Miracle-dz/UniFAR-FTUs huggingface dataset:
Reactor network synthesis with guaranteed robust performance

Typical continuous process flowsheets include reaction section, separation section and recycles. The reaction section is often the most important part of a chemical process, which may contain several interconnected reactors. The superstructure approach is a widely used model-based process design method for reactor network synthesis. It starts from a reactor network superstructure and uses mathematical models and optimization tools to select the best process design. The superstructure approach results in an optimal process flowsheet with determined connection patterns of reactors, reactor types, design parameters and operating conditions of each reactor. In this work, a systematic model-based approach for reactor network synthesis problems with guaranteed robust dynamic performance will be presented. The work is based on the superstructure approach, but in comparison to the classical methods, not only economic optimality with respect to a static objective function, but also certain specified dynamic properties, i.e. dynamic stability and response speed, are guaranteed simultaneously under parametric uncertainty. Structural alternatives in the flowsheet, i.e., how reactors are interconnected, as well as in the control system, i.e., how controlled and manipulated variables are paired, are subject to design degrees of freedom. Moreover, it is allowed that idle reactors and controllers can appear in the reactor network superstructure, so that a fixed number of non-idle reactors and controllers does not have to be assumed as a priori. The optimal reactor network design in either openor closed-loop is determined by solving a single optimization problem. The proposed approach allows an integrated treatment of parametric uncertainties, which may either result from model uncertainties, such as reaction kinetic constants or heat transfer coefficients, or from process uncertainties, including slow disturbances in load or the quality of raw materials. A robust eigenvalue constraint to guarantee the robust performance of the designed reactor network is formulated. Efficient formulations of interconnecting reactors and novel complementarity-based constraints for control structure selection are proposed. The method results in a semi-infinite mixed-integer nonlinear optimization problem with complementarity constraints, disjunctions and a robust eigenvalue constraint. A hybrid two-step solution method is proposed to solve the synthesis problem, which integrates candidate solution algorithms of related optimization problems. The proposed solution method is applied to a case study of allyl chloride production with up to ten plug flow and continuous stirred tank reactors.
A Comparison Study of Linear and Nonlinear Regression Models

Regression analysis is an important statistical tool for analyzing the relationships between dependent, and independent variables. The main goal of regression analysis is determine, and estimate parameters of a function that describe the best fit for a given data sets. There are many linear types of regression analysis models such as simple and multiple regression models. Also, there are the non-linear regression analyses such as binary and multinomial logistic regression models. This research at first, introduced many types of such models. Second, estimates the parameters of the models by using the maximum likelihood estimation, and the least square estimation methods. Also, it introduces some criteria for evaluating methods. Two suitable applications on two different data sets are conducted, and useful results are concluded.
On l∞ performance optimization: linear switched systems and systems with cone constraints

The l∞ performance of Linear Time-Invariant (LTI) systems has been one of the corner stones of the robust control theory for over the past 30 years. The l∞ performance has been studied mostly for LTI systems and the scarcity of the results for other types of systems is prominent in this area. This dissertation aims to depart from LTI systems and investigate the l∞ performance for other classes of systems. In particular, the l∞ performance of Linear Switched Systems (LSS) and of linear systems with cone constraints is studied in the first and second part of this dissertation, respectively. Part I: In Part I, we first consider the worst-case l∞ induced norm computation of LSS. That is, supσ ‖Gσ‖, where Gσ is a LSS, σ is the switching sequence, and the norm, ‖. ‖, is the l∞ induced norm. This problem can be linked to robustness of systems when the switching is arbitrary. We provide lower and upper bounds of this quantity. These bounds are hard to compute and in general conservative. Hence, we narrow our attention to special classes of LSS by defining the classes of input, output, and input-output LSS and show that for these classes, exact expressions for the worst-case l∞ induced norm can be found. Moreover, we introduce the class of generalized input-output LSS and show how their l∞ gains can be computed exactly via Linear Programming (LP). The class of generalized input-output LSS proves to be a sufficiently rich class as it is dense in the set of all stable LSS. We further derive new stability and stabilizability conditions and control synthesis in terms of LP utilizing generalized input-output LSS. The other extreme from the worst-case norm is the minimal norm, i.e., infσ ‖Gσ‖. The interest in this type of problem is motivated by situations where there may be limited sensor and/or actuator resources for filtering and control. We show that for Finite Impulse Response (FIR) switching systems the minimizing switching sequence can be chosen to be periodic. For input-only or output-only switching systems an exact characterization of the minimal l∞ gain is provided, and it is shown that the minimizing switching sequence is constant, which, as also shown, is not true for input-output switching. Moreover, we study Markov Linear Switched Systems (MLSS). These are LSS whose switching sequence is a Markov process. We introduce the notion of the stochastic l∞ gain and provide exact expression to compute it. However, this computation is challenging, as we show, and hence we resort to a more relaxed but tractable notion of l∞ mean performance. We provide tractable computation and control synthesis method with respect to the l∞ mean performance.
Integration of Abscisic Acid Signalling into Plant Responses1

: The phytohormone abscisic acid (ABA) plays a major role as an endogenous messenger in the regulation of plant’s water status. ABA is generated as a signal during a plant’s life cycle to control seed germination and further developmental processes and in response to abiotic stress imposed by salt, cold, drought, and wounding. The action of ABA can target specifically guard cells for induction of stomatal closure but may also signal systemically for adjustment towards severe water shortage. At the molecular level, the responses are primarily mediated by regulation of ion channels and by changes in gene expression. In the last years, the molecular complexity of ABA signal transduction surfaced more and more. Many proteins and a plethora of “secondary” messengers that regulate or modulate ABA-re-sponses have been identified by analysis of mutants including gene knock-out plants and by applying RNA interference technology together with protein interaction analysis. The complexity possibly reflects intensive cross-talk with other signal pathways and the role of ABA to be part of and to integrate several responses. Despite the missing unifying concept, it is becoming clear that ABA action enforces a sophisticated regulation at all levels.
Blow-up Solution for $4$-dimensional Generalized Emden-Fowler Equation with Exponential Nonlinearity

Using some nonlinear domain decomposition method, we prove the existence of singular limits for solution of generalized Emden-Fowler equation with exponential nonlinearity in fourth-dimensional given by The leading part ∆ is, usually, called Laplacian operator. The potential V ( x ) belongs to L ∞ loc ( R 4 ) it is smooth and bounded and a = a ( x ) is a given smooth function over Ω, called the Schr¨odinger wave function. Namely, we are still looking for solutions which concentrate at the points x j ∈ Ω, j = 1 , . , m as the parameter ρ tends to 0. We ﬁnd suﬃcient conditions under which, as ρ tend to 0, there exists an explicit class of solutions which admit a concentration behavior with a prescribed bubble proﬁle around some given m -points in Ω, for any given integer m . These are the so-called singular limits. The candidate m -points of concentration must be nondegenerate (in essential way) critical points of a suitable ﬁnite dimensional functional explicitly and the higher order Green’s function with respect to the imposed boundary conditions.
Adaptive Kalman Filtering for Postprocessing Ensemble Numerical Weather Predictions

AbstractForecasts from numerical weather prediction models suffer from systematic and nonsystematic errors, which originate from various sources such as subgrid-scale variability affecting large scales. Statistical postprocessing techniques can partly remove such errors. Adaptive MOS techniques based on Kalman filters (here called AMOS), are used to sequentially postprocess the forecasts, by continuously updating the correction parameters as new ground observations become available. These techniques, originally proposed for deterministic forecasts, are valuable when long training datasets do not exist. Here, a new adaptive postprocessing technique for ensemble predictions (called AEMOS) is introduced. The proposed method implements a Kalman filtering approach that fully exploits the information content of the ensemble for updating the parameters of the postprocessing equation. A verification study for the region of Campania in southern Italy is performed. Two years (2014–15) of daily meteorological observ.
A modified iterative closest point algorithm for noisy data

The problem of aligning of 3D point data is the known registration task. The most popular registration algorithm is the Iterative Closest Point (ICP) algorithm. The traditional ICP algorithm is a fast and accurate approach for rigid registration between two point clouds but it is unable to handle affine case. Recently, extension of the ICP algorithm for composition of scaling, rotation, and translation is proposed. A generalized ICP version for an arbitrary affine transformation is also suggested. In this paper, a new iterative algorithm for registration of point clouds based on the point-to-plane ICP algorithm with affine transformations is proposed. At each iteration, a closed-form solution to the affine transformation is derived. This approach allows us to get a precise solution for transformations such as rotation, translation, and scaling. With the help of computer simulation, the proposed algorithm is compared with common registration algorithms.
Analysis of Convolutions with Support Restrictions

For sequences with finite support it can be shown a one-sided stable invertibility of the convolution. This property allows a one-sided stable reconstruction of signals resp. phase from a sub-Nyquist sampling. Furthermore, it will be shown that the convolution of continuous signals with discrete signals under support restrictions generates shift orthogonal signals which can be optimal designed to a given spectral power mask.
Locality Optimizations for Regular and Irregular Applications

The fastest supercomputer in the world as of July 2016 is the Sunway TaihuLight. It can achieve a staggering performance of 93 PetaFlops. This incredible performance is achieved via massive parallelism. Today’s supercomputers and compute clusters have tens of thousands of distributed memory nodes with each node comprised of several shared memory multi/many core processors. Scaling on these massively parallel systems is not an easy task. A major performance and scalability bottleneck is the limited data movement bandwidth, which can be orders of magnitude smaller than the computation bandwidth. Developing applications to scale on these massively parallel systems requires minimizing data movement volume at different levels of memory hierarchy using locality optimization techniques. Locality optimization aims to reduce the data movement between slow and fast memory by rescheduling/remapping the original computation to reuse the data once it is in fast memory, thereby avoiding subsequent movement of the same data from slow memory. This dissertation explores multiple aspects of locality optimizations for enhancing scalability and performance of various regular and irregular applications on massively parallel computing environment. It develops distributed algorithms, lower bound techniques, and compiler and runtime frameworks for optimizing Tensor Contractions, Four-Index Transform, Convolutional Neural Networks (CNNs), and Recursive Tree
Generalised geometry for supersymmetric flux backgrounds

We present a geometric description of flux backgrounds in supergravity that preserve eight supercharges using the language of (exceptional) generalised geometry. These “exceptional Calabi–Yau” geometries generalise complex, symplectic and hyper-Kahler geometries, where integrability is equivalent to supersymmetry for the background. The integrability conditions take the form of vanishing moment maps for the “generalised diffeomorphism group”, and the moduli spaces of structures appear as hyper-Kahler and symplectic quotients. Our formalism applies to generic D=4,5,6 backgrounds preserving eight supercharges in both type II and eleven-dimensional supergravity. We include a number of examples of flux backgrounds that can be reformulated as exceptional Calabi–Yau geometries.We extend this analysis and show that generic AdS flux backgrounds in D=4,5 are also described by exceptional generalised geometry, giving what one might call “exceptional Sasaki–Einstein” geometry. These backgrounds always admit a “generalised Reeb vector” that generates a Killing symmetry of the background, corresponding to the R-symmetry of the dual field theory. We also discuss the relation between generalised structures and quantities in the dual field theory.We then consider deformations of these generalised structures. For AdS5 backgrounds in type IIB, a first-order deformation amounts to turning on three-form fluxes that preserve supersymmetry. We find the general form of these fluxes for any Sasaki–Einstein space and show that higher-order deformations are obstructed by the moment map for the symmetry group of the undeformed background. In the dual field theory, this corresponds to finding those marginal deformations that are exactly marginal. We give a number of examples and match to known expressions in the literature. We also apply our formalism to AdS5 backgrounds in M-theory, where the first-order deformation amounts to turning on a four-form flux that preserves supersymmetry.
Nutrient Limitation and Stoichiometry of Carnivorous Plants

: The cost-benefit model for the evolution of carnivo-rous plants posits a trade-off between photosynthetic costs associated with carnivorous structures and photosynthetic benefits accrued through additional nutrient acquisition. The model predicts that carnivory is expected to evolve if its marginal benefits exceed its marginal costs. Further, the model predicts that when nutrients are scarce but neither light nor water is limiting, carnivorous plants should have an energetic advantage in competition with non-carnivorous plants. Since the publication of the cost-benefit model over 20 years ago, marginal photosynthetic costs of carnivory have been demonstrated but marginal photosynthetic benefits have not. A review of published data and results of ongoing research show that nitrogen, phosphorus, and potassium often (co-)limit growth of carnivorous plants and that photosynthetic nutrient use efficiency is 20–50% of that of non-carnivorous plants. Assessments of stoichiometric relationships among limiting nutrients, scaling of leaf mass with photosynthesis and nutrient content, and photosynthetic nutrient use efficiency all suggest that carnivorous plants are at an energetic disadvantage relative to non-carnivorous plants in similar habitats. Overall, current data support some of the predictions of the cost-benefit model, fail to support others, and still others remain untested and merit future research. Rather than being an optimal solution to an adaptive problem, botanical carnivory may represent a set of limited responses constrained by both phylogenetic history and environmental stress.
Ion Channels Meet Auxin Action

: The regulation of cell division and elongation in plants is accomplished by the action of different phytohormones. Aux-in as one of these growth regulators is known to stimulate cell elongation growthin the aerial partsof the plant.Here, auxin enhances cell enlargement by increasing theextensibilityof the cell wall and by facilitating the uptake of osmolytes such as potassium ions into the cell. Starting in the late 1990s, the auxin regulation of ion channels mediating K + import into the cell has been studied in great detail. In this article we will focus on the molecular mechanisms underlying the modulation of K + transport by auxin and present a model to explain how the regulation of K + channels is involved in auxin-induced cell elongation growth.
Passivity-based attitude control with input quantization

A passivity-based controller with quantization for spacecraft attitude control is developed. This passive control scheme includes two parts which are a proportional controller for quaternion feedback and a strictly positive real controller for the angular velocity. To alleviate the errors caused by quantization, a special modification for the nonlinear quantized input is employed in the strictly positive real controller. Asymptotic stability can be guaranteed with the presented controller structure. A guideline for the controller parameter selection is provided with sensitivity analysis for the control scheme. Numerical simulation results demonstrate the effectiveness of the proposed controller.
LOAD TOLERANCE ESTIMATION IN FATIGUE RELIABILITY DESIGN

The stochastic response surface method is used to predict the relationship between the logarithmic fatigue life and the load capacity coefficient. Reliability analysis is then carried out using the first-order reliability method (FORM) based on the stochastic response surface. Applications of load tolerance estimation with respect to the reliability measure of fatigue life performance are developed. When the relationship between the reliability measure of the system and applied loads is linear or mildly nonlinear, a linear assumption turns out to be accurate and effective. Using the sensitivity information provided by FORM and the linear approximation, the critical value of the mean of α can be estimated to meet the probability requirement. The accuracy of the estimated result is also verified as shown in Figure 1 (load amplitude effect).
Hybrid continuous and periodic barrier strategies in the dual model: optimality and fluctuation identities

Avanzi et al. (2016) recently studied an optimal dividend problem where dividends are paid both periodically and continuously with different transaction costs. In the Brownian model with Poissonian periodic dividend payment opportunities, they showed that the optimal strategy is either of the pure-continuous, pure-periodic, or hybrid-barrier type. In this paper, we generalize the results of their previous study to the dual (spectrally positive L\'evy) model. The optimal strategy is again of the hybrid-barrier type and can be concisely expressed using the scale function. These results are confirmed through a sequence of numerical experiments.
Stratocumulus to Cumulus Transition by Drizzle

The stratocumulus to cumulus transition (SCT) is typically considered to be a slow, multiday process, caused primarily by dry air entrainment associated with overshooting cumulus, with minor influence of drizzle. This study revisits the role of drizzle in the SCT with large eddy simulations coupled with a two‐moment bulk microphysics scheme that includes a budget on aerosol (Na) and cloud droplet number concentrations (Nc). We show a strong precipitation‐induced modulation of the SCT by drizzle initiated in penetrative cumulus under stratocumulus. Lagrangian SCT simulations are initiated with various, moderate Na (100–250 cm−3), which produce little to no drizzle from the stratocumulus. As expected, drizzle formation in cumuli is regulated by cloud depth and Nc, with stronger dependence on cloud depth, so that, for the current case, drizzle is generated in all simulations once cumulus clouds become sufficiently deep. The drizzle generated in the cumuli washes out stratocumulus cloud water and much of the aerosol, and a cumulus state appears for approximately 10 h. With additional simulations with a fixed Nc (100 cm−3), we show that prediction of Nc is necessary for this fast SCT since it is a result of a positive feedback of collision‐coalescence‐induced aerosol depletion that enhances drizzle formation. A fixed Nc does not permit this feedback, and thus results in weak influence of drizzle on the SCT. Simulations with fixed droplet concentrations that bracket the time varying aerosol/drop concentrations are therefore not representative of the role of drizzle in the SCT.
Finite‐Time Consensus Problem for Second‐Order Multi‐Agent Systems Under Switching Topologies

This paper investigates the finite‐time consensus problem for multi‐agent systems with second‐order individual dynamics under switching topologies. A distributed continuous‐time protocol is designed to guarantee finite‐time consensus for homogeneous agents without predetermined leaders, i.e., it ensures agents asymptotically converge to an average consensus within finite time, even if the interaction topology among them is time‐varying but stepwise jointly‐connected. In particular, it introduces a distributed continuous‐time protocol to reach consensus in finite time and reduce the chattering together. Finally, the simulation results are also given to validate the proposed approach.
Numerical Subdivision Surfaces for Simulation and Data Driven Modeling of Woven Cloth

Author(s): Clyde, David | Advisor(s): Teran, Joseph | Abstract: We present the derivation details necessary for simulation of thin shells with finite strains based on the Kirchhoff-Love assumptions. With an eye towards cloth simulation, we combine this with a nonlinear orthotropic constitutive model framework. We leverage a conforming spatial discretization using Catmull-Clark subdivision surfaces to ensure convergence under refinement, which we confirm by numerical experiments. The dynamics are handled in a fully implicit fashion to allow for large timesteps and solution of quasistatic problems. Accurate constitutive modeling and parameter estimation for woven fabrics is essential in many fields. To achieve this we first design an experimental protocol for characterizing real fabrics based on commercially available tests. Next, we present a new orthotropic hyperelastic constitutive model for woven fabrics. Finally, we create a method for accurately fitting the material parameters to the experimental data. The last step is accomplished by solving inverse problems using our Catmull-Clark subdivision finite element discretization of the Kirchhoff-Love equations. Through this approach we are able to reproduce the fully nonlinear behavior corresponding to the captured data with a small number of parameters while maintaining all fundamental invariants from continuum mechanics. The resulting constitutive model can be used with any discretization and not just subdivision finite elements, which we demonstrate by providing an alternate implementation based on simple triangle meshes. We illustrate the entire process with results for five types of fabric and compare photo reference of the real fabrics to the simulated equivalents.