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The purpose of this paper is to propose and demonstrate how Tourism2vec, an adaptation of a natural language processing technique Word2vec, can serve as a tool to investigate tourism spatio-temporal behavior and quantifying tourism dynamics.

Tourism2vec, the proposed destination-tourist embedding model that learns from tourist spatio-temporal behavior is introduced, assessed and applied. Mobile positioning data from international tourists visiting Tuscany are used to construct travel itineraries, which are subsequently analyzed by applying the proposed algorithm. Locations and tourist types are then clustered according to travel patterns.

Municipalities that are similar in terms of their scores of their neural embeddings tend to have a greater number of attractions than those geographically close. Moreover, clusters of municipalities obtained from the K-means algorithm do not entirely align with the provincial administrative segmentation.

The purpose of this paper is to explore and examine discrepancies of food aesthetics portrayed on social media across different types of restaurants using a large-scale data set of food images.

A neural food aesthetic assessment model using computer vision and deep learning techniques is proposed, applied and evaluated on the food images data set. In addition, a set of photographic attributes drawn from food services and cognitive science research, including color, composition and figure–ground relationship attributes is implemented and compared with aesthetic scores for each food image.

This study finds that restaurants with different rating levels, cuisine types and chain status have different aesthetic scores. Moreover, the authors study the difference in the aesthetic scores between two groups of image posters: customers and restaurant owners, showing that the latter group tends to post more aesthetically appealing food images about the restaurant on social media than the former.

Restaurant owners may consider performing more proactive social media marketing strategies by posting high-quality food images. Likewise, social media platforms should incentivize their users to share high-quality food images.

The main contribution of this paper is to provide a novel methodological framework to assess the aesthetics of food images. Instead of relying on a multitude of standard attributes stemming from food photography, this method yields a unique one-take-all score, which is more straightforward to understand and more accessible to correlate with other target variables.

Shared leadership is an effective mechanism for managing project teams. Its performance-enhancing benefits have been demonstrated in many studies. Nonetheless, there is an obvious silence about how to promote shared leadership in Lean Six Sigma (LSS) project teams. To address this deficit, the purposes of this study are to investigate the influence of shared leadership on LSS project success and to explore how team psychological safety, project task complexity and project task interdependence influence shared leadership.

A multi-source, time-lagged survey design with a four-month interval was conducted. To do this, the authors collected data from 71 project teams (comprising 71 project managers and 352 project members) using LSS approaches in the manufacturing and service industries.

The findings show that shared leadership positively influences LSS project success. The authors also found that team psychological safety fosters the development of shared leadership and, more importantly, these effects are stronger when the tasks are more complex and more interdependent.

These findings advance our understanding of the factors that enable shared leadership and equip LSS project managers with practical techniques to improve shared leadership for the success of their projects.

This study extends the theory of shared leadership to the context of LSS project management and is among the first, to the best of the authors’ knowledge, to theoretically propose and empirically validate how to promote shared leadership in LSS project teams.

This study aims to systematically reveal the complex interaction between uncertainty and the international commodity market (CRB).

A composite uncertainty index and five categorical uncertainty indices, together with wavelet analysis and detrended cross-correlation analysis, were used. First, in the time-frequency domain, the coherency and lead-lag relationship between uncertainty and the commodity markets were investigated. Furthermore, the transmission direction of the cross-correlation over different lag periods and asymmetry in this cross-correlation under different trends were identified.

First, there is significant coherency between uncertainties and CRB mainly in the short and medium terms, with natural disaster and public health uncertainties tending to lead CRB. Second, uncertainty impacts CRB more markedly over shorter lag periods, whereas the impact of CRB on uncertainty gradually increases with longer lag periods. Third, the cross-correlation is asymmetric and multifractal under different trends. Finally, from the perspective of lag periods and trends, the interaction of uncertainty with the Chinese commodity market is significantly different from its interaction with CRB.

First, this study comprehensively constructs a composite uncertainty index based on five types of uncertainty. Second, this study provides a scientific perspective on examining the core and diverse interactions between uncertainty and CRB, as achieved by investigating the interactions of CRB with five categorical and composite uncertainties. Third, this study provides a new research framework to enable multiscale analysis of the complex interaction between uncertainty and the commodity markets.

The purpose of this paper is to study the effect of current-carrying condition on the electrochemical process and atmospheric corrosion behavior of the commercial aluminum alloys.

Potentiodynamic polarization tests were performed to study the electrochemical process of the aluminum alloys. Salt spray tests and weight loss tests were carried out to study the atmospheric corrosion behavior. The corrosion morphology of the alloys was observed, and the products were analyzed.

The corrosion process of four aluminum alloys was accelerated in the current-carrying condition. Moreover, the acceleration effect on A2024 and A7075 was much stronger than that on A1050 and A5052. The main factors would be the differences in microstructure and corrosion resistance between these alloys. As the carried current increased, the corrosion rate and corrosion current density of the aluminum alloys gradually increased, with the protection of the corrosion product film decreasing linearly.

This is a recent study on the corrosion behavior of conductors under current-carrying condition, which truly understands the corrosion status of power grid materials. Relevant results provide support for the corrosion protection and safe service of aluminum alloy in power systems.

The purpose of this paper is to construct a fuzzy logic model that acts as a decision support system to minimize inventory-related costs in the field of industrial maintenance. Achieving a balance between the unavailability and over-storage of spare parts is a problem with potentially significant consequences. That significance increases proportionally with the ever-increasing challenge of reducing overall cost. Either scenario can result in substantial financial losses because of the interruption of production or the costs of tied-up capital, also called the “solidification of capital.” Moreover, there is that additional problem of the expiry of parts on the shelf.

The proposed approach relies on inputs from experts with consideration for incompleteness and inaccuracy. Two levels of decision are considered simultaneously. The first is whether a part should be stored or ordered when needed. The second involves comparing suppliers with their batch-size offers based on user-determined criteria. A mathematical model is developed in parallel for validation.

The results indicate that the fuzzy logic approach is accurate and satisfactory for this application and that it is advantageous because of its limited sensitivity to the inaccuracy and/or incompleteness of data. In addition, the approach is practical because it requires minimal user effort.

To the best of the authors’ knowledge, the exploitation of fuzzy-logic altogether with limited sensitivity experts' inputs were never combined for the solution of this particular problem; however, this approach's positive impact is expected to be highly significant in solving a chronic problem in industry.

Nanocrystalline LaB₆, NdB₆, Nd-doped LaB₆ and La-doped NdB₆ have been studied using the density functional theory (DFT) to study their electronic, optical and mechanical characteristics. The purpose of this paper is to address this issue.

Nanocrystalline LaB₆, NdB₆, Nd-doped LaB₆ and La-doped NdB₆ have been studied using the DFT to study their electronic, optical and mechanical characteristics. The calculated lattice constants of LaB₆, NdB₆, Nd-doped LaB₆ and La-doped NdB₆ were 4.157, 4.118, 4.267 and 4.449, respectively. The lattice constant of La₇Nd₁B₆ was increased when Nd is doped into LaB₆. B p comprised the uppermost valence bands (VBs), whereas B s comprised the lowermost conduction bands (CBs). The authors’ results showed that La doping reduced the work function of NdB₆ and increased its thermionic emission characteristics.

The authors’ results showed that La doping reduced the work function of NdB₆ and increased its thermionic emission characteristics.

The work function of LaB₆ was 2.7 eV, which is higher than that of La₁Nd₇B₆ (2.64 eV).