A quantitative analysis of the impact of explicit incorporation of recency, seasonality and model specialization into fine-grained tourism demand prediction models.

Forecasting is of utmost importance for the Tourism Industry. The development of models to predict visitation demand to specific places is essential to formulate adequate tourism development plans and policies. Yet, only a handful of models deal with the hard problem of fine-grained (per attraction) tourism demand prediction.

FISETIO: A FIne-grained, Structured and Enriched Tourism Dataset for Indoor and Outdoor attractions.

This paper aims to introduce our publicly available datasets in the area of tourism demand prediction for future experiments and comparisons. Most of the previous works in the area of tourism demand forecasting are based on coarse-grained analysis (level of countries or regions) and there are very few works and consequently datasets available for fine-grained tourism analysis (level of attractions and points of interest). In this article, we present our fine-grained enriched datasets for two types of attractions - (I) indoor attractions (27 Museums and Galleries in U.

Clinical, Radiographic, and Histologic Outcome of Regenerative Endodontic Treatment in Human Teeth Using a Novel Collagen-hydroxyapatite Scaffold.

Introduction: Histologic examination of teeth after regenerative endodontic treatment (RET) shows that the type, quality, and quantity of tissues formed in the root canal space are not predictable. The aim of this study was to examine clinically, radiographically, and histologically the outcome of RET in immature noninfected human teeth using SynOss Putty (Collagen Matrix Inc, Oakland, NJ) as a scaffold.

Methods: Three pairs of maxillary/mandibular first premolars in 3 patients scheduled for extraction were included.