Search results

In order to extend the application of the original octagonal Goodman–Smith fatigue limit diagram, which is commonly used for the evaluation of structure fatigue stress in engineering, a modification of it is proposed for the structure made of S355 steel (commonly used in high-speed electric multiple units (EMUs) bogie frame).

The modification is made based on Deutscher Verband für Schweißen und verwandte Verfahren e. V. (DVS) 1612 standard and the γ-P-S-N curve, with consideration of the fatigue evaluation requirements of different survival rates and confidence levels. The verification of the modification is performed for three welded joints and for the comparison with the experimental data.

The results indicate that the design survival rate, the design safety margin and the fatigue stress evaluation of welded joint types are all improved by using the modified diagram.

There are relatively few studies on modifying octagonal Goodman–Smith fatigue limit diagram. In this paper, a modified diagram is proposed and applied in order to ensure the safety and durability of key welded structures of rail vehicles.

This study aims to test the link between enterprise digital transformation and technological innovation and the mechanisms and channels behind this link.

This study systematically examines whether and how enterprise digital transformation affects technological innovation in China.

Enterprise digital transformation effectively improves technological innovation. This result remains stable in robustness and endogeneity checks. The channel mechanisms of this promoting effect are internal (improvement of internal control quality and alleviation of agency costs) and external (increased attention of analysts and reduction of customer concentration). Moreover, this promoting effect is more significant for state-owned enterprises, small and medium-sized enterprises, enterprises in areas with low marketization and enterprises that do not enjoy digital subsidies from the government.

Enterprises need to attend to the mechanisms behind the link between digital transformation and technological innovation and to the unique effects of different enterprise attributes and capital markets, such as size, the ownership nature, the degree of regional marketization and government subsidies. Doing so will effectively promote digital transformation and technological innovation and strengthen core competitiveness.

This study provides systemic evidence of the link between enterprise digital transformation and technological innovation. The findings enrich the research literature on enterprise digitization and the factors of influencing enterprises’ technological innovation and provide a reasonable explanation for how enterprise digital transformation affects technological innovation.

In China, libraries can be grouped into three major categories: (1) the public libraries, headed by the National Library of China and including the provincial, municipal, prefectural, and county libraries; (2) the academic libraries under the control of the Ministry of Education, including the university and college libraries and middle and elementary school libraries; and (3) the libraries of the Chinese Academy of Sciences (Academic Sinica), including the science and research libraries. Academic libraries play a very special role in the country among these groups of libraries and are becoming increasingly important, as they are at the centre of information and education on each campus (Ma 1989).

The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources, e.g., sensor networks, securities exchange, electric power secondary system, etc. Concretely, the proposed framework should handle several difficult requirements including the management of gigantic data sources, the need for a fast self-adaptive algorithm, the relatively accurate prediction of multiple time series, and the real-time demand.

First, the autoregressive integrated moving average-based prediction algorithm is introduced. Second, the processing framework is designed, which includes a time-series data storage model based on the HBase, and a real-time distributed prediction platform based on Storm. Then, the work principle of this platform is described. Finally, a proof-of-concept testbed is illustrated to verify the proposed framework.

Several tests based on Power Grid monitoring data are provided for the proposed framework. The experimental results indicate that prediction data are basically consistent with actual data, processing efficiency is relatively high, and resources consumption is reasonable.

This paper provides a distributed real-time data prediction framework for large-scale time-series data, which can exactly achieve the requirement of the effective management, prediction efficiency, accuracy, and high concurrency for massive data sources.

Oppositional loyalty toward rival brands is prevalent. Although its antecedents have increasingly received scholarly attention, the literature is rather disparate. Based on identity theory, this study aims to propose that oppositional loyalty is a brand identity-driven outcome and provides a unified framework for understanding the formation and activation of brand identity in influencing oppositional loyalty.

Structural equation modeling was used to test the theoretical framework based on an online survey of 329 brand community members. Multigroup analysis was used to test the moderating effect of inter-consumer brand rivalry and brand community engagement.

The results show that self-brand similarity, brand prestige and brand uniqueness lead to consumers’ brand identity (i.e. consumer-brand identification), which, in turn, facilitates oppositional loyalty. Furthermore, the results indicate that inter-consumer brand rivalry and brand community engagement are identity-salient situations that strengthen the relationship between consumer-brand identification and oppositional loyalty.

Identity has great power in shaping consumer behaviors. Fostering consumer-brand identification is critical for firms to prevent consumers from switching to competing brands. Inter-consumer brand rivalry and brand community engagement can help firms consolidate their customer base by evoking consumers’ brand identity.

This investigation makes theoretical contributions by providing a unified theoretical framework to model the development of oppositional loyalty based on identity theory.

In this digital age and risk society, this study aims to explore innovative strategies for E-retailers during supply chain disruptions to construct a more resilient supply chain system.

Various game theoretical models are constructed to analyze four supply chain scenarios. Meanwhile, sufficient numerical analysis was conducted to observe the impact of key parameters on supply chain strategies.

Multiple crucial factors exert a comprehensive influence on E-retailers’ decisions on sourcing and pricing, leading to the diversity and complexity of decision-making conditions. First, with the increased probability of disruption, the purchase quantities of the E-retailer from different suppliers are not in a linear changing pattern, and the total purchase quantity is allocated variably between different suppliers. Second, the variation in disruption severity (partial or complete) results in the shift of decisions between single-sourcing and dual-sourcing. Responsive pricing is conducive to increasing the purchase quantity and profits under partial disruption; its advantages are diminished when completely disrupted. Third, higher commission rates usually have a detrimental impact on profit, whereas responsive pricing may mitigate this impact.

Unlike the previous single perspective, this study innovatively explores strategies from the hybrid perspective of sourcing and pricing. By extracting two key factors (disruption probability and severity), it realizes the scientific characterization of supply chain disruptions. These achievements boost theoretical innovation. Concentrating on E-retailers, it avoids the generalization of conclusions and enhances the application value.

The highly uncertain and turbulent environments nowadays intensify the paradoxical effects of supply base concentration (SBC) on improving cost efficiency while increasing idiosyncratic risk (IR). Digitalization is regarded as a remedy for this paradox, yet digitization's potentially curative effect has not been empirically tested. Leveraging the lenses of paradox theory and information processing theory (IPT), this study explores how two distinct dimensions of digitalization, i.e. digitalization intensity (DI) and digitalization breadth (DB), reconcile the paradoxical effects of SBC.

Using a panel dataset of 1,238 Chinese manufacturing firms in the period of 2012–2020, this study utilizes fixed-effects regression models to test the proposed hypotheses.

The authors discover that SBC enhances a firm's cost efficiency but induces greater IR. More importantly, there is evidence that DI restrains the amplifying effect of SBC on IR. However, DB weakens the enhancing effect of SBC on cost efficiency and aggravates the SBC's exacerbating effect on IR.

This study advances the understanding of the paradoxical effects of SBC on cost efficiency and IR from a paradox theory perspective. More importantly, to the best of the authors' knowledge, the authors' study is the first to untangle the differential roles of DI and DB in reconciling the paradox of SBC. This study also provides practitioners with nuanced insights into how the practitioners should use appropriate tactics to deploy digital technologies effectively.

The purpose of this paper is to empirically examine the impact of closing mechanism changes on market quality, investor trading behavior and market manipulation in the Shanghai stock market.

A dummy variable is constructed indicating whether the closing mechanism is call auction or continuous auction. Market quality is measured from aspects of liquidity, volatility and price continuity; investor trading behavior is scaled by order timing and order aggressiveness, and a price deviation indicator is the proxy of manipulation. Using panel regression, this study examines the impact of closing mechanism changes based on intraday transaction data from the Shanghai stock market.

The conclusions are as follows: First, market quality improves after the closing mechanism is reformed in terms of liquidity, volatility and price continuity. Second, order strategy changes significantly in the closing call market, and investors trade more aggressively in the continuous trading period before closing. Third, the closing call mechanism restrains the closing price manipulation and thus prompts an efficient closing price.

This paper examines the policy effects of closing mechanism changes from aspects of market quality, trading behavior and price manipulation, providing pieces of evidence for trading mechanism design and market supervision in emerging markets.

This paper aims to evaluate and compare the performance of different computer vision algorithms in the context of visual servoing for augmented robot perception and autonomy.

The authors evaluated and compared three different approaches: a feature-based approach, a hybrid approach and a machine-learning-based approach. To evaluate the performance of the approaches, experiments were conducted in a simulated environment using the PyBullet physics simulator. The experiments included different levels of complexity, including different numbers of distractors, varying lighting conditions and highly varied object geometry.

The experimental results showed that the machine-learning-based approach outperformed the other two approaches in terms of accuracy and robustness. The approach could detect and locate objects in complex scenes with high accuracy, even in the presence of distractors and varying lighting conditions. The hybrid approach showed promising results but was less robust to changes in lighting and object appearance. The feature-based approach performed well in simple scenes but struggled in more complex ones.

This paper sheds light on the superiority of a hybrid algorithm that incorporates a deep neural network in a feature detector for image-based visual servoing, which demonstrates stronger robustness in object detection and location against distractors and lighting conditions.

The paper aims to clarify the operation rationality of high speed trains (HSTs) under tunnel condition with the speed of 400 km/h through representative aerodynamic factors including running drag, eardrum comfort, carriages noise, aerodynamic loads on tunnel ancillary facilities and HST, micro-pressure waves, and then put forward engineering suggestions for higher speed tunnel operation based on the analysis.

Based on the field measurement data of CR400AF-C and CR400BF-J tunnel operation, correlations between each aerodynamic indicators with HST speed were established. By analyzing the safety reserve of aerodynamic indicators at 350 km/h and the sensitivity of each indicator to HST speed increasing and the indicators’ formation mechanism, the coupling relationship between various indicators was obtained.

The sensitivity of different aerodynamic indicators to speed variation differed. The aerodynamic indicators representing flow field around HST showed a linear relationship with HST speed including noise, eardrum comfort, aerodynamic load on HST body. The positive aerodynamic load on tunnel auxiliary facilities and the micro-pressure wave at the entrance of the tunnel have the same sensitivity to the 3th-power relation of HST speed. The over-limit proportion of micro-pressure wave was the highest among the indicators, and aerodynamic buffering measures were recommended for optimization. The open tunnel pressure relief structure is recommended, while allowing trains to pass through the tunnel at an unconditional speed of 380 km/h.

Comprehensive evaluation of multiple aerodynamic indicators for HST tunnel operation with higher speeds was realized. The main engineering requirements to release aerodynamic effect were identified and the optimization scheme is proposed.