Search results

The purpose of this paper is to study the cause of severe corrosion of the galvanized lightning rods in a 220 kV transformer substation, and to seek the effective corrosion inhibition measures for the hollow lightning rods.

The corrosion morphology and rust component of lightning rod was analyzed, and the corrosion process of lightning rod was researched by means of scanning electron microscopy (SEM), X-ray diffraction (XRD), ion chromatography and electrochemical tests.

The results indicated that the outer surface of hollow lightning rod was corroded slightly; however, its inner surface suffered severe corrosion because of a long time high-humidity environment inside the tube caused by the rainwater permeation. A rust layer consisted of Fe₃O₄ and a little FeOOH was accumulated on the inner surface of the hollow lightning rod. Moreover, Fe₃O₄ rust layer worked as a large cathode area which could promote the corrosion of metal substrate further. A self-accelerating corrosion process was formed on the inner surface, making the corrosion failure of lightning rod occurred and aggravated gradually.

The corrosion of inner surface of hollow lightning rod cannot be detected easily. More attention should be paid to the corrosion inhibition of lightning rod. The key of corrosion inhibition for the hollow lightning rod was to avoid the rainwater accumulation inside tube. The research results can provide guidelines on the corrosion inhibition measures selection of lightning rod in transformer substation.

Human group behavior is the driving force behind many complex social and economic phenomena. Few studies have integrated multi-dimensional travel patterns and city interest points to construct urban security risk indicators. This paper combines traffic data and urban alarm data to analyze the safe travel characteristics of the urban population. The research results are helpful to explore the diversity of human group behavior, grasp the temporal and spatial laws and reveal regional security risks. It provides a reference for optimizing resource deployment and group intelligence analysis in emergency management.

Based on the dynamics index of group behavior, this paper mines the data of large shared bikes and ride-hailing in a big city of China. We integrate the urban interest points and travel dynamic characteristics, construct the urban traffic safety index based on alarm behavior and further calculate the urban safety index.

This study found significant differences in the travel power index among ride-sharing users. There is a positive correlation between user shared bike trips and the power-law bimodal phenomenon in the logarithmic coordinate system. It is closely related to the urban public security index.

Based on group-shared dynamic index integrated alarm, we innovatively constructed an urban public safety index and analyzed the correlation of travel alarm behavior. The research results fully reveal the internal mechanism of the group behavior safety index and provide a valuable supplement for the police intelligence analysis.

This paper aims to explore the impact of a firm’s political connections on its economic value added (EVA) performance while treating connection heterogeneity and product diversification as moderators.

Based on data collected from 1,143 Chinese manufacturing listed firms in China’s A-share market from 2012 to 2017, this study conducted panel data analysis to investigate proposed relationships.

The findings provide evidence that political connections promote EVA performance of enterprises and both connection heterogeneity and product diversification negatively moderate the political connections – EVA performance relationship.

Drawing sights from the resource-based view, this study investigates the influence of corporate political connections on EVA performance, considering contingent factors of connection heterogeneity and corporate strategy (i.e. product diversification). It, thus, contributes to the literature on political connections by providing additional evidence to explaining the inconclusive findings on the political connections–firm performance relationship and extending prior research by emphasizing the moderating roles of connection heterogeneity and corporate strategy. It also complements prior research on EVA performance by exploring its antecedents.

Examines the fifteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.

This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.

The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.

This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.

The objective of this study is to investigate how the coronavirus disease 2019 (COVID-19) pandemic affects firms' financial management in China's manufacturing sector. In addition, the authors analyze the changes in various financial indicators before and during the COVID-19 pandemic. Further, the authors make a cross-country comparison of the COVID-19's impact on financial management between China and Romania.

The study uses the balanced panel data of 2,272 manufacturing listed companies from 2019 to 2020, and applies the t-test method and multiple regression method.

The results show that firms' financial performance in most manufacturing sub-sectors decreased during the observed period. In addition, the authors find that equity financing, proper liquidity management and an expanded firm scale can improve firms' financial performance. The authors further compare the results with the Romanian results, and find that the negative impact of debt-to-equity ratio on firms' financial performance in Romania is greater than that in China and the positive impact of financial autonomy ratio and working capital ratios is greater in China than that in Romania.

The findings can help corporate managers make the best financial management decision in response to crisis.

This study is one of the pioneers that analyze how manufacturing companies carried out their financial management during the COVID-19 crisis in the Chinese context, and provides a cross-country analysis of corporate financial management practices in China and Romania.

A mesoscopic phase field (PF) model is proposed to simulate the meso-failure process of lightweight concrete.

The PF damage model is applied to the meso-failure process of lightweight concrete through the ABAQUS subroutine user-defined element (UEL). And the improved staggered iteration scheme with a one-pass procedure is used to alternately solve the coupling equations.

These examples clearly show that the crack initiation of the lightweight concrete specimens mainly occurs in the ceramsite aggregates with weak strength, especially in the larger aggregates. The crack propagation paths of the specimens with the same volume fraction of light aggregates are completely different, but the crack propagation paths all pass through the ceramsite aggregates near the cracks. The results also showed that with the increase in the volume fractions of the aggregates, the slope and the peak loads of the force-deflection (F-d) curves gradually decrease, the load-bearing capacity of the lightweight concrete specimens decreases, and crack branching and coalescence are less likely during crack propagation.

The mesostructures with a mortar matrix, aggregates and an interfacial transition zone (ITZ) are generated by an automatic generation and placement program, thus incorporating the typical three-phase characteristics of lightweight concrete into the PF model.

With the rapid development of materials informatics and the Semantic Web, the semantic-driven solution has emerged to improve traditional query technology, which is hard to discover implicit knowledge from materials data. However, it is a nontrivial thing for materials scientists to construct a semantic query, and the query results are usually presented in RDF/XML format which is not convenient for users to understand. This paper aims to propose an approach to construct semantic query and visualize the query results for metallic materials domain.

The authors design a query builder to generate SPARQL query statements automatically based on domain ontology and query conditions inputted by users. Moreover, a semantic visualization model is defined based on the materials science tetrahedron to support the visualization of query results in an intuitive, dynamic and interactive way.

Based on the Semantic Web technology, the authors design an automatic semantic query builder to help domain experts write the normative semantic query statements quickly and simply, as well as a prototype (named MatViz) is developed to visually show query results, which could help experts discover implicit knowledge from materials data. Moreover, the experiments demonstrate that the proposed system in this paper can rapidly and effectively return visualized query results over the metallic materials data set.

This paper mainly discusses an approach to support semantic query and visualization of metallic materials data. The implementation of MatViz will be a meaningful work for the research of metal materials data integration.

This study investigates Hong Kong “consumers” sake tasting preferences, willingness to pay and how information commonly found on the bottle or menu affects these attributes.

This study took place inside a four-star hotel lobby restaurant. Convenience sampling was used to collect 184 valid responses from Hong Kong residents. Respondents were given four sakes two blind and two with common information found on the bottles and asked to rate each one independently.

The results suggest that alcohol content is the most crucial attribute in assessing the overall liking for consumers. In addition, information currently provided by producers and brewers has a negative effect on all assessment attributes and overall liking, but a positive effect on willingness to pay.

Sake producers, brewers, marketers, and hotel food and beverage managers should reconsider marketing strategies and the type of information provided to send better signals, increase “consumers” assessment and their overall liking. The results of this study suggest that sake brewers may want to advertise the alcohol content better to achieve higher satisfaction.

Consumers taste preferences for sake are not well understood. By applying the signalling theory the study results filled an information gap by examining how sake information commonly found on labels affects hotel guests tasting preferences and willingness to pay.

The purpose of this study is to present a highly stretchable and flexible strain sensor with simple and low cost of fabrication process and excellent dynamic characteristics, which make it suitable for human motion monitoring under large strain and high frequency.

The strain sensor was fabricated using the rubber/latex polymer as elastic carrier and single-walled carbon nanotubes (SWCNTs)/carbon black (CB) as a synergistic conductive network. The rubber/latex polymer was pre-treated in naphtha and then soaked in SWCNTs/CB/silicon rubber composite solution. The strain sensing and other performance of the sensor were measured and human motion tracking applications were tried.

These strain sensors based on aforementioned materials display high stretchability (500 per cent), excellent flexibility, fast response (approximately 45 ms), low creep (3.1 per cent at 100 per cent strain), temperature and humidity independence, superior stability and reproducibility during approximately 5,000 stretch/release cycles. Furthermore, the authors used these composites as human motion sensors, effectively monitoring joint motion, indicating that the stretchable strain sensor based on the rubber/latex polymer and the synergetic effects of mixed SWCNTs and CB could have promising applications in flexible and wearable devices for human motion tracking.

This paper presents a low-cost and a new type of strain sensor with excellent performance that can open up new fields of applications in flexible, stretchable and wearable electronics, especially in human motion tracking applications where very large strain should be accommodated by the strain sensor.