Dynamic ordering of driven vortex matter in the peak effect regime of amorphous MoGe films and 2H-NbSe2 crystals

Dynamic ordering of driven vortex matter has been investigated in the peak effect regime of both amorphous MoGe films and 2H-NbSe2 crystals by mode locking (ML) and dc transport measurements. ML features allow us to trace how the shear rigidity of driven vortices evolves with the average velocity. Determining the onset of ML resonance in different magnetic fields and/or temperatures, we find that the dynamic ordering frequency (velocity) exhibits a striking divergence in the higher part of the peak effect regime. Interestingly, this phenomenon is accompanied by a pronounced peak of dynamic critical current. Mapping out field-temperature phase diagrams, we find that divergent points follow well the thermodynamic melting curve of the ideal vortex lattice over wide field and/or temperature ranges. These findings provide a link between the dynamic and static melting phenomena which can be distinguished from the disorder induced peak effect.Comment: 9 pages, 6 figure

Modeling and forecasting gender-based violence through machine learning techniques

Gender-Based Violence (GBV) is a serious problem that societies and governments must address using all applicable resources. This requires adequate planning in order to optimize both resources and budget, which demands a thorough understanding of the magnitude of the problem, as well as analysis of its past impact in order to infer future incidence. On the other hand, for years, the rise of Machine Learning techniques and Big Data has led different countries to collect information on both GBV and other general social variables that in one way or another can affect violence levels. In this work, in order to forecast GBV, firstly, a database of features related to more than a decade’s worth of GBV is compiled and prepared from official sources available due to Spain’s open access. Then, secondly, a methodology is proposed that involves testing different methods of features selection so that, with each of the subsets generated, four techniques of predictive algorithms are applied and compared. The tests conducted indicate that it is possible to predict the number of GBV complaints presented to a court at a predictive horizon of six months with an accuracy (Root Median Squared Error) of 0.1686 complaints to the courts per 10,000 inhabitants—throughout the whole Spanish territory—with a Multi-Objective Evolutionary Search Strategy for the selection of variables, and with Random Forest as the predictive algorithm. The proposed methodology has also been successfully applied to three specific Spanish territories of different populations (large, medium, and small), pointing to the presented method’s possible use elsewhere in the world

Transverse depinning and melting of a moving vortex lattice in driven periodic Josephson junction arrays

We study the effect of thermal fluctuations in a vortex lattice driven in the periodic pinning of a Josephson junction array. The phase diagram current ($I$) vs. temperature ($T$) is studied. Above the critical current $I_c(T)$ we find a moving vortex lattice (MVL) with anisotropic Bragg peaks. For large currents $I\gg I_c(T)$, there is a melting transition of the MVL at $T_M(I)$. When applying a small transverse current to the MVL, there is no dissipation at low $T$. We find an onset of transverse vortex motion at a transverse depinning temperature $T_{tr}(I)<T_M(I)$.Comment: 4 pages, 4 figures, Figure 2 changed, added new reference

Strong oviposition preference for Bt over non-Bt maize in Spodoptera frugiperda and its implications for the evolution of resistance

BACKGROUND: Transgenic crops expressing Bt toxins have substantial benefits for growers in terms of reduced synthetic insecticide inputs, area-wide pest management and yield. This valuable technology depends upon delaying the evolution of resistance. The ‘high dose/refuge strategy’, in which a refuge of non-Bt plants is planted in close proximity to the Bt crop, is the foundation of most existing resistance management. Most theoretical analyses of the high dose/refuge strategy assume random oviposition across refugia and Bt crops. RESULTS: In this study we examined oviposition and survival of Spodoptera frugiperda across conventional and Bt maize and explored the impact of oviposition behavior on the evolution of resistance in simulation models. Over six growing seasons oviposition rates per plant were higher in Bt crops than in refugia. The Cry1F Bt maize variety retained largely undamaged leaves, and oviposition preference was correlated with the level of feeding damage in the refuge. In simulation models, damage-avoiding oviposition accelerated the evolution of resistance and either led to requirements for larger refugia or undermined resistance management altogether. Since larval densities affected oviposition preferences, pest population dynamics affected resistance evolution: larger refugia were weakly beneficial for resistance management if they increased pest population sizes and the concomitant degree of leaf damage. CONCLUSIONS: Damaged host plants have reduced attractiveness to many insect pests, and crops expressing Bt toxins are generally less damaged than conventional counterparts. Resistance management strategies should take account of this behavior, as it has the potential to undermine the effectiveness of existing practice, especially in the tropics where many pests are polyvoltinous. Efforts to bring down total pest population sizes and/or increase the attractiveness of damaged conventional plants will have substantial benefits for slowing the evolution of resistance

Order in driven vortex lattices in superconducting Nb films with nanostructured pinning potentials

Driven vortex lattices have been studied in a material with strong pinning, such as Nb films. Samples in which natural random pinning coexists with artificial ordered arrays of defects (submicrometric Ni dots) have been fabricated with different geometries (square, triangular and rectangular). Three different dynamic regimes are found: for low vortex velocities, there is a plastic regime in which random defects frustrate the effect of the ordered array; then, for vortex velocities in the range 1-100 m/s, there is a sudden increase in the interaction between the vortex lattice and the ordered dot array, independent on the geometry. This effect is associated to the onset of quasi long range order in the vortex lattice leading to an increase in the overlap between the vortex lattice and the magnetic dots array. Finally, at larger velocities the ordered array-vortex lattice interaction is suppresed again, in agreement with the behavior found in numerical simulations.Comment: 8 text pages + 4 figure

Analysis of platelets from a diet-induced obesity rat model: elucidating platelet dysfunction in obesity

Obesity is one of the main health problems in industrialized countries. The contribution of multiple factors developed in obesity can hardly be modeled in vitro. In this context, the development of animal models mimicking human obesity could be essential. The aim of the present study was to compare platelets from a diet-induced obesity (DIO) rat model with their lean control group in order to elucidate platelet dysfunction mechanisms in obesity and correlate the results with previous data from morbid obese patients. In parallel, we also established a blood collection and platelet isolation methodology to study the DIO rat model at biochemical and functional level. Optimal blood collection was obtained from vena cava and platelet isolation was based on a serial of centrifugations avoiding platelet activation. Our results show that the DIO rat model simulate obesity pathologically since weight gain, fasting glucose and platelet counts are increased in obese rats. Interestingly, platelet levels of the active form of Src (pTyr(419)) showed a tendency to increase in DIO rats pointing towards a potential dysfunction in Src family kinases-related signalling pathways in obesity. Moreover, platelets from DIO rats adhere more to collagen compared with the control group, pointing towards Glycoprotein VI (GPVI) as one of the dysregulated receptors in obesity, in agreement with our recent studies in humans. These results confirm that obesity, in line with human studies, present a platelet dysregulation, and highlight the relevance of considering novel antithrombotic drug targets in these patients, such as GPVI

Current profiles and AC losses of a superconducting strip with elliptic cross-section in perpendicular magnetic field

The case of a hard type II superconductor in the form of strip with elliptic cross-section when placed in transverse magnetic field is studied. We approach the problem in two steps, both based on the critical-state model. First we calculate numerically the penetrated current profiles that ensure complete shielding in the interior, without assuming an a priori form for the profiles. In the second step we introduce an analytical approximation that asumes that the current profiles are ellipses. Expressions linking the sample magnetization to the applied field are derived covering the whole range of applied fields. The theoretical predictions are tested by the comparison with experimental data for the imaginary part of AC susceptibility.Comment: 12 pages; 3 figure

On building physics-based AI models for the design and SHM of mooring systems

Expert systems in industrial processes are modelled using physics-based approaches, data-driven models or hybrid approaches in which however the underlying physical models generally constitute a separate block with respect to the Artificial Intelligence (AI) technique(s). This work applies the novel concept of “imbrication”-a physics-based AI approach-to the mooring system of offshore renewable energy devices to achieve a complete integration of both perspectives. This approach can reduce the size of the training dataset and computational time while delivering algorithms with higher generalization capability and explicability. We first undertake the design of the mooring system by developing a surrogate model coupled with a Bayesian optimiser. Then, we analyse the structural health monitoring of the mooring system by designing a supervised Deep Neural Network architecture. Herein, we describe the characteristics of the imbrication process, analyse preliminary results of our investigation and provide considerations for orienting further research work and sector applicability