Universidade de Pernambuco

About: Universidade de Pernambuco is a education organization based out in Recife, Brazil. It is known for research contribution in the topics: Population & Artificial neural network. The organization has 6147 authors who have published 6948 publications receiving 73648 citations.

Prevalence and associations of symptoms of upper extremities, repetitive strain injuries (RSI) and 'RSI-like condition'. A cross sectional study of bank workers in Northeast Brazil

Abstract: The repetitive strain injury syndrome (RSI) is a worldwide occupational health problem affecting all types of economic activities. We investigated the prevalence and some risk factors for RSI and related conditions, namely 'symptoms of upper limbs' and 'RSI-like condition'. We conducted a cross-sectional study with 395 bank workers in Recife, Northeast Brazil. Symptoms of upper limbs and 'RSI-like condition' were assessed by a simple questionnaire, which was used to screen probable cases of RSI. The diagnosis of RSI was confirmed by clinical examination. The associations of potential risk factors and the outcomes were assessed by multiple logistic regression analysis. We found prevalence rates of 56% for symptoms of the upper limbs and 30% for 'RSI-like condition'. The estimated prevalence of clinically confirmed cases of RSI was 22%. Female sex and occupation (as cashier or clerk) increased the risk of all conditions, but the associations were stronger for cases of RSI than for less specific diagnoses of 'RSI-like condition' and symptoms of upper limbs. Age was inversely related to the risk of symptoms of upper limbs but not to 'RSI-like' or RSI. The variation in the magnitude of risk according to the outcome assessed suggests that previous studies using different definitions may not be immediately comparable. We propose the use of a simple instrument to screen cases of RSI in population based studies, which still needs to be validated in other populations. The high prevalence of RSI and related conditions in this population suggests the need for urgent interventions to tackle the problem, which could be directed to individuals at higher risk and to changes in the work organization and environment of the general population.

Mapping SysML State Machine Diagram to Time Petri Net for Analysis and Verification of Embedded Real-Time Systems with Energy Constraints

Abstract: The main objective of this paper is to propose a solution for modeling, analysis and verification of embedded real-time systems with energy constraints. For that, we combine functionalities of the SysML models and annotation from MARTE with the advantages of using time Petri net. This formalism allows analysis and verification of functional, timing and energy requirements in early phases of the development lifecycle. In order to depict the practically usability of the proposed method, a real-world case study is presented, namely, pulse-oximeter. Experimental results have demonstrated an accuracy of 96% using the proposed formal method in comparison with the values obtained with the hardware platform.

Anxiety in the preoperative period of heart surgery.

Abstract: Objective: to characterize the patients’ anxiety in the preoperative period of heart surgery. Method: We conducted a cross-sectional study in which 106 patients, between one and fi ve days from the date of surgery, were interviewed using a socio-demographic questionnaire and the Beck Anxiety Inventory. Results: The evaluated patients accounted for 59.4% (63) in minimal anxiety and 19.8% (21) in the range considered severe, and the sample had a mean in the mild anxiety level (15.8±19.79). The women had scores (22.13±23.41) signifi cantly (p=0.003) higher than men (10.76±14.71); as well as patients who had undergone previous heart surgery (24.4±28.05 X 13.14±15.74). There was no signifi cant difference between older adults and younger patients, nor in terms of weight variations, presence of diabetes, or alcoholism. Conclusion: We reinforces the importance of nurses in recognizing the preoperative anxiety and intervene through strategies of health education and nursing visits.

Confusion Detection in Code Reviews

Abstract: Code reviews are an important mechanism for assuring quality of source code changes. Reviewers can either add general comments pertaining to the entire change or pinpoint concerns or shortcomings about a specific part of the change using inline comments. Recent studies show that reviewers often do not understand the change being reviewed and its context.Our ultimate goal is to identify the factors that confuse code reviewers and understand how confusion impacts the efficiency and effectiveness of code review(er)s. As the first step towards this goal we focus on the identification of confusion in developers' comments. Based on an existing theoretical framework categorizing expressions of confusion, we manually classify 800 comments from code reviews of the Android project. We observe that confusion can be reasonably well-identified by humans: raters achieve moderate agreement (Fleiss' kappa 0.59 for the general comments and 0.49 for the inline ones). Then, for each kind of comment we build a series of automatic classifiers that, depending on the goals of the further analysis, can be trained to achieve high precision (0.875 for the general comments and 0.615 for the inline ones), high recall (0.944 for the general comments and 0.988 for the inline ones), or substantial precision and recall (0.696 and 0.542 for the general comments and 0.434 and 0.583 for the inline ones, respectively). These results motivate further research on the impact of confusion on the code review process. Moreover, other researchers can employ the proposed classifiers to analyze confusion in other contexts where software development-related discussions occur, such as mailing lists.