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This paper aims to investigate the improvement of Sn58Bi solder properties by Ni nanoparticle to provide theoretical support in the field of electronic packaging.

In this study, nickel nanoparticles (Ni NPs) were doped into Sn58Bi solder as a reinforcing agent to prepare a composite solder. The wettability of composite solder, melting characteristics of solder alloy, microstructure of joints, mechanical properties and intermetallics growth at the interface were investigated.

Test results show that Ni had little substantial effect on the solders alloy melting temperature yet decreased the alloy undercooling. Notably, Ni enhanced solders wettability on Cu substrates. The spreading area increased by 14% at 0.6 wt.% Ni. The Ni refined the microstructure of Sn58Bi solder joints and inhibited the coarsening of the Bi-rich phase in the solder. At the same time, it also led to the production of free intermetallic compounds, increasing growth of the intermetallic compound (IMC) layer. However, upon surpassing 0.6 wt.% Ni, (Cu, Ni)₆Sn₅ accumulated and formed irregular clumps in the matrix. The grain size of the IMC layer gradually decreased as Ni was added. In shear tests, the fracture of joints occurred mainly inside the solder matrix. Sn58Bi-0.6Ni joints displayed a 40% rise in shear strength and a 25% rise in Vickers hardness. In addition, the appearance of dimples at the fracture indicated the improved flexibility of joints.

In this paper, Ni nanoparticle with different contents were added to Sn58Bi solder to prepare a composite solder, and the properties of the composite solder were investigated from different perspectives.

The purpose of this paper is to explore the changes of the scale of urban tourists in mainland China under the impact of COVID-19 and, specifically, the following questions: how did the scale of domestic tourists change nationwide and in the seven geographic regions? What are the differences in the changes among the seven geographic regions? What are the changes in the hot spot areas and spatial clustering of domestic tourists across the country?

Using the data of domestic tourist arrivals in 337 cities in mainland China from 2018 to 2021, this research analyzes the absolute differences and relative differences in the scale of domestic tourists nationwide and in seven geographic divisions with the help of indicators such as range analysis, standard deviation, coefficient of variation and Herfindahl–Hirschman Index and explores the changes in the hot spot areas and spatial concentration degree of the spatial scale of domestic tourists nationwide under the influence of the epidemic using kernel density analysis and spatial auto-correlation analysis.

The absolute differences in all seven geographical divisions continue to increase during 2018–2021. The domestic tourism in southwest China is extremely uneven. Absolute differences in the northwest and northeast regions are relatively small, and the development in attracting domestic tourists is more balanced. Relative differences in southwest China are comparatively large, with the trend of uneven development being obvious. The northeast, northwest and eastern regions of China are small, and the development is more balanced. The popularity of domestic tourism in the Beijing–Tianjin–Hebei region, as well as the Yangtze River Delta region, continues to decline and then pick up in 2021. The inland southwest region became a new domestic tourism hot spot in 2021. The size of domestic tourists from 2018 to 2021 in mainland China cities shows a significant positive spatial correlation, and there is a spatial agglomeration phenomenon, but some regional agglomeration types change from 2018 to 2021.

The impact of the epidemic on the number and spatial scale of domestic tourism in China has been clarified, which makes up for the comparison of domestic tourism changes before and after the epidemic. A clear understanding of the changes in the number and spatial scale of domestic tourists in different regions after the epidemic is conducive to the development of domestic tourism revitalization strategies in accordance with the actual situation of each province and promotes the internal circulation of Chinese tourism.

This paper tries to clarify the quantitative scale of domestic tourism in different regions after the epidemic, which is conducive to the development of domestic tourism revitalization strategies in cities in different regions according to regional characteristics and the actual situation of each province and to promote the healthy operation of the internal circulation of tourism in China. This paper also tries to show the changes of domestic tourism market hot spots, agglomeration conditions changes before and after the outbreak and the clarity of tourists’ preference space changes.

Scale of domestic tourists; Absolute difference; Relative difference; Spatial hot spot distribution; Spatial agglomeration change

本文旨在探寻疫情影响下中国大陆城市游客规模演化规律, 具体而言, 疫情影响下, 全国及七大地理分区的国内游客量规模变化如何？七大地理地区的变化有何差异？以及疫情影响下, 全国国内游客空间规模的热点区域和空间集聚程度有何变化？

利用2018-2021年中国大陆337各城市的国内游客量数据, 借助极差、标准差、变异系数、赫芬达尔指等指标分析全国及七大地理分区国内游客规模的绝对差异和相对差异; 借助核密度分析、空间自相关分析等ArcGIS分析工具, 探寻疫情影响下全国国内游客空间规模的热点区域和空间集聚程度的变化情况。

①绝对差异方面, 七大地理分区的绝对差异均持续增大。西南地区的游客量的绝对差异巨大, 国内游发展极不均衡。西北地区、东北地区绝对差异相对较小, 在吸引国内游客方面发展较为均衡。②相对差异方面, 西南地区的国内游发展相对差异较大, 发展不均衡趋势明显; 东北地区、西北地区、华东地区的国内游发展相对差异较小, 发展较为均衡。③热点区域变化方面, 京津冀地区、长三角地区的国内旅游热度持续下降, 在2021年有所回升; 内陆西南地区在2021年成为新的国内游热点区域。④2018年至2021年城市国内游客量规模均呈现出显著的空间正相关的关系, 存在着空间集聚现象, 但部分区域集聚类型在2018到2021年间发生变化。

①理论意义：明晰了疫情对中国国内旅游人次的数量规模和空间规模的影响, 弥补了当前疫情前后国内旅游业变化对比的研究; 阐明了疫情前后中国城市国内游客空间格局的变化, 拓展了研究情景, 丰富了中国旅游业时空变化的相关研究。②实践意义：明晰了疫后不同地区国内旅游人次的数量规模和空间规模变化情况, 以及国内旅游市场热点变化和游客空间偏好变化, 有利于各地区城市对症下药, 制定符合各省份实际情况的国内旅游业振兴策略, 促进中国旅游业内循环。

Although there is growing critical awareness in ELT around the rise of English as a global language, studies on teachers’ investment in critical pedagogy remain limited in mainstream ELT curricula, and the impact of such investment on teachers’ identities is not clear. To address this gap, with the inclusive paradigm of Global Englishes (GE), the paper presents a case study of an English teacher’s investment in GE at a middle school in China.

This study employed a qualitative approach. Semi-structured interview was conducted with the participant – May, focusing on her reported practices and her understanding of her roles during the investment in GE. The interview process was guided by an interview protocol which was designed based on related literature on GE, the theoretical framework, and the research context. A content analysis method was utilized to generate descriptive categories concerning participant’s identities and investment in GE.

Analysis of interview data reveals that through investment in GE, May not only constructed her identity as an English teacher, but also explored identities as a student inspirer, and a peer mentor. These identities exploration and construction were primarily driven by her increased cultural and social capital, despite facing challenges such as high-stakes test policies, native-speakerism ideology, institutional requirements, and limited resources.

Future research incorporating triangulating data such as interviews, and classroom observations, would provide a more comprehensive understanding of English teachers’ identities, and investment in GE. Besides, the findings of this study are primarily from one teacher, they may not fully represent the broader population of English teachers in China.

The findings have several implications for English teaching and teacher education. First, elementary schools, the lower grades of middle schools (grades 7 and 8), or the after-school programs may have opportunities for teachers to integrate GE in teaching. Second, teacher education programs should be designed to provide more chances that enable student teachers to invest in their learning and teaching of GE. Third, proposals for incorporating GE practices in the classroom must adopt a critical perspective, so as to to expose, deconstruct, and reconstruct power dynamics that influence teachers’ investment in GE.

The originality of this study lies in its exploration of English teachers’ investment of GE in their teaching, and the impact of this investment on their identities as English teachers. By investigating these aspects, the study addresses the research gaps in English teachers’ practice of integration of GE in mainstream ELT curricula and provides insights on how to encourage English teachers to incorporate a GE-aware perspective in their teaching.

The main aim of this study is to highlight the significance of fostering social capital and improving the quality of work life (QWL) for the well-being of healthcare workers. The second objective of this research is to address a notable gap in the current knowledge by examining the mediating influence of QWL on the relationship between work-related social capital and life satisfaction within the healthcare profession.

This study used a cross-sectional research methodology to examine the complex relationships among the variables and included a sample of 330 individuals who are employed full-time in the healthcare profession in the North Indian Region.

The study confirms all research hypotheses, showing that social capital improves work life. Thus, work-life quality improves life satisfaction significantly. The mediation analysis in this study used bootstrapping to show that work-life quality mediates the association between social capital and life satisfaction.

Addressing social support issues and using effective human resource management tactics can improve employees’ work life and satisfaction. The findings are essential in collectivistic cultures because strong workplace relationships improve professional welfare.

This study differentiates itself by analysing social capital and QWL as multi-dimensional constructs inside the workplace, ensuring the results’ correctness and validity. This study provides a distinct viewpoint for scholars and practitioners, enhancing comprehension of the correlation between life satisfaction and work-related social capital within the healthcare industry.

Prioritization of technological skills in China has led to scarce resources for art education. In this study, we tested whether personality traits were associated with creative learning and creative thinking skills, and whether these aspects of creativity were linked with academic achievement. We considered self-efficacy and 21st-century skills as mediating and moderating factors.

498 art school coaches were recruited from 12 Chinese universities. Coaches reported on their students’ Big Five personality traits, creative thinking skills, creative learning and self-efficacy, 21st-century skills and academic achievement. Data were analyzed with partial least squares structural equation modeling.

High openness, low conscientiousness, high extraversion and high agreeableness were associated with creative thinking skills, while high openness, low conscientiousness, high agreeableness and low neuroticism were associated with creative learning. Creative thinking and learning skills were both positively associated with academic achievement. Self-efficacy partially mediated the association between creative thinking skills and academic achievement. No moderation effects were identified.

Findings imply that art education would benefit from deeper consideration of individual differences and the promotion of learning environments conducive to creativity.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

In the era of the digital economy, digital platform capability plays an important role in the process of firm digitization. This study aims to investigate the relationships among digital platform capability and innovation performance from the perspective of external knowledge search and resource bricolage.

This study uses the hierarchical regression and bootstrapping methods to test the theoretical framework and research hypotheses. The survey data were collected from 629 Chinese firms.

This study finds that digital platform capability has a significant positive effect on innovation performance; external knowledge search and resource bricolage both play partial mediating roles between digital platform capability and innovation performance. There exist the serial mediation effects of “digital platform capability – external knowledge search – resource bricolage – innovation performance”.

The conclusion of this study can expand the research on digital platform capability, reveal the internal mechanism of digital platform capability in firm innovation and provide ideas and inspiration for firms to improve innovation performance and realise high-quality development.

In order to improve the computation efficiency of the four-point rainflow algorithm, a new fast four-point rainflow cycle counting algorithm (FFRA) using a novel loop iteration mode is proposed.

In this new algorithm, the loop iteration mode is simplified by reducing the number of iterations, tests and deletions. The high efficiency of the new algorithm makes it a preferable candidate in fatigue life online estimation of structural health monitoring systems.

The extensive simulation results show that the extracted cycles by the new FFRA are the same as those by the four-point rainflow cycle counting algorithm (FRA) and the three-point rainflow cycle counting algorithm (TRA). Especially, the simulation results indicate that the computation efficiency of the FFRA has improved an average of 12.4 times compared to the FRA and an average of 8.9 times compared to the TRA. Moreover, the equivalence of cycle extraction results between the FFRA and the FRA is proved mathematically by utilizing some fundamental properties of the rainflow algorithm. Theoretical proof of the efficiency improvement of the FFRA in comparison to the FRA is also given.

This merit makes the FFRA preferable in online monitoring systems of structures where fatigue life estimation needs to be accomplished online based on massive measured data. It is noticeable that the high efficiency of the FFRA attributed to the simple loop iteration, which provides beneficial guidance to improve the efficiency of existing algorithms.

In this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs.

In this study, ML algorithms were employed to predict the shear capacity and behavior of DCSWs. Various ML techniques, including linear regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), were utilized. The ML models were trained using a dataset of 462 numerical and experimental samples. Numerical models were generated and analyzed using the finite element (FE) software Abaqus. These models underwent push-over analysis, subjecting them to pure shear conditions by applying a target displacement solely to the top of the shear walls without interaction from a frame. The input data encompassed eight survey variables: geometric values and material types. The characterization of input FE data was randomly generated within a logical range for each variable. The training and testing phases employed 90 and 10% of the data, respectively. The trained models predicted two output targets: the shear capacity of DCSWs and the likelihood of buckling. Accurate predictions in these areas contribute to the efficient lateral enhancement of structures. An ensemble method was employed to enhance capacity prediction accuracy, incorporating select algorithms.

The proposed model achieved a remarkable 98% R-score for estimating shear strength and a corresponding 98% accuracy in predicting buckling occurrences. Among all the algorithms tested, XGBoost demonstrated the best performance.

In this study, for the first time, ML algorithms were employed to predict the shear capacity and behavior of DCSWs.