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The device, amplifies and sub-regionally transmits the current generated by the body temperature thermoelectric generator through a smart body temperature sensor.

The present study designs a wearable smart device regarding the relationship between temperature and emotion.

Experimental results show that the device can accurately detect changes in human body temperature under hilarious, fearful, soothing and angry emotions, so as to achieve changes in clothing colors, namely blue, red, green and brown.

Different areas of clothing produce controllable and intelligent color, so that adult emotions can be understood through changes in clothing colors, which is conducive to judging their moods and promoting social interaction.

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.

This study aims to explore how bundling knowledge resources (i.e. knowledge integration mechanisms [KIMs]) and digital resources (i.e. big data-powered artificial intelligence [BDAI]) can enhance supply chain visibility (SCV) capabilities for implementing just-in-case (JIC) practices.

Analysis of survey data from Chinese manufacturers was conducted to test the proposed hypotheses.

The results reveal a significant positive effect of KIMs on BDAI, as well as positive effects of both BDAI and KIMs on SCV. Furthermore, the results suggest that SCV partially mediates the KIMs–JIC relationship and fully mediates the BDAI–JIC relationship.

This study advances the digital SC and inventory management literature by proposing and empirically testing a digital JIC model that explores how to bundle knowledge and digital resources into SC capabilities for managing JIC inventory in uncertain and digital times.

The purpose of this paper is to examine the relationship between wine tourism experiences (i.e. winescape, winery service quality, winery brand differentiation and tourist motivation) and tourist behavioral intentions (satisfaction, loyalty and revisit intention) from both demand and supply perspectives in Ningxia, China, by using the SERVQUAL model and the self-determination theory.

The research comprises two studies with an exploratory sequential mixed-methods approach. Study 1 adopted semi-structured interviews with winery owners and managers to explore the factors related to wine tourism experiences that influence tourists’ tourism experience. The results from the Study 1 leads to Study 2, which used a survey to explore wine tourist experiences and their behavioral intentions

This study provided important information regarding the factors that influence wine tourists experience in Ningxia wine region from the perspectives of supply and demands sides. Frist, this study confirmed the various factors influence wine tourism experiences in Ningxia based on the results from Study 1 (supply perspectives). Second, by further investigation in the Study 1, this study integrates the SERVQUAL model and the self-determination theory as fundamental theoretical frameworks in Study 2 to analysis the tourist perspectives. Third, as results, the authors finally confirmed the theoretical frameworks of wine tourism in Ningxia based on dual supply-demand perspectives.

The integration of multiple research approaches enriches the research findings and provides a more robust understanding of the wine tourism experiences in Ningxia.

This paper aims to deal with the force and position tracking problem when a robot performs a task in interaction with an unknown environment and presents a hybrid control strategy based on variable admittance control and fixed-time control.

A hybrid control strategy based on variable admittance control and fixed-time control is presented. Firstly, a variable stiffness admittance model control based on proportional integral and differential (PID) is adopted to maintain the expected force value during the task execution. Secondly, a fixed-time controller based on radial basis function neural network (RBFNN) is introduced to handle the model uncertainties and ensure the fast position tracking convergence of the robot system, while the singularity problem is also avoided by designing the virtual control variable with piecewise function.

Simulation studies conducted on the robot manipulator with two degrees of freedom have verified the superior performance of the proposed control strategy comparing with other methods.

A hybrid control scheme for robot–environment interaction is presented, in which the variable stiffness admittance method is adopted to adjust the interaction force to the desired value, and the RBFNN-based fixed-time position controller without singularity problem is designed to ensure the fast convergence of the robot system with model uncertainty.

This study aims to enhance the prediction accuracy of hydroelectricity consumption in China, with a focus on addressing the challenges posed by complex and nonlinear characteristics of the data. A novel grey multivariate prediction model with structural optimization is proposed to overcome the limitations of existing grey forecasting methods.

This paper innovatively introduces fractional order and nonlinear parameter terms to develop a novel fractional multivariate grey prediction model based on the NSGM(1, N) model. The Particle Swarm Optimization algorithm is then utilized to compute the model’s hyperparameters. Subsequently, the proposed model is applied to forecast China’s hydroelectricity consumption and is compared with other models for analysis.

Theoretical derivation results demonstrate that the new model has good compatibility. Empirical results indicate that the FMGM(1, N, a) model outperforms other models in predicting the hydroelectricity consumption of China. This demonstrates the model’s effectiveness in handling complex and nonlinear data, emphasizing its practical applicability.

This paper introduces a scientific and efficient method for forecasting hydroelectricity consumption in China, particularly when confronted with complexity and nonlinearity. The predicted results can provide a solid support for China’s hydroelectricity resource development scheduling and planning.

The primary contribution of this paper is to propose a novel fractional multivariate grey prediction model that can handle nonlinear and complex series more effectively.

This study aims to explore the mechanism of boundary-spanning search on firm’s innovation performance under environmental dynamics from the perspective of strategic knowledge integration.

A survey was conducted among Chinese firm managers and R&D personnel, resulting in the collection of 315 valid samples. Hierarchical regression analysis was mainly adopted to demonstrate the hypothesized relationships, while the Sobel test and bootstrap method were used to further validate the mediating effects.

The results demonstrate that boundary-spanning search in different dimensions is a critical factor in the improvement of firm innovation performance (FIP). Two types of strategic knowledge integration are the main factors causing FIP and mediate the influence of boundary-spanning search on FIP. Furthermore, environmental dynamics moderate the relationship among boundary-spanning search, strategic knowledge integration and FIP.

Managers need to strengthen the boundary-spanning search for market and technical knowledge, which will promote firm innovative performance. Managers also need to implement strategic knowledge integration, which specifically includes using planned strategic knowledge integration to compensate for knowledge deficiencies, thereby achieving predetermined objectives; and using emergent strategic knowledge integration to update their understanding of internal and external environments, and to reset strategic objectives. In dynamic environments, managers should emphasize strategic knowledge management activities more.

From a strategic management perspective, this study categorizes strategic knowledge integration into planned and emergent forms. By applying the logic of knowledge acquisition, integration and creation, it explores how boundary-spanning search affects FIP through strategic knowledge integration as the intermediary and the boundary conditions of environmental dynamics. This not only provides a deeper understanding of the nature and effects of boundary-spanning research but also enhances the theory of strategic knowledge management.

In this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems.

Firstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models.

By altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues.

This paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export.

The primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.

In online purchase for dietary supplements, due to the lack of professional advice from pharmacists, electronic word-of-mouth (eWOM) has become an important source of information for consumers to make purchase decisions. How can firms use eWOM resources to increase sales? The purpose of this paper is to provide practical methods for firms by exploring the effects of eWOM on sales and developing a sales prediction model based on eWOM.

The data came from 120 dietary supplements on Tmall.com. The authors extracted the product sales as dependent variable and 11 eWOM factors as independent variables. The multicollinearity was tested by using variance inflation factor and least absolute shrinkage and selection operator. The multiple linear regression was used to investigate the effects of eWOM on sales. Drawing on white- and black-box approaches, six models were developed. Comparing the root mean square error, the authors selected the optimal one as their target sales prediction model.

Product ratings, total reviews and favorites are positively and strongly associated with sales. Questions and additional reviews have negative effects on sales. The random forest model has the best prediction performance.

The research focuses on eWOM of dietary supplement. First, the authors show that easily accessible eWOM from online platforms can be used to evaluate effects and predict sales. Second, the authors introduce white- and black-box models through machine learning to assess eWOM. Firms could use the described models to foster their marketing initiatives.

This study aims to develop a high-performance nanofluid that can be used in titanium alloys machining. Titanium alloys are difficult-to-cut materials and difficult to be lubricated. This study explored the lubrication performance of various carbon nanoparticles in water-based lubricants for titanium alloys.

The lubricating and antiwear properties of the developed cutting fluid were tested by a tribo-tester. The lubricant performance was evaluated through friction coefficient, wear volume and surface quality. The lubrication mechanism was analyzed through surface morphology, wettability and bonding analysis.

The lubricating performance of four kinds of carbon nanoparticles on titanium alloys was tested and the results showed that single-layer graphene had the smallest COF and wear volume. The interaction between nanoparticles and debris was an important factor that influenced the lubrication performance of nanoparticles for titanium alloy. Moreover, the hybrid nanofluid with graphene and spherical graphite in a ratio of 1:2 achieved a balance between lubricating performance and price, making it the optimal choice.

The developed lubricant containing carbon nanoparticles that can lubricate titanium alloys effectively has great potential in machining titanium alloy as a high-performance cutting fluid in the future.

This paper fulfills an identified need for water-based lubricant for titanium alloys considering the bad tribological properties.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0124/