A Dual-Band Model for the Vertical Distribution of Photosynthetically Available Radiation (PAR) in Stratified Waters

Based on the optical properties of water constituents, the vertical variation of photosynthetically available radiation (PAR) can be well modeled with hyperspectral resolution; the intensive computing load, however, demands simplified modeling that can be easily embedded in marine physical and biogeochemical models. While the vertical PAR profile in homogeneous waters can now be accurately modeled with simple parameterization, it is still a big challenge to model the PAR profile in stratified waters with limited variables. In this study, based on empirical equations and simulations, we propose a dual-band model to characterize the vertical distribution of PAR using the chlorophyll concentration (Chl). With an inclusive dataset including cruise data collected in the Southeast Pacific and BGC-Argo data in the global ocean, the model was thoroughly evaluated for its general applicability in three aspects: 1) estimating the entire PAR profile from sea-surface PAR and the Chl profile, 2) estimating the euphotic layer depth from the Chl profile, and 3) estimating PAR just below the sea surface from in situ radiometry measurements. It is demonstrated that the proposed dual-band model is capable of generating similar estimates as that from a hyperspectral model, thus offering an effective module that can be incorporated in large-scale ecosystem and/or circulation models for efficient calculations

Optimal Design of Tuned Mass-Damper-Inerter for Structure with Uncertain-but-Bounded Parameter

International audienceIn this study we focus on the H∞ optimization of a tuned mass damper inerter (TMDI), which is implemented on an harmonically forced structure of a single degree of freedom in the presence of stiffness uncertainty. Posed as a min-max optimization problem, its closed-form solutions are analytically derived via an algebraic approach that was newly developed in this work, and ready-to-use formulae of tuning parameters are provided herein for the optimal TMDI (referred to as the TMD). The accuracy of the derived solutions are examined by comparing them with the existing literature and with numerically solved solutions in both deterministic and uncertain scenarios. Our numerical investigation suggested that compared to the classic design, the proposed tuning strategy could effectively reduce the peak vibration amplitude of the host structure in the worst-case scenario. Moreover, its peak vibration amplitude decreases monotonically as the total amount of the tuned mass and inertance increases. Therefore, the incorporation of a grounded inerter in a traditional TMD could render the deteriorated performance of vibration control less important, thereby protecting the primary system against the detuning effect more effectively. Finally, the effectiveness of the proposed design under random excitation is also underlined

Mesoporous silica nanoparticles capped with graphene quantum dots as multifunctional drug carriers for photo-thermal and redox-responsive release

A novel photo-thermal and redox-responsive drug delivery carrier was developed by capping mesoporous silica nanoparticles (MSNs) with graphene quantum dots (GQDs). The disulfide bonds were introduced by amidation reaction between cystine and amino functionalized MSNs. Rhodamine B (RhB), a red fluorescent dye, was loaded into the mesopores of MSNs as the model drug and GQDs capped on MSNs as gatekeepers could prevent the release of RhB. Transmission electron microscopy (TEM), nitrogen adsorption and desorption analysis, X-ray diffraction (XRD), thermogravimetric (TG) analysis and Fourier transform infrared spectroscopy (FTIR) proved that the nanocomposites MSNs capped with GODs were achieved successfully. The nanocomposites with the size of about 100 nm have excellent photo-thermal property originated from GQDs. Moreover, the nanocomposites were endowed with remarkable redox-responsion to glutathione (GSH) from disulfide bonds, and hence the loaded drugs could release controlably. In this work, we provided an exploration of photo-thermal and redox-responsive drug delivery system and the results proved that this drug delivery system can be considered as a promising candidate for drug delivery and stimuli-responsive release.This work was funded by the Natural Science Foundation of the Science and Technology Department of Jilin Province, PR China (No. 20180101072JC), the Key Program of the Science and Technology Department of Jilin Province, PR China (No. 20180201082SF) and the scientific research Program of the Education Department of Jilin Province (P. R. China) during the 13th Five-Year Plan Period (No. JJKH20180119KJ). Dedicated to Professor Helmuth Möhwald

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. In practice, obtaining an initial model that approximates the true subsurface electrical structures remains challenging. To address this issue, we introduce a broad learning (BL) network to determine the initial model and utilize the limited memory quasi-Newton (L-BFGS) algorithm to conduct the 3-D electrical resistivity inversion task. The powerful mapping capability of the BL network enables one to find the model that elucidates the actual observed data. The single-layer BL network makes it efficient and easy to realize, leading to much faster network training compared to that using the deep learning network. Both the synthetic and field experiments suggest that the BL framework could effectively obtain the initial model based on observed data. Furthermore, in comparison to using a homogeneous medium as the initial model, the L-BFGS inversion with the BL framework-designed initial model improves the inversion accuracy of subsurface electrical structures and expedites the convergence speed of the iteration. This study provides an effective approach for fast initial model design in a data-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures