Experimental comparison of features and classifiers for Android malware detection

National Research Foundation (NRF) Singapor

Optimising piezoelectric and magnetoelectric responses on CoFe2O4/P(VDF-TrFE) nanocomposites

Magnetoelectric nanocomposite films composed of magnetostrictive CoFe2O4 nanoparticles with sizes between 35 and 55 nm embedded in P(VDF-TrFE) have been successfully prepared by a solvent casting method. The ferroelectric, piezoelectric, magnetic and magnetoelectric properties of the nanocomposite and their variation with the wt% of the ferrite filler, thickness of the composite and direction of the applied magnetic field have been investigated. Ferroelectric and piezoelectric properties are improved when small amount of ferrite nanoparticles were added to the polymeric matrix. Magnetic properties vary linearity with ferrite content. The highest magnetoelectric response of 41.3 mV/cmOe was found in the composite with 72wt% when a 2.5 kOe DC field was transversely applied to the sample surface. This value is among the highest reported in two phase particulate polymer nanocomposites. Thickness of the composite has no influence in the magnetoelectric response, allowing tailoring sensor thickness for specific applications. The good value of the magnetoelectric coefficient and the flexibility of the films make these composites suitable for applications in magnetoelectric smart devices.Fundação para a Ciência e a Tecnologia (FCT) (PTDC/CTM/69316/2006), (SFRH/BD/45265/2008).FEDER “Programa Operacional Factores de Competitividade – COMPETE” (NANO/NMed-SD/0156/2007)Basque Government Industry Department - Project Actimat (ETORTEK-IE10-272)COST Action MP1003, 2010 - The „European Scientific Network for Artificial Muscles‟ (ESNAM)

Denitrogenation process in ThMn12 nitride by in situ neutron powder diffraction

ThMn12 nitrides are good candidates for high performance permanent magnets. However, one of the remaining challenges is to transfer the good properties of the powder into a useful bulk magnet. Thus, understanding the denitrogenation process of this phase is of key importance. In this study, we investigate the magnetic and structural stability of the (Nd0.75, Pr0.25)1.2Fe10.5Mo1.5Nx compound (x=0 and 0.85) as function of temperature by means of neutron powder diffraction. Thermal dependence of the lattice parameters, formation of a-(Fe, Mo), as well as the nitrogen content in the nitrides are investigated by heating the compounds up to 1010 K. The decomposition takes place mainly via the formation of the a-(Fe, Mo) phase, which starts at around 900 K, whereas the nitrogen remains stable in the lattice. Additionally, we show that the magnetic properties of the nitrides [M(4T)=90 Am2/kg and Hc=0.55 T] are maintained after the thermal treatments up to 900 K. This study demonstrates that the ThMn12 nitrides with the Mo stabilizing element offer good prospects for a bulk magnet provided an adequate processing route is found

Magnetic behavior of metastable fcc Fe-Cu after thermal treatments

A ferromagnetic and supersaturated fcc Fe_51Cu_49 solid solution has been obtained by mechanical alloying. After subsequent thermal treatments the fcc phase undergoes a spinodal decomposition which finally, at 780 K, yields a mixture of fcc and bcc phases. In this work, a systematic magnetic study is carried out on samples at diferent decomposition states in order to determine the process of transformation into the stable phases. We observe a 20% maximum diminution on the magnetic moment with increasing temperatures of the thermal treatment. The Mössbauer spectrum taken at 8 K shows that 20% of the Fe atoms are in a nonferromagnetic state. On the other hand, upon heating up to 723 K the roomtemperature coercive field increases dramatically to 640 Oe, and after cooling down to 10 K it decreases to 270 Oe. Deviations from the T law in the temperature dependence of the magnetization have been observed. This behavior is explained by fluctuations in composition due to the spinodal decomposition, which lead to fluctuations of the magnetic order parameters, i.e., magnetic moment and Curie temperature

A Minimal Model of Metabolism Based Chemotaxis

Since the pioneering work by Julius Adler in the 1960's, bacterial chemotaxis has been predominantly studied as metabolism-independent. All available simulation models of bacterial chemotaxis endorse this assumption. Recent studies have shown, however, that many metabolism-dependent chemotactic patterns occur in bacteria. We hereby present the simplest artificial protocell model capable of performing metabolism-based chemotaxis. The model serves as a proof of concept to show how even the simplest metabolism can sustain chemotactic patterns of varying sophistication. It also reproduces a set of phenomena that have recently attracted attention on bacterial chemotaxis and provides insights about alternative mechanisms that could instantiate them. We conclude that relaxing the metabolism-independent assumption provides important theoretical advances, forces us to rethink some established pre-conceptions and may help us better understand unexplored and poorly understood aspects of bacterial chemotaxis

Evolving an optimal decision template for combining classifiers.

In this paper, we aim to develop an effective combining algorithm for ensemble learning systems. The Decision Template method, one of the most popular combining algorithms for ensemble systems, does not perform well when working on certain datasets like those having imbalanced data. Moreover, point estimation by computing the average value on the outputs of base classifiers in the Decision Template method is sometimes not a good representation, especially for skewed datasets. Here we propose to search for an optimal decision template in the combining algorithm for a heterogeneous ensemble. To do this, we first generate the base classifier by training the pre-selected learning algorithms on the given training set. The meta-data of the training set is then generated via cross validation. Using the Artificial Bee Colony algorithm, we search for the optimal template that minimizes the empirical 0–1 loss function on the training set. The class label is assigned to the unlabeled sample based on the maximum of the similarity between the optimal decision template and the sample’s meta-data. Experiments conducted on the UCI datasets demonstrated the superiority of the proposed method over several benchmark algorithms

Is there life after degeneration? The organizational life cycle of cooperatives under a ‘grow-or-die’ dichotomy

This article provides an in-depth, longitudinal analysis combining real-time and retrospective data on a set of Mondragon's industrial cooperatives that are organized as international groups. We examine the life cycle of these international cooperative groups, which is expected to evolve differently to that of small- and medium-sized cooperatives that operate exclusively on a local scale. The article is theoretically informed by the cooperative life cycle theory, as well as by recent insights from the degeneration and regeneration theses. Our analysis yields an intricate picture of the evolution of cooperatives faced with a ‘grow-or-die’ dichotomy. On the one hand, our findings reject the highly simplistic and deterministic view of the degeneration thesis by demonstrating that these cooperatives can mobilize resources to revitalize cooperative values and practices. On the other, we find that regeneration may not occur in a consistent, sequential fashion as the previous literature suggests, but rather degenerative and regenerative tendencies can occur simultaneously, even leading to long-lasting, unresolvable situations. In light of this, the article asks future research to draw on power-aware and politically informed approaches for further understanding of how cooperatives manage the tensions at each organizational stage of their life cycle, and of which organizational actors benefit, and how, from reversing some degenerative tendencies while maintaining others intact

Behavioral metabolution: the adaptive and evolutionary potential of metabolism-based chemotaxis

We use a minimal model of metabolism-based chemotaxis to show how a coupling between metabolism and behavior can affect evolutionary dynamics in a process we refer to as behavioral metabolution. This mutual influence can function as an in-the-moment, intrinsic evaluation of the adaptive value of a novel situation, such as an encounter with a compound that activates new metabolic pathways. Our model demonstrates how changes to metabolic pathways can lead to improvement of behavioral strategies, and conversely, how behavior can contribute to the exploration and fixation of new metabolic pathways. These examples indicate the potentially important role that the interplay between behavior and metabolism could have played in shaping adaptive evolution in early life and protolife. We argue that the processes illustrated by these models can be interpreted as an unorthodox instantiation of the principles of evolution by random variation and selective retention. We then discuss how the interaction between metabolism and behavior can facilitate evolution through (i) increasing exposure to environmental variation, (ii) making more likely the fixation of some beneficial metabolic pathways, (iii) providing a mechanism for in-the-moment adaptation to changes in the environment and to changes in the organization of the organism itself, and (iv) generating conditions that are conducive to speciatio