► We examine self healing of open cracks in asphalt mastic. ► We hypothesize that the main cause for healing is capillary flow of bitumen through the crack. ► We have related asphalt self healing ...rates through the Arrhenius equation.
Asphalt mastic is a self-healing material. Cracks may develop in the asphalt mixture as a result of different factors, such as repeated traffic loads or freeze–thaw cycles. But, once a crack is open in the pavement, it starts healing and, if it has enough time to complete the process, it can even close completely. Asphalt mastic self-healing properties are directly linked to temperature and to the rest periods it can be classified as a thermally induced self-healing material. Moreover, it is well known that the speed at which this phenomenon occurs is higher with the increase of temperature. In this paper, it is explained that the changes in the self-healing rates with temperature can be related by means of the Arrhenius equation. For that, an apparent activation energy for healing is needed. To calculate it, a series of asphalt mastic beams were broken and healed at different temperatures and the time when the recovery is complete was used to calculate the activation energy. Moreover, capillary flow through the crack was hypothesized as the main cause for healing. Finally, to have a visual prove of this theory, the healing process in one of the asphalt beams was examined through CT-Scan tests.
The design of active and durable catalysts for the H
O/O
interconversion is one of the major challenges of electrocatalysis for renewable energy. The oxygen evolution reaction (OER) is catalyzed by ...SrRuO
with low potentials (ca. 1.35 V
), but the catalyst's durability is insufficient. Here we show that Na doping enhances both activity and durability in acid media. DFT reveals that whereas SrRuO
binds reaction intermediates too strongly, Na doping of ~0.125 leads to nearly optimal OER activity. Na doping increases the oxidation state of Ru, thereby displacing positively O p-band and Ru d-band centers, weakening Ru-adsorbate bonds. The enhanced durability of Na-doped perovskites is concomitant with the stabilization of Ru centers with slightly higher oxidation states, higher dissolution potentials, lower surface energy and less distorted RuO
octahedra. These results illustrate how high OER activity and durability can be simultaneously engineered by chemical doping of perovskites.
Traffic sign detection systems constitute a key component in trending real-world applications, such as autonomous driving, and driver safety and assistance. This paper analyses the state-of-the-art ...of several object-detection systems (Faster R-CNN, R-FCN, SSD, and YOLO V2) combined with various feature extractors (Resnet V1 50, Resnet V1 101, Inception V2, Inception Resnet V2, Mobilenet V1, and Darknet-19) previously developed by their corresponding authors. We aim to explore the properties of these object-detection models which are modified and specifically adapted to the traffic sign detection problem domain by means of transfer learning. In particular, various publicly available object-detection models that were pre-trained on the Microsoft COCO dataset are fine-tuned on the German Traffic Sign Detection Benchmark dataset. The evaluation and comparison of these models include key metrics, such as the mean average precision (mAP), memory allocation, running time, number of floating point operations, number of parameters of the model, and the effect of traffic sign image sizes. Our findings show that Faster R-CNN Inception Resnet V2 obtains the best mAP, while R-FCN Resnet 101 strikes the best trade-off between accuracy and execution time. YOLO V2 and SSD Mobilenet merit a special mention, in that the former achieves competitive accuracy results and is the second fastest detector, while the latter, is the fastest and the lightest model in terms of memory consumption, making it an optimal choice for deployment in mobile and embedded devices.
The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can ...be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software development in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data.
Summary
The aboveground impacts of climate change receive extensive research attention, but climate change could also alter belowground processes such as the delicate balance between free‐living ...fungal decomposers and nutrient‐scavenging mycorrhizal fungi that can inhibit decomposition through a mechanism called the Gadgil effect.
We investigated how climate change‐induced reductions in plant survival, photosynthesis and productivity alter soil fungal community composition in a mixed arbuscular/ectomycorrhizal (AM/EM) semiarid shrubland exposed to experimental warming (W) and/or rainfall reduction (RR). We hypothesised that increased EM host plant mortality under a warmer and drier climate might decrease ectomycorrhizal fungal (EMF) abundance, thereby favouring the proliferation and activity of fungal saprotrophs.
The relative abundance of EMF sequences decreased by 57.5% under W+RR, which was accompanied by reductions in the activity of hydrolytic enzymes involved in the acquisition of organic‐bound nutrients by EMF and their host plants. W+RR thereby created an enhanced potential for soil organic matter (SOM) breakdown and nitrogen mineralisation by decomposers, as revealed by 127–190% increases in dissolved organic carbon and nitrogen, respectively, and decreasing SOM content in soil.
Climate aridification impacts on vegetation can cascade belowground through shifts in fungal guild structure that alter ecosystem biogeochemistry and accelerate SOM decomposition by reducing the Gadgil effect.
The phyllosphere is a wide and complex ecosystem that provides a key support for microbial diversity. Fungal communities inhabiting the leaf are functionally variable and play important roles on ...plant performance. Factors conditioning the arrival and colonization of fungal communities will determine the phyllosphere fungal composition. Plant identity, leaf functional traits and host plant phylogeny have been shown to be regulators of the microbial colonization of the leaves, and can be considered as biotic filters determining the assembly of phyllosphere fungal communities. By high‐throughput sequencing we analysed the phyllosphere fungal communities from 38 Mediterranean woody plant species in two forests of south‐eastern Iberian Peninsula. We analysed the effect of plant species and site on fungal community composition. We also tested the effect of leaf functional traits and plant phylogeny on plant species differences in their fungal communities, and on the structure of the plant–fungus interaction network. Plant species account for a larger proportion than site in the variability of the composition of phyllosphere fungal communities. Leaf traits and host phylogeny influence the arrival and colonization of phyllosphere fungal communities across plant species. Plants with pubescent leaves and phylogenetically closer harbour more similar communities of decomposers, pathogens and epiphytes. Leaf habit (i.e. evergreen versus deciduous) also influences the community composition of decomposer and epiphytic fungi. Leaf carbon, leaf water content and leaf mass per area affect differentially each functional guild. Plant–fungus interaction networks present a modular structure in which plants belonging to the same module share more fungal species and are phylogenetically closer. We provide evidence that even though phyllosphere fungal communities are complex ecosystems, fungi with contrasting relationships with the plant (decomposers, epiphytes and pathogens) respond similarly to a common subset of leaf traits that impose physical limitations to the assembly of phyllosphere fungal communities.
This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and ...Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements.
•A Deep Neural Network that is top-1 ranked in the German traffic sign benchmark.•Effectiveness analysis of Spatial Transformer Networks for traffic sign recognition.•Quantitative comparison of several stochastic gradient descent optimisation methods.
The overall performance of proton exchange membrane fuel cells is limited by the sluggish kinetics of the oxygen-reduction reaction (ORR). Among the most active PGM-free ORR electrocatalysts are ...metal-nitrogen-carbon (M-N-C), such as Fe–N–C. The Fe–N4 ensembles in these PGM-free catalysts, present in different configurations, are proposed to be the active sites for the ORR in acid. In this work, we have synthesized a Fe/N/C catalyst via thermal treatment of a polymeric CxNy precursor obtained by the wet-polymerization of melamine (a nitrogen rich molecule) and terephthaldehyde. The materials obtained (Im-FeNC-1HT and Im-FeNC-2HT) display high ORR activity in acid electrolyte compared to other Fe–N–C catalysts prepared with precursors different than 2-methylimidazole or ZIF-8. Characterization data indicate the formation of high- and low-spin Fe-Nx ensembles, with a site density of 4.4·1019 sitesFe·g−1 estimated by electrochemical stripping of NO. The ORR activity was evaluated in a RRDE configuration in 0.1 M HClO4 and in MEA configuration in a single cell.
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•A high N/C ratio precursor such as melamine used to synthesize Fe–N–C catalysts.•FeN4C10 and FeN4C12 moieties formed during the synthesis.•Site Density of exposed Fe sites determined by in situ and ex situ analyses.•ORR activity demonstrated in RDE and MEA configurations.
Federated learning is a data decentralization privacy-preserving technique used to perform machine or deep learning in a secure way. In this paper we present theoretical aspects about federated ...learning, such as the presentation of an aggregation operator, different types of federated learning, and issues to be taken into account in relation to the distribution of data from the clients, together with the exhaustive analysis of a use case where the number of clients varies. Specifically, a use case of medical image analysis is proposed, using chest X-ray images obtained from an open data repository. In addition to the advantages related to privacy, improvements in predictions (in terms of accuracy, loss and area under the curve) and reduction of execution times will be studied with respect to the classical case (the centralized approach). Different clients will be simulated from the training data, selected in an unbalanced manner. The results of considering three or ten clients are exposed and compared between them and against the centralized case. Two different problems related to intermittent clients are discussed, together with two approaches to be followed for each of them. Specifically, this type of problems may occur because in a real scenario some clients may leave the training, and others enter it, and on the other hand because of client technical or connectivity problems. Finally, improvements and future work in the field are proposed.
Estimation of muscle activity using surface electromyography (sEMG) is an important non‐invasive method that can lead to a deeper understanding of motor‐control strategies in humans. Measurement ...using multiple active electrodes is necessary to estimate not only surface muscle activity but also deep muscle activity in dynamic motion. In this paper, we propose a method for estimating muscle activity of dynamic motions based on anatomical knowledge of muscle structures. To estimate muscle activity, a large number of signal sources are set in the muscle model, and connections between the signal sources are defined a priori based on the anatomical structure of the muscles. The signal source activities are first estimated by minimizing the Kullback–Leibler divergence with a continuity cost. Then, the muscle activity is computed from the signal source activity. In the experiments, five healthy participants performed five types of motion and the forearm sEMG was measured with 20‐channel active electrodes. The estimation results for these motions were visualized in four dimensions as the three‐dimensional position of the muscle over time. The results showed that the estimation was accurate, with a reproduction rate of 95% for the measured sEMG and continuity of the muscle activity. In addition, the results suggest the advantage of the proposed method over the conventional approaches in terms of estimating the muscle activity for both dynamic and abnormal motions.
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