Positive Influence Of Education Partnerships For Teaching Integrated STEM Through Drone Competition

While enhancing the STEM career pipeline through improved quality and quantity of STEM teaching available to an ever-widening diversity is K-12 students is garnering significant attention across the U.S., there lacks widely adopted implementation and support models that efficiently make full advantage of the vast human and fiscal resources available. A wide swath of STEM education stake-holding partners—schools, businesses, government agencies, non-profit organizations, and institutions of higher education—frequently are compelled to provide support and guidance but lack easy to follow pathways in order to do so. This research study describes and documents a unique vehicle to bring often disparate partners to a unified effort under the banner of drone education designed to improve STEM and technology-oriented career pathways. Identified barriers that the collaborative partnership helped overcome to ensure success include providing: modest start-up costs for modern high-tech equipment for participating schools (drones); an infrastructure for leveraging the consistently successful approach to providing regional and statewide competitive events (precision drone flight and knowledge competitions); large-scale buildings and facilities to host competitive festivals and events (e.g., indoor sports stadiums); and K-12 teacher professional development programs along with classroom-ready instructional materials needed to nurture and sustain student drone education programs

One Pulmonary Lesion, 2 Synchronous Malignancies

Introduction. Mantle cell lymphoma (MCL) comprises approximately 3% to 10% of all non-Hodgkin lymphomas. Although there is an increased risk for secondary malignancies after treatment among non-Hodgkin lymphomas survivors, a synchronous diagnosis of primary lung cancer arising in conjunction with lymphoma at the same site has rarely been reported. We report an unusual case of primary lung adenocarcinoma with coexistent MCL within the same lung lesion. Case Presentation. A 55-year-old female with newly diagnosed stage IV-B MCL was referred for workup of a right upper lobe cavitary lesion detected during lymphoma staging. A whole-body positron-emission tomography-computed tomography scan revealed diffuse adenopathy but also identified a cavitary right upper lobe lesion atypical for lymphoma. Bronchoscopy was unremarkable with cytology (on lavage) negative for malignancy. At 2 months, a computed tomography scan of the chest showed a persistent lesion. A video-assisted thoracoscopic wedge resection was performed. Histopathological examination revealed a lepidic predominant, well-differentiated adenocarcinoma (stage T1a) and foci of lymphoid infiltrate within and adjacent to the adenocarcinoma consistent with lung involvement by MCL. Discussion. Synchronous presentation of primary lung adenocarcinoma and lymphoma at a single site is exceedingly rare. Nonresolving pulmonary lesions with features atypical for lymphoma should be viewed with caution and worked up comprehensively to rule out occult second malignancies, in order to guide a prompt diagnosis and appropriate treatment

Tiny Classifier Circuits: Evolving Accelerators for Tabular Data

A typical machine learning (ML) development cycle for edge computing is to maximise the performance during model training and then minimise the memory/area footprint of the trained model for deployment on edge devices targeting CPUs, GPUs, microcontrollers, or custom hardware accelerators. This paper proposes a methodology for automatically generating predictor circuits for classification of tabular data with comparable prediction performance to conventional ML techniques while using substantially fewer hardware resources and power. The proposed methodology uses an evolutionary algorithm to search over the space of logic gates and automatically generates a classifier circuit with maximised training prediction accuracy. Classifier circuits are so tiny (i.e., consisting of no more than 300 logic gates) that they are called "Tiny Classifier" circuits, and can efficiently be implemented in ASIC or on an FPGA. We empirically evaluate the automatic Tiny Classifier circuit generation methodology or "Auto Tiny Classifiers" on a wide range of tabular datasets, and compare it against conventional ML techniques such as Amazon's AutoGluon, Google's TabNet and a neural search over Multi-Layer Perceptrons. Despite Tiny Classifiers being constrained to a few hundred logic gates, we observe no statistically significant difference in prediction performance in comparison to the best-performing ML baseline. When synthesised as a Silicon chip, Tiny Classifiers use 8-18x less area and 4-8x less power. When implemented as an ultra-low cost chip on a flexible substrate (i.e., FlexIC), they occupy 10-75x less area and consume 13-75x less power compared to the most hardware-efficient ML baseline. On an FPGA, Tiny Classifiers consume 3-11x fewer resources.Comment: 14 pages, 16 figure

Characterisation of multiple hindered settling regimes in aggregated mineral suspensions

Aqueous suspensions of magnesium hydroxide are shown to exhibit low ζ-potential behavior and highly complex settling dynamics. Two distinct regimes of hindered settling behavior are observed on either side of a threshold concentration, ϕ*, of 2.38% v/v, which is considerably below the gel point, ϕg, observed at 5.4 ± 1.6% v/v. The low-concentration regime was characterized by a very large Richardson and Zaki exponent of 146, a factor of 10 larger than that of the high-concentration regime. Michaels and Bolger analysis of the low-concentration regime implies settling governed by large, low-density macroaggregates of 138–147 μm diameter and low intraaggregate packing fractions on the order of 0.05, which is in good agreement with in situ particle characterization undertaken using particle vision and measurement (PVM) and focused-beam reflectance measurements (FBRM). The large macroaggregates must undergo some shear densification within the higher-concentration hindered settling regime in order for the suspension to gel at a concentration of 5.4% v/v. Consequently, fluid flow past small, shear-resistant primary agglomerates, observed within the aggregates using scanning electron microscopy and flow particle image analysis, during aggregate densification may represent the limiting step for dewatering within the high-concentration regime

Radio source stacking and the infrared / radio correlation at microJy flux densities

We investigate the infrared / radio correlation using the technique of source stacking, in order to probe the average properties of radio sources that are too faint to be detected individually. We compare the two methods used in the literature to stack sources, and demonstrate that the creation of stacked images leads to a loss of information. We stack infrared sources in the Spitzer extragalactic First Look Survey (xFLS) field, and the three northern Spitzer Wide-area Infrared Extragalactic survey (SWIRE) fields, using radio surveys created at 610 MHz and 1.4 GHz, and find a variation in the absolute strength of the correlation between the xFLS and SWIRE regions, but no evidence for significant evolution in the correlation over the 24-um flux density range 150 uJy - 2 mJy. We carry out the first radio source stacking experiment using 70-um-selected galaxies, and find no evidence for significant evolution over the 70-um flux density range 10 mJy - 100 mJy.Comment: 11 pages, 12 figures. Accepted for publication in MNRA

A 610-MHz survey of the ELAIS-N1 field with the Giant Metrewave Radio Telescope - Observations, data analysis and source catalogue

Observations of the ELAIS-N1 field taken at 610 MHz with the Giant Metrewave Radio Telescope are presented. Nineteen pointings were observed, covering a total area of 9 square degrees with a resolution of 6" x 5", PA +45 deg. Four of the pointings were deep observations with an rms of 40 microJy before primary beam correction, with the remaining fifteen pointings having an rms of 70 microJy. The techniques used for data reduction and production of a mosaicked image of the region are described, and the final mosaic is presented, along with a catalogue of 2500 sources detected above 6 sigma. This work complements the large amount of optical and infrared data already available on the region. We calculate 610-MHz source counts down to 270 microJy, and find further evidence for the turnover in differential number counts below 1 mJy, previously seen at both 610 MHz and 1.4 GHz.Comment: 12 pages, 18 figures, two tables. Table 1 can be found in full via http://www.mrao.cam.ac.uk/surveys/ . Accepted for publication in MNRA

Synthesis of nuclear waste simulants by reaction precipitation: formation of caesium phosphomolybdate, zirconium molybdate and morphology modification with citratomolybdate complex

Caesium phosphomolybdate (CsPMoO·xHO) and zirconium molybdate ([ZrMoO(OH)]·2HO) solids are known to precipitate out from highly active liquors (HAL) during reprocessing of spent nuclear fuel. Here, a new synthesis for these simulants is reported; with the initial step producing spherical ceasium phosphomolybdate particles, which can then be converted into cubic Zirconium molybdate. Additionally, the addition of citric acid prior to the formation of the zirconium salt is investigated. In this case, a citratomolybdate complex is generated, leading to the synthesis of elongated cuboidal zirconium citratomolybdate ([ZrMoO(OH)]·2HO·[(MoO)O(cit)]). A key focus of this study is to explore the optimisation of reaction conditions to create a controlled environment for the particles to form with high conversion rates and with desired shape properties. Elemental and structural characterisation of the particles at various points during the synthesis, as well as post-synthesis, was undertaken to provide further insights. Ultimately, it is of importance to determine the mechanism of how these simulants are formed within the components in HAL. Establishing the influence of particle properties on HAL behaviour is key for current processing, post operational clean out (POCO) and life-time assessment of the nuclear waste facilities

Measurements of Submicron Particle Adsorption and Particle Film Elasticity at Oil-Water Interfaces

The influence of particle adsorption on liquid/liquid interfacial tension is not well understood, and much previous research has suggested conflicting behaviors. In this paper we investigate the surface activity and adsorption kinetics of charge stabilized and pH-responsive polymer stabilized colloids at oil/water interfaces using two tensiometry techniques: (i) pendant drop and (ii) microtensiometer. We found, using both techniques, that charge stabilized particles had little or no influence on the (dynamic) interfacial tension, although dense silica particles affected the "apparent" measured tension in the pendent drop, due to gravity driven elongation of the droplet profile. Nevertheless, this apparent change additionally allowed the study of adsorption kinetics, which was related qualitatively between particle systems by estimated diffusion coefficients. Significant and real interfacial tension responses were measured using ∼53 nm core-shell latex particles with a pH-responsive polymer stabilizer of poly(methyl methacrylate)-b-poly(2-(dimethylamino)ethyl methacrylate) (pMMA-b-pDMAEMA) diblock copolymer. At pH 2, where the polymer is strongly charged, behavior was similar to that of the bare charge-stabilized particles, showing little change in the interfacial tension. At pH 10, where the polymer is discharged and poorly soluble in water, a significant decrease in the measured interfacial tension commensurate with strong adsorption at the oil-water interface was seen, which was similar in magnitude to the surface activity of the free polymer. These results were both confirmed through droplet profile and microtensiometry experiments. Dilational elasticity measurements were also performed by oscillation of the droplet; again, changes in interfacial tension with droplet oscillation were only seen with the responsive particles at pH 10. Frequency sweeps were performed to ascertain the dilational elasticity modulus, with measured values being significantly higher than previously reported for nanoparticle and surfactant systems, and similar in magnitude to protein stabilized droplets.</p

Generating a predictive model for injury rates for a chemical manufacturing company

Workplace safety is not a new field of study, but the application of data analytics as a predictive tool is relatively new to the area. Beyond the obvious harm caused to workers, fatal and nonfatal injuries accrue a significant financial burden nationwide. Previous safety measures have focused largely on reactive tools. While these have done a significant job reducing nonfatal workplace injuries, they have done little to assuage fatal ones. Predictive analysis may be the next step in reducing workplace injury. By mining and processing safety-related data, predictive analysis can inform employers of high risk situations before they happen. By taking this preventative approach, employers have the chance to stop workplace incidents before their employees suffer harm. While these analyses have generally used national-level data sources and shown great success at reducing injury in some industries, such as construction, there has been little work done either in the manufacturing sector or at the company level. Using company-level data collected from a chemical manufacturing company, this team will create a predictive model for workplace incidents. Data collected from the 2017 and 2018 years will be used to train and validate a prediction model. That model will then be tested in 2019 data to determine its predictive efficiency