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
Ca 2+ imaging is a widely used microscopy technique to simultaneously study cellular activity in multiple cells. The desired information consists of cell-specific time series of pixel intensity values, in which the fluorescence intensity represents cellular activity. For static scenes, cellular signal extraction is straightforward, however multiple analysis challenges are present in recordings of contractile tissues, like those of the enteric nervous system (ENS). This layer of critical neurons, embedded within the muscle layers of the gut wall, shows optical overlap between neighboring neurons, intensity changes due to cell activity, and constant movement. These challenges reduce the applicability of classical segmentation techniques and traditional stack alignment and regions-of-interest (ROIs) selection workflows. Therefore, a signal extraction method capable of dealing with moving cells and is insensitive to large intensity changes in consecutive frames is needed. Here we propose a b-spline active contour method to delineate and track neuronal cell bodies based on local and global energy terms. We develop both a single as well as a double-contour approach. The latter takes advantage of the appearance of GCaMP expressing cells, and tracks the nucleus’ boundaries together with the cytoplasmic contour, providing a stable delineation of neighboring, overlapping cells despite movement and intensity changes. The tracked contours can also serve as landmarks to relocate additional and manually-selected ROIs. This improves the total yield of efficacious cell tracking and allows signal extraction from other cell compartments like neuronal processes. Compared to manual delineation and other segmentation methods, the proposed method can track cells during large tissue deformations and high-intensity changes such as during neuronal firing events, while preserving the shape of the extracted Ca 2+ signal. The analysis package represents a significant improvement to available Ca 2+ imaging analysis workflows for ENS recordings and other systems where movement challenges traditional Ca 2+ signal extraction workflows.

Abstract

Abstract
Tactical Sales and Operations Planning (S&OP) has emerged as an extension of the aggregate production planning, integrating mid-term decisions from procurement, production, distribution, and sales in a single plan. Despite the growing interest in the subject, past synthesizing research has focused more on the qualitative and procedural aspects of the topic rather than on modeling approaches to the problem. This paper conducts a review of the existing decision-making, i.e., optimization, models supporting S&OP. A holistic framework comprising the decisions involved in this planning activity is presented. The reviewed literature is arranged within the framework and grouped around different streams of literature which have been extending the aggregate production planning. Afterwards, the papers are classified according to the modeling approaches employed by past researchers. Finally, based on the characterization of the level of integration of different business functions provided by existing models, the review demonstrates that there are no synthesizing models characterizing the overall S&OP problem and that, even in the more comprehensive approaches, there is potential to include additional decisions that would be the basis for more sophisticated and proactive S&OP programs. We do expect this paper contributes to set the ground for more oriented and structured research in the field.

Abstract
In recent decades, the concept of urban metabolism has been widely applied at different scales. This paper proposes an optimization model, based on Data Envelopment Analysis, for the evaluation and benchmarking of countries' metabolism. The EU-28 countries are analyzed based on economic and environmental indicators, including the resources consumed (energy and materials) and environmental pressures (GHG emissions and waste) associated with the value-added from the economic activities. The empirical results produced a ranking of countries' based on their metabolic performance underlying the creation of wealth, along with the targets for the countries with lower metabolic performance. This new metabolic approach is a contribution to the design of policies for the promotion of sustainable and resilient services.