Mandarin Assessment in Chinese-English Bilingual Preschoolers

Immigrant children who grow up in linguistically and culturally diverse households are at risk for misdiagnosis for language impairment and inappropriate placement in or exclusion from special education classes. Research shows that native language testing is essential in determining eligibility for disability services, as reflected both in federal law (Individuals with Disabilities Education Improvement Act of 2004). However, despite growing agreement that native language assessment is a critical component to understanding the abilities and challenges bilingual students face, the standard assessments currently used are largely administered in Standard English and normed on monolingual English speakers. Few options are available to practitioners who work with speakers raised in multilingual households. This investigation presents a pilot study of syntax comprehension in English and Mandarin in 24 four-year-old children who live in Chinese-speaking households in New York City. The study has two aims. One is to show how children from Mandarin-speaking homes perform on language assessments in English and Mandarin. The structures selected for this investigation are coordination and relative clauses, which are cross-linguistically robust and have been previously studied in child language acquisition research in both English and Mandarin monolinguals; as well as Chinese-specific classifiers, or nominal modifiers. Results show that the children had similar accuracy on the coordination sentences in Mandarin and English; however, for the relative clause structures, children had higher accuracy in Mandarin than English, emphasizing the need for multilingual testing. This study concludes with recommendations for a touch-screen tablet based assessment that would be useful to schools and replicable to other languages, potentially addressing the gap between policy and practice

Topology Driven Improvement of FDC Build Parameters

The likeliest failure origin for advanced ceramics parts, prepared by fused deposition, is a void from improper fill. Adequate filling of each cross-section is dependent upon the deposition toolpath. Cross-sectional spaces are conventionally filled with pre-defined parameters. We propose that adaptive build parameters will control variations in geometry and property of a part. Voids, overfilling, incomplete bonding and excess traversing can be suppressed by adjusting the fill parameters for cross-sectional areas. Improved build parameters and toolpath allows for faster build time and components ofj full density. Some implementations are discussed and presented.Mechanical Engineerin

Impact of a novel after school program: Smart Fit Girls

Individuals who are highly physically active are more likely to have a greater self-esteem, better body image, and increased physical activity self-efficacy. Currently, the average PE program provides less than 12% of the recommended daily amount of physical activity, with adolescent girls being the least active. The primary purpose of this research is to explore the efficacy of an after-school program, Smart Fit Girls (SFG), which aims to improve adolescent girls physical activity habits, self-esteem and body image. A secondary purpose is to examine how physical activity and mother/daughter relationships affect adolescent girls physical and emotional health. Girls attending Riverside Middle School in Pendleton, SC and their mother or female guardian were recruited for this study. The girls were 10-14 years old, in good academic standing, and were not involved in school athletics. To explore the impact of SFG all participants and their mothers will complete two rounds (pre/post) of questionnaires and focus groups. A control group of daughters and mothers at R.C. Edwards in Clemson, SC will participate in quantitative and qualitative data collection as well. Preliminary data demonstrate an 11% increase in self-esteem in mothers and statistically significant improvements in body image between pre and post measurements in girl participants

Role of Educational Factors on College Students’ Creation Worldview

What one believes about origins is a significant component of an overall worldview. An ongoing study at Liberty University is being conducted to define and measure a creationist worldview while determining factors that influence the beliefs and attitudes about origins in a Christian college student population. The Creation Worldview Test (CWT) was administered before and after completion of a required apologetics course. Previous attendance at a creation seminar or course was associated with a stronger initial creation worldview, however prior completion of a college science course appeared to have no impact. Importantly, students who attended a public high school had a significantly weaker initial creation worldview than those who attended Christian high schools or home school. Following the apologetics course which was taught from a young-Earth Creation perspective, a large number of students showed a much stronger creation worldview. In particular, the number of students in the ‘conservative Biblical theism’ category doubled from 64 to 128 (out of 195 students in the study). These results demonstrate the importance and the clear impact of teaching students from a young-Earth Creation perspective

FAIR principles for AI models, with a practical application for accelerated high energy diffraction microscopy

A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.Comment: 10 pages, 3 figures. Comments welcome

In vivo imaging of chronic active lesions in multiple sclerosis

New clinical activity in multiple sclerosis (MS) is often accompanied by acute inflammation which subsides. However, there is growing evidence that a substantial proportion of lesions remain active well beyond the acute phase. Chronic active lesions are most frequently found in progressive MS and are characterised by a border of inflammation associated with iron-enriched cells, leading to ongoing tissue injury. Identifying imaging markers for chronic active lesions in vivo are thus a major research goal. We reviewed the literature on imaging of chronic active lesion in MS, focussing on 'slowly expanding lesions' (SELs), detected by volumetric longitudinal magnetic resonance imaging (MRI) and 'rim-positive' lesions, identified by susceptibility iron-sensitive MRI. Both SELs and rim-positive lesions have been found to be prognostically relevant to future disability. Little is known about the co-occurrence of rims around SELs and their inter-relationship with other emerging techniques such as dynamic contrast enhancement (DCE) and positron emission tomography (PET)

The beta-subunit of human chorionic gonadotrophin exists as a homodimer

The free beta-subunit of human chorionic gonadotrophin (hCGbeta) is well recognised as a product of many epithelial tumours. Recently, it has been shown that this ectopic production may have a functional relationship to tumour growth. The growth-promoting activity of hCGbeta may be explained by its structural similarity to a family of growth factors which all contain the same distinct topological fold known as the cystine-knot motif. Since the other members of this family all exhibit their activities as homo- and heterodimers, it is possible that the same may be true for hCGbeta. Using size-exclusion chromatography, low stringency SDS-PAGE and matrix assisted laser desorption/ionisation (MALDI) time-of-flight (TOF) mass spectrometry (MS) we have shown that pure preparations of hCGbeta contain hCGbetabeta homodimers. Size-exclusion chromatography revealed asymmetric elution profiles with a forward peak corresponding to the size-exclusion characteristic of a globular protein with an approximate mass of 44-54 kDa and a late shoulder centered around an elution position expected for a globular protein of approximately 29 kDa. Two immunoreactive hCGbeta species, of approximately 32 and 64 kDa, were clearly resolved by SDS-PAGE and Western blotting. When analysed by MALDI-TOF MS a |mf23 kDa monomer and a |mf46 kDa dimer were identified. Formation of hCGbetabeta homodimers is consistent with the behaviour of other cystine-knot growth factors and strengthens the inclusion of the glycoprotein hormones within this superfamily. It has yet to be determined whether it is this dimeric molecular species that is responsible for growth-promoting activity of hCGbeta preparations in tumours

The Manufacturing Data and Machine Learning Platform: Enabling Real-time Monitoring and Control of Scientific Experiments via IoT

IoT devices and sensor networks present new opportunities for measuring, monitoring, and guiding scientific experiments. Sensors, cameras, and instruments can be combined to provide previously unachievable insights into the state of ongoing experiments. However, IoT devices can vary greatly in the type, volume, and velocity of data they generate, making it challenging to fully realize this potential. Indeed, synergizing diverse IoT data streams in near-real time can require the use of machine learning (ML). In addition, new tools and technologies are required to facilitate the collection, aggregation, and manipulation of sensor data in order to simplify the application of ML models and in turn, fully realize the utility of IoT devices in laboratories. Here we will demonstrate how the use of the Argonne-developed Manufacturing Data and Machine Learning (MDML) platform can analyze and use IoT devices in a manufacturing experiment. MDML is designed to standardize the research and operational environment for advanced data analytics and AI-enabled automated process optimization by providing the infrastructure to integrate AI in cyber-physical systems for in situ analysis. We will show that MDML is capable of processing diverse IoT data streams, using multiple computing resources, and integrating ML models to guide an experiment.Comment: Two page demonstration paper. Accepted to WFIoT202