Management of Virtual Machines on Globus Grids Using GridWay

Virtual machines are a promising technology to over-come some of the problems found in current Grid infras-tructures, like heterogeneity, performance partitioning or application isolation. In this work, we present an straight-forward deployment of virtual machines in Globus Grids. This solution is based on standard services and does not re-quire additional middleware to be installed. Also, we assess the suitability of this deployment in the execution of a high throughput scientific application, the XMM-Newton Scien-tific Analysis System

GWpilot: Enabling multi-level scheduling in distributed infrastructures with GridWay and pilot jobs

Current systems based on pilot jobs are not exploiting all the scheduling advantages that the technique offers, or they lack compatibility or adaptability. To overcome the limitations or drawbacks in existing approaches, this study presents a different general-purpose pilot system, GWpilot. This system provides individual users or institutions with a more easy-to-use, easy-toinstall, scalable, extendable, flexible and adjustable framework to efficiently run legacy applications. The framework is based on the GridWay meta-scheduler and incorporates the powerful features of this system, such as standard interfaces, fair-share policies, ranking, migration, accounting and compatibility with diverse infrastructures. GWpilot goes beyond establishing simple network overlays to overcome the waiting times in remote queues or to improve the reliability in task production. It properly tackles the characterisation problem in current infrastructures, allowing users to arbitrarily incorporate customised monitoring of resources and their running applications into the system. This functionality allows the new framework to implement innovative scheduling algorithms that accomplish the computational needs of a wide range of calculations faster and more efficiently. The system can also be easily stacked under other software layers, such as self-schedulers. The advanced techniques included by default in the framework result in significant performance improvements even when very short tasks are scheduled

Electronic Descriptors for Supervised Spectroscopic Predictions

Spectroscopic properties of molecules holds great importance for the description of the molecular response under the effect of an UV/Vis electromagnetic radiation. Computationally expensive ab initio (e.g. MultiConfigurational SCF, Coupled Cluster) or TDDFT methods are commonly used by the quantum chemistry community to compute these properties. In this work, we propose a (supervised) Machine Learning approach to model the absorption spectra of organic molecules. Several supervised ML methods have been tested such as Kernel Ridge Regression (KRR), Multiperceptron Neural Networs (MLP) and Convolutional Neural Networks. The use of only geometrical descriptors (e.g. Coulomb Matrix) proved to be insufficient for an accurate training. Inspired on the TDDFT theory, we propose to use a set of electronic descriptors obtained from low-cost DFT methods: orbital energy differences, transition dipole moment between occupied and unoccupied Kohn-Sham orbitals and charge-transfer character of mono-excitations. We demonstrate that with this electronic descriptors and the use of Neural Networks we can predict not only a density of excited states, but also getting very good estimation of the absorption spectrum and charge-transfer character of the electronic excited states, reaching results close to the chemical accuracy (~2 kcal/mol or ~0.1eV)

A basic electro-topological descriptor for the prediction of organic molecule geometries by simple machine learning

This paper proposes a machine learning (ML) method to predict stable molecular geometries from their chemical composition. The method is useful for generating molecular conformations which may serve as initial geometries for saving time during expensive structure optimizations by quantum mechanical calculations of large molecules. Conformations are found by predicting the local arrangement around each atom in the molecule after trained from a database of previously optimized small molecules. It works by dividing each molecule in the database into minimal building blocks of different type. The algorithm is then trained to predict bond lengths and angles for each type of building block using an electro-topological fingerprint as descriptor. A conformation is then generated by joining the predicted blocks. Our model is able to give promising results for optimized molecular geometries from the basic knowledge of the chemical formula and connectivity. The method trends to reproduce interatomic distances within test blocks with RMSD under 0.05

A Meta-Brokering Framework for Science Gateways

Recently scientific communities produce a growing number of computation-intensive applications, which calls for the interoperation of distributed infrastructures including Clouds, Grids and private clusters. The European SHIWA and ER-flow projects have enabled the combination of heterogeneous scientific workflows, and their execution in a large-scale system consisting of multiple Distributed Computing Infrastructures. One of the resource management challenges of these projects is called parameter study job scheduling. A parameter study job of a workflow generally has a large number of input files to be consumed by independent job instances. In this paper we propose a meta-brokering framework for science gateways to support the execution of such workflows. In order to cope with the high uncertainty and unpredictable load of the utilized distributed infrastructures, we introduce the so called resource priority services. These tools are capable of determining and dynamically updating priorities of the available infrastructures to be selected for job instances. Our evaluations show that this approach implies an efficient distribution of job instances among the available computing resources resulting in shorter makespan for parameter study workflows

Protective role of mindfulness, self-compassion and psychological flexibility on the burnout subtypes among psychology and nursing undergraduate students

Aims: To explore the relationship between mindfulness, self-compassion and psychological flexibility, and the burnout subtypes in university students of the Psychology and Nursing degrees, and to analyse possible risk factors for developing burnout among socio-demographic and studies-related characteristics. Design: Cross-sectional study conducted on a sample of 644 undergraduate students of Nursing and Psychology from two Spanish universities. Methods: The study was conducted between December 2015 and May 2016. Bivariate Pearson''s correlations were computed to analyse the association between mindfulness facets, self-compassion and psychological flexibility, and levels of burnout. Multivariate linear regression models and bivariate and multivariate binary logistic regressions were also computed. Results: The three subtypes of burnout presented significant correlations with psychological flexibility, self-compassion and some mindfulness facets. Psychological flexibility, self-compassion and the mindfulness facets of observing and acting with awareness were significantly associated to burnout. Among the risk factors, ‘year of study’ was the only variable to show significantly higher risk for every burnout subtype. Conclusion: The significant associations found between mindfulness, self-compassion, psychological flexibility and burnout levels underline the need of including these variables as therapeutic targets when addressing the burnout syndrome in university students. Impact. Undergraduate students, especially those of health sciences, often experience burnout. This study delves into the protective role of some psychological variables: mindfulness, self-compassion and psychological flexibility. These should be considered as potentially protective skills for developing burnout, and therefore, undergraduate students could be trained on these abilities to face their studies and their future profession to prevent experiencing burnout syndrome. © 2021 John Wiley & Sons Lt

Frenetic, under-challenged, and worn-out burnout subtypes among brazilian primary care personnel: Validation of the Brazilian “burnout clinical subtype questionnaire” (BCSQ-36/BCSQ-12)

Primary healthcare personnel show high levels of burnout. A new model of burnout has been developed to distinguish three subtypes: frenetic, under-challenged, and worn-out, which are characterized as overwhelmed, under-stimulated, and disengaged at work, respectively. The aim of this study was to assess the psychometric properties of the long/short Brazilian versions of the “Burnout Clinical Subtypes Questionnaire” (BCSQ-36/BCSQ-12) among Brazilian primary healthcare staff and its possible associations with other psychological health-related outcomes. An online cross-sectional study conducted among 407 Brazilian primary healthcare personnel was developed. Participants answered a Brazil-specific survey including the BCSQ-36/BCSQ-12, “Maslach Burnout Inventory-General Survey”, “Utrecht Work Engagement Scale”, “Hospital Anxiety/Depression Scale”, “Positive-Negative Affect Schedule”, and a Visual Analogue Scale of guilt at work. The bifactor was the model with the best fit to the data using the BCSQ-36, which allowed a general factor for each subtype. The three-correlated factors model fit better to the BCSQ-12. Internal consistence was appropriate, and the convergence between the long-short versions was high. The pattern of relationships between the burnout subtypes and the psychological outcomes suggested a progressive deterioration from the frenetic to the under-challenged and worn-out. In sum, the Brazilian BCSQ-36/BCSQ-12 showed appropriate psychometrics to be used in primary healthcare personnel

Diets containing sea cucumber (Isostichopus badionotus) meals are hypocholesterolemic in young rats

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