Abstract

Abstract
Centro de Reabilitacao Profissional de Gaia (CRPG) is a Portuguese public association that promotes the social, educational, occupational, and rehabilitation aspects of people with disabilities. The CRPG project presented in this paper is directly related to the Rehabilitation and reintegration of labor accidents and occupational diseases (RAC/LCA) victims. The solution presented in this paper heavily relied on user research of the institution's procedures and the patients themselves. Their habits, goals, needs, and mental models allowed a better understanding of the heterogeneity of patient's injuries, which usually affected memory, motion, and cognition in different ways and severity. The User Centered-Design methodological approach also encompasses a comparative analysis of mobile applications in Google Play and Apple App Store related to disabilities and features some main features identified by CRPG; interviews with patients and with CRPG stakeholders; personas and contextual scenarios development, requirements, and features definition, workflow, wireflows, lo and high fidelity prototype, formative evaluation with end users. The usability and accessibility test results suggest the viability and efficacy of multiple interaction modalities in improving the accessibility of the application, ensuring a broader reach to the CRPG community with future implications as good practices in the inclusiveness of applications in other contexts.

Abstract
Tension remains between the observed and modeled properties of substellar objects, but objects in binary orbits, with known dynamical masses, can provide a way forward. HD 72946 B is a recently imaged brown dwarf companion to a nearby, solar-type star. We achieve similar to 100 mu as relative astrometry of HD 72946 B in the K band using VLTI/GRAVITY, unprecedented for a benchmark brown dwarf. We fit an ensemble of measurements of the orbit using orbitize! and derive a strong dynamical mass constraint M B = 69.5 +/- 0.5 M Jup assuming a strong prior on the host star mass M A = 0.97 +/- 0.01 M circle dot from an updated stellar analysis. We fit the spectrum of the companion to a grid of self-consistent BT-Settl-CIFIST model atmospheres, and perform atmospheric retrievals using petitRADTRANS. A dynamical mass prior only marginally influences the sampled distribution of effective temperature, but has a large influence on the surface gravity and radius, as expected. The dynamical mass alone does not strongly influence retrieved pressure-temperature or cloud parameters within our current retrieval setup. Independently of the cloud prescription and prior assumptions, we find agreement within +/- 2 sigma between the C/O of the host (0.52 +/- 0.05) and brown dwarf (0.43-0.63), as expected from a molecular cloud collapse formation scenario, but our retrieved metallicities are implausibly high (0.6-0.8) in light of the excellent agreement of the data with the solar-abundance model grid. Future work on our retrieval framework will seek to resolve this tension. Additional study of low surface gravity objects is necessary to assess the influence of a dynamical mass prior on atmospheric analysis.

Abstract
We consider a continuous lifetime model for investor whose lifetime is a random variable. We assume the investor has an access to the social welfare system, the financial market and the life insurance market. The investor aims to find the optimal strategies that maximize the expected utility obtained from consumption, investing in the financial market, buying life insurance, registering in the social welfare system, the size of his estate in the event of premature death and the size of his fortune at time of retirement if he lives that long. We use dynamic programming techniques to derive a second-order nonlinear partial differential equation whose solution is the maximum objective function. We use special case of discounted constant relative risk aversion utilities to find an explicit solutions for the optimal strategies. Finally, we have shown a numerical solution for the problem under consideration and study some properties for the optimal strategies.

Abstract
Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.

Abstract