Abstract
The current technological advances have pushed the quantification of exercise intensity to new era of physical exercise sciences. Monitoring physical exercise is essential in the process of planning, applying, and controlling loads for performance optimization and health. A lot of research studies applied various statistical approaches to estimate various physiological indices, to our knowledge, no studies found to investigate the relationship of facial color changes and increased exercise intensity. The aim of this study was to develop a non-contact method based on computer vision to determine the heart rate and, ultimately, the exercise intensity. The method was based on analyzing facial color changes during exercise by using RGB, HSV, YCbCr, Lab, and YUV color models. Nine university students participated in the study (mean age = 26.88 +/- 6.01 years, mean weight = 72.56 +/- 14.27 kg, mean height = 172.88 +/- 12.04 cm, six males and three females, and all white Caucasian). The data analyses were carried out separately for each participant (personalized model) as well as all the participants at a time (universal model). The multiple auto regressions, and a multiple polynomial regression model were designed to predict maximum heart rate percentage (maxHR%) from each color models. The results were analyzed and evaluated using Root Mean Square Error (RMSE), F-values, and R-square. The multiple polynomial regression using all participants exhibits the best accuracy with RMSE of 6.75 (R-square = 0.78). Exercise prescription and monitoring can benefit from the use of these methods, for example, to optimize the process of online monitoring, without having the need to use any other instrumentation.

Abstract
Simple Summary Radiographic diagnosis is essential for the genetic control of canine hip dysplasia (HD). The Federation Cynologique Internationale (FCI) scoring HD scheme is based on objective and qualitative radiographic criteria. Subjective interpretations can lead to errors in diagnosis and, consequently, to incorrect selective breeding, which in turn impacts the gene pool of dog breeds. The aim of this study was to use a computer method to calculate the Hip Congruency Index (HCI) to objectively estimate radiographic hip congruency for future application in the development of computer vision models capable of classifying canine HD. The HCI measures the percentage of acetabular coverage that is occupied by the femoral head. Normal hips are associated with an even, parallel joint surface that translates into reduced acetabular free space, which increases with hip subluxation and becomes maximal in hip dislocation. We found statistically significant differences in mean HCI values among all five FCI categories. These results demonstrate that the HCI reliably reflects the different degrees of congruency associated with HD. Therefore, it is expected that when used in conjunction with other HD evaluation parameters, such as Norberg angle and assessment of osteoarthritic signs, it can improve the diagnosis by making it more accurate and unequivocal. Accurate radiographic screening evaluation is essential in the genetic control of canine HD, however, the qualitative assessment of hip congruency introduces some subjectivity, leading to excessive variability in scoring. The main objective of this work was to validate a method-Hip Congruency Index (HCI)-capable of objectively measuring the relationship between the acetabulum and the femoral head and associating it with the level of congruency proposed by the Federation Cynologique Internationale (FCI), with the aim of incorporating it into a computer vision model that classifies HD autonomously. A total of 200 dogs (400 hips) were randomly selected for the study. All radiographs were scored in five categories by an experienced examiner according to FCI criteria. Two examiners performed HCI measurements on 25 hip radiographs to study intra- and inter-examiner reliability and agreement. Additionally, each examiner measured HCI on their half of the study sample (100 dogs), and the results were compared between FCI categories. The paired t-test and the intraclass correlation coefficient (ICC) showed no evidence of a systematic bias, and there was excellent reliability between the measurements of the two examiners and examiners' sessions. Hips that were assigned an FCI grade of A (n = 120), B (n = 157), C (n = 68), D (n = 38) and E (n = 17) had a mean HCI of 0.739 +/- 0.044, 0.666 +/- 0.052, 0.605 +/- 0.055, 0.494 +/- 0.070 and 0.374 +/- 0.122, respectively (ANOVA, p < 0.01). Therefore, these results show that HCI is a parameter capable of estimating hip congruency and has the potential to enrich conventional HD scoring criteria if incorporated into an artificial intelligence algorithm competent in diagnosing HD.

Abstract
Compared to rodents, sheep offer several attractive features as an experimental model for testing different medical and surgical interventions related to pathological gait caused by neurological diseases and injuries. To use sheep for development of novel treatment strategies in the field of neuroscience, it is key to establish the relevant kinematic features of locomotion in this species. To use sheep for development of novel treatment strategies in the field of neuroscience, it is crucial to understand fundamental baseline characteristics of locomotion in this species. Despite their relevance for medical research, little is known about the locomotion in the ovine model, and next to nothing about the three-dimensional (3D) kinematics of the hindlimb. This study is the first to perform and compare two-dimensional (2D) and 3D hindlimb kinematics of the sagittal motion during treadmill walking in the ovine model. Our results show that the most significant differences took place throughout the swing phase of the gait cycle were for the distal joints, ankle and metatarsophalangeal joint, whereas the hip and knee joints were much less affected. The results provide evidence of the inadequacy of a 2D approach to the computation of joint kinematics in clinically normal sheep during treadmill walking when the interest is centered on the hoof's joints. The findings from the present investigation are likely to be useful for an accurate, quantitative and objective assessment of functionally altered gait and its underlying neuronal mechanisms and biomechanical consequences.

Abstract
Nowadays, social networks are one of the biggest ways of sharing real time information. These networks, have several groups focused on sharing information about road incidents and other traffic events. The work here presented aims the creation of an AI model capable of identifying publications related to traffic events in a specific road, based on publications shared on social networks. A predictive model was obtained by training a deep learning model for the detection of publications related with road incidents with an average accuracy of 95%. The model deployed as a service is already fully functional and is operating in 24/7 while awaits a final integration with the road management system of a company where it will be used to support the Control Center team in the decision making. © 2022 Elsevier B.V.. All rights reserved.

Abstract
Virtual reality (VR) potential to isolate users from the real world while producing a rich virtual environment where users act similarly to how they would, in reality, is still being investigated in several fields. In this work, we investigated the effects of product contextualisation and gender under an immersive VR application where users can explore in-depth a commercial product with a hands-on experience. An experimental between-subjects study was performed with 38 participants between 18 and 28 years. The product tested consisted of a double-door refrigerator equipped with a touchscreen. Two independent variables were studied: Context (the refrigerator was filled with food products and placed in a kitchen), Neutral Context (empty refrigerator displayed in an empty white room), and Gender (Female and Male). As for the dependent variables, we considered how clarified users felt about the product functionalities, its size, the extent users remember details and characteristics of the refrigerator, and the user's subjective workload. The evidence shows that contextualisation and gender have no impact on any dependent variables. Therefore, we concluded that presenting a product in its context does not benefit significantly benefit it. Thus, opting for a neutral context would be preferable to save computational costs and human resources necessary to build and run the higher complexity environments required to contextualise the product.

Abstract
Customer relationship management is critical for organizations. Public institutions, in particular municipalities, are no exception to this. Since the process of implementing a CRM system is not risk-free, it is important to know the factors that influence its success. From studies conducted, it was possible to verify that there is a gap in the literature regarding the influential factors of the successful adoption of CRM systems in public institutions (CzRM). Also, through interviews conducted in some municipalities and CRM suppliers, it was possible to identify the relevant factors for the adoption of CRM systems. The purpose of this article is to present the influence factors of the success of the implementation of CzRM systems. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.