Distinctive Electric Properties of Group 14 Oxides: SiO2, SiO, and SnO2.

The oxides of group 14 have been widely used in numerous applications in glass, ceramics, optics, pharmaceuticals, and food industries and semiconductors, photovoltaics, thermoelectrics, sensors, and energy storage, namely, batteries. Herein, we simulate and experimentally determine by scanning kelvin probe (SKP) the work functions of three oxides, SiO2, SiO, and SnO2, which were found to be very similar. Electrical properties such as electronic band structure, electron localization function, and carrier mobility were also simulated for the three crystalline oxides, amorphous SiO, and surfaces. The most exciting results were obtained for SiO and seem to show Poole-Frankel emissions or trap-assisted tunneling and propagation of surface plasmon polariton (SPP) with nucleation of solitons on the surface of the Aluminum. These phenomena and proposed models may also describe other oxide-metal heterojunctions and plasmonic and metamaterials devices. The SiO2 was demonstrated to be a stable insulator interacting less with the metals composing the cell than SnO2 and much less than SiO, configuring a typical Cu/SiO2/Al cell potential well. Its surface charge carrier mobility is small, as expected for an insulator. The highest charge carrier mobility at the lowest conduction band energy is the SnO2's and the most symmetrical the SiO's with a similar number of electron holes at the conduction and valence bands, respectively. The SnO2 shows it may perform as an n-type semiconductor.

Diachronic profile of startup companies through social media.

Social media platforms have become powerful tools for startups, helping them find customers and raise funding. In this study, we applied a social media intelligence-based methodology to analyze startups' content and to understand how their communication strategies may differ during their scaling process. To understand if a startup's social media content reflects its current business maturation position, we first defined an adequate life cycle model for startups based on funding rounds and product maturity. Using Twitter as the source of information and selecting a sample of known Portuguese IT startups at different phases of their life cycle, we analyzed their Twitter data. After preprocessing the data, using latent Dirichlet allocation, topic modeling techniques enabled the categorization of the data according to the topics arising in the published contents of the startups, making it possible to discover that contents can be grouped into five specific topics: "Fintech and ML," "IT," "Business Operations," "Product/Service R&D," and "Bank and Funding." By comparing those profiles against the startup's life cycle, we were able to understand how contents change over time. This provided a diachronic profile for each company, showing that while certain topics remain prevalent in the startup's scaling, others depend on a particular phase of the startup's cycle. Our analysis revealed that startups' social media content differs along their life cycle, highlighting the importance of understanding how startups use social media at different stages of their development.

Effectiveness of COVID-19 Vaccines: Evidence from the First-Year Rollout of Vaccination Programs.

The COVID-19 pandemic has raised a number of new realities, sets of data, and opportunities for data-driven approaches, decisions, and conclusions. One particular area for which developments and data have been made available in record time is related to vaccines and their impacts on health conditions and saving lives. In this article, we use public domain information to study the prevalence of vaccines in different countries and how they can save lives. We conclude that there are different clusters of countries, for some of which solid statistical models were built, and show that vaccination rates provide significant contributions to saving lives in such countries, with impacts that can be computed by simulations based upon these models.

A hotel's customers personal, behavioral, demographic, and geographic dataset from Lisbon, Portugal (2015-2018).

This data article describes a hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

Hotel booking demand datasets.

This data article describes two datasets with hotel demand data. One of the hotels (H1) is a resort hotel and the other is a city hotel (H2). Both datasets share the same structure, with 31 variables describing the 40,060 observations of H1 and 79,330 observations of H2. Each observation represents a hotel booking. Both datasets comprehend bookings due to arrive between the 1st of July of 2015 and the 31st of August 2017, including bookings that effectively arrived and bookings that were canceled. Since this is hotel real data, all data elements pertaining hotel or costumer identification were deleted. Due to the scarcity of real business data for scientific and educational purposes, these datasets can have an important role for research and education in revenue management, machine learning, or data mining, as well as in other fields.