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The purpose of this paper is to present a scan matching simultaneous localization and mapping (SLAM) algorithm based on particle filter to generate the grid map online. It mainly focuses on reducing the memory consumption and alleviating the loop closure problem.

The proposed method alleviates the loop closure problem by improving the accuracy of the robot’s pose. First, two improvements were applied to enhance the accuracy of the hill climbing scan matching. Second, a particle filter was used to maintain the diversity of the robot’s pose and then to supply potential seeds to the hill climbing scan matching to ensure that the best match point was the global optimum. The proposed method reduces the memory consumption by maintaining only a single grid map.

Simulation and experimental results have proved that this method can build a consistent map of a complex environment. Meanwhile, it reduced the memory consumption and alleviates the loop closure problem.

In this paper, a new SLAM algorithm has been proposed. It can reduce the memory consumption and alleviate the loop closure problem without lowering the accuracy of the generated grid map.

For autonomous vehicles, trajectory prediction of surrounding vehicles is beneficial to improving the situational awareness of dynamic and stochastic traffic environments, which is a crucial and indispensable element to realize highly automated driving.

In this paper, the overall framework consists of two parts: first, a novel driver characteristic and intention estimation (DCIE) model is built to indicate the higher-level information of the vehicle using its low-level motion variables; then, according to the estimation results of the DCIE model, a classified Gaussian process model is established for probabilistic vehicle trajectory prediction under different motion patterns.

The whole method is later applied and analyzed in the highway lane-change scenarios with the parameters of models learned from the public naturalistic driving data set. Compared with other traditional methods, the performance of this proposed approach is proved superior, demonstrated by the higher accuracy in the long prediction horizon and a more reasonable description of uncertainty.

This hierarchical approach is proposed to make trajectory prediction accurately both in the short term and long term, which can also deal with the uncertainties caused by the perception system or indeterminate vehicle behaviors.

Because submaps including a subset of the global map contain more environmental information, submap-based graph simultaneous localization and mapping (SLAM) has been studied by many researchers. In most of those studies, helpful environmental information was not taken into consideration when designed the termination criterion of the submap construction process. After optimizing the graph, cumulative error within the submaps was also ignored. To address those problems, this paper aims to propose a two-level optimized graph-based SLAM algorithm.

Submaps are updated by extended Kalman filter SLAM while no geometric-shaped landmark models are needed; raw laser scans are treated as landmarks. A more reasonable criterion called the uncertainty index is proposed to combine with the size of the submap to terminate the submap construction process. After a submap is completed and a loop closure is found, a two-level optimization process is performed to minimize the loop closure error and the accumulated error within the submaps.

Simulation and experimental results indicate that the estimated error of the proposed algorithm is small, and the maps generated are consistent whether in global or local.

The proposed method is robust to sparse pedestrians and can be adapted to most indoor environments.

In this paper, a two-level optimized graph-based SLAM algorithm is proposed.

The purpose of this paper is to accomplish the low cost mass‐production of flexible radio frequency identification (RFID) tag inlays.

An anisotropic conductive paste (ACP) is prepared by mixing uniform micro‐sized spherical conductive particles, latent curing agent and other additives into a thermoset epoxy resin. RFID tag inlays are assembled with the paste through flip‐chip technology. The microstructural analysis of bonded joints, bond strength testing, and high‐temperature and humidity aging testing are employed to evaluate the performance of the inlays.

It was found that the chips are hard assembled on the antennae by the ACP. Flexible RFID tag inlays assembled using the presented method have good reliability when working under high frequency (13.56 MHz) conditions.

The method presented is a promising new way for packaging flexible RFID tag inlays with ACP. Through the use of flip‐chip technology, large‐scale production is possible with low manufacturing costs.

The paper details a simple way to prepare an anisotropic conductive paste and to assemble flexible RFID tag inlays. The technique uses flip‐chip technology with the paste as the electrical and mechanical interconnection material. It presents a simple and fast method of assembly for flexible RFID tag inlays on a large‐scale with low cost.

This study aims to revisit the relationship between Shariah-compliant firms and earnings management. Specifically, the authors examine whether Shariah-certified firms have lower earnings management than non-Shariah-certified firms and how often a firm must hold its certification to observe considerably reduced earnings management. This study also explores how senior management ethnic dualism affects the association of Shariah certification and earnings management.

The authors analyze the hypothesized association between Shariah certification and earnings management using a panel regression model and several robustness tests, including the Heckman selection model. The sample consists of 547 nonfinancial firms listed on the Bursa Malaysia stock exchange, with 5,478 firm-year observations over the 2001–2016 sample period.

Shariah certification is found to mitigate earnings management, particularly for firms that consistently retain their Shariah status. The longer firms retain their Shariah certification continually, the lower the earnings management. Additionally, the results indicate that the negative impact of Shariah certification on earnings management is driven by ethnic duality when a specific ethnic group dominates the top management.

Firms’ commitment to religious-based screening and continuation of certification plays a significant role in improving earnings quality. Firms are committed to abiding by the Shariah code of conduct instead of using the Shariah status for reputation purposes to attract investors.

For investors, the continuous compliance status is a crucial indicator of a firm’s commitment to comply with Shariah principles and to mitigate earnings management. Regarding policy implications, Shariah-compliance guidelines can constrain earnings manipulation, especially among firms lacking ethnic diversity.

The study shows that Shariah certification must be maintained consecutively to reduce earnings management. Shariah certification’s governance function is crucial in ethnically homogeneous firms, primarily when one ethnic group dominates the senior management.

This paper aims to understand the role that money plays in polygamous marriages among the Hui ethnic group in Northwest China.

This study conducted in-depth interviews, focus group discussions and observations based on snowball sampling of individuals who voluntarily agreed to participate from June to December 2010, and during the summer of 2011, in Qinghai in Northwest China. Follow-up interviews and observations were conducted in 2015.

This study examines how love and money intersect and work together to sustain the participants’ polygamous marriages. The study concludes that material desires unite love with money to make love consumable. With the help of money, love between the sexes is transformed into desirable consumption through economic activities associated with leisure, gift giving and religious beliefs to articulate individualism

This study is the first to explore money’s role in the experience of polygamy among the Hui ethnic group in China.

It is a huge technical and engineering challenge to realize the precise assembly of thousands of large optics in high power solid-state laser system. Using the 400-mm aperture-sized transport mirror as a case, this paper aims to present an intelligent numerical computation methodology for mounting performance analysis and modeling of large optics in a high-power laser system for inertial confinement fusion (ICF).

Fundamental principles of modeling and analysis of the transport mirror surface distortion are proposed, and a genetic algorithm-based computation framework is proposed to evaluate and optimize the assembly and mounting performance of large laser optics.

The stringent specifications of large ICF optics place very tight constraints upon the transport mirror’s assembly and mounts. The operational requirements on surface distortion [peak-to-valley and root mean square (RMS)] can be met as it is appropriately assembled by the close loop of assembly-inspection-optimization-fastening. In the end, the experimental study validates the reliability and effectiveness of the transport mirror mounting method.

In the assembly design and mounting performance evaluation of large laser optics, the whole study has the advantages of accurate evaluation and intelligent optimization on nano-level optical surface distortion, which provides a fundamental methodology for precise assembly and mounting of large ICF optics.

Given the scarcity of studies regarding religious food as a destination attraction and limited research on tourist halal food experience, this study aims to explore and compare halal food experience perceived by Muslim and non-Muslim tourists in a non-Islamic destination.

This study was carried out in a halal food street in Yuanjia Village, China. It used a qualitative approach and interviewed 16 Muslim tourists and 20 non-Muslim tourists.

Six themes and 18 attributes of halal food experience were identified. The findings revealed that Muslim tourists saw the reassuring options and religious value of halal food as important experiences. By contrast, the experiences of abundant choices, value for money, sensory pleasure and unique charm were frequently mentioned by non-Muslim tourists. The nature of halal food, the context of China (i.e. Chinese halal food culture) and the feature of research site (i.e. food operation of Yuanjia Village) work together to create such experiences.

To the best of the authors’ knowledge, this is one of the first studies to explore and compare halal food experiences of Muslim and non-Muslim tourists in a non-Islamic country (China). This study suggests that halal food could be an appealing destination attraction, even in non-Islamic destinations. Thus, this study contributes to a better understanding of the halal food experiences and assists destination marketers in promoting halal food.

Smart farming technologies (SFTs) can increase yields and reduce the environmental impacts of farming by improving the efficient use of inputs. This paper is to estimate farmers' preference and willingness to pay (WTP) for a well-defined SFT, smart drip irrigation (SDI) technology.

This study conducted a discrete choice experiment (DCE) among 1,300 maize farmers in North China to understand their WTP for various functions of SDI using mixed logit (MIXL) models.

The results show that farmers have a strong preference for SDI in general and its specific functions of smart sensing and smart control. However, farmers do not have a preference for the function of region-level agronomic planning. Farmers' preferences for different functions of SDI are heterogeneous. Their preference was significantly associated with their education, experience of being village cadres and using computers, household income and holding of land and machines. Further analysis show that farmers' WTP for functions facilitated by hardware is close to the estimated prices, whereas their WTP for functions wholly or partially facilitated by software is substantially lower than the estimated prices.

Findings from the empirical study lead to policy implications for enhancing the design of SFTs by integrating software and hardware and optimizing agricultural extension strategies for SFTs with digital techniques such as videos.

This study provides initial insights into understanding farmers' preferences and WTP for specific functions of SFTs with a DCE.

The prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a basis for gas dispatch and reduce the production cost of enterprises.

In this paper, a new method using the ensemble empirical mode decomposition (EEMD) and the back propagation neural network is proposed. Unfortunately, this method does not achieve the ideal prediction. Further, using the advantages of long short-term memory (LSTM) neural network for long-term dependence, a prediction method based on EEMD and LSTM is proposed. In this model, the gas consumption series is decomposed into several intrinsic mode functions and a residual term (r(t)) by EEMD. Second, each component is predicted by LSTM. The predicted values of all components are added together to get the final prediction result.

The results show that the root mean square error is reduced to 0.35%, the average absolute error is reduced to 1.852 and the R-squared is reached to 0.963.

A new gas consumption prediction method is proposed in this paper. The production data collected in the actual production process is non-linear, unstable and contains a lot of noise. But the EEMD method has the unique superiority in the analysis data aspect and may solve these questions well. The prediction of gas consumption is the result of long-term training and needs a lot of prior knowledge. Relying on LSTM can solve the problem of long-term dependence.