Sound patterns in the speech of two Brazilian-Portuguese speaking children are compared with early production patterns in English-learning children as well as English and Brazilian-Portuguese (BP) characteristics. The relationship between production system effects and ambient language influences in the acquisition of early sound patterns is of primary interest, as English and BP are characterized by differing phonological systems. Results emphasize the primacy of production system effects in early acquisition, although even the earliest word forms show evidence of perceptual effects from the ambient language in both BP children. Use of labials and coronals and low and midfront vowels in simple syllable shapes is consistent with acquisition data for this period across languages. However, potential ambient language influences include higher frequencies of dorsals, use of multisyllabic words, and different phone types in syllable-offset position. These results suggest that to fully understand early acquisition of sound systems one must account for both production system effects and perceptual effects from the ambient language.
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http://dx.doi.org/10.1177/00238309020450020401 | DOI Listing |
Int J Environ Res Public Health
December 2024
College of Public Health Sciences, Chulalongkorn University, Bangkok 10330, Thailand.
Breast cancer is the most prevalent malignancy among women. Certain air pollutants have carcinogenic and estrogenic properties that can contribute to breast cancer development. This systematic review aimed to investigate the association between air pollution and breast cancer based on epidemiological evidence.
View Article and Find Full Text PDFBMJ Qual Saf
January 2025
National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, District of Columbia, USA.
Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classification system for categorising generative AI patient safety errors. Our classification system is organised around two AI system stages (input and output) with specific error types by stage.
View Article and Find Full Text PDFLight Sci Appl
January 2025
State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics, Peking University, Beijing, 100871, China.
Metamaterials have revolutionized wave control; in the last two decades, they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified functionality. Although deep-learning techniques play an increasingly important role in metamaterial inverse design, measurement post-processing and end-to-end optimization, their role is ultimately still limited to approximating specific mathematical relations; the metamaterial is still limited to serving as proxy of a human operator, realizing a predefined functionality. Here, we propose and experimentally prototype a paradigm shift toward a metamaterial agent (coined metaAgent) endowed with reasoning and cognitive capabilities enabling the autonomous planning and successful execution of diverse long-horizon tasks, including electromagnetic (EM) field manipulations and interactions with robots and humans.
View Article and Find Full Text PDFAtten Percept Psychophys
December 2024
Cognitive Psychology, Institute of Psychology, University of Hildesheim, Universitaetsplatz 1, D-31141, Hildesheim, Germany.
The attentional blink (AB) paradigm is frequently used to investigate temporal attention. Essentially, rapid serial visual streams of several distractors and two targets are presented. The accuracy in detecting the second target stimulus (T2) decreases in the time window between 100 and 500 ms following accurate detection of the first target stimulus (T1).
View Article and Find Full Text PDFJ Am Med Inform Assoc
December 2024
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States.
Objectives: To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe.
Materials And Methods: This prospective quality improvement study was conducted at a large academic medical center with 45 physicians from 8 ambulatory disciplines over 3 months. Utilization and documentation times were derived from electronic health record (EHR) use measures.
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