This article discusses the historic relationship between the practical arts (technology) and the mental (liberal) arts, suggesting that Converging Technologies is a new higher education paradigm that integrates the arts, humanities, and sciences with modern technology. It explains that the paradigm really includes all fields in higher education from philosophy to art to music to modern languages and beyond. To implement a transformation of this magnitude, it is necessary to understand the psychology of change in academia. Union College in Schenectady, New York, implemented a Converging Technologies Educational Paradigm in five steps: (1) create a compelling vision, (2) communicate the vision, (3) empower the faculty, (4) create short-term successes, and (5) institutionalize the results. This case study of Union College demonstrates it is possible to build a pillar of educational excellence based on Converging Technologies.
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http://dx.doi.org/10.1196/annals.1382.005 | DOI Listing |
Sci Rep
January 2025
Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy.
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January 2025
Zhongyuan University of Technology, Zhengzhou, 450007, China.
This paper studies the practical prescribed-time control problem for dual-arm robots handling an object with output constraints. Firstly, by utilizing the property that the sum of internal forces in the grasping space is zero, the system model is obtained and decomposed into the contact force model and free motion model, which are orthogonal to each other. Furthermore, by combining the performance function and constraint function, the original system tracking error is transformed to a new one, whose boundedness can ensure that the original system variable converges to the predetermined range within the specified time.
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January 2025
Department of Electrical and Electronics, Faculty of Engineering, Alberoni University, Kapisa, Afghanistan.
This study first proposes an innovative method for optimizing the maximum power extraction from photovoltaic (PV) systems during dynamic and static environmental conditions (DSEC) by applying the horse herd optimization algorithm (HHOA). The HHOA is a bio-inspired technique that mimics the motion cycles of an entire herd of horses. Next, the linear active disturbance rejection control (LADRC) was applied to monitor the HHOA's reference voltage output.
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January 2025
Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea; Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea; Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea; POSCO-POSTECH-RIST Convergence Research Center for Flat Optics and Metaphotonics, Pohang 37673, Republic of Korea. Electronic address:
Band topology has emerged as a novel tool for material design across various domains, including photonic and phononic systems, and metamaterials. A prominent model for band topology is the Su-Schrieffer-Heeger (SSH) chain, which reveals topological in-gap states within Bragg-type gaps (BG) formed by periodic modification. Apart from classical BGs, another mechanism for bandgap formation in metamaterials involves strong coupling between local resonances and propagating waves, resulting in a local resonance-induced bandgap (LRG).
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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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