Optimization of the extraordinary magnetoresistance in semiconductor-metal hybrid structures for magnetic-field sensor applications

Semiconductor-metal hybrid structures can exhibit a very large geometrical magnetoresistance effect, the so-called extraordinary magnetoresistance (EMR) effect. In this paper, we analyze this effect by means of a model based on the finite element method and compare our results with experimental data. In particular, we investigate the important effect of the contact resistance $\rho_c$ between the semiconductor and the metal on the EMR effect. Introducing a realistic $\rho_c=3.5\times 10^{-7} \Omega{\rm cm}^2$ in our model we find that at room temperature this reduces the EMR by 30% if compared to an analysis where $\rho_c$ is not considered.Comment: 4 pages; manuscript for MSS11 conference 2003, Nara, Japa

Emission of Scission Neutrons in the Sudden Approximation

At a certain finite neck radius during the descent of a fissioning nucleus from the saddle to the scission point, the attractive nuclear forces can no more withstand the repulsive Coulomb forces producing the neck rupture and the sudden absorption of the neck stubs by the fragments. At that moment, the neutrons, although still characterized by their pre-scission wave functions, find themselves in the newly created potential of their interaction with the separated fragments. Their wave functions become wave packets with components in the continuum. The probability to populate such states gives evidently the emission probability of neutrons at scission. In this way, we have studied scission neutrons for the fissioning nucleus $^{236}$U, using two-dimensional realistic nuclear shapes. Both the emission probability and the distribution of the emission points relative to the fission fragments strongly depend on the quantum numbers of the pre-scission state from which the neutron is emitted. In particular it was found that states with $\Omega \pi$ = 1/2+ dominate the emission. Depending on the assumed pre- and post-scission configurations and on the emission-barrier height, 30 to 50% of the total scission neutrons are emitted from 1/2+ states. Their emission points are concentrated in the region between the newly separated fragments. The upper limit for the total number of neutrons per scission event is predicted to lie between 0.16 and 1.73 (depending on the computational assumptions).Comment: 31 pages, 16 figures, 2 table

Symmetries of Quadrupole-Collective Vibrational Motion in Transitional Even-Even 124−134Xenon Nuclei

Projectile-Coulomb excitation of Xe isotopes has been performed at ANL using the Gammasphere array for the detection of γ-rays. The one-quadrupole phonon 2+ 1,ms mixed-symmetry state (MSS) has been traced in the stable N=80 isotones down to 134Xe. First, the data on absolute E2 andM1 transition rates quantify the amount of F-spin symmetry in these nuclei and provide a new local measure for the pn-QQ interaction. Second, the evolution of the 2+ 1,ms state has been studied along the sequence of stable even-even 124−134Xe isotopes that are considered to form a shape transition path from vibrational nuclei with vibrational U(5) symmetry near N=82 to γ-softly deformed shapes with almost O(6) symmetry. Third, our data on more than 50 absolute E2 transition rates between off-yrast low-spin states of 124,126Xe enable us to quantitatively test O(6) symmetry in these nuclei. As a result we find that O(6) symmetry is more strongly broken in the A=130 mass region than previously thought. The data will be discussed

Synthesizing a Solution Space for Prescriptive Design Knowledge Codification

One of Design Science Research’s (DSR) principal purposes is to generate and codify design knowledge. Codification in DSR is done by providing clear chunks of prescriptive knowledge that guide the design of future solutions, including instructions on how to design (parts of) artifacts. Although various codification mechanisms have emerged over the last years, design principles are among the most prominent mechanisms. Yet, distinguishing between different codification mechanisms is often blurry, hindering designers from making informed decisions regarding appropriate mechanisms for their research aim and leveraging the full potential of the prescriptive knowledge. We seek to bridge the challenge of selecting from the fuzzy array of codification mechanisms by proposing an inductively generated solution space. We provide a taxonomy to organize essential elements of prescriptive knowledge based on an analysis of design-oriented literature in four meta-dimensions (i.e., communication, application, development, and justification). These meta-dimensions make transparent how codified prescriptive design knowledge works. Overall, the taxonomy guides designers in reflecting on and selecting from the set of suitable elements for their statements. Also, providing a synthesis of options for codifying prescriptive design knowledge will simplify the identification and advance the positioning of DSR contributions

Fink: early supernovae Ia classification using active learning

We describe how the Fink broker early supernova Ia classifier optimizes its ML classifications by employing an active learning (AL) strategy. We demonstrate the feasibility of implementation of such strategies in the current Zwicky Transient Facility (ZTF) public alert data stream. We compare the performance of two AL strategies: uncertainty sampling and random sampling. Our pipeline consists of 3 stages: feature extraction, classification and learning strategy. Starting from an initial sample of 10 alerts (5 SN Ia and 5 non-Ia), we let the algorithm identify which alert should be added to the training sample. The system is allowed to evolve through 300 iterations. Our data set consists of 23 840 alerts from the ZTF with confirmed classification via cross-match with SIMBAD database and the Transient name server (TNS), 1 600 of which were SNe Ia (1 021 unique objects). The data configuration, after the learning cycle was completed, consists of 310 alerts for training and 23 530 for testing. Averaging over 100 realizations, the classifier achieved 89% purity and 54% efficiency. From 01/November/2020 to 31/October/2021 Fink has applied its early supernova Ia module to the ZTF stream and communicated promising SN Ia candidates to the TNS. From the 535 spectroscopically classified Fink candidates, 459 (86%) were proven to be SNe Ia. Our results confirm the effectiveness of active learning strategies for guiding the construction of optimal training samples for astronomical classifiers. It demonstrates in real data that the performance of learning algorithms can be highly improved without the need of extra computational resources or overwhelmingly large training samples. This is, to our knowledge, the first application of AL to real alerts data.Comment: 8 pages, 7 figures - submitted to Astronomy and Astrophysics. Comments are welcom