Abstract
Hyperspectral aerial imagery is becoming increasingly available due to both technology evolution and a somewhat affordable price tag. However, selecting a proper UAV + hyperspectral sensor combo to use in specific contexts is still challenging and lacks proper documental support. While selecting an UAV is more straightforward as it mostly relates with sensor compatibility, autonomy, reliability and cost, a hyperspectral sensor has much more to be considered. This note provides an assessment of two hyperspectral sensors (push-broom and snapshot) regarding practicality and suitability, within a precision viticulture context. The aim is to provide researchers, agronomists, winegrowers and UAV pilots with dependable data collection protocols and methods, enabling them to achieve faster processing techniques and helping to integrate multiple data sources. Furthermore, both the benefits and drawbacks of using each technology within a precision viticulture context are also highlighted. Hyperspectral sensors, UAVs, flight operations, and the processing methodology for each imaging type' datasets are presented through a qualitative and quantitative analysis. For this purpose, four vineyards in two countries were selected as case studies. This supports the extrapolation of both advantages and issues related with the two types of hyperspectral sensors used, in different contexts. Sensors' performance was compared through the evaluation of field operations complexity, processing time and qualitative accuracy of the results, namely the quality of the generated hyperspectral mosaics. The results shown an overall excellent geometrical quality, with no distortions or overlapping faults for both technologies, using the proposed mosaicking process and reconstruction. By resorting to the multi-site assessment, the qualitative and quantitative exchange of information throughout the UAV hyperspectral community is facilitated. In addition, all the major benefits and drawbacks of each hyperspectral sensor regarding its operation and data features are identified. Lastly, the operational complexity in the context of precision agriculture is also presented.

Abstract
The sense of presence is an important aspect of experiences in Virtual Reality (VR), an emerging technology in education, leading this construct to be increasingly researched in parallel to learning purposes. However, there is not a consensus in the literature on the outcomes of this association. Aiming to outline a panorama in this regard, a systematic literature review was conducted, with a comprehensive analysis of 140 primary studies recovered from five worldwide databases. The analysis shows an overview of 24 years of areas, factors, and methodological approaches that seem to be more inclined to benefit from the sense of presence toward learning purposes. We contribute to the advancement of state of the art by providing an understanding of the relationship among these variables, identifying potential ways to benefit from the sense of presence to further leverage the use of VR for learning purposes.

Abstract
Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients' health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values.

Abstract
The main goal of this paper is to investigate the effect of different types of self-representations through floating members (hands vs. hands + feet), virtual full body (hands + feet vs. full-body avatar), walking fidelity (static feet, simulated walking, real walking), and number of tracking points used (head + hands, head + hands + feet, head + hands + feet + hip) on the sense of presence and embodiment through questionnaires. The sample consisted of 98 participants divided into a total of six conditions in a between-subjects design. The HTC Vive headset, controllers, and trackers were used to perform the experiment. Users were tasked to find a series of hidden objects in a virtual environment and place them in a travel bag. We concluded that (1) the addition of feet to floating hands can impair the experienced realism (p = 0.039), (2) both floating members and full-body avatars can be used without affecting presence and embodiment (p > 0.05) as long as there is the same level of control over the self-representation, (3) simulated walking scores of presence and embodiment were similar when compared to static feet and real walking tracking data (p > 0.05), and (4) adding hip tracking overhead, hand and feet tracking (when using a full-body avatar) allows for a more realistic response to stimuli (p = 0.002) and a higher overall feeling of embodiment (p = 0.023).

Abstract
Antibiotic resistance is a major health concern of the 21st century. The misuse of antibiotics over the years has led to their increasing presence in the environment, particularly in water resources, which can exacerbate the transmission of resistance genes and facilitate the emergence of resistant microorganisms. The objective of the present work is to develop a chemosensor for screening of sulfonamides in environmental waters, targeting sulfamethoxazole as the model analyte. The methodology was based on the retention of sulfamethoxazole in disks containing polystyrene divinylbenzene sulfonated sorbent particles and reaction with p-dimethylaminocinnamaldehyde, followed by colorimetric detection using a computer-vision algorithm. Several color spaces (RGB, HSV and CIELAB) were evaluated, with the coordinate a_star, from the CIELAB color space, providing the highest sensitivity. Moreover, in order to avoid possible errors due to variations in illumination, a color palette is included in the picture of the analytical disk, and a correction using the a_star value from one of the color patches is proposed. The methodology presented recoveries of 82-101% at 0.1 mu g and 0.5 mu g of sulfamethoxazole (25 mL), providing a detection limit of 0.08 mu g and a quantification limit of 0.26 mu g. As a proof of concept, application to in-field analysis was successfully implemented.

Abstract
Video games have become one of the predominant forms of entertainment in our society, but they have also impacted many other of its social and cultural aspects [...]