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We evaluated the impact of exposure to a second language on infants' emerging speech production skills. We compared speech produced by three groups of 12-month-old infants while they interacted with interlocutors who spoke to them in Spanish and English monolingual English-learning infants who had previously received 5 hours of exposure to a second language (Spanish), English- and Spanish-learning simultaneous bilinguals, and monolingual English-learning infants without any exposure to Spanish. Our results showed that the monolingual English-learning infants with short-term exposure to Spanish and the bilingual infants, but not the monolingual English-learning infants without exposure to Spanish, flexibly matched the prosody of their babbling to that of a Spanish- or English-speaking interlocutor. Our findings demonstrate the nature and extent of benefits for language learning from early exposure to two languages. We discuss the implications of these findings for language organization in infants learning two languages.Although there is a body of work investigating code-switching (alternation between two languages in production) in the preschool period, it largely relies on case studies or very small samples. The current work seeks to extend extant research by exploring the development of code-switching longitudinally from 31 to 39 months of age in two distinct groups of bilingual children Spanish-English children in San Diego and French-English children in Montréal. In two studies, consistent with previous research, children code-switched more often between than within utterances and code-switched more content than function words. Additionally, children code-switched more from Spanish or French to English than the reverse. Importantly, the factors driving the rate of code-switching differed across samples such that exposure was the most important predictor of code-switching in Spanish-English children whereas proficiency was the more important predictor in French-English children.Loose programming enables analysts to program with concepts instead of procedural code. Data transformations are left underspecified, leaving out procedural details and exploiting knowledge about the applicability of functions to data types. To synthesize workflows of high quality for a geo-analytical task, the semantic type system needs to reflect knowledge of geographic information systems (GIS) at a level that is deep enough to capture geo-analytical concepts and intentions, yet shallow enough to generalize over GIS implementations. Recently, core concepts of spatial information and related geo-analytical concepts were proposed as a way to add the required abstraction level to current geodata models. The core concept data types (CCD) ontology is a semantic type system that can be used to constrain GIS functions for workflow synthesis. However, to date, it is unknown what gain in precision and workflow quality can be expected. In this article we synthesize workflows by annotating GIS tools with these types, specifying a range of common analytical tasks taken from an urban livability scenario. We measure the quality of automatically synthesized workflows against a benchmark generated from common data types. Results show that CCD concepts significantly improve the precision of workflow synthesis.Because of the heath measures taken during the outbreak of Covid-19, the lack of educational methods has become the primary concern among educational professionals who have been using technology as a motivational tool. Gamification is very important because it helps students to represent their study contents and enrich their experiences of higher education when learning in-person is unavailable during the Covid-19 period. This study seeks to present an Android-based gamification app to evaluate the effect of using gamification and e-quizzes on college students' learning. We used the visual blocks language from the MIT App Inventor platform to develop an application, available at (https//play.google.com/store/apps/details?id=appinventor.ai_mekomerofofo.projectGamification). The participants were students from level 2 who used digital lessons for learning MATLAB. The study included gamified learning and non-gamified learning, both integrated into lesson plans, to investigate the differences in learners' performance. Two types of quizzes were used for instruction gamified e-quizzes and paper-based quizzes. The outcomes plainly showed that using the new gamified e-quiz was more effective than using paper-based quizzes. They are better for assessing the learning performance of the students in question, specifically in terms of formative assessment. It is very important for instructors to apply games as a modern and innovation-oriented tool through which students can be engaged in an attractive, competitive experience.Daily maximum 8-hour average (MDA8) ozone (O3) concentrations are well-known to be influenced by local meteorological conditions, which vary across both daily and seasonal temporal scales. this website Previous studies have adjusted long-term trends in O3 concentrations for meteorological effects using various statistical and mathematical methods in order to get a better estimate of the long-term changes in O3 concentrations due to changes in precursor emissions such as nitrogen oxides (NOX) and volatile organic compounds (VOCs). In this work, the authors present improvements to the current method used by the United States Environmental Protection Agency (US EPA) to adjust O3 trends for meteorological influences by making refinements to the input data sources and by allowing the underlying statistical model to vary locally using a variable selection procedure. The current method is also expanded by using a quantile regression model to adjust trends in the 90th and 98th percentiles of the distribution of MDA8 O3 concentrations, allowing for a better understanding of the effects of local meteorology on peak O3 levels in addition to seasonal average concentrations. The revised method is used to adjust trends in the May to September mean, 90th percentile, and 98th percentile MDA8 O3 concentrations at over 700 monitoring sites in the U.S. for years 2000 to 2016. The utilization of variable selection and quantile regression allow for a more in-depth understanding of how weather conditions affect O3 levels in the U.S. This represents a fundamental advancement in our ability to understand how interannual variability in weather conditions in the U.S. may impact attainment of the O3 National Ambient Air Quality Standards (NAAQS).