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We model the electron density in the topside of the ionosphere with an improved machine learning (ML) model and compare it to existing empirical models, specifically the International Reference Ionosphere (IRI) and the Empirical‐Canadian High Arctic Ionospheric Model (E‐CHAIM). In prior work, an artificial neural network (NN) was developed and trained on two solar cycles worth of Defense Meteorological Satellite Program data (113 satellite‐years), along with global drivers and indices to predict topside electron density. In this paper, we highlight improvements made to this NN, and present a detailed comparison of the new model to E‐CHAIM and IRI as a function of location, geomagnetic condition, time of year, and solar local time. We discuss precision and accuracy metrics to better understand model strengths and weaknesses. The updated neural network shows improved mid‐latitude performance with absolute errors lower than the IRI by 2.5 × 10⁹to 2.5 × 10¹⁰e⁻/m³, modestly improved performance in disturbed geomagnetic conditions with absolute errors reduced by about 2.5 × 10⁹ e⁻/m³at high Kp compared to the IRI, and high Kp percentage errors reduced by >50% when compared to E‐CHAIM.

 

The central hypothesis of the genotype–phenotype relationship is that the phenotype of a developing organism (i.e., its set of observable attributes) depends on its genome and the environment. However, as we learn more about the genetics and biochemistry of living systems, our understanding does not fully extend to the complex multiscale nature of how cells move, interact, and organize; this gap in understanding is referred to as the genotype-to-phenotype problem. The physics of soft matter sets the background on which living organisms evolved, and the cell environment is a strong determinant of cell phenotype. This inevitably leads to challenges as the full function of many genes, and the diversity of cellular behaviors cannot be assessed without wide screens of environmental conditions. Cellular mechanobiology is an emerging field that provides methodologies to understand how cells integrate chemical and physical environmental stress and signals, and how they are transduced to control cell function. Biofilm forming bacteria represent an attractive model because they are fast growing, genetically malleable and can display sophisticated self-organizing developmental behaviors similar to those found in higher organisms. Here, we propose mechanobiology as a new area of study in prokaryotic systems and describe its potential for unveiling new links between an organism’s genome and phenome. 

The use of wh-words, including wh-questions and wh-clauses, can be linguistically, conceptually, and interactively challenging to preschoolers. Young children develop mastery of wh-words as they formulate and hear these words during daily interactions in contexts such as preschool classrooms. Observational approaches limit researchers' ability to comprehensively capture the classroom conversations, including wh-words. In the current study, we report the results of the first study using the automated speech recognition (ASR) system coupled with location sensors designed to quantify teachers' wh-words in the literacy activity areas of a preschool classroom. We found that the ASR system is a viable solution to automatically quantify the number of adult wh-words used in preschool classrooms. Our findings demonstrated that the most frequently used adult wh-word type was "what." Classroom adults used more wh-words during time point 1 compared to time point 2. Lastly, a child at risk for developmental delays heard more wh-words per minute than a typically developing child. Future research is warranted to further improve the efforts 

Adult-child interaction is an important component for language development in young children. Teachers responsible for the language acquisition of their students have a vested interest in improving such conversation in their classrooms. Advancements in speech technology and natural language processing can be used as an effective tool by teachers in pre-school classrooms to acquire large amounts of conversational data, receive feedback from automated conversational analysis, and amend their teaching methods. Measuring engagement among pre-school children and teachers is a challenging task and not well defined. In this study, we focus on developing criteria to measure conversational turn-taking and topic initiation during adult-child interactions in preschool environments. However, counting conversational turns, conversation initiations, or vocabulary alone is not enough to judge the quality of a conversation and track language acquisition. It is necessary to use a combination of the three and include a measurement of the complexity of vocabulary. The next iterative of this problem is to deploy various solutions from speech and language processing technology to automate these measurements. * (2022 ASEE Best Student Paper Award Winner) 

Speech and language development in children are crucial for ensuring effective skills in their long-term learning ability. A child’s vocabulary size at the time of entry into kindergarten is an early indicator of their learning ability to read and potential long-term success in school. The preschool classroom is thus a promising venue for assessing growth in young children by measuring their interactions with teachers as well as classmates. However, to date limited studies have explored such naturalistic audio communications. Automatic Speech Recognition (ASR) technologies provide an opportunity for ’Early Childhood’ researchers to obtain knowledge through automatic analysis of naturalistic classroom recordings in measuring such interactions. For this purpose, 208 hours of audio recordings across 48 daylong sessions are collected in a childcare learning center in the United States using Language Environment Analysis (LENA) devices worn by the preschool children. Approximately 29 hours of adult speech and 26 hours of child speech is segmented using manual transcriptions provided by CRSS transcription team. Traditional as well as End-to-End ASR models are trained on adult/child speech data subset. Factorized Time Delay Neural Network provides a best Word-Error-Rate (WER) of 35.05% on the adult subset of the test set. End-to-End transformer models achieve 63.5% WER on the child subset of the test data. Next, bar plots demonstrating the frequency of WH-question words in Science vs. Reading activity areas of the preschool are presented for sessions in the test set. It is suggested that learning spaces could be configured to encourage greater adult-child conversational engagement given such speech/audio assessment strategies. 

Molecular visualization and structure-function discussions present a valuable lens for research, practice, and education in chemistry and biology. Currently, molecular structural data, visualization tools and resources are underutilized by students and faculty. A new community, Molecular CaseNet, is engaging undergraduate educators in chemistry and biology to collaboratively develop case studies for interdisciplinary learning on real world topics. Use of molecular case studies will help biologists focus on chemical (covalent and non-covalent) interactions underlying biological processes/cellular events and help chemists consider biological contexts of chemical reactions. Experiences in developing and using molecular case studies will help uncover current challenges in discussing biological/chemical phenomena at the atomic level. These insights can guide future development of necessary scaffolds for exploring molecular structures and linked bioinformatics resources.