Teachers are charged with connecting classroom science to real-world applications; however, opportunities for teachers to experience real-world applications in the life and biosciences are limited. When provided the opportunity to engage in hands-on learning experiences, teachers' capacity to connect classroom activities to real-world applications increases. Through partnerships within the local STEM Ecosystem, the Teacher Enrichment Initiatives (TEI) provides teachers with experiences that enable them to strengthen the connections between classroom science and careers. Over the course of TEI's 25+ years, these collaborations between our teachers and partners have resulted in the production of over 350 individual hands-on, inquiry-based curriculum activities. In addition, the TEI has played a pivotal role in the growth of the STEM workforce by disseminating best practices and providing teacher professional development (TPD) experiences designed to improve teacher knowledge of STEM career options and the associated educational pathways for their students while enhancing teacher professionalism. Since 2011, the TEI has hosted 15 TPD conferences and 300 workshops with over 1,800 K-12 teachers participating annually. The number of students impacted through teacher participation exceeds 300,000, covering all grade levels. To date, over 83 scientists, engineers, and STEM-career professionals have participated in TEI sponsored events.
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http://dx.doi.org/10.15695/jstem/v3i2.06 | DOI Listing |
Alzheimers Res Ther
January 2025
Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611002, Tamil Nadu, India.
In response to the pressing need for the detection of Monkeypox caused by the Monkeypox virus (MPXV), this study introduces the Enhanced Spatial-Awareness Capsule Network (ESACN), a Capsule Network architecture designed for the precise multi-class classification of dermatological images. Addressing the shortcomings of traditional Machine Learning and Deep Learning models, our ESACN model utilizes the dynamic routing and spatial hierarchy capabilities of CapsNets to differentiate complex patterns such as those seen in monkeypox, chickenpox, measles, and normal skin presentations. CapsNets' inherent ability to recognize and process crucial spatial relationships within images outperforms conventional CNNs, particularly in tasks that require the distinction of visually similar classes.
View Article and Find Full Text PDFSci Total Environ
January 2025
Department of Civil Engineering, City College of New York, New York, NY 10031, United States.
Odor emissions, primarily from anthropogenic activities like waste treatment and industrial processes, pose significant challenges in urban areas, particularly near water resource recovery facilities. While these emissions are generally not toxic, they can adversely affect community wellbeing and investment, prompting stricter regulations in some regions. For example, New York State's hydrogen sulfide guidelines are more stringent than federal standards.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFISA Trans
January 2025
Toronto Metropolitan University, Toronto, Canada. Electronic address:
This research introduces an innovative approach to optimal control for a class of linear systems with input saturation. It leverages the synergy of Takagi-Sugeno (T-S) fuzzy models and reinforcement learning (RL) techniques. To enhance interpretability and analytical accessibility, our approach applies T-S models to approximate the value function and generate optimal control laws while incorporating prior knowledge.
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