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This paper aims to calibrate the mounted parameters between the LIDAR and the motor in a low-cost 3D LIDAR device. It proposes the model of the aimed 3D LIDAR device and analyzes the influence of all mounted parameters. The study aims to find a way more accurate and simple to calibrate those mounted parameters.

This method minimizes the coplanarity and area of the plane scanned to estimate the mounted parameters. Within the method, the authors build different cost function for rotation parameters and translation parameters; thus, the parameter estimation problem of 4-degree-of-freedom (DOF) is decoupled into 2-DOF estimation problem, achieving the calibration of these two types of parameters.

This paper proposes a calibration method for accurately estimating the mounted parameters between a 2D LIDAR and rotating platform, which realizes the estimation of 2-DOF rotation parameters and 2-DOF translation parameters without additional hardware.

Unlike previous plane-based calibration techniques, the main advantage of the proposed method is that the algorithm can estimate the most and more accurate parameters with no more hardware.

This study aims to compare rapid‐growth companies with slow‐growth ones in order to provide fresh insights into the attributes of Chinese rapid‐growth companies through an integrated research framework, which was adapted from the framework proposed by Wiklund and by Barringer.

To obtain data for analysis, the research selected the top 30 rapid‐growth firms and the bottom 30 slow‐growth firms from a data bank which contains complete financial data of 533 firms from 2001 to 2003. All data concerning rapid‐growth were coded either one or zero, and were analyzed by Fisher's Exact Test to find out the frequency of important growth variables.

It is found that the major differences between rapid‐growth and slow‐growth firms lie in different favorite firm resources and capacities, task environment, and entrepreneurial strategies. They can create greater value for success with certain entrepreneurs, and might spur those entrepreneurs to push their firms onto a rapid growth road. In addition, the variable of creating unique value emerges as a strong predictor of rapid growth. Entrepreneur orientation strategy is not found to be significantly facilitating rapid growth. This illustrates that all growth companies value highly entrepreneurship‐oriented strategy.

These research findings confirm that growth is not a random event and that entrepreneurs are not necessarily natural. These research findings will promote entrepreneurship education and entrepreneurship in China.

There are two originalities in this research. First, the authors developed a new integrated framework based on previous research achievements. Second, it is the first empirical study concerning company growth variables in China.

Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy.

This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image.

The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods.

In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.

Effectively engaging patients is critical for the sustainable development of online health communities (OHCs). Although physicians’ general knowledge-sharing, which is free to the public, represents essential resources of OHCs that have been shown to promote patient engagement, little is known about whether such knowledge-sharing can backfire when superfluous knowledge-sharing is perceived as overwhelming and anxiety-provoking. Thus, this study aims to gain a comprehensive understanding of the role of general knowledge-sharing in OHCs by exploring the spillover effects of the depth and breadth of general knowledge-sharing on patient engagement.

The research model is established based on a knowledge-based view and the literature on knowledge-sharing in OHCs. Then the authors test the research model and associated hypotheses with objective data from a leading OHC.

Although counterintuitive, the findings revealed an inverted U-shape relationship between general knowledge-sharing (depth and breadth of knowledge-sharing) and patient engagement that is positively associated with physicians’ number of patients. Specifically, the positive effects of depth and breadth of general knowledge-sharing increase and then decrease as the quantity of general knowledge-sharing grows. In addition, physicians’ offline and online professional status negatively moderated these curvilinear relationships.

This study further enriches the literature on knowledge-sharing and the operations of OHCs from a novel perspective while also offering significant specific implications for OHCs practitioners.

Online health question-and-answer (Q&A) forums have developed a new business model whereby listeners (peer patients) can pay to read health information derived from consultations between askers (focal patients) and answerers (physicians). However, research exploring the mechanism behind peer patients' purchase decisions and the specific nature of the information driving these decisions has remained limited. This study aims to develop a theoretical model for understanding how peer patients make such decisions based on limited information, i.e. the first question displayed in each focal patient-physician interaction record, considering argument quality (interrogative form and information details) and source credibility (patient experience of focal patients), including the contingent role of urgency.

The model was tested by text mining 1,960 consultation records from a popular Chinese online health Q&A forum on the Yilu App. These records involved interactions between focal patients and physicians and were purchased by 447,718 peer patients seeking health-related information until this research.

Patient experience embedded in focal patients' questions plays a significant role in inducing peer patients to purchase previous consultation records featuring exchanges between focal patients and physicians; in particular, increasingly detailed information is associated with a reduced probability of making a purchase. When focal patients demonstrate a high level of urgency, the effect of information details is weakened, while the interrogative form is strengthened.

The originality of this study lies in its exploration of the monetization mechanism forming the trilateral relationship between askers (focal patients), answerers (physicians) and listeners (peer patients) in the business model “paying to view others' answers” in the online health Q&A forum and the moderating role of urgency in explaining the mechanism of how first questions influence peer patients' purchasing behavior.

Research on the impact of the engagement of online medical teams (OMTs) on patient evaluation, for example, satisfaction, remains insufficient. This study attempts to recognize the underlying mechanism of how OMTs’ engagement influences patient satisfaction by adopting social support as the mediator. This study also scrutinizes the moderating effects of the transactive memory system (TMS) on the link between OMTs’ engagement and social support.

We utilized a linear model that had fixed effects controlled at the team level for analysis. A bootstrapping approach using 5,000 samples was employed to test the mediation effect.

Our results reveal that OMTs’ engagement improves informational and emotional support, thereby promoting patient satisfaction. Specialization and credibility strengthen the impact of OMTs’ engagement on informational and emotional support. Simultaneously, coordination has an insignificant influence on the link between OMTs’ engagement and social support.

This study contributes to the literature on OMTs, social support, and TMS, providing insights into patients’ perceptions of OMTs’ engagement during online team consultation. This study also generates several implications for the practice of online health communities and OMTs.

This paper aims to examine how the official discourse of frugality evolved in China between 1979 and 2015.

The study uses historical and textual analysis. It divides the Chinese official discourse on frugality between 1979 and 2015 into four periods: 1979-1992, 1993-2002, 2003-2012 and 2013-2015.

A Chinese official discourse on frugality persisted between 1979 and 2015, even though during the same period, China transformed from a socialist economy of central planning and insufficient supply to a market economy of excessive supply and weak consumer demand. The intensity of this official discourse frequently vacillated, adjusting to both economic and political conditions of the time as part of the larger political-economic contestation between competing ideas and policies.

There have been calls for more studies on how frugality discourses have evolved in international markets, especially in terms of how they are shaped by local historical antecedents and long-standing tensions. Through the Chinese case, this article illuminates why some traditional values persist and obtain a paradoxical co-existence with consumerist ethos in our modern society.

Free knowledge sharing in the online health community has been widely documented. However, whether free knowledge sharing can help physicians accumulate popularity and further the accumulated popularity can help physicians attract patients remain unclear. To unveil these gaps, this study aims to examine how physicians' popularity are affected by their free knowledge sharing, how the relationship between free knowledge sharing and popularity is moderated by professional capital, and how the popularity finally impacts patients' attraction.

The authors collect a panel dataset from Hepatitis B within an online health community platform with 10,888 observations from April 2020 to August 2020. The authors develop a model that integrates free knowledge sharing, popularity, professional capital, and patients' attraction. The hierarchical regression model is used to for examining the impact of free knowledge sharing on physicians' popularity and further investigating the impact of popularity on patients' attraction.

The authors find that the quantity of articles acted as the heuristic cue and the quality of articles acted as the systematic cue have positive effect on physicians' popularity, and this effect is strengthened by physicians' professional capital. Furthermore, physicians' popularity positively influences their patients' attraction.

This study reveals the aggregation of physicians' popularity and patients' attraction within online health communities and provides practical implications for managers in online health communities.

The purpose of this study is to examine the role of retail investors’ green attention in promoting corporate environmental investments (EIs) using a communication sample on “Hudongyi” from 2011 to 2022.

In this paper, Python is used to capture data and text analysis techniques to obtain green attention information. In the word-matching process, words are matched in the target document one by one based on the preset dictionary and vocabulary rules. In addition to employing fixed effects, this study also incorporates instrumental variables using two-stage least squares (2SLS) estimation and applies the Heckman two-step method to verify the regression results.

First, this paper empirically examines the positive influence of retail investors’ green attention on EIs. Second, the findings show that retail investors’ green attention promotes EIs through decreasing principal-agent costs and principal-principal costs. Third, the results show that retail investor’s supervision effect is strengthened under the following three circumstances: executives with stronger green conception, corporations with less information asymmetry and areas with higher level of investor protection.

Our findings broaden the scope of prior research by exploring the impact of retail investor activism on nonfinancial outcomes, contributing to understanding the “black box” of how investor attention fosters EIs. Moreover, by leveraging the power of technology, retail investors have evolved from being the “silent majority” to being actively engaged. The internet has empowered retail investors by providing them with access to information and enabling them to exercise “voice” rights by appealing companies to engage in pro-environmental activities. Our study can provide useful suggestions for the green development of listed companies in China, as well as in other emerging countries.

Unlike other studies that focus on the deterrent effect and corporate financial outcomes of retail investors, we focus on the supervisory effect of retail investors and verify its role in driving EIs. This fills the knowledge gap in prior studies and contributes new insights to explain EIs and extends the understanding of retail investor activism.