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The robust stabilization problem with respect to both dynamic and parametric uncertainty for linear deterministic systems is analyzed in the present article. The robust design methods consider either the dynamic modelling in frequency domain of the uncertainty or, its parametric representation in the state space realisation. Suitable analysis approaches for parametric uncertainty modelling are provided by Kharitonov and Edge-type theorems. Under some specific assumptions, these methods allow to determine the whole admissible domain of the uncertain parameters for which a system is stable. It shall describe a method that combines the advantages of the control techniques with ones given by the polytopic representation of parametric uncertainty. A modified ∞ loop-shaping approach allowing to solve control problems in which robust stabilization, sensitivity reduction, and model following design objectives are formulated is presented and it allows to handle tracking design specifications. The modified loop-shaping procedure allows to design a controller that provides a) robust stability with respect to the normalized left coprime factorization (NLCF); b) reduced sensitivity with respect to output disturbance on a specified range of frequencies, and c) tracking of the output of a given ideal model. The article is finished with a case study in which a two degrees-of-freedom control system with respect to the pitch angle for the longitudinal dynamics of a UAV (ARCHER V1.7) is designed using the modified ∞ loop-shaping procedure.

Copper (Cu) solvent extraction (SX) from sulphate leach solution was investigated using Robust design (Taguchi design). The effect of some parameters, including pH (1.25–5), phase ratio (O/A: 1/3–2/1), concentration of the extractant (2.5–10% v/v) and extraction duration (5–20 min) were examined. In the investigation of SX, the ranges and experimental parameters chosen are as follows: pH, 3.75; concentration of the extractant, 7.5% v/v; Aqueous /Organic ratio, 3/1 and extraction duration, 10 min. Under these conditions, Cu extraction of 94.62% was achieved. Variance analysis technique (ANOVA) suggested that, the most effective parameter was pH and the less effective parameter was the aqueous /organic ratio. Using of 1.5 M H 2 SO 4 allowed for a stripping efficiency of 98% Cu from the organic phase. Graphical Abstract

The closed-loop constant pressure drop control valve is widely used in aero-engine fuel servo metering systems. However, the available constant pressure drop control valve cannot realize servo tracking without static error and, often, a high proportional gain is used to reduce the static error and improve the servo tracking performance, which reduces the stability margin. In this paper, an integral constant pressure drop control valve is designed, which consists of an integral controller and a stabilizing controller. Moreover, a robust LQR design method is proposed to complete the design task. Firstly, the controlled plant’s state–space model is derived, and the augmented model with tracking error is established based on the robust servo system design theory. Secondly, a servo controller with dual functions of integral control and stabilization control is constructed and decoupled, in which the stabilizing controller guarantees the asymptotic stability as well as the anti-disturbance performance, and the integral controller realizes the servo tracking without static error. Finally, based on the robust LQR design method, two key design parameters, including the integral control gain and the stabilization control gain, are designed to complete the design task. The simulation results indicate that, even when suffering 50 mm2 metered flow area step disturbance and 1 MPa inlet pressure step change, the designed integral constant pressure drop control valve can realize the function of servo tracking without static error. The static error is almost 0, the settling time is within 0.01 s, the overshoot is within 10%, and the phase margin is more than 55°.

To consider the influence of uncertainty in the design process of automotive acoustic packages, the robustness of the acoustic packaging system performance must be improved, and the low-efficiency problem of the two-layer nesting robustness optimization model must be solved. This article proposes a highly efficient robustness optimization design method for improving the performance of the automotive acoustic package. First, the full vehicle model was established based on the statistical energy analysis method, and the accuracy of the model was verified through acoustic transfer function (ATF) testing. The parameters affecting the sound absorption and insulation performance of the key acoustic packaging parts were selected as the uncertain parameters, and their sensitivity was analyzed. The possibility degree method of interval numbers was introduced to convert the two-layer nesting robustness optimization model to a single-layer optimization model, and the efficient robust design of the automotive acoustic packaging was realized. The acoustic packaging parts of a sport utility vehicle (SUV) were analyzed and optimized using the proposed efficient and robust optimization method. After optimization, the total mass of the acoustic packaging parts decreased by 10.8%, the radius of the perturbation interval of the interior noise decreased by more than 25% compared with the initial value, and the robustness of the system greatly improved.

Transdermal drug delivery has attracted much attention as an alternative to intravenous and oral methods of delivery. But the main barrier is stratum corneum. Terpenes classes of chemical enhancers are used in transdermal formulations for facilitating penetration of drugs. The aim of the study is to evaluate terpenes as skin penetration enhancers and correlate its relationship with permeation and lipophilicity. In this study, alfuzosin hydrochloride (AH) hydrogels were prepared with terpenes using Taguchi orthogonal array experimental design. The formulations contained one of eight terpenes, based on their lipophilicity (log P 2.13-5.36). The percutaneous permeation was studied in rat skin using diffusion cell technique. Flux, cumulative amount, lag time and skin content of AH were measured over 24 hours and compared with control gels. Nerolidol with highest lipophilicity (log P 5.36 ± 0.38) showed highest cumulative amount (Q(24)) of 647.29 ± 18.76 μg/cm(2) and fluxrateof 28.16 ± 0.64 μg/cm(2)/hour. It showed decreased lag time of 0.76 ± 0.15 hours. Fenchone (2.5%) (log P 2.13 ± 0.30) produced the longest lag time 4.8 ± 0.20 hours. The rank order of enhancement effect was shown as nerolidol > farnesol > limonene > linalool > geraniol > carvone > fenchone > menthol. Lowest skin content was seen with carvone. Increase in lipophilicity of terpenes showed increase in flux, cumulative amount (Q(24)), and enhancement ratio which was significant with P < 0.000. But lag time was decreased and no correlation was found between lipophilicity and skin content. Histological studies showed changes in dermis which can be attributed to disruption of lipid packing of stratum corneum due to effect of nerolidol within lipid lamellae. It was found that small alcoholic terpenes with high degree of unsaturation enhance permeation of hydrophilic drugs, liquid terpenes enhance better than solid terpenes and terpenes with high lipophilicity are good penetration enhancers.

SUMMARY Due to the diversity of operating conditions of real‐time power system, traditional power oscillation damping controller (PODC) of static synchronous compensator (STATCOM), which only considers single operating condition, could not provide sufficient damping when operating condition changes. To solve this problem, this paper proposes a robust design method for PODC. The total least squares‐estimation of signal parameters via rotational invariance techniques (TLS‐ESPRIT), which performs well in term of anti‐noise and accuracy, is suggested to identify the transfer function and residues of the system. The residue‐based approach considers several typical operating conditions so that the designed PODC could adapt to multiple operating conditions. The simulation result demonstrates that, compared with the traditional method, PODC of STATCOM designed by the proposed method damps the low‐frequency oscillation more effectively and is more adaptable to the change of operating conditions. Copyright © 2013 John Wiley & Sons, Ltd.

This study is concerned with the robust stabilisation problem of networked delta operator systems with time delays and packet losses. To avoid the inherent drawback of the standard shift-domain model at high sampling rates, the uncertain delta operator model is employed to describe the plant of networked control systems. Under this framework, by guaranteeing the deceasement of Lyapunov functional at each updating period rather than at each sampling period, a sufficient robust stability condition in terms of linear matrix inequalities is presented. Based on this criterion, the robust stabilising controller design method is also provided. The results acquired in this study are network parameters dependent in the sense that they depend on not only the bounds of time delays but also the bound of maximum consecutive packet losses. Numerical examples are given to show the effectiveness and potential of the developed theoretical results.

With the availability and ease of small molecule production and design continuing to improve, robust, high-throughput methods for screening are increasingly necessary to find pharmacologically relevant compounds amongst the masses of potential candidates. Here, we demonstrate that a primary oxygen glucose deprivation assay in primary cortical neurons followed by secondary assays (i.e. post-treatment protocol in organotypic hippocampal slice cultures and cortical neurons) can be used as a robust screen to identify neuroprotective compounds with potential therapeutic efficacy. In our screen about 50% of the compounds in a library of pharmacologically active compounds displayed some degree of neuroprotective activity if tested in a pre-treatment toxicity assay but just a few of these compounds, including Carbenoxolone, remained active when tested in a post-treatment protocol. When further examined, Carbenoxolone also led to a significant reduction in infarction size and neuronal damage in the ischemic penumbra when administered six hours post middle cerebral artery occlusion in rats. Pharmacological testing of Carbenoxolone-related compounds, acting by inhibition of 11-β-hydroxysteroid dehydrogenase-1 (11β-HSD1), gave rise to similarly potent in vivo neuroprotection. This indicates that the increase of intracellular glucocorticoid levels mediated by 11β-HSD1 may be involved in the mechanism that exacerbates ischemic neuronal cell death, and inhibiting this enzyme could have potential therapeutic value for neuroprotective therapies in ischemic stroke and other neurodegenerative disorders associated with neuronal injury.

Heavy trucks contribute approximately 18% of all vehicle contribution to air pollution and approximately 49% of nitric oxides (NOx) 62% of the particulate matter (PM). Diesel emissions of NOx and particulates have been steadily improving along with reductions in worldwide emissions standards. A Diesel Particulate Filter (DPF) is an important component of a diesel exhaust system. The DPF minimizes particulates in the exhaust stream. Robust Engineering Parameter Design methods were applied to optimize the catalyst slurry coating process for the DPF. The initial production scale‐up run was unsatisfactory with a process capability index (Cpk) less than 1.0. Three Nominal‐the‐Best Robust Engineering Parameter Design experiments were conducted on the process, one in Tulsa, Oklahoma and two in Florange, France. Very similar process improvements and learning's were obtained for each. Both Tulsa and Florange processes confirmed with 18 dB gains in Signal‐to‐Noise (S/N) Ratios and on target performance.

The ability to design efficient enzymes from scratch would have a profound effect on chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past decade makes us optimistic that this ambition is within reach. The development of artificial enzymes containing metal cofactors and noncanonical organocatalytic groups shows how protein structure can be optimized to harness the reactivity of nonproteinogenic elements. In parallel, computational methods have been used to design protein catalysts for diverse reactions on the basis of fundamental principles of transition state stabilization. Although the activities of designed catalysts have been quite low, extensive laboratory evolution has been used to generate efficient enzymes. Structural analysis of these systems has revealed the high degree of precision that will be needed to design catalysts with greater activity. To this end, emerging protein design methods, including deep learning, hold particular promise for improving model accuracy. Here we take stock of key developments in the field and highlight new opportunities for innovation that should allow us to transition beyond the current state of the art and enable the robust design of biocatalysts to address societal needs. Recent progress in computational enzyme design, active site engineering and directed evolution are reviewed, highlighting methodological innovations needed to deliver improved designer biocatalysts.