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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
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The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
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The corrosion of steel reinforcement within concrete is a process influenced by the material's inherent properties and external factors. The high pH level of around 13, provided by calcium hydroxide present in concrete, initially protects the steel reinforcement by promoting the formation of a passive iron oxide layer on its surface.
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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
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A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning.

Qingnan Sun, Marko V Jankovic, Joao Budzinski

    IEEE Journal of Biomedical and Health Informatics
    |December 21, 2018
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    Summary
    This summary is machine-generated.

    An AI algorithm, the adaptive basal-bolus algorithm (ABBA), offers personalized insulin dosing for type 1 diabetes management using either self-monitoring of blood glucose (SMBG) or continuous glucose monitoring (CGM) data.

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    Area of Science:

    • Artificial Intelligence in Medicine
    • Diabetes Technology
    • Computational Endocrinology

    Background:

    • Type 1 diabetes (T1D) management relies on accurate glucose monitoring.
    • Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are primary methods for glucose measurement.
    • Personalized insulin dosing remains a challenge in T1D care.

    Purpose of the Study:

    • To develop and validate an adaptive basal-bolus algorithm (ABBA) for personalized insulin dose suggestions in T1D.
    • To assess the ABBA's performance using both SMBG and CGM data inputs.
    • To evaluate the algorithm's ability to optimize glucose control independent of monitoring technology.

    Main Methods:

    • The adaptive basal-bolus algorithm (ABBA) was developed using reinforcement learning, a type of artificial intelligence.
    • The ABBA was validated in silico using a simulated population of 100 adults with T1D.
    • Simulations incorporated realistic daily meal patterns and variability in insulin sensitivity, meal timing, and glucose measurements over three months.

    Main Results:

    • The ABBA demonstrated comparable performance when using either CGM or SMBG data as input.
    • The algorithm provided personalized suggestions for basal rates and prandial insulin doses.
    • The total daily insulin dose was not significantly influenced by the ABBA's recommendations.

    Conclusions:

    • AI-driven algorithmic approaches, like the ABBA, show promise for personalized adaptive insulin optimization in T1D.
    • Glucose control can be achieved effectively regardless of the specific glucose monitoring technology used (CGM or SMBG).
    • The ABBA represents a significant step towards intelligent, technology-independent diabetes management systems.