This study presents a novel method based in ray tracing for analyzing wave propagation in composites, specifically tailored for Structural Health Monitoring applications. The method offers distinct advantages over the commonly used Finite Element Method mainly in computational resource utilization, that has become a limiting factor for this kind of analyses. The ray tracing method is evaluated against a number of example cases representing structural details such as thickness changes, stringers or simulated damages and it highlights the significance of ray tracing to study wave propagation under these conditions and how it can serve as a valuable tool for structural health monitoring.

Early detection of stress can prevent us from suffering from a long-term illness such as depression and anxiety. This article presents a scoping review of stress detection based on electrodermal activity (EDA) and machine learning (ML). From an initial set of 395 articles searched in six scientific databases, 58 were finally selected according to various criteria established. The scoping review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, preprocessing, processing and feature extraction. Finally, all the ML techniques applied to the features of this signal have been studied for stress detection. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high performance values. On the contrary, it has been evidenced that unsupervised learning is not very common in the detection of stress through EDA. This scoping review concludes that the use of EDA for the detection of arousal variation (and stress detection) is widely spread, with very good results in its prediction with the ML methods found during this review.

The relative proportions between nutrients affects the fate of them for metabolic interconversion, storage and turnover or, essentially for energy, in a process of partition modulated by hormonal and metabolic factors. A basic common chow model was used: ST or standard diet, HF high-fat and HP or high-protein had a common substrate. A CF cafeteria-type diet was added for comparison. Rats (female and male) received the diet for 30d. Nutrient intake and body composition were measured. Plasma glucose, lactate, testosterone (T) and estradiol (E2) were measured. Common groups energy intake, was largely based in carbohydrates CH, yielding 6C units, which were fully oxidized or stored as 2C fragments (fatty acids). Lactate (3C) was partially stored as glycerol of TAG. Accrual of 2C far exceeded about ten-fold that of 3C. Amino acid (AA) catabolism yielded 2C, 3C and 4C-5C fragments, the latter acting as Krebs cycle anaplerotic intermediates (KCAI), that facilitated the oxidation of 2C in detriment of TAG accumulation Lactate levels, were related to lactic dehydrogenase activities in muscle and liver. TAG accrual was maximal in CF, quite differently from HF with similar diet lipid proportion. Maximal rates of lipid storage were found in ST (and CF) groups. Females showed a higher ability to oxidize CH and to store less TAG. T increased N and protein deposition, whilst E2 decreased them favoring AA oxidation and the availability of KCAI. In the rat, an effective energy partition scheme requires that about half of the energy is provided by CH (polysaccharide), and needs enough protein to provide KCAI for a smooth 2C oxidation.

This work analyzes the visibility and scientific impact of publications related to agricultural value chains. The incidence of bibliometric indicators allows for the interpretation of bibliographic information generated worldwide. Objective: The objective of this research is to analyze the published literature and bibliometric indicators on agricultural value chains. The Web of Science database was used to extract value chains data. The study analyzed articles published between 2010 and 2020. The keywords used are "agricultural value chains'' and articles from journals or studies related to the subject were selected for bibliometric analysis and methodological review. In the search for the keyword, a total of 4208 results were extracted, of which 1,669 records were considered for analysis. The bibliometric analysis of the data reveals that Wageningen University (55) has the highest number of publications, followed by Chinese Acad Sci (26). The author Klerkx L (9) has the highest number of records, followed by Hellin J (7). With respect to the countries with the greatest contributions on the subject are: the People's Republic of China, Germany, Italy, France and the United States. The study contributes to the analysis of bibliometrics and provides a methodological review of published journal articles on agricultural value chains. This bibliographic study presents the history of research development in agricultural value chains.

This work is part of an ongoing research project to develop an unmanned flying social robot to monitor dependants at home in order to detect the person’s state and bring the necessary assistance. In this sense, this paper focuses on the description of a virtual reality (VR) simulation platform for the monitoring process of an avatar in a virtual home by a rotatory-wing autonomous unmanned aerial vehicle (UAV). This platform is based on a distributed architecture composed of three modules communicated through the Message Queue Telemetry Transport (MQTT) protocol: the UAV Simulator implemented in MATLAB/Simulink, the VR Visualiser developed in Unity, and the new emotion recognition (ER) System developed in Python. Using a face detection algorithm and a convolutional neural network (CNN), the ER System is able to detect the person’s face in the image captured by the UAV’s on-board camera and classify the emotion among seven possible ones (surprise, fear, happiness, sadness, disgust, anger or neutral expression). The experimental results demonstrate the correct integration of this new computer vision module within the VR platform, as well as the good performance of the designed CNN, with around 85% in the F1-score, a mean of the precision and recall of the model. The developed emotion detection system can be used in the future implementation of the assistance UAV that monitors dependent people in a real environment, since the methodology used is valid for images of real people.

Abstract: Despite existing several techniques for distributed sensing (temperature and strain) using standard Single Mode optical Fiber (SMF), compensating or decoupling both effects is mandatory for many applications. Currently, most of the decoupling techniques require special optical fibers and are difficult to implement with high spatial resolution distributed techniques, such as ϕ-PA-OFDR. So, this work’s objective is to study the feasibility of decoupling temperature and strain out of a ϕ-PA-OFDR readouts taken over an SMF. For this purpose, the readouts will be subjected to a study using several Machine Learning algorithms, among them, Deep Neural Networks. The motivation which underlies this target is the current blockage in the widespread use of Fiber Optic Sensors in situations where both strain and temperature change, due to the coupled dependence of currently developed sensing methods. Instead of using other types of sensors or even other interrogation methods, the objective of this work is to analyze the available information in order to develop a sensing method capable of providing information about strain and temperature simultaneously.

Unmanned aerial vehicles (UAVs) represent a new model of social robots for home care of dependent persons. In this regard, this article introduces a study on people’s feeling of safety and comfort while watching the monitoring trajectory of a quadrotor dedicated to determining their condition. Three main parameters are evaluated: the relative monitoring altitude, the monitoring velocity and the shape of the monitoring path around the person (ellipsoidal or circular). For this purpose, a new trajectory generator based on a state machine, which is successfully implemented and simulated in MATLAB/Simulink®, is described. The study is carried out with 37 participants using a virtual reality (VR) platform based on two modules, UAV Simulator and VR Visualiser, both communicating through the MQTT protocol. The participants’ preferences have been a high relative monitoring altitude, a high monitoring velocity and a circular path. These choices are a starting point for the design of trustworthy socially assistive UAVs flying in real homes.

The mortality of birds resulting from collisions and electrocutions with overhead lines, such as power lines, and phone line among others, has been implicated in the decline of various avian species globally. Specifically, overhead line collisions pose a significant threat to the conserva-tion of the Canarian houbara bustard (Chlamydotis undulata fuertaventurae), an endangered sub-species endemic to the Canary Islands. This study centres on the postmortem findings of Canarian houbara bustards that have collided with overhead lines, providing insights into the post-collision outcomes for these birds. A com-plete standardized necropsy of nine Canarian houbara bustards revealed that polytrauma was the cause of death in all cases. The most notable gross lesions associated with trauma included bone fractures, soft tissue lacerations, haemorrhages, luxations, and hemocoelom, with certain body regions being more frequently affected. Histopathology, immunohistochemistry, and en-tomology analysis confirmed that numerous birds survived the initial trauma, exhibiting varia-ble survival intervals before succumbing to their injuries. We concluded that when a houbara bustard collide with an overhead line it frequently survives the initial trauma. The histopathology, immunohistochemistry, or entomologic analysis may be helpful to approximate the timing interval between trauma and death.