Purpose The purpose of this paper is to focus on how corporate social responsibility (CSR) (i.e. responsibility to customers, employees and society) influences customer behavioural loyalty in the hotel industry. The mediating effects of brand image and customer trust on the relationship between CSR and customer behavioural loyalty are also considered. Design/methodology/approach In total, 298 valid responses to questionnaire surveys were collected from a convenience sample in China in 2017. A structural equation model was used to test the hypotheses. Findings Hotel customer behavioural loyalty can be enhanced by CSR performance. Performance in each of the three CSR domains positively impacted customer behavioural loyalty to different degrees. The impact of CSR on the customer had the strongest influence on Chinese customers’ behavioural loyalty among the three CSR domains of customer, employee and society. Brand image and customer trust were found to be mediators of the relationship between CSR performance and customer behavioural loyalty. Originality/value The current research contributes to the literature by demonstrating that CSR activities are not all equally effective. Results reveal that the society dimension of CSR had the strongest impact on Chinese customers’ brand image of hotels among the three CSR dimensions investigated. In terms of Chinese hotel customers’ trust, the CSR–customer dimension plays the most effective role. The findings also support the notion that Chinese consumers are beginning to use CSR information to evaluate hotels.
To further prolong the lifetime of wireless sensor network (WSN), researchers from various countries have proposed many clustering routing protocols. However, the total network energy consumption of most protocols is not well minimized and balanced. To alleviate this problem, this paper proposes an energy-efficient clustering routing protocol in WSNs. To begin with, this paper introduces a new network structure model and combines the original energy consumption model to construct a new method to determine the optimal number of clusters for the total energy consumption minimization. Based on the balanced energy consumption, then we optimize the AGglomerative NESting (AGNES) algorithm, including: (1) introduction of distance variance, (2) the dual-cluster heads (D-CHs) division of the energy balance strategy, and (3) the node dormancy mechanism. In addition, the CHs priority function is constructed based on the residual energy and position of the node. Finally, we simulated this protocol in homogeneous networks (the initial energy = 0.4 J, 0.6 J and 0.8 J) and heterogeneous networks (the initial energy = 0.4⁻0.8 J). Simulation results show that our proposed protocol can reduce the network energy consumption decay rate, prolong the network lifetime, and improve the network throughput in the above two networks.
In this paper, a technique based on the combination of genetic algorithm (GA) with high frequency simulation software (HFSS) is presented to perform optimization. The technique is realized by MATLAB and VB script of HFSS. Then, the technique is used to guide the design and optimization of broadband microstrip antenna. The feasibility of the technique is validated by the example.
This study aimed to evaluate and compare the performances of the random forest (RF) and support vector regression (SVR) models combined with different feature selection methods, including recursive feature elimination (RFE), simulated annealing feature selection (SAFS), and selection by filtering (SBF) in predicting soil pH in Anhui Province, East China. We also used the ALL original features to build the RF and SVR models as a comparison. A total of 140 samples were selected, following the principles of randomness, uniformity, and representativeness, to consider the combination of landscape elements, such as topography, parent material, and land use. Auxiliary data, including climatic, topographic, and vegetation indexes, were used for predicting soil pH. The results showed that compared with the use the ALL original modeling features (ALL-RF, ALL-SVR), the combination of the three feature selection algorithms with RF and SVR can eliminate some redundant features and effectively improve the prediction accuracy of the soil pH model. For the RF model, the RMSE and the MAE of the calibration of the RFE-RF model were 0.73 and 0.57 and had the highest R2 in four different RF models. The testing set of the RFE-RF model had an R2 of 0.61, which was better than that of the ALL-RF (R2 = 0.45) model and lower than those of the SAFS-RF (R2 = 0.71) and SBF-RF (R2 = 0.69) models. For the SVR model, the RFE-RF model was more robust and had better generalization ability. The accuracy of digital soil mapping can be improved through feature selection.
Change point detection in dynamic networks aims to detect the points of sudden change or abnormal events within the network. It has garnered substantial interest from researchers due to its potential to enhance the stability and reliability of real-world networks. Most change point detection methods are based on statistical characteristics and phased training, and some methods are required to set the percent of change points. Meanwhile, existing methods for change point detection suffer from two limitations. On one hand, they struggle to extract snapshot features that are crucial for accurate change point detection, thereby limiting their overall effectiveness. On the other hand, they are typically tailored for specific network types and lack the versatility to adapt to networks of varying scales. To solve these issues, we propose a novel unified end-to-end framework called Variational Graph Gaussian Mixture model (VGGM) for change point detection in dynamic networks. Specifically, VGGM combines Variational Graph Auto-Encoder (VGAE) and Gaussian Mixture Model (GMM) through joint training, incorporating a Mixture-of-Gaussians prior to model dynamic networks. This approach yields highly effective snapshot embeddings via VGAE and a dedicated readout function, while automating change point detection through GMM. The experimental results, conducted on both real-world and synthetic datasets, clearly demonstrate the superiority of our model in comparison to the current state-of-the-art methods for change point detection.
L'objectif de cette these est de trouver la forme optimale d'une antenne planaire ou d'un reseau d'antennes planaires a partir de contraintes imposees (diagramme de rayonnement, gain ou directivite) ou de reconstruire la forme a partir de mesures experimentales. L'algorithme d'optimisation developpe est base sur une methode de type gradient et la reconstruction des par une methode d'ensembles de niveaux (Level Sets) ou contours actifs. Le probleme direct est resolu en utilisant une formulation integrale du probleme electromagnetique et une methode d'elements finis pour la discretisation. Le gradient de forme est calcule en utilisant deux methodes differentes. Tout d'abord, une methode par differences finies basee sur la derivee a un nœud du maillage, pour une modification infinitesimale des elements triangulaires du contour, suivant la direction de la normale exterieure. La deuxieme methode est basee sur le gradient topologique pour le calcul de la deformation des contours. Une methode d'ensembles de niveaux avec bande etroite a ete developpee pour faire evoluer le contour des antennes utilisant la vitesse de deformation calculee a partir du gradient de forme. Differentes configurations d'antennes et reseaux d'antennes planaires ont ete utilisees pour etudier les performances de l'algorithme d'optimisation. Des techniques de type saut de frequence et multifrequence ont ete utilisees pour optimiser la forme dans une bande de frequence. L'optimisation de forme pour la miniaturisation d'antennes planaires concerne de nombreuses applications, en particulier, pour les reseaux reflecteurs
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