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The purpose of this study is to design a spherical sensor which can detect the surge from various directions to lay the foundation of the research of surge.

This paper designed a spherical sensor to detect the impact force generated by the surge. To realize the depth and stability control of the shallow underwater vehicles, it is necessary to do research and analysis on the surge in shallow waters. The spherical sensor with novel structure was skillfully composed of 24 cantilever beam pressure-type strain sensors. It is powerful to detect the surge from various directions simultaneously.

It is proved that the spherical sensor can accurately collect the surge data from multiple directions through experiments, which laid the foundation of the anti-surge study.

Surge is not a new topic. But there is no effective tool to detect surge. The research of this paper is an attempt to provide an available tool to analyze surge. The research limitation is that the pool where the spherical sensor is installed is a little small. In the future, a bigger pool can be used.

A deep and comprehensive analysis of surge can be conducted according to the surge data detected by the spherical sensor to achieve the essential features of surge. This lays the foundation for the accurate control of Autonomous Underwater Vehicles (AUVs), especially fixed depth and stability control.

As the control accuracy of AUVs increases, the AUVs can perform much more difficult tasks such as port monitoring, underwater salvaging, underwater pipeline maintenance and so on. These can be applied in commercial applications or in the national defense of many countries.

A novel spherical sensor using 24 cantilever beam pressure-type strain sensors to detect the surge was designed. The spherical sensor was installed in the physical surge simulator to collect surge data and conduct an analysis of the collected data.

In view of the unknown environmental parameters and uncertain interference during gripping by the manipulator, it is difficult to obtain an effective gripping force with the traditional impedance control method. To avoid this dilemma, the purpose of this study is to propose an adaptive control strategy based on an adaptive neural network and a PID search optimization algorithm for unknown environments.

The method is based on a variable impedance model, and a new impedance model is established using a radial basis function (RBF) neural network to estimate unknown parameters of the impedance model. The approximation errors of the adaptive neural network and the uncertain disturbance are effectively suppressed by designing the adaptive rate. In the meantime, auxiliary variables are constructed for Lyapunov stability analysis and adaptive controller design, and PSA is used to ensure the stability of the adaptive impedance control system. Based on the Lyapunov stability criterion, the adaptive im-pedance control system is proved to have progressive tracking convergence property.

Through comparative simulations and experiments, the superiority of the proposed adaptive control strategy in position and force tracking has been verified. For objects with low flexibility and light-weight (such as a coke, a banana and a nectarine), this control method demonstrates errors of less than 10%.

This paper uses RBF neural networks to estimate unknown parameters of the impedance model in real-time, enhancing system adaptability. Neural network weights are updated online to suppress errors and disturbances. Auxiliary variables are designed for Lyapunov stability analysis. The PSA algorithm is used to adjust controller parameters in real-time. Additionally, comparative simulations and experi-ments are designed to analyze and validate the performance of controller.

The aim of this study is to explore students' expectations and perceived effectiveness of computer-assisted review tools, and the differences in reliability and validity between human evaluation and automatic evaluation, to find a way to improve students' English writing ability.

Based on the expectancy disconfirmation theory (EDT) and Intelligent Computer-Assisted Language Learning (ICALL) theory, an experiment is conducted through the observation method, semistructured interview method and questionnaire survey method. In the experiment, respondents were asked to write and submit four essays on three online automated essay evaluation (AEE) systems in total, one essay every two weeks. Also, two teacher raters were invited to score the first and last papers of each student. The respondents' feedbacks were investigated to confirm the effectiveness of the AEE system; the evaluation results of the AEE systems and teachers were compared; descriptive statistics was used to analyze the experimental data.

The experiment revealed that the respondents held high expectations for the computer-assisted evaluation tools, and the effectiveness of computer scoring feedback on students was higher than that of teacher scoring feedback. Moreover, at the end of the writing project, the students' independent learning ability and English writing ability were significantly improved. Besides, there was a positive correlation between students' initial expectations of computer-assisted learning tools and the final evaluation of learning results.

The innovation lies in the use of observation methods, questionnaire survey methods, data analysis, and other methods for the experiment, and the combination of deep learning theory, EDT and descriptive statistics, which has particular reference value for future works.

This study aims to explore suitable anode materials used in the electrochemical system for indigo dyeing wastewater, to achieve optimal treatment performances.

The single factor experiment was used to explore the optimum process parameters for electrochemical decolorization of indigo dyeing wastewater by changing the applied voltage, electrolysis time and electrolyte concentration. At the voltage of 9 V, the morphology of flocs with different electrolytic times was observed and the effect of electrolyte concentration on decolorization rate in two electrolyte systems was also investigated. Further analysis of chemical oxygen demand (COD) removal rate, anode weight loss and sediment quantity after electrochemical treatment of indigo dyeing wastewater were carried out.

Comprehensive considering the decolorization degree and COD removal rate of the wastewater, the aluminum electrode showed the best treatment effect among several common anode materials. With aluminum electrode as an anode, under conditions of applied voltage of 9 V, electrolysis time of 40 min and sodium sulfate concentration of 6 g/L, the decolorization percentage obtained was of 94.59% and the COD removal rate reached at 84.53%.

In the electrochemical treatment of indigo dyeing wastewater, the aluminum electrode was found as an ideal anode material, which provided a reference for the choice of anodes. The electrodes used in this study were homogenous material and the composite material anode needed to be further researched.

It provided an effective and practical anode material choice for electrochemical degradation of indigo dyeing wastewater.

Combined with the influence of applied voltage, electrolysis time and electrolyte concentration and anode materials on decolorization degree and COD removal rate of indigo dyeing wastewater, providing a better electrochemical treatment system for dyehouse effluent.

Previous research about online recommendation systems has focused largely on their impact on customers' purchase decisions regarding the products being recommended, but it has mostly ignored how they may affect focal product evaluation. This research aimed to examine the influence of recommendation type (i.e. substitute-based vs complement-based) on focal product evaluation dependent on the brand image (i.e. warm vs competent).

Four laboratory experiments were conducted. Study 1 adopted an implicit association task. Studies 2 and 3 used a 2 (image: warmth vs competence) × 2 (product display: complements vs substitutes) between-subjects experimental design. Study 4 used a 2 (decision stage) × 2 (image) × 2 (product display) × continuous (need for cognition) between-subjects design.

Study 1 demonstrated a general “complementation (competition)—warmth (competence)” association. Studies 2 and 3 found that when a focal product had a warm (competent) image, complement-based (substitute-based) recommendations led customers to evaluate it more favorably than substitute-based (complement-based) recommendations. Study 3 further demonstrated that processing fluency mediates the above effect. Study 4 showed that this effect relies on heuristic processing and disappears for those who are in the screening stage or have a high need for cognition.

Theoretically, this research extends the understanding of the stereotype content model of focal product brand image, the feelings-as-information process, and moderating roles of processing stage and need for cognition in e-commerce contexts. Practically, the findings provide online retailers a guideline for customizing their recommendation systems.

The purpose of this paper is to present a novel method for fabric defect detection.

The method based on joint low-rank and spare matrix recovery, since patterned fabric is manufactured by a set of predefined symmetry rules, and it can be seen as the superposition of sparse defective regions and low-rank defect-free regions. A robust principal component analysis model with a noise term is designed to handle fabric images with diverse patterns robustly. The authors also estimate a defect prior and use it to guide the matrix recovery process for accurate extraction of various fabric defects.

Experiments on plain and twill, dot-, box- and star-patterned fabric images with various defects demonstrate that the method is more efficient and robust than previous methods.

The authors present a RPCA-based model for fabric defects detection, and show how to incorporate defect prior to improve the detection results. The authors also show that more robust detection and less running time can be obtained by introducing a noise term into the model.

The main purpose of this paper is to analyse the different factors affecting Sino-African trade based on the gravity model, and propose some solutions to improve the problems.

The paper is based on an extended gravity model, including trade agreement and recession as explanatory variables. The impacts of trade agreement and economic recession on Sino-African imports and exports are examined.

The results show that the product of GDP affects African exports to China significantly and negatively, and affects African imports from China positively. Real exchange rate affects African exports to China positively, and affects African imports from China negatively. Population affect African exports to China significantly and positively, and affect African imports from China positively. Recession have negative effects on both African imports from China and exports to China but is only significant for imports. Agreement affects African imports from China and exports to China positively. Our findings confirm the impact of economic recession, and imply that the structure of African product exported to China should be improved, and trade agreements should be reinforced.

This paper contributes and extends the literature on Sino-African trade by improving the traditional gravity model to include the impact of all trade agreements, and their aggregating effects on trade. The paper also seeks to assess the trade impact of economic recession through a dynamic gravity model approach for which there has been no research done to our knowledge. In this regard, it provides new understanding of the trade pattern between China and Africa, and ways in improving the Sino-African bilateral trade.

The purpose of this paper is to present a novel method for minor fabric defects detection.

This paper proposes a PETM-CNN algorithm. PETM-CNN is designed based on self-similar estimation algorithm and Convolutional Neural Network. The PE (Patches Extractor) algorithm extracts patches that are possible to be defective patches to preprocess the fabric image. Then a TM-CNN (Triplet Metric CNN) method is designed to predict labels of the patches and the final label of the image. The TM-CNN can perform better than normal CNN.

This algorithm is superior to other algorithms on the data set of fabric images with minor defects. The proposed method achieves accurate classification of fabric images whether it has minor defects or not. The experimental results show that the approach is effective.

Traditional fabric defects detection is not effective as minor defects detection, so this paper develops a method of minor fabric images classification based on self-similar estimation and CNN. This paper offers the first investigation of minor fabric defects.

Tags help promote customer engagement on video-sharing platforms. Video tag recommender systems are artificial intelligence-enabled frameworks that strive for recommending precise tags for videos. Extant video tag recommender systems are uninterpretable, which leads to distrust of the recommendation outcome, hesitation in tag adoption and difficulty in the system debugging process. This study aims at constructing an interpretable and novel video tag recommender system to assist video-sharing platform users in tagging their newly uploaded videos.

The proposed interpretable video tag recommender system is a multimedia deep learning framework composed of convolutional neural networks (CNNs), which receives texts and images as inputs. The interpretability of the proposed system is realized through layer-wise relevance propagation.

The case study and user study demonstrate that the proposed interpretable multimedia CNN model could effectively explain its recommended tag to users by highlighting keywords and key patches that contribute the most to the recommended tag. Moreover, the proposed model achieves an improved recommendation performance by outperforming state-of-the-art models.

The interpretability of the proposed recommender system makes its decision process more transparent, builds users’ trust in the recommender systems and prompts users to adopt the recommended tags. Through labeling videos with human-understandable and accurate tags, the exposure of videos to their target audiences would increase, which enhances information technology (IT) adoption, customer engagement, value co-creation and precision marketing on the video-sharing platform.

The proposed model is not only the first explainable video tag recommender system but also the first explainable multimedia tag recommender system to the best of our knowledge.

Job satisfaction is essential in understanding turnover intentions. Previous studies reveal that highly educated hospitality employees generally have lower levels of job satisfaction, indicating that the antecedents of job satisfaction may be different from hospitality managers and frontline employees. This study compared the different antecedents of job satisfaction for housekeeping managers and employees.

This study used a mixed-methods approach for a two-part study. The researchers recruited housekeeping managers for the exploratory survey. The results of open-end questions helped us build a custom dictionary for the text mining of comments from Glassdoor.com. Finally, a multilinear regression of themes from housekeeping employees’ ratings on Glassdoor.com was conducted to understand the antecedents of job satisfaction for housekeeping managers and employees.

The results of the exploratory survey indicated that the housekeeping department has an urgent need for organizational support and training. The text-mining revealed organizational support impacts both managers and frontline employees, while training impacts managers more than employees. Finally, the regression analysis showed compensation, business outlook, senior management, and career opportunity impacted both groups. However, work-life balance only influenced managers.

With a large number of employees at low salaries, housekeeping departments have a higher-than-average turnover rate for lodging. This study is among the first to compare the antecedents of managers’ and frontline employees’ job satisfaction in the housekeeping department, extending Social Exchange Theory. It provides suggestions for the housekeeping department to decrease turnover intentions.