Reação de Cultivares de Oliveira a Meloidogyne mayaguensis.

bitstream/item/31511/1/comunicado-224.pd

Factored Online Planning in Many-Agent POMDPs

In centralized multi-agent systems, often modeled as multi-agent partially observable Markov decision processes (MPOMDPs), the action and observation spaces grow exponentially with the number of agents, making the value and belief estimation of single-agent online planning ineffective. Prior work partially tackles value estimation by exploiting the inherent structure of multi-agent settings via so-called coordination graphs. Additionally, belief estimation methods have been improved by incorporating the likelihood of observations into the approximation. However, the challenges of value estimation and belief estimation have only been tackled individually, which prevents existing methods from scaling to settings with many agents. Therefore, we address these challenges simultaneously. First, we introduce weighted particle filtering to a sample-based online planner for MPOMDPs. Second, we present a scalable approximation of the belief. Third, we bring an approach that exploits the typical locality of agent interactions to novel online planning algorithms for MPOMDPs operating on a so-called sparse particle filter tree. Our experimental evaluation against several state-of-the-art baselines shows that our methods (1) are competitive in settings with only a few agents and (2) improve over the baselines in the presence of many agents.Comment: Extended version (includes the Appendix) of the paper accepted at AAAI-2

Plaster Layout Process in Civil Works with a Focus on Clean Production

The constant expansion of civil construction and the increasing use of plaster gives rise to a solid waste generation problem causing difficulties for the disposal or reuse of this material. The generation of plaster waste represents an economic problem, with serious consequences and impacts. In order to contribute to sustainability, this study sought to evaluate the reduction of plaster waste in an apartment construction project, employing the layout method. With the adequate arrangement of plates, a reduction of 4.41% in the use of plaster could be obtained, This reduction will consequently result in the minimization of waste from civil works, bringing invaluable economic and environmental benefits

IMPACT OF ROUX-EN-Y GASTRIC BYPASS SURGERY (RYGB) ON METABOLIC SYNDROME COMPONENTS AND ON THE USE OF ASSOCIATED DRUGS IN OBESE PATIENTS

ABSTRACT BACKGROUND The prevalence of obesity and metabolic syndrome is increasing worldwide and both behavior modification and drug therapy have low adherence. Gastric bypass has shown effective results in both reducing weight and improving comorbidities. OBJECTIVE To evaluate the impact of Roux-en-Y Gastric Bypass Surgery (RYGB) on both metabolic syndrome components and the use of associated drugs in obese patients. METHODS Historical cohort of patients subjected to Roux-en-Y Gastric Bypass Surgery (RYGB) between January 2007 and March 2014 in a private clinic. The sample consisted of 273 obese class II and III individuals, 86.4% of whom were female, with age ≥20 years, followed up for 2 months after surgery. Sociodemographic, anthropometric, biochemical, clinical, and drug-use data were collected from patients’ medical records. RESULTS Significant differences were found in weight, body mass index and waist circumference, after 60 postoperative days. Components for metabolic syndrome diagnosis (hypertension P=0.001; hyperglycemia P<0.001; hypertriglyceridemia P=0.006) were reduced after 60 days of postoperative, with the exception HDL-c (P=0.083). There was a significant reduction in the use of antihypertensive (P<0.001), hypoglycemic (P=0.013), lipid lowering (P<0.001), and antiobesity (P=0.010) drugs and increased use of gastroprotective drugs, vitamins, and minerals (P<0.001) after 60 postoperative days. CONCLUSION Patients subjected to Roux-en-Y Gastric Bypass Surgery exhibited both weight loss and significant improvement not only in metabolic syndrome components (except for HDL-c) but in the use of drugs associated with obesity and metabolic syndrome

Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions