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Cold atmospheric plasmas (CAPs) are increasingly used for applications requiring the processing of heat- and pressure-sensitive (bio)materials. A key challenge in model-based control of CAPs arises from the high computational requirements of theoretical plasma models as well as lack of mechanistic understanding of plasma-surface interactions. Thus, control strategies that rely on simple, physics-based models that can be adapted to mitigate plant-model mismatch will be particularly advantageous for CAP applications. This paper presents an optimal control approach for controlling the nonlinear and cumulative effects of CAPs delivered to a target surface using a simple system model. Through parsimonious input parameterization, the solution to the optimal control problem (OCP) is given by an arc sequence that does not include any singular arcs. A data-driven adaptive algorithm based on modifier adaptation is proposed to deal with the structural plant-model mismatch by estimating the mismatch in the cost and constraints of the OCP. The adaptive approach is shown to converge to a Karush-Kuhn-Tucker (KKT) point of the OCP for the true system. Moreover, a control strategy based on feedback linearization and derivative estimation is proposed for online tracking of path constraints in the presence of disturbances and model uncertainty. The proposed approach is demonstrated by simulations and real-time control experiments on a kHz-excited atmospheric pressure plasma jet in Helium, in which the plasma treatment time is minimized while delivering a desired amount of nonlinear thermal effects to the target surface.

Optimal control problems are prevalent in model-based control, state and parameter estimation, and experimental design for complex dynamical systems. An approach for obtaining solutions to these problems is based on the notion of parsimonious input parameterization and comprises two tasks: the enumeration of arc sequences followed by the computation of optimal values of a small number of decision variables for each sequence. This paper proposes an efficient global solution method for single-input optimal control problems for nonlinear dynamical systems with a potentially large number of states or complex dynamics via sum-of-squares polynomials and parallel computing. The method approximates the problem for a given arc sequence as a polynomial optimization problem that can be efficiently solved to global optimality via semidefinite programming. It is established that the difference between the cost obtained by the proposed method and the globally optimal cost of the original problem is bounded and depends on the polynomial approximation error. The method is illustrated by simulation examples of a reaction system and a rocket.

Optimal experiment design (OED) aims to optimize the information content of experimental observations by designing the experimental conditions. In Bayesian OED for parameter estimation, the design selection is based on an expected utility metric that accounts for the joint probability distribution of the uncertain parameters and the observations. This work presents solution methods for two approximate formulations of the Bayesian OED problem based on Kullback–Leibler divergence for the particular case of Gaussian prior and observation noise distributions and the general case of arbitrary prior distributions and arbitrary observation noise distributions when the observation noise corresponds to arbitrary functions of the states and random variables with an arbitrary multivariate distribution. The proposed methods also allow satisfying path constraints with a specified probability. The solution approach relies on the reformulation of the approximate Bayesian OED problem as an optimal control problem (OCP), for which a parsimonious input parameterization is adopted to reduce the number of decision variables. An efficient global solution method for OCPs via sum-of-squares polynomials and parallel computing is then applied, which is based on approximating the cost of the OCP by a polynomial function of the decision variables and solving the resulting polynomial optimization problem to global optimality in a tractable way via semidefinite programming. It is established that the difference between the cost obtained by solving the polynomial optimization problem and the globally optimal cost of the OCP is bounded and depends on the polynomial approximation error.

Models of nonlinear dynamical systems are typically composed of unknown and known parts due to the existence of mismatch between the true nonlinear system dynamics and their models. This paper presents a multivariable control strategy for nonlinear systems that deals explicitly with the existence of incomplete models in a data-driven framework. The proposed strategy uses feedback linearization and continuous-time estimation of the unknown derivatives to achieve satisfactory closed-loop performance for fast sampling applications with simple tuning of design parameters in the absence of a fully known system model. It is shown that this strategy possesses useful steady-state properties, such as elimination of steady-state error and rejection of constant input disturbances without any integral term, and ensures exponential convergence to the setpoints. Another contribution of this paper is the development of methods for quantitative analysis of the stability and performance of the control strategy in the continuous-time case. The approach is illustrated via a simulated example of reactor control. The proposed control strategy is broadly applicable and simplifies control design significantly for a large variety of systems.

Optimal experiment design (OED) aims to optimize the information content of experimental observations for various types of applications by designing the experimental conditions. In Bayesian OED for parameter estimation, the design selection is based on an expected utility metric that accounts for the joint probability distribution of the uncertain parameters and the observations. This work presents an approximation of the Bayesian OED problem based on Kullback–Leibler divergence that is amenable to global optimization. The experiment design adopts a parsimonious input parametrization that reduces the number of design variables. This leads to a tractable polynomial optimization problem that can be solved to global optimality via the concept of sum-of-squares polynomials.

Background: Genotyping and amyloid fibril detection in tissues are generally considered the diagnostic gold standard in transthyretin-related amyloidosis. Patients carry less stable TTR homotetramers prone to dissociation into non-native monomers, which rapidly self-assemble into oligomers and, ultimately, amyloid fibrils. Thus, the initial event of the amyloid cascade produces the smallest transthyretin species: the monomers. This creates engineering opportunities for diagnosis that remain unexplored. Methods: We hypothesise that molecular sieving represents a promising method for isolating and concentrating trace TTR monomers from the tetramers present in plasma samples. Subsequently, immunodetection can be utilised to distinguish monomeric TTR from other low molecular weight proteins within the adsorbed fraction. A two-step assay was devised (ImmunoSieve assay), combining molecular sieving and immunodetection for sensing monomeric transthyretin. This assay was employed to analyse plasma microsamples from 10 individuals, including 5 pre-symptomatic carriers of TTR-V30M, the most prevalent amyloidosis-associated TTR variant worldwide, and 5 healthy controls. Results: The ImmunoSieve assay enable sensitive detection of monomeric transthyretin in plasma microsamples. Moreover, the circulating monomeric TTR levels were significantly higher in carriers of amyloidogenic TTR mutation. Conclusions: Monomeric TTR can function as a biomarker for evaluating disease progression and assessing responses to therapies targeted at stabilising native TTR. [ABSTRACT FROM AUTHOR]

This study investigates the sensitivity of the square cup forming process. It analyses how the uncertainties in the material properties, friction and process conditions affect the results of the square cup, such as equivalent plastic strain, geometry change, thickness reduction, punch force and springback. The cup flange and the die curvature region are identified as highly affected areas, while the cup bottom is least affected by the uncertainties. Two sensitivity analysis techniques, PAWN and Sobol indices, are compared. In particular, the study shows that PAWN indices require a significantly smaller number of simulations than Sobol indices, making them a more efficient choice for sensitivity analysis. While both PAWN and Sobol indices generally give comparable results, discrepancies arise in the analysis of springback, where PAWN indices show superior accuracy, particularly when dealing with multimodal distributions. This observation highlights the importance of selecting the appropriate sensitivity analysis method based on the nature of the data being analysed. These results provide insights for optimizing stamping processes to reduce production time and costs. [ABSTRACT FROM AUTHOR]

Introduction: Conjunctival cysts of the orbit development are a rare complication in enucleated patients. They result from the entrapment of conjunctival epithelium within the orbit. Case Presentation: We report the case of a 25-year-old man with a history of enucleation for a retinoblastoma of the right eye who presented with difficulty in fitting his eye prothesis. On his past medical records, there was no reference to the placement of any orbital implant at the time of the surgery. Biomicroscopy of the right eye revealed a thickened bulbar conjunctiva, an inferior symblepharon, and a translucid central area with vascularization. Imaging was remarkable for a cystic cavity filling the whole right orbit. Biopsy revealed the diagnosis of a conjunctival cyst, and drainage was performed, alleviating the patient's symptoms. Conclusion: Orbital conjunctival cysts may pose a clinical problem, and treatment modalities include surgical excision, absolute alcohol injection, and trichloroacetic acid injection. [ABSTRACT FROM AUTHOR]

Due to the growth in the consumption of avocado in recent years, the amount of waste caused by avocado peel and seed has increased. Avocado waste can be transformed into valuable products such as energy, biofuels, and biological products using integrated processes in a biorefinery. This paper considers the detailed modeling, simulation, and optimization of a biorefinery for the production of phenolic compounds, bioethanol, biological xylitol, syngas, and electrical power from avocado seed and peel, using Aspen Plus in equation-oriented mode as a process simulation tool. For a biorefinery in nominal conditions, it is possible to achieve a gross profit of approximately 30 × 10 6 USD/year for capital costs of USD 31.4 × 10 6 , while the combined effect of process optimization and heat integration allows reaching a gross profit of 37 × 10 6 USD/year for capital costs of USD 30.7 × 10 6 . These results of the optimized plant show its potential to take advantage of avocado seed and peel in a profitable and sustainable way. This detailed equation-based model paves the way for superstructure optimization of a biorefinery for avocado waste processing. [ABSTRACT FROM AUTHOR]