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In this work we study the asymmetric heat flow, i.e., thermal rectification, of a one-dimensional, mass-graded system consisting of acoupled harmonic oscillator lattice (ballistic spacer) and two diffusive leads attached to the boundaries of the former with both nearest-neighbor and next-nearest-neighbor (NNN) interactions. The latter enhance the rectification properties of the system and specially its independence on system size. The system presents a maximum rectification efficiency for a very precise value of the parameter that controls the coupling strength of the NNN interactions that depend on the temperature range wherein the device operates. The origin of this maximum value is the asymmetric local heat flow response corresponding to the NNN contribution at both sides of the lighter mass-loaded diffusive lead as quantified by the spectral properties. Upon variation of the system's parameters the performance of the device is always enhanced in the presence of NNN interactions., Comment: 9 pages, 14 figures

Over the last decade, there has been increased attention on early education, but real progress for children and families has remained out of reach. We want America's children to become lifelong learners who are able to think critically and inventively, manage their emotions and impulses, and make smart decisions by drawing upon a rich knowledge base about how the world works. To make this goal a reality for all children, New America makes eight recommendations, suggests specific actions, and pinpoints which actors--federal, state, and local policymakers, as well as educators and administrators--should help move the work forward. [Additional funding was provided by the Richard E. and Nancy P. Marriott Foundation and Trust for Learning.]

Chicago Public Schools (CPS) is in the midst of a teacher shortage crisis. At the same time, the district is looking for ways to increase the racial and linguistic diversity of the educator workforce. A new bilingual teacher residency program is attempting to help close these gaps and ease current shortages by recruiting from within and tapping paraeducators who show promise and interest in becoming licensed teachers. This paper explores how CPS designed and implemented the program in partnership with National Louis University and the National Center for Teacher Residencies.

The 2015 American Indian and Alaska Native (AI/AN) Head Start Family and Child Experiences Survey (FACES)--also referred to as AI/AN FACES 2015--is the first national study of Region XI Head Start children, families, and programs. Members of the AI/AN FACES 2015 workgroup discussed and provided input on the AI/AN FACES 2015 design, implementation, and dissemination of findings, and worked to ensure that Native voices were at the forefront. During the nearly two years of extensive planning that preceded AI/AN FACES 2015, the workgroup added questions regarding children's experience of Native language and culture into the AI/AN FACES 2015 protocol. This report includes data from survey items informed by the workgroup about Native language and cultural experiences at home and in the community. In this brief, the authors address two questions: (1) What Native language experiences did AI/AN children in Region XI Head Start have at home and in their community?; and (2) What Native cultural experiences did AI/AN children in Region XI Head Start have at home and in their community? To answer these questions, parent survey data for AI/AN children only are examined in this report. The data presented in this brief inform our understanding of the Native language and cultural experiences that AI/AN children in Region XI Head Start have at home and in their communities. They tell the story of Region XI Head Start children's Native language and cultural experiences through the eyes of their parents.

The COVID-19 pandemic has challenged authorities at different levels of government administration around the globe. When faced with diseases of this severity, it is useful for the authorities to have prediction tools to estimate in advance the impact on the health system and the human, material, and economic resources that will be necessary. In this paper, we construct an extended Susceptible-Exposed-Infected-Recovered model that incorporates the social structure of Mar del Plata, the $4^\circ$ most inhabited city in Argentina and head of the Municipality of General Pueyrred\'on. Moreover, we consider detailed partitions of infected individuals according to the illness severity, as well as data of local health resources, to bring these predictions closer to the local reality. Tuning the corresponding epidemic parameters for COVID-19, we study an alternating quarantine strategy, in which a part of the population can circulate without restrictions at any time, while the rest is equally divided into two groups and goes on successive periods of normal activity and lockdown, each one with a duration of $\tau$ days. Besides, we implement a random testing strategy over the population. We found that $\tau = 7$ is a good choice for the quarantine strategy since it matches with the weekly cycle as it reduces the infected population. Focusing on the health system, projecting from the situation as of September 30, we foresee a difficulty to avoid saturation of ICU, given the extremely low levels of mobility that would be required. In the worst case, our model estimates that four thousand deaths would occur, of which 30\% could be avoided with proper medical attention. Nonetheless, we found that aggressive testing would allow an increase in the percentage of people that can circulate without restrictions, being the equipment required to deal with the additional critical patients relatively low.

The flat-spectrum radio quasar 3C~454.3 throughout the years has presented very high activity phases (flares) in which the different wavebands increase their flux dramatically. In this work, we perform multiwavelength analysis from radio to gamma-rays and study the Mg~II~$\lambda 2798$\AA\ emission line and the UV~Fe~II band from 2008-2018. We found that an increase in the 43 GHz flux density of the quasi-stationary component C, coincides with the estimated time at which a superluminal blob ejected from the radio core (which caused the brightest flare of 2010) collides with the quasi-stationary component (at a projected distance of $\sim4.6$ pc from the radio core). The spectral index different behavior in the first ($5000 < \text{JD}-2450000 < 5600$) and second ($6600 < \text{JD}-2450000 < 7900$) flaring periods suggest changes in the physical conditions. The complex nature of the second period can be a result of a superposition of multiple events at different locations. The Mg~II has an anti-correlation with the UV-continuum while Fe~II correlates positively. Except by the time of the brightest flare of 2010, when both have a strong response at high continuum luminosities. Our results suggest that the dominant gamma-ray emission mechanism for the first flaring period is External Compton. For the second flaring period the seed photons emission region is co-spatial with the gamma-ray emission region. However, a SED study using a multizone jet emission model is required to confirm the nature of each significant flare during the second period., Comment: 23 pages, 10 figures, 3 tables, accepted for publication in ApJ

Biological systems achieve precise control over ambient fluids through the self-organization of active protein structures including flagella, cilia, and cytoskeletal networks. In active structures individual proteins consume chemical energy to generate force and motion at molecular length scales. Self-organization of protein components enables the control and modulation of fluid flow fields on micron scales. The physical principles underlying the organization and control of active-matter driven fluid flows are poorly understood. Here, we apply an optically-controlled active-matter system composed of microtubule filaments and light-switchable kinesin motor proteins to analyze the emergence of persistent flow fields in a model active matter system. Using light, we form contractile microtubule networks of varying shape. We analyze the fluid flow fields generated by a wide range of microtubule network geometries and explain the resulting flow fields within a unified theoretical framework. We specifically demonstrate that the geometry of microtubule flux at the boundary of contracting microtubule networks predicts the steady-state fluid flow fields across polygonal network geometries through finite-element simulations. Our work provides a foundation for programming microscopic fluid-flows with controllable active matter and could enable the engineering of versatile and dynamic microfluidic devices.

A significant fraction of all $\gamma$-ray sources detected by the Large Area Telescope aboard the \fer\ satellite is still lacking a low-energy counterpart. In addition, there is still a large population of $\gamma$-ray sources with associated low-energy counterparts that lack firm classifications. In the last 10 years we have undertaken an optical spectroscopic campaign to address the problem of unassociated/unidentified $\gamma$-ray sources (UGSs), mainly devoted to observing blazars and blazar candidates because they are the largest population of $\gamma$-ray sources associated to date. Here we describe the overall impact of our optical spectroscopic campaign on sources associated in \fer-LAT catalogs, coupled with objects found in the literature. In the literature search, we kept track of efforts by different teams that presented optical spectra of counterparts or potential counterparts of \fer-LAT catalog sources. Our summary includes an analysis of an additional 30 newly-collected optical spectra of counterparts or potential counterparts of \fer-LAT sources of previously unknown nature.New spectra were acquired at the Blanco 4-m and OAN-SPM 2.1-m telescopes, and those available in the Sloan Digital Sky Survey (data release 15) archive. All new sources with optical spectra analyzed here are classified as blazars. Thanks to our campaign, we altogether discovered and classified 394 targets with an additional 123 objects collected from a literature search. We began our optical spectroscopic campaign between the release of the second and third \fer-LAT source catalogs (2FGL and 3FGL, respectively), and classified about 25\% of the sources with uncertain nature and discovered a blazar-like potential counterpart for $\sim$10\% of UGSs listed therein. In the 4FGL catalog, about 350 \fer-LAT sources are classified to date thanks to our campaign. [incomplete abstract], Comment: 31 pages, 12 tables, 32 figures, 2 appendices, accepted for publication on A&A (pre-proofs version)

The proper classification of plasma regions in near-Earth space is crucial to perform unambiguous statistical studies of fundamental plasma processes such as shocks, magnetic reconnection, waves and turbulence, jets and their combinations. The majority of available studies have been performed by using human-driven methods, such as visual data selection or the application of predefined thresholds to different observable plasma quantities. While human-driven methods have allowed performing many statistical studies, these methods are often time-consuming and can introduce important biases. On the other hand, the recent availability of large, high-quality spacecraft databases, together with major advances in machine-learning algorithms, can now allow meaningful applications of machine learning to in-situ plasma data. In this study, we apply the fully convolutional neural network (FCN) deep machine-leaning algorithm to the recent Magnetospheric Multi Scale (MMS) mission data in order to classify ten key plasma regions in near-Earth space for the period 2016-2019. For this purpose, we use available intervals of time series for each such plasma region, which were labeled by using human-driven selective downlink applied to MMS burst data. We discuss several quantitative parameters to assess the accuracy of both methods. Our results indicate that the FCN method is reliable to accurately classify labeled time series data since it takes into account the dynamical features of the plasma data in each region. We also present good accuracy of the FCN method when applied to unlabeled MMS data. Finally, we show how this method used on MMS data can be extended to data from the Cluster mission, indicating that such method can be successfully applied to any in situ spacecraft plasma database., Comment: 18 pages, 6 figures

Kapton HN films, adopted worldwide due to their superior thermal durability (up to 400 {\deg}C), allow the high temperature sintering of nanoparticle based metal inks. By carefully selecting inks and Kapton substrates, outstanding thermal stability and anti-delaminating features are obtained in both aqueous and organic solutions and were applied to four novel devices: a solid state ion selective nitrate sensor, an ssDNA based mercury aptasensor, a low cost protein sensor, and a long lasting organic thin film transistor (OTFT). Many experimental studies on parameter combinations were conducted during the development of the above devices. The results showed that the ion selective nitrate sensor displayed a linear sensitivity range with a limit of detection of 2 ppm. The mercury sensor exhibited a linear correlation between the RCT values and the increasing concentrations of mercury. The protein printed circuit board (PCB) sensor provided a much simpler method of protein detection. Finally, the OTFT demonstrated a stable performance with mobility values for the linear and saturation regimes, and the threshold voltage. These devices have shown their value and reveal possibilities that could be pursued.

Planets are common objects in the Universe, observationally as well as theoretically. However, the standard theory of their formation encounters many difficulties, such as dust fall and disk lifetime problems. We positively analyze them, expecting that those problems as a whole may indicate some consistent effective model. Thus we propose a dynamical model of the planet formation based on the assumption that the inner void of gas is commonly formed in the disk without specifying any Physical origin. The basic processes of this model are the dust fall, the accumulation, and the slingshots. The dust in the protoplanetary disk rapidly falls as it grows to the meter size. Then, all of them stops at the outer edge of the void where the gas friction disappears. Such dust clusters rapidly coalesce with each other and easily cause the runaway in the dense and coherent environment. Then the huge clusters are formed there and they are the first generation planets Hot-Jupiters. They immediately slingshot smaller clusters around them towards the outer regions. They are Rockey-Planets, Cold-Gas-Giants, Ice-Giants, and `Trans Neptunian objects including `Kuiper belt/Oort cloud objects`, depending on the original core mass or the distance blown. Combining numerical calculations of the slingshots and coagulation equations, we obtain the planet population diagram, including the possibility of the massive thermal metamorphosis, the origin of the variety of planetary systems, and the possibility of stray planets/objects., Comment: 16 pages, 10 figures

In this work we prove the following: let $K$ be a convex body in the Euclidean space $\mathbb{R}^n$, $n\geq 3$, contained in the interior of the unit ball of $\mathbb{R}^n$, and let $p\in \mathbb{R}^n$ be a point such that, from each point of $\mathbb{S}^{n-1}$, $K$ looks centrally symmetric and $p$ appears as the center, then $K$ is a ball.

In this work we prove that either a sequence of axis of symmetry or a sequence of hyperplanes of symmetry of a convex body $K$ in the Euclidian space $\mathbb{E}^d, n\geq 3$, are enough to guarantee that $K$ is either a generalized body of revolution or a sphere.

Models based on neural networks and machine learning are seeing a rise in popularity in space physics. In particular, the forecasting of geomagnetic indices with neural network models is becoming a popular field of study. These models are evaluated with metrics such as the root-mean-square error (RMSE) and Pearson correlation coefficient. However, these classical metrics sometimes fail to capture crucial behavior. To show where the classical metrics are lacking, we trained a neural network, using a long short-term memory network, to make a forecast of the disturbance storm time index at origin time $t$ with a forecasting horizon of 1 up to 6 hours, trained on OMNIWeb data. Inspection of the model's results with the correlation coefficient and RMSE indicated a performance comparable to the latest publications. However, visual inspection showed that the predictions made by the neural network were behaving similarly to the persistence model. In this work, a new method is proposed to measure whether two time series are shifted in time with respect to each other, such as the persistence model output versus the observation. The new measure, based on Dynamical Time Warping, is capable of identifying results made by the persistence model and shows promising results in confirming the visual observations of the neural network's output. Finally, different methodologies for training the neural network are explored in order to remove the persistence behavior from the results., Comment: Accepted for publication in Frontiers in Astronomy and Space Sciences, section Space Physics

Disruption prediction and mitigation is of key importance in the development of sustainable tokamakreactors. Machine learning has become a key tool in this endeavour. In this paper multiple machinelearning models will be tested and compared. A particular focus has been placed on their portability.This describes how easily the models can be used with data from new devices. The methods used inthis paper are support vector machine, 2-tiered support vector machine, random forest, gradient boostedtrees and long-short term memory. The results show that the support vector machine performanceis marginally better among the standard models, while the gradient boosted trees performed the worst.The portable variant of each model had lower performance. Random forest obtained the highest portableperformance. Results also suggest that disruptions can be detected as early as 600ms before the event.An analysis of the computational cost showed all models run in less than 1ms, allowing sufficient timefor disruption mitigation., Comment: 11 pages, 10 figures, 3 tables, submitted to Journal of Plasma Physics special issue on EFTC 2019 conference

One of the goals of machine learning is to eliminate tedious and arduous repetitive work. The manual and semi-automatic classification of millions of hours of solar wind data from multiple missions can be replaced by automatic algorithms that can discover, in mountains of multi-dimensional data, the real differences in the solar wind properties. In this paper we present how unsupervised clustering techniques can be used to segregate different types of solar wind. We propose the use of advanced data reduction methods to pre-process the data, and we introduce the use of Self-Organizing Maps to visualize and interpret 14 years of ACE data. Finally, we show how these techniques can potentially be used to uncover hidden information, and how they compare with previous manual and automatic categorizations., Comment: Submitted to: Frontiers in Astronomy and Space Sciences, Space Physics, Research Topic Machine Learning in Heliophysics

The fourth Fermi Large Area Telescope Source Catalog (4FGL) lists over 5000 gamma-ray sources with statistical significance above 4$\sigma$. About 23% of the sources listed in this catalog are unidentified/unassociated gamma-ray sources while ~26% of the sources are classified as blazar candidates of uncertain type (BCUs), lacking optical spectroscopic information. To probe the blazar nature of candidate counterparts of UGSs and BCUs, we started our optical spectroscopic follow up campaign in 2012, which up to date account for more than 350 observed sources. In this paper, the tenth of our campaign, we report on the spectroscopic observations of 37 sources, mostly BCUs, whose observations were carried out predominantly at the Observatorio Astron\'omico Nacional San Pedro M\'artir and the Southern Astrophysical Research Observatory between August 2018 and September 2019. We confirm the BL Lac nature of 27 sources and the flat spectrum radio quasar nature of three sources. The remaining ones are classified as six BL Lacs galaxy-dominated and one normal galaxy. We were also able to measure the redshifts for 20 sources, including 10 BL Lacs. As in previous analyses, the largest fraction of BCUs revealed to be BL Lac objects., Comment: 24 pages, 14 figures, 3 tables (Pre-proof version)