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Velma S02E08 1080p HEVC x265-MeGustaThe origin of the sleuth and member of the Mystery Inc. gang, Velma.Language: English 345.41 MB | 00:25:08 | 3942 Kbps | V_MPEGH/ISO/HEVC | 1920×1080 | A_EAC3, 48 Khz, 6 channelsGenre: Animation, Adventure, Comedy
A self-taught vegan chef known for her sustainable cooking practices, Chef Priyanka Naik’s journey from the tech world to the Food Network is a testament to the power of passion and perseverance. Hear her insights into how embracing your unique path ca…
Inspired by nature’s remarkable ability to form intricate minerals, researchers have unlocked transformative strategies for creating next-generation biosensors with exceptional sensitivity, selectivity, and biocompatibility. By mimicking how organisms orchestrate mineral growth, biomimetic and bioinspired materials are significantly impacting biosensor design. Engineered bioinspired materials offer distinct advantages over their natural counterparts, boasting superior tunability, precise controllability, and the ability to integrate specific functionalities for enhanced sensing capabilities. This remarkable versatility enables the construction of various biosensing platforms, including optical sensors, electrochemical sensors, magnetic biosensors, and nucleic acid detection platforms, for diverse applications. Additionally, bioinspired materials facilitate the development of smartphone-assisted biosensing platforms, offering user-friendly and portable diagnostic tools for point-of-care applications. This review comprehensively explores the utilization of naturally occurring and engineered biominerals and materials for diverse biosensing applications. We highlight the fabrication and design strategies that tailor their functionalities to address specific biosensing needs. This in-depth exploration underscores the transformative potential of biominerals and materials in revolutionizing biosensing, paving the way for advancements in healthcare, environmental monitoring, and other critical fields.
The use of the global mean first-passage time (GMFPT) in random walks on networks has been widely explored in the field of statistical physics, both in theory and practical applications. The GMFPT is the estimated interval of time needed to reach a state j in a system from a starting state i. In contrast, there exists an intrinsic measure for a stochastic process, known as Kemeny’s constant, which is independent of the initial state. In the literature, it has been used as a measure of network efficiency. This article deals with a graph-spectrum-based method for finding both the GMFPT and Kemeny’s constant of random walks on spiro-ring networks (that are organic compounds with a particular graph structure). Furthermore, we calculate the Laplacian matrix for some specific spiro-ring networks using the decomposition theorem of Laplacian polynomials. Moreover, using the coefficients and roots of the resulting matrices, we establish some formulae for both GMFPT and Kemeny’s constant in these spiro-ring networks.
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