Deconstructing Online Hospitality Review Systems

Online hospitality reviews have an important impact on consumers’ travel and hospitality booking decisions in the Internet age. A well-designed online hospitality review system is crucial to reduce the uncertainty of consumers’ decision making, to grasp the actual needs of consumers, and to improve the quality experience of platforms. In this context, this research conducts an empirical study on the design features of online hospitality review systems based on the Kano model. First, the paper analyzes the design features of online hospitality review systems. Then, the paper proposes an improved method to classify design features on the basis of the Kano questionnaire design and survey data. Finally, the paper quantitatively measures their importance in online hospitality review systems. Results can provide scientific basis for online travel platforms or hospitality operators to optimize the design of online hospitality review systems and to obtain reference value to increase the satisfaction of consumers’ decision making.

An Improved Method of Automated Nonparametric Content Analysis for Social Science

Some scholars build models to classify documents into chosen categories. Others, especially social scientists who tend to focus on population characteristics, instead usually estimate the proportion of documents in each category—using either parametric “classify-and-count” methods or “direct” nonparametric estimation of proportions without individual classification. Unfortunately, classify-and-count methods can be highly model-dependent or generate more bias in the proportions even as the percent of documents correctly classified increases. Direct estimation avoids these problems, but can suffer when the meaning of language changes between training and test sets or is too similar across categories. We develop an improved direct estimation approach without these issues by including and optimizing continuous text features, along with a form of matching adapted from the causal inference literature. Our approach substantially improves performance in a diverse collection of 73 datasets. We also offer easy-to-use software that implements all ideas discussed herein.

Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+, Mg2+, Na+, and K+). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the Sn and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.

An improved method to determine basic probability assignment based on fuzzy mathematics

In D-S evidence theory, the determination of the basic probability assignment function (BPA) is the first and important step. However, the generation of BPA is still a problem to be solved. Based on the concepts in fuzzy mathematics, this paper proposes an improved BPA generation method. By introducing the value of the intersection point of membership function of different targets under the same index to describe the overlap degree of targets, the assignment of unknown items is optimized on this basis. This article applies it to target recognition of robot hands. The results show that the proposed method is more reliable and more accurate.

A Vehicle and Pedestrian Detection Method Based on Improved YOLOv4-Tiny

Aiming at the problems of low detection accuracy and poor recognition effect of small-scale targets in traditional vehicle and pedestrian detection methods, a vehicle and pedestrian detection method based on improved YOLOv4-Tiny is proposed. On the basis of YOLOv4-Tiny, the 8-fold down sampling feature layer was added for feature fusion, the PANet structure was used to perform bidirectional fusion for the deep and shallow features from the output feature layer of backbone network, and the detection head for small targets was added. The results show that the mean average precision of the improved method has reached 85.93%, and the detection performance is similar to that of YOLOv4. Compared with the YOLOv4-Tiny, the mean average precision of the improved method is increased by 24.45%, and the detection speed reaches 67.83FPS, which means that the detection effect is significantly improved and can meet the real-time requirements.

Solution-phase decomposition of ferrocene into wüstite-iron oxide core-shell nanoparticles

We report an improved method for the controlled solvent-phase decomposition of ferrocene into highly crystalline monodisperse iron oxide nanoparticles at relatively low temperatures. Solution-phase decomposition of ferrocene into nanoparticles has...