Abstract
One of the principal complications of patients that suffer from Diabetes Mellitus (DM) and that can lead to ulceration is the Diabetic foot. As tissue inflammation causes temperature variation, several studies show that thermography can be used to detect complications in diabetic foot and help predicting the risk of ulceration. It is known that, although healthy individuals present characteristic plantar temperature variation patterns, the same does not happen with diabetic patients, for which a particular pattern can not be found; thus, making the measurement of the temperature variation more difficult. Given that, it is important to research in this field in order to obtain methods that can detect atypical variations of the temperature in the sole of the foot. With this in mind, the objective of this work is to present a methodology to analyze the distribution of temperature in thermograms of the foot's plant and classify it as belonging to a DM individual with risk of ulceration or a healthy individual. After foot partitioning with a clustering algorithm, basic statistical descriptors are computed for each cluster. A binary classifier to predict the risk of ulceration in the diabetic foot was evaluated with the different descriptors; both a quantitative temperature index and a classification threshold are calculated for each descriptor. To evaluate the performance of the classifier, experiments were conducted using a public dataset (containing 45 thermograms of healthy individuals and 122 images of DM ones); the following metrics were obtained: Accuracy = 78%, AUC = 86% and F-measure = 84%, with the best descriptor.

Abstract
Robotics navigation and perception for forest management are challenging due to the existence of many obstacles to detect and avoid and the sharp illumination changes. Advanced perception systems are needed because they can enable the development of robotic and machinery solutions to accomplish a smarter, more precise, and sustainable forestry. This article presents a state-of-the-art review about unimodal and multimodal perception in forests, detailing the current developed work about perception using a single type of sensors (unimodal) and by combining data from different kinds of sensors (multimodal). This work also makes a comparison between existing perception datasets in the literature and presents a new multimodal dataset, composed by images and laser scanning data, as a contribution for this research field. Lastly, a critical analysis of the works collected is conducted by identifying strengths and research trends in this domain.

Abstract

Abstract

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
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.