), a major active tricyclic furocoumarin extracted from Psoralea corylifolia, is widely used as an antineoplastic agent in treatment of leukemia and other cancers. This study is aim to find the protective effect of psoralen polymer lipid nanoparticles (PSO-PLN) on doxorubicin-induced myocardial toxicity in mice. The model of myocardial toxicity induced by DOX was established. The experiment was divided into 6 groups: normal saline group, DOX + Sulfotanshinone Sodium, DOX + PSO-PLN (3 mg/kg), DOX + PSO-PLN (6 mg/kg), DOX + PSO-PLN (9 mg/ kg), DOX group. DOX alone treated mice lead to a significant decrease in the body weight, heart weight, and increase in the serum levels of lactate dehydrogenase (LDH), creatine kinase (CK) and malondialdehyde (MDA) markers of cardiotoxicity. However, DOX reduced glutathione (GSH) content and activities of antioxidant enzymes, including superoxide dismutase (SOD) and glutathione peroxidase (GPX), were recovered by PSO-PLN. And PSO-PLN also decreased markers of cardiotoxicity in the serum. Western blotting data showed that the protective effects of PSO-PLN might be mediated via regulation of protein kinase A (PKA) and p38. Our study suggest that PSO-PLN possesses antioxidant activities, inactivating PKA and p38 effect, which in turn protect the heart from the DOX-induced cardiotoxicity.

Warning students with poor performance in advance based on historical academic data, namely, the academic abnormality prediction is important task in education. The majority of existing methods focus on digging out abnormal complex clues from historical data, while ignoring two basic considerations:(1)these works fail to handle unrecorded/missing data when this part is sparse; (2)these works ignore the complex relationship between courses. The different courses are used as the attention weight vector for abnormality prediction, but they do not notice the mutual influence between courses. To this end, we contribute a Hybrid Neural Network Model based on High-Order Attention Mechanism, called HHA, to address the academic abnormality prediction problem. Specifically, we first exploit Generative Adversarial Network(GAN) to mine hidden factors in the unrecorded/missing data reasonably by simulating student behavior. Thereafter, a high-order attention mechanism is proposed to measure the importance of course and course combination. Lastly, a multi-layer projection abstracts feature and classifies whether the student is abnormal. By experimenting on real-world dataset, we demonstrate the effectiveness and rationality of our proposed model.

Copper slag is a by-product waste during copper pyrometallurgy. The piling-stack of copper slag not only occupies a large quantity of plowland, but causes potential danger for local residents. In order to reduce the storage of copper slag, this study prepared concretes by replacing ordinary cement by copper slag. The mechanical properties, phase composition, micrographs, pozzolanic activity and volume stability of the compound cementitious materials were evaluated with fly ash as control. It was found that when the content of copper slag was 5%-10%, the best mechanical properties of concretes were obtained. The phase composition of hydration products of copper slag and fly ash based cementitious materials was almost identical. The micrographs and pozzolanic activity showed that copper slag had stronger reactivity than fly ash. Copper slag based pastes had good volume stability. Copper slag presented a potential application in mortars and concretes.

Sag is an important indicator of the operational health of a transmission line, and its timely measurement is of great significance to maintain the stability and reliability of power systems. However, traditional contact measurements may be affected by the electromagnetic interference of conductors. In contrast, measurement methods without direct electrical contact with the subject provide greater portability and flexibility. This paper presents a study of a transmission line sag measurement and simulation based on non-contact electric field sensing. The finite element method was used to analyze the conductor distribution, establish the coupling relationships among the electric field, transmission line, and measurement point, propose a sag inverse calculation model, and assess the impact of the transmission line parameter on the curved drooping measurement. Simultaneously, sag measurement schemes for single-round and dual-circuit lines were designed for multi-conductive lines, and measurement array studies were conducted. The vertical component of the electric field in space measured by the array was obtained, which could be used to perform conductor sag measurement simply and efficiently. The proposed method will facilitate the monitoring of the overhead transmission line status, which is conducive to the effective operation of the entire system.

This study aims at comparing the hydration characteristics between two kinds of cements, the Portland slag cement (PSC) containing 50 wt.% ground granulated blast furnace slag (GGBFS), and the supersulfated cement (SSC) containing 85 wt.% GGBFS. The reason caused the different hydration properties with the same type raw materials were analyzed by determining the compressive strength of the pastes, the hydration heat release and the hydration products at different curing ages. The results show that in PSC system, alkali activation was predominating and it mainly generated hydrated calcium silicate (C-S-H gels) and calcium hydroxide (CH). While in SSC system, the hydration mechanism was the combination of sulfate and alkali excitation, it mainly generated C-S-H gels and ettringite. The compressive strength of PSC was higher than that of SSC at every curing age. However, the difference of the compressive strength between PSC and SSC narrowed gradually as curing age went on. The early compressive strength of PSC was high while at late hydration age it increased slowly. The early compressive strength of SSC was low, while the later compressive strength increased quikly. Compared with PSC, the hydration heat evolution rate of SSC was slower and the hydration heat of SSC was relatively less low. Moreover, the second exothermic peak of SSC delayed. DOI: http://dx.doi.org/10.5755/j01.ms.24.4.17746

Advanced sensing and measurement techniques are key technologies to realize a smart grid. The giant magnetoresistance (GMR) effect has revolutionized the fields of data storage and magnetic measurement. In this work, a design of a GMR current sensor based on a commercial analog GMR chip for applications in a smart grid is presented and discussed. Static, dynamic and thermal properties of the sensor were characterized. The characterizations showed that in the operation range from 0 to ±5 A, the sensor had a sensitivity of 28 mV·A−1, linearity of 99.97%, maximum deviation of 2.717%, frequency response of −1.5 dB at 10 kHz current measurement, and maximum change of the amplitude response of 0.0335%·°C−1 with thermal compensation. In the distributed real-time measurement and monitoring of a smart grid system, the GMR current sensor shows excellent performance and is cost effective, making it suitable for applications such as steady-state and transient-state monitoring. With the advantages of having a high sensitivity, high linearity, small volume, low cost, and simple structure, the GMR current sensor is promising for the measurement and monitoring of smart grids.

The backreaming operation plays a significant role in safe drilling for horizontal wellbores, while it may cause severe stuck pipe accidents. To lower the risk of the stuck pipe in backreaming operations, the mechanism of cuttings transport needs to be carefully investigated. In this research, a transient cuttings transport with multiple flow patterns model is developed to predict the evolution of cuttings transported in the annulus while backreaming. The established model can provide predictions of the distribution of cuttings bed along the wellbore considering the bulldozer effect caused by large-size drilling tools (LSDTs). The sensitivity analyses of the size of LSDTs, and backreaming operating parameters are conducted in Section 4. And a new theory is proposed to explain the mechanism of cuttings transport in the backreaming operation, in which both the bit and LSDTs have the "cleaning effect" and "plugging effect". The results demonstrate that the cuttings bed in annuli is in a state of dynamic equilibrium, but the overall trend and the distribution pattern are obvious. First, larger diameters and longer drilling tools could lead to a higher risk of the stuck pipe. Second, we find that it is not the case that the higher flow rate is always better for hole cleaning, so three flow-rate intervals are discussed separately under the given conditions. When the "dangerous flow rate" (<33 L/s in Case 4) is employed, the cuttings bed completely blocks the borehole near the step surface and causes a stuck pipe directly. If the flow rate increases to the "low flow rate" interval (33e35 L/s in Case 4), a smaller flow rate instead facilitates borehole cleaning. If the flow rate is large enough to be in the "high flow rate" interval (>35 L/s in Case 4), the higher the flow rate, the better the cleaning effect of cuttings beds. Third, an interval of tripping velocity called "dangerous velocity" is proposed, in which the cuttings bed accumulation near the LSDTs is more serious than those of other tripping velocities. As long as the applied tripping velocity is not within the "dangerous velocity" (0.4e0.5 m/s in Case 5) interval in the backreaming operation, the risk of the stuck pipe can be controlled validly. Finally, through the factors analyses of the annular geometry, particle properties, and fluid properties in Section 5, it can be found that the "low flow rate", "high flow rate" and "dangers flow rate" tend to decrease and the "dangerous velocity" tends to increase with the conditions more favorable for hole cleaning. This study has some guiding significance for risk prediction and parameter setting of the backreaming operation. [ABSTRACT FROM AUTHOR]