INTRODUCTION Patient appointment systems are of great importance for efficiently managing outpatient clinics as well as patient satisfaction as an indirect indicator for quality care rendered. PURPOSE To describe the hourly block and standard sequential scheduling appointment systems at two different hospitals, as well as to assess the patients' opinions regarding their waiting time in both s...

In this paper, we tackle air quality forecasting by using machine learning approaches to 1 predict the hourly concentration of air pollutants (e.g., Ozone, PM2.5 and Sulfur Dioxide). Machine 2 learning, as one of the most popular techniques, is able to efficiently train a model on big data by using 3 large-scale optimization algorithms. Although there exists some works applying machine learning...

Sea surface temperature (SST) is crucial in ocean research and marine activities. It makes predicting SST of paramount importance. While highly affected by different oceanic, atmospheric, climatic parameters, few papers have investigated time-series prediction based on multiple features. This paper utilized multi features air pressure, water temperature, wind direction, speed for hourly using d...

This article presents a set of empirical models capable of extracting metrics quantifying the short-term variability of the solar resource based upon site/time specific satellite-derived hourly irradiance data. The model is derived from over 92,000 experimental hourly data points at twenty-four sites in the United States. The model returns four metrics characterizing intra-hourly variability, i...

Over subhourly time scales, extreme precipitation events play a critical role for many sectors impacted by climate change; however, it is unclear how these will evolve in warmer climate. Here, we perform simulations using regional model over the greater Alpine region at kilometer-scale resolution. By analyzing intensities with short accumulation times, show that can capture observed percentiles...