A dataset of recorded electricity outages by United States county 2014-2022.

In this Data Descriptor, we present county-level electricity outage estimates at 15-minute intervals from 2014 to 2022. By 2022 92% of customers in the 50 US States, Washington DC, and Puerto Rico are represented. These data have been produced by the Environment for Analysis of Geo-Located Energy Information (EAGLE-ITM), a geographic information system and data visualization platform created at Oak Ridge National Laboratory to map the population experiencing electricity outages every 15 minutes at the county level. Although these data do not cover every US customer, they represent the most comprehensive outage information ever compiled for the United States. The rate of coverage increases through time between 2014 and 2022. We present a quantitative Data Quality Index for these data for the years 2018-2022 to demonstrate temporal changes in customer coverage rates by FEMA region and indicators of data collection gaps or other errors.

What predicts word reading in Arabic?

Efficient and accurate word reading ability is critical for later reading success. As such, it is important to understand the component skills that underlie strong word reading ability. Although a growing research base points to the importance of phonological processing, morphological processing and orthographic processing for accurate and fluent word reading in Arabic, there are few studies that examine all three areas at one time to better understand their role in word reading. Additionally, it remains unclear whether the contribution of the various processes might differ across the early years when children are learning to read. 1,098 pupils in grades 1-3 participated in this study and took tests for phonological processing, morphological processing, orthographic processing, and word reading accuracy and fluency. According to the findings of regression analyses, the relative contribution of these underlying processes differed according to the method used to test word reading and the student's grade level. Regarding accuracy, several subscales of phonological processing and two measures of orthographic processing accounted for significant differences in word reading accuracy for first graders. For second grade students, nonword repetition, elision, and all three measures of orthographic processing accounted for variance. In third grade, elision and memory for digits, word creation and morpheme identification, and letter/sound identification and orthographic fluency were significant predictors of word reading accuracy. In terms of fluency, two subscales of phonological processing, two measures of orthographic processing, and two measures of morphological processing explained significant differences in word reading fluency for first graders. For second grade students, nonword repetition, elision, RAN-digits, isolation, segmenting and all the measures of orthographic processing and word creation explained unique variance in word reading fluency. In third grade, elision, RAN-letters, RAN-digits and phoneme isolation, all measures of orthographic processing and morphological processing, explained variance in word reading fluency. Implications and future directions in research are discussed.

An Exploratory Approach to Deriving Nutrition Information of Restaurant Food from Crowdsourced Food Images: Case of Hartford.

Deep learning models can recognize the food item in an image and derive their nutrition information, including calories, macronutrients (carbohydrates, fats, and proteins), and micronutrients (vitamins and minerals). This technology has yet to be implemented for the nutrition assessment of restaurant food. In this paper, we crowdsource 15,908 food images of 470 restaurants in the Greater Hartford region on Tripadvisor and Google Place. These food images are loaded into a proprietary deep learning model (Calorie Mama) for nutrition assessment. We employ manual coding to validate the model accuracy based on the Food and Nutrient Database for Dietary Studies. The derived nutrition information is visualized at both the restaurant level and the census tract level. The deep learning model achieves 75.1% accuracy when compared with manual coding. It has more accurate labels for ethnic foods but cannot identify portion sizes, certain food items (e.g., specialty burgers and salads), and multiple food items in an image. The restaurant nutrition (RN) index is further proposed based on the derived nutrition information. By identifying the nutrition information of restaurant food through crowdsourced food images and a deep learning model, the study provides a pilot approach for large-scale nutrition assessment of the community food environment.

Identifying Dyslexia: Link between Maze Learning and Dyslexia Susceptibility Gene, DCDC2, in Young Children.

Dyslexia is a common learning disability that affects processing of written language despite adequate intelligence and educational background. If learning disabilities remain untreated, a child may experience long-term social and emotional problems, which influence future success in all aspects of their life. Dyslexia has a 60% heritability rate, and genetic studies have identified multiple dyslexia susceptibility genes (DSGs). DSGs, such as DCDC2, are consistently associated with the risk and severity of reading disability (RD). Altered neural connectivity within temporoparietal regions of the brain is associated with specific variants of DSGs in individuals with RD. Genetically altering DSG expression in mice results in visual and auditory processing deficits as well as neurophysiological and neuroanatomical disruptions. Previously, we demonstrated that learning deficits associated with RD can be translated across species using virtual environments. In this 2-year longitudinal study, we demonstrate that performance on a virtual Hebb-Williams maze in pre-readers is able to predict future reading impairment, and the genetic risk strengthens, but is not dependent on, this relationship. Due to the lack of oral reporting and use of letters, this easy-to-use tool may be particularly valuable in a remote working environment as well as working with vulnerable populations such as English language learners.

Social support, resilience, and quality of life for families with children with intellectual disabilities.

A family's quality of life (FQOL) has been shown to impact the quality of life for the child with intellectual disabilities. Therefore, it is important to understand the factors that contribute to FQOL to inform the types of interventions and supports that are provided to families. The goal of this study was to determine whether social support and resilience account for variance in Family Quality of Life as reported by mothers of children with intellectual disabilities. Eight-eight Qatari mothers responded to three surveys, the Brief Resilience Scale, the 2-Way Social Support Scale, and the Beach Center Family Quality of Life Scale. Regression results indicate that giving and receiving social support accounted for significant variance in FQOL, explaining 62 % of the variance. Resilience was not a significant predictor. Implications for research and practice are discussed.

The Relationship of Special Education Teacher Performance on Observation Instruments With Student Outcomes.

In this study, we examined the relationship of special education teachers' performance on the Recognizing Effective Special Education Teachers (RESET) Explicit Instruction observation protocol with student growth on academic measures. Special education teachers provided video-recorded observations of three instructional lessons along with data from standardized, curriculum-based academic measures at the beginning, middle, and end of the school year for the students in the instructional group. Teachers' lessons were evaluated by external, trained raters. Data were analyzed using many-faceted Rasch measurement (MFRM), correlation, and multiple regression. Teacher performance on the overall protocol did not account for statistically significant variance in student growth beyond that of students' beginning of the year academic performance. Teacher performance on an abbreviated protocol comprised of items that had average or higher item difficulties on the MFRM analysis accounted for an additional 4.5% of variance beyond that of beginning of the year student performance. Implications for further research are discussed.

Developing a Multi-Dimensional Early Elementary Mathematics Screener and Diagnostic Tool: The Primary Mathematics Assessment.

There is a critical need to identify primary level students experiencing difficulties in mathematics to provide immediate and targeted instruction that remediates their deficits. However, most early math screening instruments focus only on the concept of number, resulting in inadequate and incomplete information for teachers to design intervention efforts. We propose a mathematics assessment that screens and provides diagnostic information in six domains that are important to building a strong foundation in mathematics. This article describes the conceptual framework and psychometric qualities of a web-based assessment tool, the Primary Math Assessment (PMA). The PMA includes a screener to identify students at risk for poor math outcomes and a diagnostic tool to provide a more in-depth profile of children's specific strengths and weaknesses in mathematics. The PMA allows teachers and school personnel to make better instructional decisions by providing more targeted analyses.

Translating dyslexia across species.

Direct relationships between induced mutation in the DCDC2 candidate dyslexia susceptibility gene in mice and changes in behavioral measures of visual spatial learning have been reported. We were interested in determining whether performance on a visual-spatial learning and memory task could be translated across species (study 1) and whether children with reading impairment showed a similar impairment to animal models of the disorder (study 2). Study 1 included 37 participants who completed six trials of four different virtual Hebb-Williams maze configurations. A 2 × 4 × 6 mixed factorial repeated measures ANOVA indicated consistency in performance between humans and mice on these tasks, enabling us to translate across species. Study 2 included a total of 91 participants (age range = 8-13 years). Eighteen participants were identified with reading disorder by performance on the Woodcock-Johnson III Tests of Achievement. Participants completed six trials of five separate virtual Hebb-Williams maze configurations. A 2 × 5 × 6 mixed factorial ANCOVA (gender as covariate) indicated that individuals with reading impairment demonstrated impaired visuo-spatial performance on this task. Overall, results from this study suggest that we are able to translate behavioral deficits observed in genetic animal models of dyslexia to humans with reading impairment. Future studies will utilize the virtual environment to further explore the underlying basis for this impairment.

Alternative models of learning disabilities identification: considerations and initial conclusions.

The final session of the National Research Center on Learning Disabilities (NRCLD) Responsiveness-to-Intervention (RTI) Symposium, "What are alternative models to LD identification other than RTI?" included four papers that discussed concerns over the exclusive reliance on an RTI approach to learning disability identification, considerations for analyzing proposed LD identification models, and various alternatives to LD identification. The work of the participating panelists is summarized in this discussant paper, and next steps for the NRCLD in light of these presentations are suggested.

Absorption and CD spectroscopy and modeling of various LH2 complexes from purple bacteria.

The absorption (OD) and circular dichroism (CD) spectra of LH2 complexes from various purple bacteria have been measured and modeled. Based on the lineshapes of the spectra we can sort the LH2 complexes into two distinguishable groups: "acidophila"-like (type 1) and "molischianum"-like (type 2). Starting from the known geometric structures of Rhodopseudomonas (Rps.) acidophila and Rhodospirillum (Rsp.) molischianum we can model the OD and CD spectra of all species by just slightly varying some key parameters: the interaction strength, the energy difference of alpha- and beta-bound B850 bacteriochlorophylls (BChls), the orientation of the B800 and B850 BChls, and the (in)homogeneous broadening. Although the ring size can vary, the data are consistent with all the LH2 complexes having basically very similar structures.