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Wine tourism is gaining increasing popularity among Chinese tourists, making it necessary to thoroughly examine tourist behavior. While online reviews posted by wine tourists have been extensively studied from the perspectives of destinations and wineries, the perspective of the tourists themselves has been overlooked. To address this gap, this study aims to identify significant attributes intrinsic to the tourism experiences of Chinese wine tourists by adopting a text-mining approach from a tourist-centric perspective.

The authors use topic modeling to extract these attributes, calculate topic intensity to understand tourists’ attention distribution across these attributes and conduct topical sentiment analysis to evaluate tourists’ satisfaction levels with each attribute. The authors perform importance-performance analyses (IPAs) using topic intensity and sentiment scores. Furthermore, the authors conduct semistructured in-depth interviews with Chinese wine tourists to gain insights into the underlying reasons behind the key findings.

The study identifies eleven attributes for domestic wine tourists and seven attributes for outbound wine tourists. From the reviews of both domestic and outbound tourists, three common attributes have been identified: “scenic view”, “wine tasting and purchase” and “wine knowledge”.

According to the results of the IPAs, there is a pressing need for enhancements in the wine tasting and purchasing experience at domestic wine attractions. Additionally, managers of domestic wine attractions should continue to prioritize the positive aspects of the family trip experience and scenic views. On the other hand, for outbound wine attractions, it is crucial for managers to maintain their efforts in providing opportunities for wine knowledge acquisition, ensuring scenic views and upholding the reputation of wine regions.

First, this study breaks new ground by adopting a tourist-centric perspective to extract significant attributes from real wine tourism reviews. Second, the authors conduct a comparative analysis between Chinese wine tourists who travel domestically and those who travel abroad. The third novel aspect of this study is the application of IPA based on textual review data in the context of wine tourism. Fourth, by integrating topic modeling with qualitative interviews, the authors use a mixed-method approach to gain deeper insights into the experiences of Chinese wine tourists.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

The safety performance of cooperative robots is particularly important. This paper aims to study collision detection and response of cooperative robots, which meet the lightweight requirements of cooperative robots and help to ensure the safety of humans and robots.

This paper proposes a collision detection, recognition and response method based on dynamic models. First, this paper identifies the dynamic model of the robot. Second, an external torque observer is established based on the model, and a dynamic threshold collision detection method is designed to reduce the interference of model uncertainty on collision detection. Finally, a collision position and direction estimation method is designed, and a robot collision response strategy is proposed to reduce the harm caused by collisions to humans.

Comparative experiments are conducted on static threshold and dynamic threshold collision detection, and the results showed that the static threshold only detected one collision while the dynamic threshold could detect all collisions. Conducting collision position and direction estimation and collision response experiments, and the results show that this method can determine the location and direction of collision occurrence, and enable the robot to achieve collision separation.

This paper designs a dynamic threshold collision detection method that does not require external sensors. Compared with static threshold collision detection methods, this method can significantly improve the sensitivity of collision detection. This paper also proposes a collision position direction estimation method and collision separation response strategy, which can enable robots to achieve post collision separation and improve the safety of cooperative robots.

This paper aims to clarify the size and morphology of transition metal dichalcogenides has an impact on lubrication performance of Cu-based composites. This study is intended to show that Cu-based electrical contact materials containing Nb_0.91Ti0.09Se₂ have better electrical and tribological properties than those containing NbSe₂. The tribological properties of Cu-based with different Ti-dopped NbSe₂ content were also discussed.

The NbSe₂ and Nb_0.91Ti_0.09Se₂ particles were fabricated by thermal solid state reaction method. The powder metallurgy technique was used to fabricate composites with varying Nb_0.91Ti_0.09Se₂ mass fraction. The phase composition of Cu-based composites was identified by X-ray diffraction, and the morphology of NbSe₂/Nb_0.91Ti_0.09Se₂ and the worn surface of composites were characterized by scanning electron microscopy and transmission electron microscopy. In addition, the tribological properties of composites were appraised using a ball-on-disk multi-functional tribometer. The data of friction coefficient and resistivity were analyzed and the corresponding conclusion was drawn.

In comparison with the pure copper, Cu-based composites containing Nb_0.91Ti_0.09Se₂/NbSe₂ had a lower friction coefficient, illustrating the Nb_0.91Ti_0.09Se₂ with nano-size particles prepared in this work is a perfect choice for the fabrication of excellent electrical contact composites. Compared to composites with NbSe₂, composites containing Nb_0.91Ti_0.09Se₂ have better tribological and electrical properties.

Because of the use of thermal solid state reaction method, the size of NbSe₂ and Nb_0.91Ti_0.09Se₂ is relatively large. Therefore, the fabrication of finer particles of Nb_0.91Ti_0.09Se₂ is encouraged.

In this paper, the authors discuss the tribological and electrical properties of Cu-based composites, and the value of optimum obtained as Nb_0.91Ti_0.09Se₂ content is 15 Wt.%.

This paper aims to study the synergistic effect of graphene sponge on the thermal properties and shape stability of composite phase change material (PCM).

Graphene oxide sponge is first prepared from an aqueous solution of graphene oxide by freeze-drying method. The oxidized graphene sponge is reduced by hydrazine hydrate. Finally, use vacuum impregnation method to introduce paraffin into graphene sponge to prepare composite PCM.

Graphene sponge is used to improve the shape stability of paraffin wax and improves the thermal conductivity and latent heat of the composite PCM. The thermal conductivity increases by 200 per cent and the composite PCM has excellent reliability in 100 melt-freezing cycles.

A simple way for fabricating composite PCM with high thermal conductivity and latent heat which has the potential to be used as thermal storage materials without container encapsulation has been developed by using graphene sponge and paraffin.

The materials and preparation methods with special structure and properties in this paper provide a new idea for the research of PCM, which can be widely used in the fields of energy conversion and storage.