Showing papers by "University of Extremadura published in 2020"

Image Segmentation Using Deep Learning: A Survey

Abstract: Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.

TRY plant trait database : Enhanced coverage and open access

Abstract: Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Graph Convolutional Networks for Hyperspectral Image Classification

Abstract: Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification, owing to their ability to capture spatial-spectral feature representations. Nevertheless, their ability in modeling relations between samples remains limited. Beyond the limitations of grid sampling, graph convolutional networks (GCNs) have been recently proposed and successfully applied in irregular (or non-grid) data representation and analysis. In this paper, we thoroughly investigate CNNs and GCNs (qualitatively and quantitatively) in terms of HS image classification. Due to the construction of the adjacency matrix on all the data, traditional GCNs usually suffer from a huge computational cost, particularly in large-scale remote sensing (RS) problems. To this end, we develop a new mini-batch GCN (called miniGCN hereinafter) which allows to train large-scale GCNs in a mini-batch fashion. More significantly, our miniGCN is capable of inferring out-of-sample data without re-training networks and improving classification performance. Furthermore, as CNNs and GCNs can extract different types of HS features, an intuitive solution to break the performance bottleneck of a single model is to fuse them. Since miniGCNs can perform batch-wise network training (enabling the combination of CNNs and GCNs) we explore three fusion strategies: additive fusion, element-wise multiplicative fusion, and concatenation fusion to measure the obtained performance gain. Extensive experiments, conducted on three HS datasets, demonstrate the advantages of miniGCNs over GCNs and the superiority of the tested fusion strategies with regards to the single CNN or GCN models. The codes of this work will be available at this https URL for the sake of reproducibility.

Current trends in economy, sustainable development, and energy: a circular economy view

Abstract: Nowadays, it is widely recognized that the current production and consumption models are no longer adequate because of the inefficient use of resources (especially energy) and its dramatical consequences, environmental load and social inequality (UNEP 2011). In this context, Circular Economy (CE) has started to be considered a sustainable economic paradigm (Geissdoerfer et al. 2017), enabled by novel business models (Lewandowski 2016) and responsible consumers (Borrello et al. 2017). As highlighted by McDowall et al. 2017), the origin of the concept emerged in Europe in the 1980s and 1990s, together with early policies of the EuropeanUnionmember states, drawing on ideas that can be traced to the 1970s. Driven by a desire to divert waste from landfill, the Netherlands and Germany pioneered concepts of waste prevention and reduction, with the waste hierarchy introduced to the Dutch Parliament in 1979. However, even acknowledging that CE was rooted in environmental economics, there is today a huge interdisciplinary framework underpinning CE. This new framework offers good prospect for gradual involvement of the present economic system, including production and consumption models. We can say that at the moment, CE has the supreme goal of promoting sustainable development through the dissociation of environmental pressure from economic growth (Ghisellini et al. 2016; Schandl et al. 2016; Bruel et al. 2019). According to Ghisellini et al. (2016), CE is a new economic system aiming to increase the efficiency of resource use to achieve improvements in the existing balance between economy, environment and society. In this line, the concept of CE has recently been proposed as a new development strategy that aims to mitigate the contradiction between the rapid economic growth and the shortage of raw materials and energy (Christis et al. 2019). Although there is no commonly accepted definition of CE so far, the core of CE is the closed flow of materials and the use of raw materials and energy through multiple phases (Gan et al. 2013). The ‘3R’ principles—reduction, reuse, and recycling of materials and energy—are often cited to describe the three possible approaches in practice (the goals of reduce, reuse, and especially recycle have become the only acceptable ways of disposing of waste (Yong 2007, Yellishetty et al. 2011, Samiha 2013, Ahmadi 2017, Huang et al. 2018). This concept has been pursued by many countries’ environmental policy makers as a potential strategy to solve existing environmental problems (Preston and Carr 2018). As one of the main objectives of governments is to promote the sustainable development of economy and society, it also helps to achieve sustainable environmental protection (Yuan et al. 2006). Once the high economic potential of CE has been recognized, in the European Union, different CE Action Plans have been proposed as new initiatives, changes or adaptations to legislation, mainly related to waste and landfills at the beginning. In 2015, a range of policy measures collectively known as the Circular Economy Package, and later replaced by the Closing the Loop-An Action Plan for the CE, were approved in Europe (COM2015). At present times the CE is a priority for Europe. Nearly 1 billion from Horizon 2020’s final Work Responsible editor: Philippe Garrigues

The global burden of falls: global, regional and national estimates of morbidity and mortality from the Global Burden of Disease Study 2017

Abstract: Background Falls can lead to severe health loss including death. Past research has shown that falls are an important cause of death and disability worldwide. The Global Burden of Disease Study 2017 (GBD 2017) provides a comprehensive assessment of morbidity and mortality from falls. Methods Estimates for mortality, years of life lost (YLLs), incidence, prevalence, years lived with disability (YLDs) and disability-adjusted life years (DALYs) were produced for 195 countries and territories from 1990 to 2017 for all ages using the GBD 2017 framework. Distributions of the bodily injury (eg, hip fracture) were estimated using hospital records. Results Globally, the age-standardised incidence of falls was 2238 (1990–2532) per 100 000 in 2017, representing a decline of 3.7% (7.4 to 0.3) from 1990 to 2017. Age-standardised prevalence was 5186 (4622–5849) per 100 000 in 2017, representing a decline of 6.5% (7.6 to 5.4) from 1990 to 2017. Age-standardised mortality rate was 9.2 (8.5–9.8) per 100 000 which equated to 695 771 (644 927–741 720) deaths in 2017. Globally, falls resulted in 16 688 088 (15 101 897–17 636 830) YLLs, 19 252 699 (13 725 429–26 140 433) YLDs and 35 940 787 (30 185 695–42 903 289) DALYs across all ages. The most common injury sustained by fall victims is fracture of patella, tibia or fibula, or ankle. Globally, age-specific YLD rates increased with age. Conclusions This study shows that the burden of falls is substantial. Investing in further research, fall prevention strategies and access to care is critical.

Micelle-directed chiral seeded growth on anisotropic gold nanocrystals

Abstract: Surfactant-assisted seeded growth of metal nanoparticles (NPs) can be engineered to produce anisotropic gold nanocrystals with high chiroptical activity through the templating effect of chiral micelles formed in the presence of dissymmetric cosurfactants Mixed micelles adsorb on gold nanorods, forming quasihelical patterns that direct seeded growth into NPs with pronounced morphological and optical handedness Sharp chiral wrinkles lead to chiral plasmon modes with high dissymmetry factors (~020) Through variation of the dimensions of chiral wrinkles, the chiroptical properties can be tuned within the visible and near-infrared electromagnetic spectrum The micelle-directed mechanism allows extension to other systems, such as the seeded growth of chiral platinum shells on gold nanorods This approach provides a reproducible, simple, and scalable method toward the fabrication of NPs with high chiral optical activity

Global Geographic Distribution and Host Range of Fusarium circinatum, the Causal Agent of Pine Pitch Canker

Abstract: Fusarium circinatum, the causal agent of pine pitch canker (PPC), is currently one of the most important threats of Pinus spp. globally. This pathogen is known in many pine-growing regions, including natural and planted forests, and can affect all life stages of trees, from emerging seedlings to mature trees. Despite the importance of PPC, the global distribution of F. circinatum is poorly documented, and this problem is also true of the hosts within countries that are affected. The aim of this study was to review the global distribution of F. circinatum, with a particular focus on Europe. We considered (1) the current and historical pathogen records, both positive and negative, based on confirmed reports from Europe and globally; (2) the genetic diversity and population structure of the pathogen; (3) the current distribution of PPC in Europe, comparing published models of predicted disease distribution; and (4) host susceptibility by reviewing literature and generating a comprehensive list of known hosts for the fungus. These data were collated from 41 countries and used to compile a specially constructed geo-database. A review of 6297 observation records showed that F. circinatum and the symptoms it causes on conifers occurred in 14 countries, including four in Europe, and is absent in 28 countries. Field observations and experimental data from 138 host species revealed 106 susceptible host species including 85 Pinus species, 6 non-pine tree species and 15 grass and herb species. Our data confirm that susceptibility to F. circinatum varies between different host species, tree ages and environmental characteristics. Knowledge on the geographic distribution, host range and the relative susceptibility of different hosts is essential for disease management, mitigation and containment strategies. The findings reported in this review will support countries that are currently free of F. circinatum in implementing effective procedures and restrictions and prevent further spread of the pathogen.

Skip-Connected Covariance Network for Remote Sensing Scene Classification

Abstract: This paper proposes a novel end-to-end learning model, called skip-connected covariance (SCCov) network, for remote sensing scene classification (RSSC) The innovative contribution of this paper is to embed two novel modules into the traditional convolutional neural network (CNN) model, ie, skip connections and covariance pooling The advantages of newly developed SCCov are twofold First, by means of the skip connections, the multi-resolution feature maps produced by the CNN are combined together, which provides important benefits to address the presence of large-scale variance in RSSC data sets Second, by using covariance pooling, we can fully exploit the second-order information contained in such multi-resolution feature maps This allows the CNN to achieve more representative feature learning when dealing with RSSC problems Experimental results, conducted using three large-scale benchmark data sets, demonstrate that our newly proposed SCCov network exhibits very competitive or superior classification performance when compared with the current state-of-the-art RSSC techniques, using a much lower amount of parameters Specifically, our SCCov only needs 10% of the parameters used by its counterparts

More than smell. COVID-19 is associated with severe impairment of smell, taste, and chemesthesis

Abstract: Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, generally lacked quantitative measurements, were mostly restricted to data from single countries. Here, we report the development, implementation and initial results of a multi-lingual, international questionnaire to assess self-reported quantity and quality of perception in three distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, 8 other, ages 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change+/-100) revealed a mean reduction of smell (-79.7+/- 28.7, mean+/- SD), taste (-69.0+/- 32.6), and chemesthetic (-37.3+/- 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell, but also affects taste and chemesthesis. The multimodal impact of COVID-19 and lack of perceived nasal obstruction suggest that SARS-CoV-2 infection may disrupt sensory-neural mechanisms.

Plant Phenolics: Bioavailability as a Key Determinant of Their Potential Health-Promoting Applications.

Abstract: Phenolic compounds are secondary metabolites widely spread throughout the plant kingdom that can be categorized as flavonoids and non-flavonoids. Interest in phenolic compounds has dramatically increased during the last decade due to their biological effects and promising therapeutic applications. In this review, we discuss the importance of phenolic compounds' bioavailability to accomplish their physiological functions, and highlight main factors affecting such parameter throughout metabolism of phenolics, from absorption to excretion. Besides, we give an updated overview of the health benefits of phenolic compounds, which are mainly linked to both their direct (e.g., free-radical scavenging ability) and indirect (e.g., by stimulating activity of antioxidant enzymes) antioxidant properties. Such antioxidant actions reportedly help them to prevent chronic and oxidative stress-related disorders such as cancer, cardiovascular and neurodegenerative diseases, among others. Last, we comment on development of cutting-edge delivery systems intended to improve bioavailability and enhance stability of phenolic compounds in the human body.

Quantification of the Antioxidant Activity of Plant Extracts: Analysis of Sensitivity and Hierarchization Based on the Method Used.

Abstract: Plants have a large number of bioactive compounds with high antioxidant activity. Studies for the determination of the antioxidant activity of different plant species could contribute to revealing the value of these species as a source of new antioxidant compounds. There is a large variety of in vitro methods to quantify antioxidant activity, and it is important to select the proper method to determine which species have the highest antioxidant activity. The aim of this work was to verify whether different methods show the same sensitivity and/or capacity to discriminate the antioxidant activity of the extract of different plant species. To that end, we selected 12 species with different content of phenolic compounds. Their extracts were analyzed using the following methods: 2,2-di-phenyl-1-picrylhydrazyl (DPPH) radical scavenging capacity assay, ferric reducing (FRAP) assay, Trolox equivalent antioxidant capacity (ABTS) assay, and reducing power (RP) assay. The four methods selected could quantify the antioxidant capacity of the 12 study species, although there were differences between them. The antioxidant activity values quantified through DPPH and RP were higher than the ones obtained by ABTS and FRAP, and these values varied among species. Thus, the hierarchization or categorization of these species was different depending on the method used. Another difference established between these methods was the sensitivity obtained with each of them. A cluster revealed that RP established the largest number of groups at the shortest distance from the root. Therefore, as it showed the best discrimination of differences and/or similarities between species, RP is considered in this study as the one with the highest sensitivity among the four studied methods. On the other hand, ABTS showed the lowest sensitivity. These results show the importance of selecting the proper antioxidant activity quantification method for establishing a ranking of species based on this parameter.

Rationally Designed Long-Wavelength Absorbing Ru(II) Polypyridyl Complexes as Photosensitizers for Photodynamic Therapy

Abstract: The utilization of photodynamic therapy (PDT) for the treatment of various types of cancer has gained increasing attention over the last decades. Despite the clinical success of approved photosensitizers (PSs), their application is sometimes limited due to poor water solubility, aggregation, photodegradation, and slow clearance from the body. To overcome these drawbacks, research efforts are devoted toward the development of metal complexes and especially Ru(II) polypyridine complexes based on their attractive photophysical and biological properties. Despite the recent research developments, the vast majority of complexes utilize blue or UV-A light to obtain a PDT effect, limiting the penetration depth inside tissues and, therefore, the possibility to treat deep-seated or large tumors. To circumvent these drawbacks, we present the first example of a DFT guided search for efficient PDT PSs with a substantial spectral red shift toward the biological spectral window. Thanks to this design, we have unveiled a Ru(II) polypyridine complex that causes phototoxicity in the very low micromolar to nanomolar range at clinically relevant 595 nm, in monolayer cells as well as in 3D multicellular tumor spheroids.

Educación a distancia en tiempos de COVID-19: Análisis desde la perspectiva de los estudiantes universitarios

Abstract: This paper focuses on the students of traditional face-to-face universities and on the implemented distance learning models during the lockdown period caused by the COVID-19’ crisis. We aim here to analyze the impact of the personal and family context on digital equity, to identify the teaching model received, and to know their assessment and perception of this model. The research is a mixed study of descriptive scope in which qualitative and quantitative methods are combined. Firstly, a survey was carried out with students from the University of Extremadura (n=548) and, then, online interviews were conducted to members of the university governance. The results indicate that students from families with a low educational level have fewer opportunities to use digital technologies. Virtual lessons, which students have received, have essentially been based on presentations uploaded to the virtual campus with asynchronous interactions. The negative assessment of distance learning is explained by the apparent reverse relationship between time spent studying and academic performance and by the lack of teachers’ adaptation to students’ personal and academic circumstances. In conclusion, the university must move towards more collaborative and student-centered models.

A Single Model CNN for Hyperspectral Image Denoising

Abstract: Denoising is a common preprocessing step prior to the analysis and interpretation of hyperspectral images (HSIs). However, the vast majority of methods typically adopted for HSI denoising exploit architectures originally developed for grayscale or RGB images, exhibiting limitations when processing high-dimensional HSI data cubes. In particular, traditional methods do not take into account the high spectral correlation between adjacent bands in HSIs, which leads to unsatisfactory denoising performance as the rich spectral information present in HSIs is not fully exploited. To overcome this limitation, this article considers deep learning models—such as convolutional neural networks (CNNs)—to perform spectral–spatial HSI denoising. The proposed model, called HSI single denoising CNN (HSI-SDeCNN), efficiently takes into consideration both the spatial and spectral information contained in HSIs. Experimental results on both synthetic and real data demonstrate that the proposed HSI-SDeCNN outperforms other state-of-the-art HSI denoising methods. Source code: https://github.com/mhaut/HSI-SDeCNN

Changes in soil organic carbon under perennial crops.

Abstract: This study evaluates the dynamics of soil organic carbon (SOC) under perennial crops across the globe. It quantifies the effect of change from annual to perennial crops and the subsequent temporal changes in SOC stocks during the perennial crop cycle. It also presents an empirical model to estimate changes in the SOC content under crops as a function of time, land use, and site characteristics. We used a harmonized global dataset containing paired-comparison empirical values of SOC and different types of perennial crops (perennial grasses, palms, and woody plants) with different end uses: bioenergy, food, other bio-products, and short rotation coppice. Salient outcomes include: a 20-year period encompassing a change from annual to perennial crops led to an average 20% increase in SOC at 0-30 cm (6.0 ± 4.6 Mg/ha gain) and a total 10% increase over the 0-100 cm soil profile (5.7 ± 10.9 Mg/ha). A change from natural pasture to perennial crop decreased SOC stocks by 1% over 0-30 cm (-2.5 ± 4.2 Mg/ha) and 10% over 0-100 cm (-13.6 ± 8.9 Mg/ha). The effect of a land use change from forest to perennial crops did not show significant impacts, probably due to the limited number of plots; but the data indicated that while a 2% increase in SOC was observed at 0-30 cm (16.81 ± 55.1 Mg/ha), a decrease in 24% was observed at 30-100 cm (-40.1 ± 16.8 Mg/ha). Perennial crops generally accumulate SOC through time, especially woody crops; and temperature was the main driver explaining differences in SOC dynamics, followed by crop age, soil bulk density, clay content, and depth. We present empirical evidence showing that the FAO perennialization strategy is reasonable, underscoring the role of perennial crops as a useful component of climate change mitigation strategies.

Advances in Orthotic and Prosthetic Manufacturing: A Technology Review.

Abstract: In this work, the recent advances for rapid prototyping in the orthoprosthetic industry are presented. Specifically, the manufacturing process of orthoprosthetic aids are analysed, as thier use is widely extended in orthopedic surgery. These devices are devoted to either correct posture or movement (orthosis) or to substitute a body segment (prosthesis) while maintaining functionality. The manufacturing process is traditionally mainly hand-crafted: The subject’s morphology is taken by means of plaster molds, and the manufacture is performed individually, by adjusting the prototype over the subject. This industry has incorporated computer aided design (CAD), computed aided engineering (CAE) and computed aided manufacturing (CAM) tools; however, the true revolution is the result of the application of rapid prototyping technologies (RPT). Techniques such as fused deposition modelling (FDM), selective laser sintering (SLS), laminated object manufacturing (LOM), and 3D printing (3DP) are some examples of the available methodologies in the manufacturing industry that, step by step, are being included in the rehabilitation engineering market—an engineering field with growth and prospects in the coming years. In this work we analyse different methodologies for additive manufacturing along with the principal methods for collecting 3D body shapes and their application in the manufacturing of functional devices for rehabilitation purposes such as splints, ankle-foot orthoses, or arm prostheses.

Understanding the carbonation of concrete with supplementary cementitious materials: a critical review by RILEM TC 281-CCC

Abstract: Blended cements, where Portland cement clinker is partially replaced by supplementary cementitious materials (SCMs), provide the most feasible route for reducing carbon dioxide emissions associated with concrete production. However, lowering the clinker content can lead to an increasing risk of neutralisation of the concrete pore solution and potential reinforcement corrosion due to carbonation. carbonation of concrete with SCMs differs from carbonation of concrete solely based on Portland cement (PC). This is a consequence of the differences in the hydrate phase assemblage and pore solution chemistry, as well as the pore structure and transport properties, when varying the binder composition, age and curing conditions of the concretes. The carbonation mechanism and kinetics also depend on the saturation degree of the concrete and CO2 partial pressure which in turn depends on exposure conditions (e.g. relative humidity, volume, and duration of water in contact with the concrete surface and temperature conditions). This in turn influence the microstructural changes identified upon carbonation. This literature review, prepared by members of RILEM technical committee 281-CCC carbonation of concrete with supplementary cementitious materials, working groups 1 and 2, elucidates the effect of numerous SCM characteristics, exposure environments and curing conditions on the carbonation mechanism, kinetics and structural alterations in cementitious systems containing SCMs.

Psychological Impact of COVID-19 Emergency on Health Professionals: Burnout Incidence at the Most Critical Period in Spain.

Abstract: Background: The health profession is a burnout producer due to the continuous contact with pain and suffering. In addition, excessive workloads can generate stress and psychological distress. Objectives: The aim of this study was to determine the degree of burnout and its main triggers in health professionals in Spain at the most critical period of the COVID-19 emergency. Method: A quantitative research was developed through a simple random sampling in different Spanish hospitals through the period of greatest impact of the pandemic (N = 157). Data were collected using a standardized questionnaire from Maslach burnout inventory (MBI) containing 22 items, which measures three subscales: emotional burnout, depersonalization, and self-fulfillment. Results: depersonalization values reached 38.9%. A total of 90.4% of the health professionals considered that psychological care should be provided from the work centers. Furthermore, 43.3% of the health professionals estimated that they might need psychological treatment in the future. Finally, 85.4% stated that the lack of personal protective equipment (PPE) generated an increase in stress and anxiety. Conclusion: This study demonstrates the need to consider specific mental health care services and training in crises to avoid possible psychological disorders. The information obtained is also valuable for the development of future prevention protocols and training of health personnel to face pandemics of these characteristics or emergency scenarios. Having the necessary physical means for their protection, as well to updated regular and accurate information, is essential to avoid feelings of fear and uncertainty. This would promote the health of these professionals.

Historical sunspot records

Abstract: Sunspot observations are available in fairly good numbers since 1610, after the invention of the telescope. This review is concerned with those sunspot observations of which longer records and drawings in particular are available. Those records bear information beyond the classical sunspot numbers or group sunspot numbers. We begin with a brief summary on naked-eye sunspot observations, in particular those with drawings. They are followed by the records of drawings from 1610 to about 1900. The review is not a compilation of all known historical sunspot information. Some records contributing substantially to the sunspot number time series may therefore be absent. We also glance at the evolution of the understanding of what sunspots actually are, from 1610 to the 19th century. The final part of the review illuminates the physical quantities that can be derived from historical drawings.

Hydrogels for Bioprinting: A Systematic Review of Hydrogels Synthesis, Bioprinting Parameters, and Bioprinted Structures Behavior.

Abstract: Nowadays, bioprinting is rapidly evolving and hydrogels are a key component for its success. In this sense, synthesis of hydrogels, as well as bioprinting process, and cross-linking of bioinks represent different challenges for the scientific community. A set of unified criteria and a common framework are missing, so multidisciplinary research teams might not efficiently share the advances and limitations of bioprinting. Although multiple combinations of materials and proportions have been used for several applications, it is still unclear the relationship between good printability of hydrogels and better medical/clinical behavior of bioprinted structures. For this reason, a PRISMA methodology was conducted in this review. Thus, 1,774 papers were retrieved from PUBMED, WOS, and SCOPUS databases. After selection, 118 papers were analyzed to extract information about materials, hydrogel synthesis, bioprinting process, and tests performed on bioprinted structures. The aim of this systematic review is to analyze materials used and their influence on the bioprinting parameters that ultimately generate tridimensional structures. Furthermore, a comparison of mechanical and cellular behavior of those bioprinted structures is presented. Finally, some conclusions and recommendations are exposed to improve reproducibility and facilitate a fair comparison of results.

Detection and Mitigation of DoS and DDoS Attacks in IoT-Based Stateful SDN : An Experimental Approach

Abstract: The expected advent of the Internet of Things (IoT) has triggered a large demand of embedded devices, which envisions the autonomous interaction of sensors and actuators while offering all sort of smart services. However, these IoT devices are limited in computation, storage, and network capacity, which makes them easy to hack and compromise. To achieve secure development of IoT, it is necessary to engineer scalable security solutions optimized for the IoT ecosystem. To this end, Software Defined Networking (SDN) is a promising paradigm that serves as a pillar in the fifth generation of mobile systems (5G) that could help to detect and mitigate Denial of Service (DoS) and Distributed DoS (DDoS) threats. In this work, we propose to experimentally evaluate an entropy-based solution to detect and mitigate DoS and DDoS attacks in IoT scenarios using a stateful SDN data plane. The obtained results demonstrate for the first time the effectiveness of this technique targeting real IoT data traffic.

Dripping, jetting and tip streaming

Abstract: Dripping, jetting and tip streaming have been studied up to a certain point separately by both fluid mechanics and microfluidics communities, the former focusing on fundamental aspects while the latter on applications. Here, we intend to review this field from a global perspective by considering and linking the two sides of the problem. First, we present the theoretical model used to study interfacial flows arising in droplet-based microfluidics, paying attention to three elements commonly present in applications: viscoelasticity, electric fields and surfactants. We review both classical and current results of the stability of jets affected by these elements. Mechanisms leading to the breakup of jets to produce drops are reviewed as well, including some recent advances in this field. We also consider the relatively scarce theoretical studies on the emergence and stability of tip streaming in open systems. Second, we focus on axisymmetric microfluidic configurations which can operate on the dripping and jetting modes either in a direct (standard) way or via tip streaming. We present the dimensionless parameters characterizing these configurations, the scaling laws which allow predicting the size of the resulting droplets and bubbles, as well as those delimiting the parameter windows where tip streaming can be found. Special attention is paid to electrospray and flow focusing, two of the techniques more frequently used in continuous drop production microfluidics. We aim to connect experimental observations described in this section of topics with fundamental and general aspects described in the first part of the review. This work closes with some prospects at both fundamental and practical levels.

Modulators of the Personal and Professional Threat Perception of Olympic Athletes in the Actual COVID-19 Crisis

Abstract: The COVID-19 pandemic is now a major global health issue, affecting world population and high-performance athlete too. The aim of the present research was to analyze the effect of psychological profile, academic schedule, and gender in the perception of personal and professional threat of Olympic and Paralympic athletes facing the 2021 Tokyo Olympiad in the actual COVID-19 crisis. We analyzed in 136 Olympic (26.4 ± 6.2 years) and 39 Paralympic athletes (31.8 ± 9.3 years) academic and sport variables, individual perceptions about COVID-19 crisis, personality, loneliness, psychological inflexibility, and anxiety. Paralympic athletes perceived higher negative impact in their training and performance by the confinement than Olympic athletes (+24.18, p < 0.005, r = 0.60). Neuroticism and psychological inflexibility presented the greatest negative feelings for female athletes (+32.59, p < 0.000, r = 0.13) and the perception that quarantine would negatively affect their sports performance. Finally professional athletes showed lower values in personality tests (Agreeableness factor) about COVID-19 crisis than non-professionals (-40.62, p < 0.012, r = 0.88).

Hybrid first and second order attention Unet for building segmentation in remote sensing images

Abstract: Recently, building segmentation (BS) has drawn significant attention in remote sensing applications. Convolutional neural networks (CNNs) have become the mainstream analysis approach in this field owing to their powerful representative ability. However, owing to the variation in building appearance, designing an effective CNN architecture for BS still remains a challenging task. Most of CNN-based BS methods mainly focus on deep or wide network architectures, neglecting the correlation among intermediate features. To address this problem, in this paper we propose a hybrid first and second order attention network (HFSA) that explores both the global mean and the inner-product among different channels to adaptively rescale intermediate features. As a result, the HFSA can not only make full use of first order feature statistics, but also incorporate the second order feature statistics, which leads to more representative feature. We conduct a series of comprehensive experiments on three widely used aerial building segmentation data sets and one satellite building segmentation data set. The experimental results show that our newly developed model achieves better segmentation performance over state-of-the-art models in terms of both quantitative and qualitative results.

Optimization and Implementation of the Proportional-Resonant Controller for Grid-Connected Inverter With Significant Computation Delay

Abstract: This paper describes the tuning process of the proportional-resonant controller, taking into account the significant computational delay from the digital control system. Different structures of the controller and related contradicting results are discussed. Particular attention is paid to the stability domain and its dependence on different parameters of the proportional-resonant controller. The main outcome of this paper consists in the tuning approach guidelines for selection of controller parameters in case of significant digital system delay. An optimal controller structure and start-up current optimization are proposed. All results are confirmed by simulation and experimental setup.

Spatio-temporal fusion for remote sensing data: an overview and new benchmark

Abstract: Spatio-temporal fusion (STF) aims at fusing (temporally dense) coarse resolution images and (temporally sparse) fine resolution images to generate image series with adequate temporal and spatial resolution. In the last decade, STF has drawn a lot of attention and many STF methods have been developed. However, to datethe STF domain still lacks benchmark datasets, which is a pressing issue that needs to be addressed in order to foster the development of this field. In this review, we provide (for the first time in the literature) a robust benchmark STF dataset that includes three important characteristics: (1) diversity of regions, (2) long timespan, and (3) challenging scenarios. We also provide a survey of highly representative STF techniques, along with a detailed quantitative and qualitative comparison of their performance with our newly presented benchmark dataset. The proposed dataset is public and available online.

Deep Metric Learning Based on Scalable Neighborhood Components for Remote Sensing Scene Characterization

Abstract: With the development of convolutional neural networks (CNNs), the semantic understanding of remote sensing (RS) scenes has been significantly improved based on their prominent feature encoding capabilities. While many existing deep-learning models focus on designing different architectures, only a few works in the RS field have focused on investigating the performance of the learned feature embeddings and the associated metric space. In particular, two main loss functions have been exploited: the contrastive and the triplet loss. However, the straightforward application of these techniques to RS images may not be optimal in order to capture their neighborhood structures in the metric space due to the insufficient sampling of image pairs or triplets during the training stage and to the inherent semantic complexity of remotely sensed data. To solve these problems, we propose a new deep metric learning approach, which overcomes the limitation on the class discrimination by means of two different components: 1) scalable neighborhood component analysis (SNCA) that aims at discovering the neighborhood structure in the metric space and 2) the cross-entropy loss that aims at preserving the class discrimination capability based on the learned class prototypes. Moreover, in order to preserve feature consistency among all the minibatches during training, a novel optimization mechanism based on momentum update is introduced for minimizing the proposed loss. An extensive experimental comparison (using several state-of-the-art models and two different benchmark data sets) has been conducted to validate the effectiveness of the proposed method from different perspectives, including: 1) classification; 2) clustering; and 3) image retrieval. The related codes of this article will be made publicly available for reproducible research by the community.

Soil Science Challenges in a New Era: A Transdisciplinary Overview of Relevant Topics

Abstract: Transdisciplinary approaches that provide holistic views are essential to properly understand soil processes and the importance of soil to society and will be crucial in the future to integrate distinct disciplines into soil studies. A myriad of challenges faces soil science at the beginning of the 2020s. The main aim of this overview is to assess past achievements and current challenges regarding soil threats such as erosion and soil contamination related to different United Nations sustainable development goals (SDGs) including (1) sustainable food production, (2) ensure healthy lives and reduce environmental risks (SDG3), (3) ensure water availability (SDG6), and (4) enhanced soil carbon sequestration because of climate change (SDG13). Twenty experts from different disciplines related to soil sciences offer perspectives on important research directions. Special attention must be paid to some concerns such as (1) effective soil conservation strategies; (2) new computational technologies, models, and in situ measurements that will bring new insights to in-soil process at spatiotemporal scales, their relationships, dynamics, and thresholds; (3) impacts of human activities, wildfires, and climate change on soil microorganisms and thereby on biogeochemical cycles and water relationships; (4) microplastics as a new potential pollutant; (5) the development of green technologies for soil rehabilitation; and (6) the reduction of greenhouse gas emissions by simultaneous soil carbon sequestration and reduction in nitrous oxide emission. Manuscripts on topics such as these are particularly welcomed in Air, Soil and Water Research.