Teachers\u27 Perceptions of Manipulatives During Middle School Math Instruction

In a Colorado school district, school personnel and parents were concerned that middle school math proficiency levels were low for 2011-2014 and math teachers were not using manipulatives in their classes to increase math performance. The district\u27s math coordinator did not foresee providing specific professional development (PD) for math manipulative use to address these concerns. Without this PD, math teachers may be ill-quipped to teach math concepts when using manipulatives, which, in turn, could lead to further poor math performance. The purpose of this qualitative bounded collective case study was to explore middle school teachers\u27 perceptions of PD and perceived self-efficacy regading the implementation of manipulatives. Knowles\u27s andragogy and Piaget\u27s cognitive development theories framed this study. A homogeneous sample of 12 voluntary participants with more than 5 years teaching middle school math, both with and without access to manipulatives, volunteered to participate in this study. Data from observations, interviews, and archival documents were analyzed using comparative and inductive analyses and were analytically coded. Participants reported a need for PD that focused on physical and virtual manipulatives (PM and VM) and a low perceived self-efficacy regarding manipulatives use during math instruction. A blended PD using face-to-face and distance learning formats was designed to increase math teachers\u27 knowledge of and perceived self-efficacy with PM and VM for math instruction. This endeavor may contribute to positive social change by reforming PD opportunities to support teachers\u27 practice and self-efficacy using manipulatives during math instruction, ultimately increasing student performance

Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary “Real Life” Results

The aim of our study is the development of an automatic tool for the prioritization of COVID-19 diagnostic workflow in the emergency department by analyzing chest X-rays (CXRs). The Convolutional Neural Network (CNN)-based method we propose has been tested retrospectively on a single-center set of 542 CXRs evaluated by experienced radiologists. The SARS-CoV-2 positive dataset (n = 234) consists of CXRs collected between March and April 2020, with the COVID-19 infection being confirmed by an RT-PCR test within 24 h. The SARS-CoV-2 negative dataset (n = 308) includes CXRs from 2019, therefore prior to the pandemic. For each image, the CNN computes COVID-19 risk indicators, identifying COVID-19 cases and prioritizing the urgent ones. After installing the software into the hospital RIS, a preliminary comparison between local daily COVID-19 cases and predicted risk indicators for 2918 CXRs in the same period was performed. Significant improvements were obtained for both prioritization and identification using the proposed method. Mean Average Precision (MAP) increased (p < 1.21 × 10(−21) from 43.79% with random sorting to 71.75% with our method. CNN sensitivity was 78.23%, higher than radiologists’ 61.1%; specificity was 64.20%. In the real-life setting, this method had a correlation of 0.873. The proposed CNN-based system effectively prioritizes CXRs according to COVID-19 risk in an experimental setting; preliminary real-life results revealed high concordance with local pandemic incidence

When collateral vessels matter: asymptomatic Leriche syndrome

While acute arterial occlusion causes life-threatening ischemia and organ damage requiring urgent revascularization, the incidental identification of arterial occlusions in asymptomatic patients represents a therapeutic dilemma in clinicians. Does chronic asymptomatic artery occlusion require specific treatment

Fabrication and First Full Characterisation of Timing Properties of 3D Diamond Detectors

Tracking detectors at future high luminosity hadron colliders are expected to be able to stand unprecedented levels of radiation as well as to efficiently reconstruct a huge number of tracks and primary vertices. To face the challenges posed by the radiation damage, new extremely radiation hard materials and sensor designs will be needed, while the track and vertex reconstruction problem can be significantly mitigated by the introduction of detectors with excellent timing capabilities. Indeed, the time coordinate provides extremely powerful information to disentangle overlapping tracks and hits in the harsh hadronic collision environment. Diamond 3D pixel sensors optimised for timing applications provide an appealing solution to the above problems as the 3D geometry enhances the already outstanding radiation hardness and allows to exploit the excellent timing properties of diamond. We report here the first full timing characterisation of 3D diamond sensors fabricated by electrode laser graphitisation in Florence. Results from a 270MeV pion beam test of a first prototype and from tests with a β source on a recently fabricated 55×55μm2 pitch sensor are discussed. First results on sensor simulation are also presented

MQTT-Auth: a Token-based Solution to Endow MQTT with Authentication and Authorization Capabilities

Security in the Internet of Things is a current hot topic and it may comprise different aspects such as confidentiality and integrity of personal data, as well as the authentication and the authorization to access smart objects that are spreading more and more in our every-day lives. In this work we focus on MQTT (Message Queue Telemetry Transport), a message-based communication protocol explicitly designed for low-power machine-to-machine communications and based on the publish-subscribe paradigm. First of all, we provide an accurate analysis of some of the most recent security solutions and improvements of MQTT found in the literature. Secondly, we describe in detail a novel secure solution, called MQTT-Auth, to protect specific topics in MQTT. This solution is based on the AugPAKE security algorithm for guaranteeing confidentiality, and onto two tokens which permit to authenticate the usage of a topic and to guarantee authorization in accessing a topic respectively. MQTT-Auth can also be easily extended to a hierarchical structure of topics and entities. Finally, we compare MQTT-Auth with some solutions for securing MQTT being present in the relevant literature, and we provide some details on how MQTT-Auth has been implemented and successfully tested

Improved Methodology to Estimate the Power Transfer Efficiency in an Inductively Coupled Radio Frequency Ion Source

The International Thermonuclear Experimental Reactor neutral beam injector includes an ion source which can produce D− ion beams for 1 h, accelerated at the energy of 1 MeV. An ion source consists of a driver where the plasma is produced by the application of the radio frequency (RF) power to an inductive coil. This paper presents an improved methodology which provides an estimation of the power transfer efficiency to the plasma of the driver. The developed methodology is based on different mechanisms which are responsible for the plasma heating (ohmic and stochastic) and an electrical model describing the power transfer to the plasma. As a first approximation in a previous work, a transformer model was assumed as an electrical model. In this paper, a main improvement is introduced based on the development of a multi-filament model which takes into account the mutual coupling between the RF coil, the plasma, and the passive metallic structure. The methodology is applied to the negative ion optimization 1 (NIO1), a flexible negative ion source, currently in operation at Consorzio RFX, Italy. The results from the two models, transformer and multi-filament, are presented and compared in terms of plasma equivalent resistance and power transfer efficiency. It is found that results obtained from both the transformer and the multi-filament model follow the same trend in comparison with the applied frequency and the other plasma parameters like electron density, temperature, and gas pressure. However, lower values of the plasma equivalent resistance and power transfer efficiency are observed with the multi-filament model. The multi-filament model reproduces a more realistic experimental scenario where the power losses due to the generation of the eddy currents in the metallic structure are considered