Influência das mudanças climáticas na estrutura funcional da comunidade fitoplanctônica em um reservatório da região do semiárido

The Brazilian Northeast is the most vulnerable region to climatic variability risks. For the Brazilian semi-arid is expected a reduction in the overall rates of precipitation and an increase in the number of dry days. These changes predicted by the IPCC (2007) will intensify the rainfall and droughts period that could promote the dominance of cyanobacteria, thus affecting the water quality of reservoirs, that are most used for water supply, in the semi-arid. The aim of this study was to evaluate the effects of increasing temperature combined with nutrient enrichment on the functional structure of the phytoplankton community of a mesotrophic reservoir in the semi-arid, in the worst case scenario of climate change predicted by the IPCC (2007). Two experiments were performed, one in a rainy season and another in the dry season. In the water sampled, nutrients (nitrate and orthophosphate) were added in different concentrations. The microcosms were submitted to two different temperatures, five-year average of air temperature in the reservoir (control) and 4°C above the control temperature (warming). The results of this study showed that warming and nutrient enrichment benefited mainly the functional groups of cyanobacteria. During the rainy season it was verified the increasing biomass of small functional groups of unicellular and opportunists algae such as F (colonial green algae with mucilage) and X1 (nanoplanktonic algae of eutrophic lake systems). It was also observed an increasing in total biomass, in the richness and diversity of the community. In the dry season experiment there was a greater contribution in the relative biomass of filamentous algae, with a replacement of the group S1 (non-filamentous cyanobacteria with heterocytes) for H1 (filamentous cyanobacteria with heterocytes) in nutrient- enriched treatments. Moreover, there was also loss in total biomass, species richness and diversity of the community. The effects of temperature and nutrients manipulation on phytoplankton community of reservoir Ministro João Alves provoked changes in species richness, the diversity of the community and its functional composition, being the dry period which showed the highest susceptibility to the increase in the contribution of potentially toxic cyanobacteria with heterocytes

Controle preditivo robusto baseado em desigualdades matriciais lineares aplicado a um sistema de tanques acoplados

This work deals with an on-line control strategy based on Robust Model Predictive Control (RMPC) technique applied in a real coupled tanks system. This process consists of two coupled tanks and a pump to feed the liquid to the system. The control objective (regulator problem) is to keep the tanks levels in the considered operation point even in the presence of disturbance. The RMPC is a technique that allows explicit incorporation of the plant uncertainty in the problem formulation. The goal is to design, at each time step, a state-feedback control law that minimizes a 'worst-case' infinite horizon objective function, subject to constraint in the control. The existence of a feedback control law satisfying the input constraints is reduced to a convex optimization over linear matrix inequalities (LMIs) problem. It is shown in this work that for the plant uncertainty described by the polytope, the feasible receding horizon state feedback control design is robustly stabilizing. The software implementation of the RMPC is made using Scilab, and its communication with Coupled Tanks Systems is done through the OLE for Process Control (OPC) industrial protocol

Fuzzy angel: uma arquitetura distribuída de telemedicina para monitoramento de pacientes com esclerose lateral amiotrófica

The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.

Fuzzy angel: uma arquitetura distribuída de telemedicina para monitoramento de pacientes com esclerose lateral amiotrófica

The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.