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The light detection and ranging sensor has been widely deployed in the area of simultaneous localization and mapping (SLAM) for its remarkable accuracy, but obvious drift phenomenon and large accumulated error are inevitable when using SLAM. The purpose of this study is to alleviate the accumulated error and drift phenomenon in the process of mapping.

A novel light detection and ranging SLAM system is introduced based on Normal Distributions Transform and dynamic Scan Context with switch. The pose-graph optimization is used as back-end optimization module. The loop closure detection is only operated in the scenario, while the path satisfies conditions of loop-closed.

The proposed algorithm exhibits competitiveness compared with current approaches in terms of the accumulated error and drift distance. Further, supplementary to the place recognition process that is usually performed for loop detection, the authors introduce a novel dynamic constraint that takes into account the change in the direction of the robot throughout the total path trajectory between corresponding frames, which contributes to avoiding potential misidentifications and improving the efficiency.

The proposed system is based on Normal Distributions Transform and dynamic Scan Context with switch. The pose-graph optimization is used as back-end optimization module. The loop closure detection is only operated in the scenario, while the path satisfies condition of loop-closed.

The representation of Heterogeneous Object (HO) is divided into two categories: Data model (DM) and material evaluation paradigm (MEP). A hybrid methodology with geometry model and volumetric dataset to represent heterogeneous properties is proposed in this paper. Geometry model of an object can guarantee the accuracy of the final HO slices; and volumetric dataset lends the flexible manipulability and other advantages to HO representation. Two MEPs, namely distance field (DF) based and Fixed Reference Features & Active Grading Source(s) (FRF&AGS) are presented to facilitate the process of HO representation according to the designer)s input parameters. The DM can be modified interactively with users until the final satisfactory result is obtained. In this paper, a scheme of HO slicing is described. In this method, we utilize the slices contour of geometrical model as constraint to reconstruct the HO slices, which can theoretically achieve the same accuracy with the geometrical shape. Some examples of Heterogeneous object represented with our scheme are provided.

The purpose of this paper is to develop a preliminary model of instructional leadership in the Chinese educational context and explore the ways in which Chinese school principals locate their instructional-leadership practices in response to traditional expectations and the requirements of recent reforms.

In-depth interviews were conducted with 22 selected primary school principals in Shenzhen and Guangzhou. A qualitative analysis was conducted to categorize the major leadership practices enacted by these principals.

An initial model of instructional leadership in China with six major dimensions is constructed. The paper also illustrates and elaborates on three dimensions with the greatest context-specific meanings for Chinese principals.

The paper explores the ways in which Chinese principals enact their instructional leadership in a context in which “the west wind meets the east wind”; that is, when they are required to accommodate both imported reform initiatives and traditional expectations. The paper contributes to the sparse existing research on principals’ instructional leadership in non-western cultural and social contexts.

The purpose of this study of Google and Baidu in mainland China is to identify the key contingencies to firm strategic responses to technical and institutional pressures. From an institutional logic perspective and Hirschman's model of exit, voice, and loyalty, this research proposes a few propositions which are intended to explain why foreign and local companies adopt different responses to similar institutional requirements or under similar institutional pressures.

This study applies a historical chronological method by the recognition of certain types of events and key strategic activities conducted by two sample organizations from their foundations in mainland China till March 2010 when Google exited from China's internet search market. These activities were identified as the measurement of firm strategic responses to institutional pressures. Data were gathered from various sources, including documents published by sample organizations, online and media reports, etc.

It is found that firms adopted similar responses to technical pressures which were determined by characteristics of the internet industry. However, their responses to institutional pressures, which were driven by the state logic for control of the internet, were dramatically different. As a multinational corporation, Google was faced with inherent tensions between home and host institutional requirements. When the state control pressures increased, Google eventually selected a voice and exit strategy. Baidu, as a local leading player in China's internet market, adopted a loyalty strategy through closer collaboration with local institutional constituents, including government agencies and clients, in addition to its investment in creating corporate images and reputation among local internet users.

This research explores the dynamic and diverse responses of foreign and local companies to institutional pressures and advances our understanding of political properties in firm strategies and the importance of firm nationality in strategy making.

This study aims to investigate the influence of soy protein isolate hydrolysates (SPIH) obtained using 4 h hydrolysis under 200 MPa on proximate composition, cooking loss, textural properties, color, water distribution, microstructure, thiobarbituric acid reactive substance (TBARS) value and carbonyl and sulfhydryl contents of emulsion sausages.

Sausages with SPIHs at four concentrations (0, 1.0, 2.0 and 3.0%) were prepared, and the sausage with 0.01% butylated hydroxyanisole (BHA) was used as a positive control. Some sausages were selected for the analyses of quality characteristics and microcosmic properties. Other sausages were stored under 4 °C for 0, 7, 14, 21 and 28 days to investigate the oxidative stability.

The addition of SPIHs at various levels (0–3.0%) or 0.01% BHA did not affect the proximate composition (protein, fat and ash) of emulsion sausages. The addition of 2.0% SPIH decreased cooking loss and increased moisture content, hardness, springiness, chewiness, resilience and L* value, compared to the sausages without SPIH and with 0.01% BHA (p < 0.05). Furthermore, low-field nuclear magnetic resonance results suggested that sausages with 2.0% SPIH had the shortest T2 relaxation time. In addition, 2.0% SPIH and 0.01% BHA could inhibit the oxidation of emulsion sausages when compared with the sample without SPIH (p < 0.05). Moreover, there were no differences between sausages with 2.0% SPIH and 0.01% BHA (p > 0.05).

These findings confirmed that the 2.0% SPIH obtained under 200 MPa can be used as a natural additive to improve quality properties and antioxidant potential of emulsion sausages during storage.

To date there is little understanding of the factors that impact the survival of foreign IPOs after they list on US stock exchanges. In this study, we examine how foreign IPO survival is contingent on institutional factors associated with the firm’s home country. We also explore how corporate governance and organizational identity influence the survival of foreign IPOs in the United States. Results suggest that the US institutional environment supports foreign firms with more independent and professional leadership, and that knowledge-intense organizations have higher chances of long-term success after listing on US exchanges.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

Social media analytics uses data mining platforms, tools and analytics techniques to collect, monitor and analyze massive amounts of social media data to extract useful patterns, gain insight into market requirements and enhance business intelligence. The purpose of this paper is to propose a framework for social media competitive intelligence to enhance business value and market intelligence.

The authors conducted a case study to collect and analyze a data set with nearly half million tweets related to two largest retail chains in the world: Walmart and Costco in the past three months during December 1, 2014-February 28, 2015.

The results of the case study revealed the value of analyzing social media mentions and conducting sentiment analysis and comparison on individual product level. In addition to analyzing the social media data-at-rest, the proposed framework and the case study results also indicate that there is a strong need for creating a social media data application that can conduct real-time social media competitive intelligence for social media data-in-motion.

So far there is little research to guide businesses for social media competitive intelligence. This paper proposes a novel framework for social media competitive intelligence to illustrate how organizations can leverage social media analytics to enhance business value through a case study.

Fraudulent financial statements and the manipulation of stock prices can seriously affect investors' judgment of company performance, especially in stock markets in emerging economies. Apart from financial reports, which run the risk of being misreported, are there any other information sources that the public can trust when it comes to the truth about a company's performance? The purpose of this paper is to address this issue by assessing the correlation between online recruitment information and company performance and provide investors with a new framework to assist them in making decisions and to identify fraud.

The research extracted the recruitment information of normal and fraudulent companies separately from the internet by employing techniques of natural language processing, opinion mining and competitive intelligence. A statistical tool was then used to study whether there is a difference in the correlation between the recruitment information intensity (RII) and the annually averaged stock price (AASP) of normal and fraudulent firms.

The experiments showed that recruitment information intensity is significantly correlated to company's stock performance for normal firms, which indicates that the company's recruitment activities are consistent with their performance. But for fraudulent companies, the fact that the result is quite the opposite may imply that the RII discloses the truth when managers make misreports.

The findings suggest that the intensity of a company's recruitment information is a valuable element for investors in evaluating the firm, and it also can be used as a reliable tool to assist in identifying fraudulent companies.

This paper provided a novel way for the public to break information barriers to reach the truth of companies' performance and avoid misleading fraudulent finance statements. It is also a useful application of natural language processing techniques.