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This paper proposes a self-supervised monocular depth estimation algorithm under multiple constraints, which can generate the corresponding depth map end-to-end based on RGB images. On this basis, based on the traditional visual simultaneous localisation and mapping (VSLAM) framework, a dynamic object detection framework based on deep learning is introduced, and dynamic objects in the scene are culled during mapping.

Typical SLAM algorithms or data sets assume a static environment and do not consider the potential consequences of accidentally adding dynamic objects to a 3D map. This shortcoming limits the applicability of VSLAM in many practical cases, such as long-term mapping. In light of the aforementioned considerations, this paper presents a self-supervised monocular depth estimation algorithm based on deep learning. Furthermore, this paper introduces the YOLOv5 dynamic detection framework into the traditional ORBSLAM2 algorithm for the purpose of removing dynamic objects.

Compared with Dyna-SLAM, the algorithm proposed in this paper reduces the error by about 13%, and compared with ORB-SLAM2 by about 54.9%. In addition, the algorithm in this paper can process a single frame of image at a speed of 15–20 FPS on GeForce RTX 2080s, far exceeding Dyna-SLAM in real-time performance.

This paper proposes a VSLAM algorithm that can be applied to dynamic environments. The algorithm consists of a self-supervised monocular depth estimation part under multiple constraints and the introduction of a dynamic object detection framework based on YOLOv5.

A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand rate is unknown. That is, optimal inventory levels are decided using these two approaches at consecutive time intervals. Simulations were conducted to compare the total inventory cost using a Bayesian approach and a non-Bayesian approach to a theoretical minimum cost over a variety of demand rate conditions including the challenging slow moving or intermittent type of spare parts. Although Bayesian approaches are often recommended, this study’s results reveal that under conditions of large variability across the demand rates of spare parts, the inventory cost using the Bayes model was not superior to that using the non-Bayesian approach. For spare parts with homogeneous demand rates, the inventory cost using the Bayes model for forecasting was generally lower than that of the non-Bayesian model. Practitioners may still opt to use the non-Bayesian model since a prior distribution for the demand does not need to be identified.

To understand the meaning of a sentence, humans can focus on important words in the sentence, which reflects our eyes staying on each word in different gaze time or times. Thus, some studies utilize eye-tracking values to optimize the attention mechanism in deep learning models. But these studies lack to explain the rationality of this approach. Whether the attention mechanism possesses this feature of human reading needs to be explored.

The authors conducted experiments on a sentiment classification task. Firstly, they obtained eye-tracking values from two open-source eye-tracking corpora to describe the feature of human reading. Then, the machine attention values of each sentence were learned from a sentiment classification model. Finally, a comparison was conducted to analyze machine attention values and eye-tracking values.

Through experiments, the authors found the attention mechanism can focus on important words, such as adjectives, adverbs and sentiment words, which are valuable for judging the sentiment of sentences on the sentiment classification task. It possesses the feature of human reading, focusing on important words in sentences when reading. Due to the insufficient learning of the attention mechanism, some words are wrongly focused. The eye-tracking values can help the attention mechanism correct this error and improve the model performance.

Our research not only provides a reasonable explanation for the study of using eye-tracking values to optimize the attention mechanism but also provides new inspiration for the interpretability of attention mechanism.

The purpose of this paper is to explore the effects of online advertising spending on automobile sales through both search and non-search advertising.

Sales data of the top 52 vehicle models were collected in two consecutive years in China. The advertising spending data of both formats were collected from a leading consulting company and a major search engine company. Then several empirical models were proposed to evaluate the effects of online advertising on automobile sales. Two extended models were further investigated for search advertising.

The results revealed that both formats of online advertising have significantly positive effects on automobile sales. However, excessive spending on non-search advertising does not help sales and a moderate budget is preferred. On the other hand, spending on search advertising has no such constraint to improve the vehicle sales.

The empirical findings have proved the importance of online advertising to the automobile companies and thus can help companies improve their decision making in online advertising allocation strategies.

This study provides a better understanding of the relationship between online advertising spending and automobile sales, and helps business to define sophisticated online advertising strategies to improve sales performance.

Corporate investment behavior increases the uncertainty of a company’s operation and performance. The purpose of this paper is to investigate how analyst recommendations respond to corporate uncertainty caused by investment behavior and what motivates analysts to react as they do.

The authors test two motivation hypotheses: the hypothesis that analysts are currying favor with management to obtain private information and the hypothesis that analysts have conflicts of interest due to connections. Using Chinese analyst-level data from 2007 to 2015, the authors find that overall investment levels, R&D investment and M&A events are significantly positively correlated with analyst recommendations, suggesting that analysts tend to react optimistically to corporate investment behavior.

Analysts are only optimistic about companies with low information transparency, suggesting that analysts may be trying to curry favor with management to gain access to private information. The authors find that analysts with stronger recommendations have more private information and analysts with more private information publish more accurate earnings forecasts, which supports the hypothesis that analysts curry favor with management through optimistic recommendations to obtain more private information. This is consistent with the logic that the difficulty of earnings forecasting increases under uncertain conditions, increasing the demand for private information. The authors then group the analysts according to their underwriting connections, securities company’s proprietary connections and fund connections, and find that the positive correlation between corporate investment behavior and analyst recommendations exists only in the unconnected groups. This is evidence against the hypothesis that analysts have conflicts of interest due to their connections.

First, the authors link the optimism of analysts with the uncertainty of analysts’ information inputs to partially unpack the black box of analysts’ analyses. Second, the authors test the two hypotheses mentioned. There is a lack of comparative studies on the influence of different motivations on the behavior of analysts.

The purpose of this paper is to study the relationship between institutional holdings and analyst coverage in the context of the heterogeneous nature of institutional investors.

Similar to prior studies (e.g. Ke and Ramalingegowda; Ramalingegowda and Yu), this paper obtains institutional investors' trading classifications (transient, dedicated, and quasi‐indexing) from Brian Bushee directly. To examine the hypotheses, the paper uses a two‐step instrumental variable approach demonstrated in O'Brien and Bhushan to mitigate the simultaneity relationship between the change in analyst coverage and the change in the number of heterogeneous institutional investors.

The findings suggest that such relations are different among transient, dedicated, and quasi‐indexing institutional investors. Specifically, there are three major results. First, a change to the number of analyst coverage has the lowest impact on the change in the number of dedicated institutional investors. Second, a change in the number of transient institutional investors has a higher impact on change in analyst coverage than those for change in the number of dedicated and quasi‐indexing institutional investors. Third, changes to analysts' buy or sell recommendations have the least impact on the change in the number of dedicated institutions, relative to transient and quasi‐indexing institutions.

The findings suggest that institutional investors are not homogeneous. Research studies on institutional investors need to disentangle the differences among different types of institutions.

The paper provides a comprehensive study on different institutional investors and analyst coverage. The findings show the complex nature of the interaction between institutional investors and analyst coverage.

In recent years, with the steady growth of China’s economy and world’s vacation market, and powered by the growing and significant large demands of Chinese overseas travel, an emergent trend of international development by major Chinese tourism groups has attracted notice by scholars and practitioners and this is obvious especially in the private sector. With this as the background, this paper aims to explore how China’s leading tourism companies expand their business internationally through a case study approach.

The purpose of this paper is to explore the legitimacy needs and legitimation strategies of Internet start-ups in the context of industry dynamism.

The purpose of this paper is to explore the legitimacy needs and legitimation strategies of Internet start-ups in the context of industry dynamism.

The authors found that Internet start-ups are in great need of acquiring market and relational legitimacy at their nascent stages. Conformance to the environment is widely adopted by them as a legitimacy-enhancing strategy. There is an inverted “U” relationship between the maturity of the industry and the proactivity of any legitimation strategy in the sector. In the face of high- and low-level industry maturity, start-ups tend to employ prudent strategies to build up legitimacy. While in medium-mature industries, ventures are more likely to adopt proactive and aggressive strategies.

Due to the very nature of case methodology, this study is based on a small number of observations and it is set in the context of the Internet industry. The generalizability of its findings needs to be reinforced by further concrete studies.

This paper suggests that industry dynamism should be taken into account carefully when implementing a choice of legitimation strategies.

This study makes an attempt to further our understanding of how industry dynamism influences firms’ choices of legitimation strategies.

The purpose of this paper is to build a conceptual framework which provides both practical and theoretical insights into drivers of consumer use of social network sites (SNSs) as an electronic word-of-mouth (eWOM) tool and its impact on brand image (BI) and purchase intention (PI).

For assessment of the research constructs, the measures proposed in the social psychology and marketing literature were adapted. These scales were examined for the face validity by the authors’ academic colleagues. Using the survey data collected from the students of the Islamic Azad University, these scales were further examined in terms of convergent and discriminant validity as well as reliability. Structural equation modelling technique was used to test the effect of eWOM antecedents on consumer eWOM intention and the eWOM impact on BI and consumer PI.

The results confirmed significance of the constructs consumer trust, informational influence, sense of belonging, altruism, moral obligation, and knowledge self-efficacy for consumer engagement in eWOM. The results further indicated that eWOM, in turn, played a significant role in shaping BI in the mind of consumers and their PI.

Obviously, university students do not exactly represent the whole population of SNSs members. The eWOM intention can be investigated across generations in terms of intergenerational differences. The authors admit that in addition to the constructs used in this study there are other equally important constructs that can be adapted from relevant fields (e.g. psychology) as the eWOM antecedents. As for eWOM consequences, different aspects of brand equity could be additionally explored and investigated.

The results of this study would help marketing department of companies and advertising agencies make advertisements in accordance with consumer characteristics. The results would also benefit providers of online platforms by giving them insight into behaviour of their members. Finally, the finding that eWOM intention is likely to create PI among consumers is of particular interest to producers of goods.

Given its bidimensional perspective to eWOM behaviour (i.e. considering both antecedents and consequences of eWOM), this study provides valuable insights into the phenomenon eWOM in the context of social networks as well as a basis for further research in this field.

This paper aims to investigate the bonding of the photonic integrated circuit (PIC) chip with the heat sink using the AlNi self-propagating soldering method.

Compared to industrial optical modules, optical modules for aerospace applications require better reliability and stability, which is hard to achieve via the dispensing adhesive process that is used for traditional industrial optical modules. In this paper, 25 µm SAC305 solder foils and the AlNi nanofoil heat source were used to bond the back of the PIC chip with the heat sink. The temperature field and temperature history were analyzed by the finite element analysis (FEA) method. The junction-to-case thermal resistance is 0.0353°C/W and reduced by 85% compared with the UV hybrid epoxy joint.

The self-propagating reaction ends within 2.82 ms. The maximum temperature in the PIC operating area during the process is 368.5°C. The maximum heating and cooling rates of the solder were 1.39 × 107°C/s and −5.15 × 106°C/s, respectively. The microstructure of SAC305 under self-propagating reaction heating is more refined than the microstructure of SAC305 under reflow. The porosity of the heat sink-SAC305-PIC chip self-propagating joint is only 4.7%. Several metastable phases appear as AuSn3.4 and AgSn3.

A new bonding technology was used to form the bonding between the PIC chip with the heat sink for the aerospace optical module. The reliability and thermal resistance of the joint are better than that of the UV hybrid epoxy joint.