Avaliação de um Programa de Formação em Habilidades Sociais Docentes na perspectiva de professores e alunos

The objective of this research, of qualitative and quantitative nature, is the evaluation of a Social Skills Formation Program for Teachers - PFHS-D (Portuguese initials), from the perspective of teachers and their high school students. The research is justified by the need to conduct studies on teacher education, focused on affective interaction with basic education adolescent students, which is lacking in interventions and continually challenged by public policies that lead to a precariousness of teaching work. As a method to evaluate the PFHS-D, specific tests and questionnaires were used, obtaining data from teachers and students, looking for signs of improvement or worsening in the interactions and in the Social Skills of teachers. Five teachers and 13 students from a school in the northwest of the State of São Paulo participated in this research. The results showed that there was a significant acquisition of new socially skilled behaviors, confirmed both by assessments conducted with teachers and their students.El objetivo de esta investigación, de cuño cualitativo y cuantitativo, es la evaluación de un Programa de Capacitación en Habilidades Sociales para Maestros (PFHS-D), desde la perspectiva de los maestros y sus estudiantes (de la escuela secundaria). La investigación se justifica por la necesidad de realizar estudios sobre la formación del profesorado, dirigidos a la interacción afectiva com estudiantes adolescentes em educación básica, com uma falta de intervenciones y continuamente cuestionados por políticas públicas que conducen a uma precariedade del trabajo docente. Como método para evaluar el PFHS-D, se utilizaron pruebas y cuestionarios específicos, obteniendo datos de maestros y estudiantes, buscando signos de mejora o empeoramiento en las interacciones y en las habilidades sociales de los maestros. Cinco maestros y 13 estudiantes de una escuela en el noroeste del estado de São Paulo participaron en esta investigación. Los resultados mostraron que hubo una adquisición significativa de nuevos comportamientos socialmente calificados, confirmados tanto por las evaluaciones realizadas con los maestros como con sus alumnos.O objetivo desta pesquisa, de cunho qualitativo e quantitativo, é a avaliação de um Programa de Formação em Habilidades Sociais Docentes - PFHS-D, pela perspectiva dos professores e de seus alunos do Ensino Médio. A pesquisa se justifica pela necessidade de conduzir estudos sobre formação de professores, voltados para a interação afetiva com alunos adolescentes da educação básica, carente de intervenções e desafiada continuamente pelas políticas públicas que conduzem a uma precarização do trabalho docente. Como método para avaliar o PFHS-D utilizou-se testes específicos e questionários obtendo dados com docentes e alunos, buscando sinais de melhora ou piora nas interações e nas Habilidades Sociais dos professores. Participaram desta pesquisa 5 professores e 13 alunos de uma escola do noroeste do Estado de São Paulo. Os resultados apontaram que houve aquisição significativa de novos comportamentos socialmente hábeis, confirmados tanto pelas avaliações conduzidas com os professores quanto com seus alunos

Optimization of the effective light attenuation length of YAP:Ce and LYSO:Ce crystals for a novel geometrical PET concept

Abstract The effective light attenuation length in thin bars of polished YAP:Ce and LYSO:Ce scintillators with lengths of the order of 10 cm has been studied for various wrappings and coatings of the crystal lateral surfaces. This physical parameter plays a key role in a novel 3D PET concept based on axial arrays of long scintillator bars read out at both ends by Hybrid Photodetectors (HPDs) since it influences the spatial, energy and time resolutions of such a device. In this paper we show that the effective light attenuation length of polished crystals can be reduced by wrapping their lateral surfaces with Teflon, or tuned to the desired value by depositing a coating of Cr or Au of well-defined thickness. The studies have been carried out with YAP and LYSO long scintillator bars, read out by standard photomultiplier tubes. Even if the novel PET device will use different scintillators and HPD readout, the results described here prove the feasibility of an important aspect of the concept and provide hints on the potential capabilities of the device

Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes, fundamental to nuclear fusion energy, advanced manufacturing, green transportation and others, often involve one mole or more atoms, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: a.) Detectors; b.) U-RadIT modalities; c.) Data and algorithms; and d.) Applications. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT makes increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.Comment: 51 pages, 31 figures; Overview of ultrafast radiographic imaging and tracking as a part of ULITIMA 2023 conference, Mar. 13-16,2023, Menlo Park, CA, US

4-Dimensional Trackers

4-dimensional (4D) trackers with ultra fast timing (10-30 ps) and very fine spatial resolution (O(few $\mu$m)) represent a new avenue in the development of silicon trackers, enabling new physics capabilities beyond the reach of the existing tracking detectors. This paper reviews the impact of integrating 4D tracking capabilities on several physics benchmarks both in potential upgrades of the HL-LHC experiments and in several detectors at future colliders, and summarizes the currently available sensor technologies as well as electronics, along with their limitations and directions for R$\&$D

Hit Optimization of 5‑Substituted‑<i>N</i>‑(piperidin-4-ylmethyl)‑1<i>H</i>‑indazole-3-carboxamides: Potent Glycogen Synthase Kinase‑3 (GSK-3) Inhibitors with in Vivo Activity in Model of Mood Disorders

Novel treatments for bipolar disorder with improved efficacy and broader spectrum of activity are urgently needed. Glycogen synthase kinase 3β (GSK-3β) has been suggested to be a key player in the pathophysiology of bipolar disorder. A series of novel GSK-3β inhibitors having the common <i>N</i>-[(1-alkylpiperidin-4-yl)­methyl]-1<i>H</i>-indazole-3-carboxamide scaffold were prepared taking advantage of an X-ray cocrystal structure of compound <b>5</b> with GSK-3β. We probed different substitutions at the indazole 5-position and at the piperidine-nitrogen to obtain potent ATP-competitive GSK-3β inhibitors with good cell activity. Among the compounds assessed in the <i>in vivo</i> PK experiments, <b>14i</b> showed, after i.p. dosing, encouraging plasma PK profile and brain exposure, as well as efficacy in a mouse model of mania. Compound <b>14i</b> was selected for further <i>in vitro</i>/<i>in vivo</i> pharmacological evaluation, in order to elucidate the use of ATP-competitive GSK-3β inhibitors as new tools in the development of new treatments for mood disorders

Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes, fundamental to nuclear fusion energy, advanced manufacturing, green transportation and others, often involve one mole or more atoms, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: a.) Detectors; b.) U-RadIT modalities; c.) Data and algorithms; and d.) Applications. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification, and U-RadIT optimization