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A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body and quality evaluation. This paper aims to discuss the aforementioned idea.

First, the differential body is set on a rotation platform before measuring. Then one laser sensor called as “primary sensor”, is installed on the intern of the differential body. The spherical surface and four holes on the differential body are sampled by the primary sensor when the rotation platform rotates one revolution. Another sensor called as “secondary sensor”, is installed above to sample the external cylinder surface and the planar surface on the top of the differential body, and the external cylinder surface and the planar surface are high in manufacturing precision, which are used as datum surfaces to compute the errors caused by the motion of the rotation platform. Finally, the sampled points from the primary sensor are compensated to improve the measurement accuracy.

A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body. Based on the characteristics of the measurement data, a gradient image-based method is proposed to distinguish different objects from laser measurement data. A case study is presented to validate the measurement principle and data processing approach.

The study investigates the possibility of correction of sensor data by the measurement results of multiple sensors to improving measurement accuracy. The proposed technique enables the error analysis and compensation by the geometric correlation relationship of various features on the measurand.

The proposed error compensation principle by using multiple sensors proved to be useful for the design of new measurement device for special part inspection. The proposed approach to describe the measuring data by image also is proved to be useful to simplify the measurement data processing.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

Based on the attention-based view (ABV), this study examines the mechanism of external pressure and internal managerial interpretation affecting the promotion of green entrepreneurial orientation (GEO) of agricultural enterprises.

Based on data collected from 208 agricultural enterprises in China, the conceptual model was tested by using hierarchical regression.

The results show that managerial interpretation can affect the promotion of GEO. Command and control regulation, market-based regulation and green market pressure are important external pressures that affect the promotion of GEO. In addition, managerial interpretation mediates the relationship between command and control regulation and GEO, market-based regulation and GEO, as well as green market pressure and GEO.

This study proposes a key path for promoting the adoption and implementation of GEO by agricultural enterprises. The research results provide experience for emerging and developing countries to promote the GEO of agricultural enterprises, which is helpful to alleviate the environmental problems caused by the development of agricultural enterprises.

For the first time, this study introduced the ABV into the research of GEO. The research results enrich the theoretical perspective of GEO and expand the research field of the ABV. In addition, this study fills the research gap that existing research has not paid enough attention to the internal driving factors of GEO and opens the black box between the external pressure and GEO.

The purpose of this work is to design the wire beam electrode (WBE) of P110 steel and study its corrosion behavior and mechanism under high temperature and pressure.

Packaging materials of the new type P110 steel WBE and high pressure stable WBE structure were designed. A metallurgical microscope (XJP-3C) and scanning electron microscopy (EV0 MA15 Zeiss) with an energy dispersive spectrometer were used to analyze the microstructure and composition of the P110 steel. The electrochemical workstation (CS310, CorrTest Instrument Co., Ltd) with a WBE potential and current scanner was used to analyze the corrosion mechanism of P110 steel.

According to the analysis of Nyquist plots at different temperatures, the corrosion resistance of P110 steel decreases with the increase of temperature under atmospheric pressure. In addition, R_p of P110 steel under high pressure is maintained in the range of 200 ∼ 375 Ωcm², while that under atmospheric pressure is maintained in the range of 20 ∼ 160 Ωcm², indicating that the corrosion products on P110 steel under high pressure is denser, which improves the corrosion resistance of P110 steel to a certain extent.

The WBE applied in high temperature and pressure environment is in blank. This work designed and prepared a WBE of P110 steel for high temperature and pressure environment, and the corrosion mechanism of P110 steel was revealed by using the designed WBE.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

The recent livestreaming commerce has magnified the role of influencer marketing, where the influencers are partnering with brands for product promotion. This study examines the impact of influencer attributes, interaction strategies and parasocial relationships on impulsive buying in livestreaming commerce.

A survey with 368 livestreaming commerce users was analyzed using the symmetric-thinking approach – partial least squares structural equation modeling (PLS-SEM) and asymmetric thinking approach – fuzzy set qualitative comparative analysis (fsQCA).

The results of PLS-SEM indicate that influencer trustworthiness, influencer interactivity and self-disclosure determine parasocial relationships, which in turn influence impulsive buying. The fsQCA finding returned three configurations with various combinations of the causal conditions (i.e. influencer attributes, interaction strategies, parasocial relationships, perceived fit uncertainty and perceived quality uncertainty) explaining the formation of impulsive buying.

These findings provide unique linear and nonlinear insights to explain the combinatory effects of influencer attributes, interaction strategies, parasocial relationships, perceived fit uncertainty and perceived quality uncertainty on impulsive buying in livestreaming commerce.

Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.

G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.

G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.

An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.

The purpose of this paper is to explore the impact and mechanism of WeChat public platforms articles (abbreviated as WPP) on blood donation behavior using data of WPPA and donation behavior data.

This paper uses multiple linear regression methods, web crawlers and natural language processing technology. It first quantifies the impact of WPP published articles on donation behavior. On this basis, it then selects data from the day of article publication to further study the impact of article dissemination on donation behavior from the perspective of reading quantity, and analyzes the influencing factors of article reading quantity.

The results show that on the same day that an article is published, there is an increase of 13.8 and 14.3% in blood donation volume and fan registrations, respectively. The mediating effect exists. However, the day after an article is published, there is no longer any effect on blood donations. With a 1% increase in reading quantity, blood donation volume on the day of article publication increases by 0.13%, and this positive impact is promoted by the quality of the articles. A conc ise articles title and body and rich images help drive reading quantity. Moreover, blood donors prefer to read articles about blood dynamics and donation promotion, while articles about news, announcements and administrative affairs make them less inclined to read.

First, it focuses on WPPA, quantifies the impact of articles on blood donation behavior and analyzes the mechanism. Second, the authors study the impact and timeliness of social media article dissemination to address the insufficiency of existing research. Third, the study provides a scientific basis for the editing and publishing of articles, helping blood banks improve the effectiveness of publicity and recruitment.

Negative brand engagement represents a pervasive and persistent feature of interactivity in online contexts. Although existing research suggests that consumer negativity is potentially more impactful or detrimental to brands than its positive counterpart, few studies have examined negative brand-related cognitions, feelings and behaviours. Building on the concept of brand engagement, this study aims to operationalise negative online brand engagement.

This paper presents the results of nine studies that contributed to the development and validation of the proposed scale. Building on the concept of engagement, Studies 1–3 enhanced the construct conceptualisation and generated items. Study 4 involved validation with an academic expert panel. The process of measure operationalisation and validation with quantitative data was completed in Studies 5–8. Finally, the scale's nomological validity was assessed in Study 9.

The results confirm the multidimensional nature of negative online brand engagement. The validated instrument encompasses four dimensions (cognition, affection, online constructive behaviour and online destructive behaviour), captured by 17 items.

Progress in understanding and dealing with negative online brand engagement has been hampered by disagreements over conceptualisation and the absence of measures that capture the phenomenon. This work enhances managerial understanding of negativity fostering strategies that protect brand engagement and improve firm performance.