Abstract
One of the most intriguing facets of the climate system is that it exhibits variability across all temporal and spatial scales; pronounced examples are temperature and precipitation. The structure of this variability, however, is not arbitrary. Over certain spatial and temporal ranges, it can be described by scaling relationships in the form of power laws in probability density distributions and autocorrelation functions. These scaling relationships can be quantified by scaling exponents which measure how the variability changes across scales and how the intensity changes with frequency of occurrence. Scaling determines the relative magnitudes and persistence of natural climate fluctuations. Here, we review various scaling mechanisms and their relevance for the climate system. We show observational evidence of scaling and discuss the application of scaling properties and methods in trend detection, climate sensitivity analyses, and climate prediction.

Abstract
High-maturity software development processes and development environments with automated data collection can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes, and devise improvement actions. However, conducting the analysis manually is challenging because of the potentially large amount of data to analyze, the effort and expertise required, and the lack of benchmarks for comparison. In this article, we present ProcessPAIR, a novel method with tool support designed to help developers analyze their performance data with higher quality and less effort. Based on performance models structured manually by process experts and calibrated automatically from the performance data of many process users, it automatically identifies and ranks performance problems and potential root causes of individual subjects, so that subsequent manual analysis for the identification of deeper causes and improvement actions can be appropriately focused. We also show how ProcessPAIR was successfully instantiated and used in software engineering education and training, helping students analyze their performance data with higher satisfaction (by 25%), better quality of analysis outcomes (by 7%), and lower effort (by 4%), as compared to a traditional approach (with reduced tool support).

Abstract
Performance and power efficiency in edge and embedded systems can benefit from specialized hardware. To avoid the effort of manual hardware design, we explore the generation of accelerator circuits from binary instruction traces for several Instruction Set Architectures.

Abstract
The adherence to inhaled controller medications is of critical importance to achieve good clinical results in patients with chronic respiratory diseases. To objectively verify the adherence, a detection tool was previously developed and integrated in the mobile application InspirerMundi, based on image processing methods. In this work, a new approach for enhanced adherence verification was developed. In a first phase template matching is employed to confirm the inhaler positioning and to locate the dose counter. In a second phase Google ML Kit framework is used for the detection of each numerical dose in the dose counter. The proposed approach was validated through a new detection tool pilot implementation, using a set of images collected by patients using the application in their daily life. Performance of each of the two phases was evaluated for a set of commonly used inhaler devices. Promising results were achieved showing the potential of mobile embedded sensors without the need for external devices.

Abstract
The Brazilian National Institutes are strategic elements for the growth and development of Brazilian society since they have the purpose of meeting social and economic demands. However, for this purpose to be materialized, it is essential to develop strategies and mechanisms that consider the current educational context, marked in large part by the transformation of education into a product and the increased awareness of students who expect to have their own needs met in terms of achievement and satisfaction. Based on this premise, this research aims to present an indicator for measuring student satisfaction of students from the Federal Institute of Education, Science and Technology of Piaui-Campus Oeiras (FIEPI-Campus Oeiras), in order to provide evidence of how satisfaction has presented itself in relation to the different educational profiles present in the institution. The study was conducted with a sample of 290 students from FIEPI-Campus Oeiras. The instrument used for data collection was a questionnaire structured in two sections, in which the first was intended to obtain information to characterize the sample and the second section, composed of 14 items, aimed at measuring students' satisfaction with the institution. Descriptive, exploratory, and inferential statistical techniques were used for the data treatment and for the validation of the results. The results indicate that the students are slightly satisfied with the institution and that the average satisfaction is different in relation to the courses and technological axes.

Abstract