Optimal H infinity insulin injection control for blood glucose regulation in diabetic patients.

The theory of H infinity optimal control has the feature of minimizing the worst-case gain of an unknown disturbance input. When appropriately modified, the theory can be used to design a "switching" controller that can be applied to insulin injection for blood glucose (BG) regulation. The "switching" controller is defined by a collection of basic insulin rates and a rule that switches the insulin rates from one value to another. The rule employed an estimation of BG from noisy measurements, and the subsequent optimization of a performance index that involves the solution of a "jump" Riccati differential equation and a discrete-time dynamic programming equation. With an appropriate patient model, simulation studies have shown that the controller could correct BG deviation using clinically acceptable insulin delivery rates.     

Optimal H/sup /spl infin// insulin injection control for blood glucose regulation in diabetic patients

The theory of H/sup /spl infin// optimal control has the feature of minimizing the worst-case gain of an unknown disturbance input. When appropriately modified, the theory can be used to design a "switching" controller that can be applied to insulin injection for blood glucose (BG) regulation. The "switching" controller is defined by a collection of basic insulin rates and a rule that switches the insulin rates from one value to another. The rule employed an estimation of BG from noisy measurements, and the subsequent optimization of a performance index that involves the solution of a "jump" Riccati differential equation and a discrete-time dynamic programming equation. With an appropriate patient model, simulation studies have shown that the controller could correct BG deviation using clinically acceptable insulin delivery rates.     

Fuzzy-based controller for glucose regulation in type-1 diabetic patients by subcutaneous route

This paper presents an advisory/control algorithm for a type-1 diabetes mellitus (TIDM) patient under an intensive insulin treatment based on a multiple daily injections regimen (MDIR). The advisory/control algorithm incorporates expert knowledge about the treatment of this disease by using Mamdani-type fuzzy logic controllers to regulate the blood glucose level (BGL). The overall control strategy is based on a two-loop feedback strategy to overcome the variability in the glucose-insulin dynamics from patient to patient. An inner-loop provides the amount of both rapid/short and intermediate/long acting insulin (RSAI and ILAI) formulations that are programmed in a three-shots daily basis before meals. The combined preparation is then injected by the patient through a subcutaneous route. Meanwhile, an outer-loop adjusts the maximum amounts of insulin provided to the patient in a time-scale of days. The outer-loop controller aims to work as a supervisor of the inner-loop controller. Extensive closed-loop simulations are illustrated, using a detailed compartmental model of the insulin-glucose dynamics in a TIDM patient with meal intake.     

A stochastic model to assess the variability of blood glucose time series in diabetic patients self-monitoring

Several studies have shown that patients suffering from Diabetes Mellitus can significantly delay the onset and slow down the progression of diabetes micro- and macro-angiopathic complications through intensive monitoring and treatment. In general, intensive treatments imply a careful blood glucose level (BGL) self-monitoring. The analysis of BGL measurements is one of the most important tasks in order to assess the glucose metabolic control and to revise the therapeutic protocol. Recent clinical studies have shown the correlation between the glucose variability and the long-term diabetes related complications. In this paper, we propose a stochastic model to extract the time course of such variability from the self-monitoring BGL time series. This information can be conveniently combined with other analysis to evaluate the adequacy of the therapeutic protocol and to highlight periods characterized by an increasing glucose instability. The method here proposed has been validated on two simulated data sets and tested with success in the retrospective analysis of three patients' data sets.     

氟伐他汀对2型糖尿病合并高胆固醇血症患者的抗氧化作用

目的调查氟伐他汀对2型糖尿病合并高胆固醇血症患者体内氧化压力的影响以及与降脂作用的相关性。方法糖尿病合并高脂血症患者41例,男21例,女20例,平均年龄（56．7±6．0）岁,均在日本弘前大学附属医院收集。每晚服用20mg氟伐他汀,于用药前及用药后4、8、12周分别测定血浆中低密度脂蛋白（LDL）,LDL size、LHPO、TBARS的动态变化。结果血浆LHPO,TBARS、LDL—C在用药的第8周（分别为19．4±8．1 vs43.3±13．1nmol／mg LDLpm;5．33±0．97vs8．83±1．11nmol／mL;125±9vs157±12mg／dL,P〈0．05）和12周（分别为14．4±5．1vs43．3±13．1nmol／mg LDLpro;2．3±0．3vs8．83±1．11nmol／mL;131±5vs157±12mg／dL,P〈0．05）明显降低,LDL size无统计学差异。结论氟伐他汀能够显著降低患者血中的LHPO、TBARS、LDL,表明对2型糖尿病合并高脂血症患者有抗氧化作用．且与其抗血脂的作用相关．     

From cells to laminate: probing and modeling residual stress evolution in thin silicon photovoltaic modules using synchrotron X‐ray micro‐diffraction experiments and finite element simulations

Fracture of silicon crystalline solar cells has recently been observed in increasing percentages especially in solar photovoltaic (PV) modules involving thinner silicon solar cells (<200 μm). Many failures due to fracture have been reported from the field because of environmental loading (snow, wind, etc.) as well as mishandling of the solar PV modules (during installation, maintenance, etc.). However, a significantly higher number of failures have also been reported during module encapsulation (lamination) indicating high residual stress in the modules and thus more prone to cell cracking. We report here, through the use of synchrotron X‐ray submicron diffraction coupled with physics‐based finite element modeling, the complete residual stress evolution in mono‐crystalline silicon solar cells during PV module integration process. For the first time, we unravel the reason for the high stress and cracking of silicon cells near soldered inter‐connects. Our experiments revealed a significant increase of residual stress in the silicon cell near the solder joint after lamination. Moreover, our finite element simulations show that this increase of stress during lamination is a result of highly localized bending of the cell near the soldered inter‐connects. Further, the synchrotron X‐ray submicron diffraction has proven to be a very effective way to quantitatively probe mechanical stress in encapsulated silicon solar cells. Thus, this technique has ultimately enabled these findings leading to the enlightening of the role of soldering and encapsulation processes on the cell residual stress. This model can be further used to suggest methodologies that could lead to lower stress in encapsulated silicon solar cells, which are the subjects of our continued investigations. Copyright © 2017 John Wiley & Sons, Ltd.     

A constitutive single crystal model for the silicon mechanical behavior: Applications to the stress induced by silicided lines and STI in MOS technologies

In this study, a single crystal model for the silicon mechanical behavior is used in finite element simulation, performed with the FEM code ZeBuLon. The constitutive equations are taken from the well known model of Alexander and Haasen applied to each slip system along the {1 1 1} planes in the directions. After calibration, thermal-softening and strain rate-softening have been investigated. Two applications are reviewed.The effect of silicide-induced stress is studied with the model. During the cooling down, simulation shows that gliding is activated in a narrow temperature window. This viscoplastic regime enables strain localization. The residual stress field is compared with an elastic simulation. Then, in a second application two layouts, where the STI pattern is different, are simulated and the results are checked against the leakage current measured.     

A spatiotemporal, patient individualized simulation model of solid tumor response to chemotherapy in vivo: the paradigm of glioblastoma multiforme treated by temozolomide

A novel four-dimensional, patient-specific Monte Carlo simulation model of solid tumor response to chemotherapeutic treatment in vivo is presented. The special case of glioblastoma multiforme treated by temozolomide is addressed as a simulation paradigm. Nevertheless, a considerable number of the involved algorithms are generally applicable. The model is based on the patient's imaging, histopathologic and genetic data. For a given drug administration schedule lying within acceptable toxicity boundaries, the concentration of the prodrug and its metabolites within the tumor is calculated as a function of time based on the drug pharamacokinetics. A discretization mesh is superimposed upon the anatomical region of interest and within each geometrical cell of the mesh the most prominent biological "laws" (cell cycling, necrosis, apoptosis, mechanical restictions, etc.) are applied. The biological cell fates are predicted based on the drug pharmacodynamics. The outcome of the simulation is a prediction of the spatiotemporal activity of the entire tumor and is virtual reality visualized. A good qualitative agreement of the model's predictions with clinical experience supports the applicability of the approach. The proposed model primarily aims at providing a platform for performing patient individualized in silico experiments as a means of chemotherapeutic treatment optimization.