Microstructure and chemical composition of Roman orichalcum coins emitted after the monetary reform of Augustus (23 B.C.)

A collection of ancient Roman orichalcum coins, i.e., a copper-zinc alloy, minted under the reigns from Caesar to Domitianus, have been characterised using scanning electron microscopy (SEM-EDS) and electron microprobe analysis (EMPA). We studied, for the first time, coins emitted by Romans after the reforms of Augustus (23 B.C.) and Nero (63-64 A.D). These coins, consisting of asses, sestertii, dupondii and semisses, were analysed using non- and invasive analyses, aiming to explore microstructure, corrosive process and to acquire quantitative chemical analysis. The results revealed that the coins are characterized by porous external layers, which are affected by dezincification and decuprification processes. As pictured by the X-ray maps, the elemental distribution of Cu and Zn shows patterns of depletion that in some cases penetrate in deep up to 1 mm. The composition of the un-corroded nucleus is a Cu-Zn alloy containing up to 30% of Zn, typical of coins produced via cementation process

Harnack inequality and regularity for degenerate quasilinear elliptic equations

We prove Harnack inequality and local regularity results for weak solutions of a quasilinear degenerate equation in divergence form under natural growth conditions. The degeneracy is given by a suitable power of a strong $A_\infty$ weight. Regularity results are achieved under minimal assumptions on the coefficients and, as an application, we prove $C^{1,\alpha}$ local estimates for solutions of a degenerate equation in non divergence form

K-means Clustering to Study How Student Reasoning Lines Can Be Modified by a Learning Activity Based on Feynman\u2019s Unifying Approach

Research in Science Education has shown that often students need to learn how to identify differences and similarities between descriptive and explicative models. The development and use of explicative skills in the field of thermal science has always been a difficult objective to reach. A way to develop analogical reasoning is to use in Science Education unifying conceptual frameworks. In this paper we describe a 20-hour workshop focused on Feynman\u2019s Unifying Approach and the two-level system. We measure its efficacy in helping undergraduate chemical engineering students explain phenomena by applying an explanatory model. Contexts involve systems for which a process is activated by thermally overcoming a well-defined potential barrier. A questionnaire containing six open-ended questions was administered to the students before instruction. A second one, similar but focused on different physical content was administered after instruction. Responses were analysed using k-means Cluster Analysis and students\u2019 inferred lines of reasoning about the analysed phenomena were studied. We conclude that students reasoning lines seem to have clearly evolved to explicative ones and it is reasonable to think that the Feynman Unifying Approach has favoured this change

Prospective elementary teachers\u2019 perceptions of the processes of modeling: A case study

In this paper we discuss a study on the approaches to modeling of students of the 4-year elementary school teacher program at the University of Palermo, Italy. The answers to a specially designed questionnaire are analyzed on the basis of an a priori analysis made using a general scheme of reference on the epistemology of mathematics and physics. The study is performed by using quantitative data analysis methods, i.e. factorial analysis of the correspondences and implicative analysis. A qualitative analysis of key words and terms used by students during interviews is also used to examine some aspects that emerged from the quantitative analysis. The students have been classified on the basis of their different epistemological approaches to knowledge construction, and implications between different conceptual strategies used to answer the questionnaire have been highlighted. The study\u2019s conclusions are consistent with previous research, but the use of quantitative data analysis allowed us to classify the students into three \u201cprofiles\u201d related to different epistemological approaches to knowledge construction, and to show the implications of the different conceptual strategies used to answer the questionnaire, giving an estimation of the classification or implication \u201cstrength.\u201d Some hints on how a course for elementary school physics and mathematics education can be planned to orient the future teachers to the construction of models of explanation are reported

A New Approach to Investigate Students\u2019 Behavior by Using Cluster Analysis as an Unsupervised Methodology in the Field of Education

The problem of taking a set of data and separating it into subgroups where the ele- ments of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied through the statistical method of cluster analysis. In this paper we want to discuss the application of this method to the field of education: particularly, we want to present the use of cluster analysis to separate students into groups that can be recognized and characterized by common traits in their answers to a questionnaire, without any prior knowledge of what form those groups would take (unsupervised classification). We start from a detailed study of the data processing needed by cluster analysis. Then two methods commonly used in cluster analysis are before described only from a theoretical point a view and after in the Section 4 through an example of application to data coming from an open-ended questionnaire administered to a sample of university students. In particular we de- scribe and criticize the variables and parameters used to show the results of the clus- ter analysis methods

Activated Tryptophan-Kynurenine metabolic system in the human brain is associated with learned fear

Fear is widely recognized as a defensive emotion that has developed as a consequence of its adaptive function in protecting the entire animal kingdom from danger, ensuring the survival (Bouton, 2002; Milad and Quirk, 2002). Although fear associated with specific stimuli is an innate emotion (e.g., a loud noise triggering fear in infants, fear of potential predators), it can also be acquired rapidly and permanently to enable an appropriate and adaptive response to new or unpredictable environmental situations (LeDoux, 2000; Beckers et al., 2013)

Neuropharmacological Modulation of N-methyl-D-aspartate, Noradrenaline and Endocannabinoid Receptors in Fear Extinction Learning: Synaptic Transmission and Plasticity

Learning to recognize and respond to potential threats is crucial for survival. Pavlovian threat conditioning represents a key paradigm for investigating the neurobiological mechanisms of fear learning. In this review, we address the role of specific neuropharmacological adjuvants that act on neurochemical synaptic transmission, as well as on brain plasticity processes implicated in fear memory. We focus on novel neuropharmacological manipulations targeting glutamatergic, noradrenergic, and endocannabinoid systems, and address how the modulation of these neurobiological systems affects fear extinction learning in humans. We show that the administration of N-methyl-D-aspartate (NMDA) agonists and modulation of the endocannabinoid system by fatty acid amide hydrolase (FAAH) inhibition can boost extinction learning through the stabilization and regulation of the receptor concentration. On the other hand, elevated noradrenaline levels dynamically modulate fear learning, hindering long-term extinction processes. These pharmacological interventions could provide novel targeted treatments and prevention strategies for fear-based and anxiety-related disorders

Edge channel mixing induced by potential steps in an integer quantum Hall system

We investigate the coherent mixing of co-propagating edge channels in a quantum Hall bar produced by step potentials. In the case of two edge channels it is found that, although a single step induces only a few percent mixing, a series of steps could yield 50% mixing. In addition, a strong mixing is found when the potential height of a single step allows a different number of edge channels on the two sides of the step. Charge density probability has been also calculated even for the case where the step is smoothened.Comment: final version: 7 pages, 6 figure

The Influence of Vicarious Fear-Learning in “Infecting” Reactive Action Inhibition

Since the dawn of cognitive neuroscience, emotions have been recognized to impact on several executive processes, such as action inhibition. However, the complex interplay between emotional stimuli and action control is not yet fully understood. One way to measure inhibitory control is the stop-signal task (SST), which estimates the ability to cancel outright an action to the presentation of a stop signal by means of the stop-signal reaction times (SSRTs). Impaired as well as facilitated action control has been found when faced with intrinsic emotional stimuli as stop signals in SSTs. Here, we aimed at investigating more deeply the power of negative stimuli to influence our action control, testing the hypothesis that a previously neutral stimulus [i.e., the image of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)], which has been conditioned through vicarious fear learning, has the same impact on reactive action inhibition performance as an intrinsically negative stimulus (i.e., a fearful face or body). Action control capabilities were tested in 90 participants by means of a SST, in which the stop signals were represented by different negative stimuli. Results showed that the SARS-CoV-2 image enhanced the ability to suppress an ongoing action similarly to observing fearful facial expressions or fearful body postures. Interestingly, we found that this effect was predicted by impulsivity traits: for example, the less self-control the participants had, the less they showed emotional facilitation for inhibitory performance. These results demonstrated that vicarious fear learning has a critical impact on cognitive abilities, making a neutral image as threatening as phylogenetically innate negative stimuli and able to impact on our behavioral control

Hierarchical Model for the Evolution of Cloud Complexes

The structure of cloud complexes appears to be well described by a "tree structure" representation when the image is partitioned into "clouds". In this representation, the parent-child relationships are assigned according to containment. Based on this picture, a hierarchical model for the evolution of Cloud Complexes, including star formation, is constructed, that follows the mass evolution of each sub-structure by computing its mass exchange (evaporation or condensation) with its parent and children, which depends on the radiation density at the interphase. For the set of parameters used as a reference model, the system produces IMFs with a maximum at too high mass (~2 M_sun) and the characteristic times for evolution seem too long. We show that these properties can be improved by adjusting model parameters. However, the emphasis here is to illustrate some general properties of this nonlinear model for the star formation process. Notwithstanding the simplifications involved, the model reveals an essential feature that will likely remain if additional physical processes are included. That is: the detailed behavior of the system is very sensitive to variations on the initial and external conditions, suggesting that a "universal" IMF is very unlikely. When an ensemble of IMFs corresponding to a variety of initial or external conditions is examined, the slope of the IMF at high masses shows variations comparable to the range derived from observational data. (Abridged)Comment: Latex, 29 pages, 13 figures, accepted for publication in Ap