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The purpose of this paper is to develop high-performance Au-coated Ag alloy wires (ACAA wires) and demonstrate the effect of Au coating layers on the bonding performance and oxidation resistance for stable and reliable electronic packaging applications.

ACAA wire with a diameter of approximately 25 µm and Au layer thickness of approximately 100 nm were prepared by the continuous casting, plating and wire drawing method. The bonding performance of the ACAA wires were studied through bonding on 3,535 chips. The oxidation resistance of ACAA wires and Ag alloy wires (AA wires) were comparatively studied by means of chemical oxidation tests, accelerated life tests and electrochemical tests systematically.

ACAA wires could form axi-symmetrical spherical free air balls with controllable diameter of 1.5∼2.5 times of the wire diameter after electric flame-off process. The ball shear strength of ACAA wire was higher than that of AA wires. Most importantly, because of the surface Au coating layer, the oxidation resistance of ACAA wires was much enhanced.

ACAA wires with different lengths of heat affected zone were not developed in this study, which limited their application with different loop height requirements.

With higher bonding strength and oxidation resistance, ACAA wires would be a better choice than previous reported AA wire in chip packaging which require high stability and reliability.

This paper provides a kind of novel ACAA wire, which possess the merits of high bonding strength and reliability, and show great potential in electronic packaging applications.

The purpose of the study is to address the problems of low accuracy and missed detection of occluded pedestrians and small target pedestrians when using the YOLOX general object detection algorithm for pedestrian detection. This study proposes a multi-level fine-grained YOLOX pedestrian detection algorithm.

First, to address the problem of the original YOLOX algorithm in obtaining a single perceptual field for the feature map before feature fusion, this study improves the PAFPN structure by adding the ResCoT module to increase the diversity of the perceptual field of the feature map and divides the pedestrian multi-scale features into finer granularity. Second, for the CSPLayer of the PAFPN, a weight gain-based normalization-based attention module (NAM) is proposed to make the model pay more attention to the context information when extracting pedestrian features and highlight the salient features of pedestrians. Finally, the authors experimentally determined the optimal values for the confidence loss function.

The experimental results show that, compared with the original YOLOX algorithm, the AP of the improved algorithm increased by 2.90%, the Recall increased by 3.57%, and F1 increased by 2% on the pedestrian dataset.

The multi-level fine-grained YOLOX pedestrian detection algorithm can effectively improve the detection of occluded pedestrians and small target pedestrians.

The authors introduce a multi-level fine-grained ResCoT module and a weight gain-based NAM attention module.

The purpose of this paper is to present an on-orbit frequency identification method for spacecraft directly using attitude maneuver data. Natural frequency of flexible solar arrays plays an important role in attitude control design of spacecraft with solar arrays, and its precision will directly affect the accuracy of attitude maneuver. However, when the flexibility of the solar arrays is large, because of air damping, gravity effect etc., the frequency obtained by ground test shows great error compared with the on-orbit real value. One solution to this problem is to conduct on-orbit identification during which proper identification methods are used to obtain the parameters of interest based on the real on-orbit data of spacecraft.

The observer/Kalman filter identification and eigensystem realization algorithm are used as identification methods, and the attitude maneuver controller is designed using the rigid-body dynamics method.

Two conclusions are drawn in this paper according to results of numerical simulations. The first one is that the attitude controller based on the rigid-body dynamics method is effective in attitude maneuver of the spacecraft. The second one is that the on-orbit parameter identification can be directly achieved by using attitude maneuver data of spacecraft without adding additional missions.

Based on the methods proposed in this paper, it is convenient to obtain the natural frequencies of the spacecraft using the data of the attitude maneuver, which may greatly reduce the cost of on-orbit identification test.

The way of obtaining natural frequencies based on attitude maneuver data of spacecraft provides high originality and value for practical application.

In shield tunneling projects, human, shield machine and underground environment are tightly coupled and interacted. Accidents often occur under dysfunctional interactions among them. Therefore, this paper aims to develop a multi-agent based safety computational experiment system (SCES) and use it to identify the main influential factors of various aspects of human, shield machine and underground environment.

The methods mainly comprised computational experiments and multi-agent technologies. First, a safety model with human-machine-environment interaction consideration is developed through the multi-agent technologies. On this basis, SCES is implemented. Then computational experiments are designed and performed on SCES for analyzing safety performance and identifying the main influential factors.

The main influential factors of two common accidents are identified. For surface settlement, the main influential factors are ranked as experience, soil density, soil cohesion, screw conveyor speed and thrust force in descending order of influence levels; for mud cake on cutter, they are ranked as soil cohesion, experience, cutter speed and screw conveyor speed. These results are consistent with intuition and previous studies and demonstrate the applicability of SCES.

The proposed SCES provides comprehensive risk factor identification for shield tunneling projects and also insights to support informed decisions for safety management.

A safety model with human-machine-environment interaction consideration is developed and computational experiments are used to analyze the safety performance. The novel method and model could contribute to system-based safety research and promote systematic understanding of the safety performance of shield tunneling projects.

Since the present university leading cadre capability evaluations are mainly static, there is a lack of evaluation methods to address dynamic and multi‐scale‐based situations. Some important factors to address people's performances, such as the developing trends and potentialities are considered. The purpose of this paper is to attempt to propose a dynamic evaluation model based on multivariate delay connection number.

First, evaluation results of previous periods are taken into the current period and values of uncertain numbers are revised. Next, a dynamic evaluation model is built for university leading cadre. Last, a numerical example is used to prove its feasibility and effectiveness.

This paper promotes a corrected equal portion value method of uncertain numbers i1(τ), … ,in(τ) from the adjacent two periods to multi periods, and builds a dynamic evaluation model, which proved simple‐structured and easy to apply.

The new model is applied in the evaluation of university leading cadre, and could give a more comprehensive, dynamic, scientific analysis for evaluation by model application.

The paper develops a novel evaluation model based on a multivariate delay connection number, which expands traditional methods from a static situation to a dynamic and multi‐scale‐based situation.

The research on social media-based academic communication has made great progress with the development of the mobile Internet era, and while a large number of research results have emerged, clarifying the topology of the knowledge label network (KLN) in this field and showing the development of its knowledge labels and related concepts is one of the issues that must be faced. This study aims to discuss the aforementioned issue.

From a bibliometric perspective, 5,217 research papers in this field from CNKI from 2011 to 2021 are selected, and the title and abstract of each paper are subjected to subword processing and topic model analysis, and the extended labels are obtained by taking the merged set with the original keywords, so as to construct a conceptually expanded KLN. At the same time, appropriate time window slicing is performed to observe the temporal evolution of the network topology. Specifically, the basic network topological parameters and the complex modal structure are analyzed empirically to explore the evolution pattern and inner mechanism of the KLN in this domain. In addition, the ARIMA time series prediction model is used to further predict and compare the changing trend of network structure among different disciplines, so as to compare the differences among different disciplines.

The results show that the degree sequence distribution of the KLN is power-law distributed during the growth process, and it performs better in the mature stage of network development, and the network shows more stable scale-free characteristics. At the same time, the network has the characteristics of “short path and high clustering” throughout the time series, which is a typical small-world network. The KLN consists of a small number of hub nodes occupying the core position of the network, while a large number of label nodes are distributed at the periphery of the network and formed around these hub nodes, and its knowledge expansion pattern has a certain retrospective nature. More knowledge label nodes expand from the center to the periphery and have a gradual and stable trend. In addition, there are certain differences between different disciplines, and the research direction or topic of library and information science (LIS) is more refined and deeper than that of journalism and media and computer science. The LIS discipline has shown better development momentum in this field.

KLN is constructed by using extended labels and empirically analyzed by using network frontier conceptual motifs, which reflects the innovation of the study to a certain extent. In future research, the influence of larger-scale network motifs on the structural features and evolutionary mechanisms of KLNs will be further explored.

The purpose of this paper is to focus on the cultural cognition of Chinese Zhong-yong thinking, which is deeply rooted in Chinese Confucius culture, and to examine how entrepreneurs’ Zhong-yong thinking is related to new venture performance through guanxi network, and also examine how environmental turbulence affects the influencing mechanism.

This paper follows an empirical design. Data are collected from a survey administered to entrepreneurs in new ventures of China. Regression analysis is used to test the hypothesis.

Results show that entrepreneurs’ Zhong-yong thinking is positively related to guanxi and new venture performance, and guanxi mediates the relationship between entrepreneurs’ Zhong-yong thinking and new venture performance. In addition, environmental turbulence moderates the relationship between entrepreneurs’ Zhong-yong thinking and guanxi such that the relationship is stronger under higher technological turbulence or lower market turbulence.

This research uses cross-sectional data, so causal conclusions cannot be made. In addition, more moderators should be considered.

The present study enriches the understanding of how entrepreneurs’ Zhong-yong thinking affects new ventures, which helps entrepreneur understand how to strategize according to external environment and develop what kind of cognitive style to deal with complex situation of their own venture.

This study is a pioneer in exploring non-Western cognitive style–Zhong-yong thinking in entrepreneurial context. It not only enriches the understanding of how Chinese wisdom affects organizational strategy and organizational performance but also advances the cognition research in the field of entrepreneurship.

Despite the importance of hosts who contribute to the success of accommodation sharing through sharing underutilized space with guests, current literature sheds little light on what exactly incentivizes hosts to grow their properties. The purpose of this study is to investigate the effects of multifaceted motivations including financial benefits, online social interaction and membership seniority and their interplay on hosts’ multiple listing behavior.

The study is instantiated on real-world business data collected from an accommodation-sharing platform in China. The data set includes 3,199 observations of 252 multi-listing hosts in Beijing who managed 815 properties from September 2012 to October 2016.

The study discloses that financial benefits, online social interaction and membership seniority significantly incentivize hosts to list multiple properties on the accommodation-sharing platform. In particular, the social incentive is the most important driver among the three. With a 1 per cent increase in online social interactions, the number of properties operated by a host would increase by 13.5 per cent. While the financial benefits and online social interaction motivate hosts to engage in the multi-listing behavior, such effects are significantly mitigated as the membership seniority increases.

This study adds to the extant literature a unique yet less researched perspective of supply expansion driven by hosts. It also provides important practical implications for managing multiple properties for a healthy and viable accommodation-sharing community.

While a majority of the extant research on the sharing economy primarily takes a consumer-related perspective, this study addresses a different and original topic about hosts’ multiple-listing behavior that drives the supply of accommodation sharing. It is a first empirical investigation of the increase of accommodation sharing supply with host motivations explained.

The purpose of this study is to restore the spatial distribution of overseas remittance businesses in Shantou during the 1940s. It explores various socioeconomic factors that influenced the concentration of local remittance business investment in real estate. By reconstructing the spatial distribution of remittance business activities in Shantou, this study hopes to lay a foundation for further analysis of the business strategies of Chaoshan merchants.

This research draws on information from the published Swatow Guide, archival sources and cadastral maps to identify the location of remittance enterprises and the native place and overseas networks of property owners.

This study reveals that the spatial distribution of the remittance enterprises was determined by the native place origins of local property owners, and that the inflow of overseas Chinese capital contributed to real estate development in Shantou.

Despite the limited access to Chinese official archives, this paper manages to identify several building blocks and neighbors in Shantou for spatial analysis.

This study is the first attempt to use the geographical information system (GIS) method in Chinese urban history research and hopes to establish a larger historical database of Shantou as a sample for comparison.

This investigation advances the spatial study of urban history and overseas Chinese remittances in the maritime society of South China.

This paper aims to identify a wide array of utility-based attributes of Airbnb listings and measures the effects of these attributes on consumers’ valuation of Airbnb listings.

A hedonic price model was developed to test the effects of a group of utility-based attributes on the price of Airbnb listings, including the characteristics of Airbnb listings, attributes of hosts, reputation of listings and market competition. The authors examined attributes as they relate to the price of Airbnb listings and, therefore, estimated consumers’ willingness to pay for the specific attributes. The model was tested by using a dataset of 5,779 Airbnb listings managed by 4,602 hosts in 41 census tracts of Austin, Texas in the USA over a period from Airbnb’s launch in Texas up until November 2015.

The authors found that the functional characteristics of Airbnb listings were significantly associated to the price of the listings, and that three of five behavioral attributes of hosts were statistically significant. However, the effect of reputation of listings on the price of Airbnb listings was weak.

This study inspires what they call a factor-endowment valuation of Airbnb listings. It shows that the intrinsic attributes that an Airbnb listing endows are the primary source of consumer utilities, and thus consumer valuation of the listing is grounded on its functionality as an accommodation. This conclusion can shed light on the examination of competition between Airbnb and hotel accommodations that are built on the same or similar intrinsic attributes.