The aim of this study was to analyze the psychological impact of COVID-19 in the university community during the first weeks of confinement. A cross-sectional study was conducted. The Depression ...Anxiety Stress Scale (DASS-21) was employed to assess symptoms of depression, anxiety and stress. The emotional impact of the situation was analyzed using the Impact of Event Scale. An online survey was fulfilled by 2530 members of the University of Valladolid, in Spain. Moderate to extremely severe scores of anxiety, depression, and stress were reported by 21.34%, 34.19% and 28.14% of the respondents, respectively. A total of 50.43% of respondents presented moderate to severe impact of the outbreak. Students from Arts & Humanities and Social Sciences & Law showed higher scores related to anxiety, depression, stress and impact of event with respect to students from Engineering & Architecture. University staff presented lower scores in all measures compared to students, who seem to have suffered an important psychological impact during the first weeks of the COVID-19 lockdown. In order to provide timely crisis-oriented psychological services and to take preventive measures in future pandemic situations, mental health in university students should be carefully monitored.
•College students reflected a possible psychological impact of the COVID-19 lockdown.•Symptoms of common mental health disorders were reported by 20-35% of respondents.•Around the half of respondents presented moderate to severe impact of the outbreak.•Students from the Engineering and Architecture area showed lower symptomatic scores.•Mental health from students should be monitored to mitigate the impact of the crisis.
The rational design of heterogeneous catalysts relies on the efficient survey of mechanisms by density functional theory (DFT). However, massive reaction networks cannot be sampled effectively as ...they grow exponentially with the size of reactants. Here we present a statistical principal component analysis and regression applied to the DFT thermochemical data of 71 CFormula: see text-CFormula: see text species on 12 close-packed metal surfaces. Adsorption is controlled by covalent (Formula: see text-band center) and ionic terms (reduction potential), modulated by conjugation and conformational contributions. All formation energies can be reproduced from only three key intermediates (predictors) calculated with DFT. The results agree with accurate experimental measurements having error bars comparable to those of DFT. The procedure can be extended to single-atom and near-surface alloys reducing the number of explicit DFT calculation needed by a factor of 20, thus paving the way for a rapid and accurate survey of whole reaction networks on multimetallic surfaces.
•A systematic literature review of recent works on the use of machine learning in precision livestock farming.•Opportunities for machine learning in the livestock sector.•The most used ML techniques ...for the analysis of grazing and animal health.•The most used forms of data acquisition in PLF.
This article presents a systematic literature review of recent works on the use of machine learning (ML) in precision livestock farming (PLF), focusing on two areas of interest: grazing and animal health. This review: (i) highlights opportunities for ML in the livestock sector; (ii) shows the current sensors, software and techniques for data analysis; (iii) details the increasing openness of data sources. It was found that the use of ML in PLF is in a stage of development and has several research challenges. Examples of such challenges are: (i) to develop hybrid models for diagnosis and prescription as a tool for the prevention and control of animal diseases; (ii) to bring together the grazing and animal health issues; (iii) to give autonomy to PLF using autonomous cycles of data analysis tasks and meta-learning; and (iv) to bring together soil and pasture variables because, for both, animal health and animal grazing, the variables used are only behavioral and environmental.
Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in ...the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of “Autonomous Cycles of Data Analysis Tasks”, which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.
A digital twin is a virtual representation of a physical object or process capable of collecting information from the real environment to represent, validate and simulate the physical twin’s present ...and future behavior. It is a key enabler of data-driven decision making, complex systems monitoring, product validation and simulation and object lifecycle management. As an emergent technology, its widespread implementation is increasing in several domains such as industrial, automotive, medicine, smart cities, etc. The objective of this systematic literature review is to present a comprehensive view on the DT technology and its implementation challenges and limits in the most relevant domains and applications in engineering and beyond.
Weighing management in cattle farming is important for farmers, as it allows them to accurately monitor the growth and development of their animals. It is also a valuable tool that allows farmers to ...maximize the production and welfare of their animals. However, it is difficult for the farmer to detect if the herd of animals being weighed is gaining the ideal weight for a given breed and age. In addition, normally, when a new breed of cattle is introduced to a farm, there is very little data. This article proposes a meta-learning framework (MTL) for identification models used in the fattening process of animals to detect anomalies in cattle weight. The proposed MTL framework has a knowledge base of Meta-Models on Identification models based on machine learning techniques, which is used to select the identification model to use when a new breed of cattle arrives on the farm. This knowledge base is updated, either because a previous identification model has been successfully adapted to the new breed, or a new identification model has had to be generated, allowing the framework to continuously improve its performance over time. Particularly, this article presents in detail the process of adaptation of the previous identification models to new breeds carried out by our MTL framework. Besides, to test our approach, a case study is presented, using records of animals raised and fattened at the ”El Rosario” farm, located in the municipality of Monteria (Córdoba-Colombia). The results are very encouraging in terms of the ability of our framework to adapt the identification models to different possible scenarios in the process of detecting anomalous weights. In general, the identification models generated with our proposal had an R2 of 90.8%, which suggests that the models can explain the variability observed in the data.
•A meta-learning framework allows an autonomous adaptation of machine learning models.•The meta-learning framework has a useful knowledge base of Identification models.•A meta-learning framework adapt identification models for animal fattening processes.•The identification models detect anomalies in cattle weight in different scenarios.•The meta-learning framework confers self-organization to Precision Livestock Farming.
We studied the short-term psychological effects of the COVID-19 crisis and the quarantine on 3550 adults from the Spanish population in a cross-sectional survey. Symptoms of anxiety, depression, and ...stress were analyzed using the 21-item version of the Depression Anxiety Stress Scale. Symptoms of posttraumatic stress disorder were analyzed using the Impact of Event Scale. Symptomatic scores of anxiety, depression, and stress were observed in 20% to 30% of respondents. Symptomatic scores indicating psychological stress were found in 47.5% of respondents. Similar to the findings of other multiple studies, confinement has been found to have significant emotional impact in the Spanish population.
Purpose
To accurately estimate the partial volume fraction of free water in the white matter from diffusion MRI acquisitions not demanding strong sensitizing gradients and/or large collections of ...different b‐values. Data sets considered comprise ∼32‐64 gradients near b=1000s/mm2 plus ∼6 gradients near b=500s/mm2.
Theory and Methods
The spherical means of each diffusion MRI set with the same b‐value are computed. These means are related to the inherent diffusion parameters within the voxel (free‐ and cellular‐water fractions; cellular‐water diffusivity), which are solved by constrained nonlinear least squares regression.
Results
The proposed method outperforms those based on mixtures of two Gaussians for the kind of data sets considered. W.r.t. the accuracy, the former does not introduce significant biases in the scenarios of interest, while the latter can reach a bias of 5%–7% if fiber crossings are present. W.r.t. the precision, a variance near 10%, compared to 15%, can be attained for usual configurations.
Conclusion
It is possible to compute reliable estimates of the free‐water fraction inside the white matter by complementing typical DTI acquisitions with few gradients at a lowb‐value. It can be done voxel‐by‐voxel, without imposing spatial regularity constraints.
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