Reliability evaluation of III-V concentrator solar cells

Concentrator solar cells have been proposed as an interesting way of reducing the cost of photovoltaic electricity. However, in order to compete with conventional solar modules it is necessary not only to reduce costs but also to evaluate and increase the present reliability. Concentrator solar cells work at higher temperature, solar radiation and current stress than conventional solar cells and a carefully reliability analysis is needed. In this paper a reliability analysis procedure, that is being developed, is presented

Late vacuum choice and slow roll approximation in gravitational particle production during reheating

In the transition between inflation and reheating, the curvature scalar typically undergoes oscillations which have significant impact on the density of gravitationally produced particles. The commonly used adiabatic vacuum prescription for the extraction of produced particle spectra becomes a non-reliable definition of vacuum in the regimes for which this oscillatory behavior is important. In this work, we study particle production for a scalar field non-minimally coupled to gravity, taking into account the complete dynamics of spacetime during inflation and reheating. We derive an approximation for the solution to the mode equation during the slow-roll of the inflaton and analyze the importance of Ricci scalar oscillations in the resulting spectra. Additionally, we propose a prescription for the vacuum that allows to safely extrapolate the result to the present, given that the test field interacts only gravitationally. Lastly, we calculate the abundance of dark matter this mechanism yields and compare it to observations.Comment: 24 pages, 11 figure

Metabolic and Stress Responses in Senegalese Soles (Solea senegalensis Kaup) Fed Tryptophan Supplements: E ects of Concentration and Feeding Period

The objective of this study was to assess the impact of di erent dietary Trp concentrations on the stress and metabolism response of juvenile Senegalese soles (Solea senegalensis). Fish (38.1 1.9 g) were fed di erent Trp-enriched feeds (0%, 1% and 2% Trp added) for two and eight days, and later exposed to air stress for three min. Samples were taken pre- and 1 h post-stress (condition). Plasma cortisol, lactate, glucose and proteins were significantly a ected by the sampling time, showing higher values at 1 h post-stress. Trp concentration in food also had significant e ects on lactate and glucose levels. However, the feeding period did not a ect these parameters. Post-stress values were higher than in the pre-stress condition for every plasma parameter, except for lactate in two days and 1% Trp treatment. Nevertheless, cortisol, glucose and lactate did not vary significantly between pre- and post-stress samplings in fish fed the 1% Trp-enriched diet for two days. The lack of variability in cortisol response was also due to the high pre-stress value, significantly superior to pre-stress control. The exposure time to Trp feeding did not significantly a ect any enzyme activity; however, Trp added and condition influenced protein-related enzyme activities. In spite of decreasing stress markers, Trp-enriched diets altered the protein metabolism

Identities of Choi-Lee-Srivastava involving the Euler-Mascheroni’s constant

We give an elementary deduction of the Choi-Lee-Srivastava’s identities involving the Euler Mascheroni’s constant, thus from them is immediate the identity of Wilf

Lanczos Potential for The Weyl Tensor

For arbitrary spacetimes with Petrov types O, N and III, we indicate general results about the  Lanczos potential for the corresponding Weyl tensor

A Physically-Based Fractional Diffusion Model for Semi-Dilute Suspensions of Rods in a Newtonian Fluid

[EN] The rheological behaviour of suspensions involving interacting (functionalized) rods remains nowadays incompletely understood, in particular with regard to the evolution of the elastic modulus with the applied frequency in small-amplitude oscillatory flows. In a previous work, we addressed this issue by assuming a fractional diffusion mechanism, however the approach followed was purely phenomenological. The present work revisits the topic from a phys ical viewpoint, with the aim of justifying the fractional nature of diffusion. After accomplishing this first objective, we explore by means of numerical ex periments the consequences of the proposed fractional modelling approach in linear and non-linear rheology.Nadal, E.; Aguado-López, JV.; Abisset-Chavanne, E.; Chinesta Soria, FJ.; Keunings, R.; Cueto, E. (2017). A Physically-Based Fractional Diffusion Model for Semi-Dilute Suspensions of Rods in a Newtonian Fluid. Applied Mathematical Modelling. 51:58-67. https://doi.org/10.1016/j.apm.2017.06.009S58675

A model-based approach to multi-domain monitoring data aggregation

The essential propellant for any closed-loop management mechanism is data related to the managed entity. While this is a general evidence, it becomes even more true when dealing with advanced closed-loop systems like the ones supported by Artificial Intelligence (AI), as they require a trustworthy, up-to-date and steady flow of state data to be applicable. Modern network infrastructures provide a vast amount of disparate data sources, especially in the multi-domain scenarios considered by the ETSI Industry Specification Group (ISG) Zero Touch Network and Service Management (ZSM) framework, and proper mechanisms for data aggregation, pre-processing and normalization are required to make possible AI-enabled closed-loop management. So far, solutions proposed for these data aggregation tasks have been specific to concrete data sources and consumers, following ad-hoc approaches unsuitable to address the vast heterogeneity of data sources and potential data consumers. This paper presents a model-based approach to a data aggregator framework, relying on standardized data models and telemetry protocols, and integrated with an open-source network orchestration stack to support their incorporation within network service lifecycles.The research leading to these results received funding from the European Union’s Horizon 2020 research and innovation programme under grant agree-ment no 871808 (INSPIRE-5Gplus) and no. 856709 (5GROWTH). The paper reflects only the authors’ views. The Commission is not responsible for any use that may be made of the information it contains

A pipeline architecture for feature-based unsupervised clustering using multivariate time series from HPC jobs

[Abstract]: Time series are key across industrial and research areas for their ability to model behaviour across time, making them ideal for a wide range of use cases such as event monitoring, trend prediction or anomaly detection. This is even more so due to the increasing monitoring capabilities in many areas, with the subsequent massive data generation. But it is also interesting to consider the potential of time series for Machine Learning processing, often fused with Big Data, to search for useful information and solve real-world problems. However, time series can be studied individually, representing a single entity or variable to be analysed, or in a grouped fashion, to study and represent a more complex entity or scenario. In this latter case we are dealing with multivariate time series, which usually imply different approaches when dealt with. In this paper, we present a pipeline architecture to process and cluster multiple groups of multivariate time series. To implement this, we apply a multi-process solution composed by a feature-based extraction stage, followed by a dimension reduction, and finally, several clustering algorithms. The pipeline is also highly configurable in terms of the stage techniques to be used, allowing to perform a search with several combinations for the most promising results. The pipeline has been experimentally applied to batches of HPC jobs from different users of a supercomputer, with the multivariate time series coming from the monitoring of several node resource metrics. The results show how it is possible to apply this multi-process information fusion to create different meaningful clusters from the batches, using only the time series, without any labelling information, thus being an unsupervised scenario. Optionally, the pipeline also supports an outlier detection stage to find and separate jobs that are radically different when compared to others on a dataset. These outliers can be removed for a better clustering, and later reviewed looking for anomalies, or if numerous, fed back to the pipeline to identify possible groupings. The results also include some outliers found in the experiments, as well as scenarios where they are clustered, or ignored and not removed at all. In addition, by leveraging Big Data technologies like Spark, the pipeline is proven to be scalable by working with up to hundreds of jobs and thousands of time series.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2021/30This research was funded by the Ministry of Science and Innovation of Spain (PID2019-104184RB-I00/AEI/10.13039/501100011033), and by Xunta de Galicia, Spain and FEDER funds of the European Union (Centro de Investigación de Galicia accreditation 2019–2022, ref. ED431G 2019/01; Consolidation Program of Competitive Reference Groups, ref. ED431C 2021/30). Funding for open access charge: Universidade da Coruña/CISUG

A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

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