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Metalearning of numerical algorithms for a given task consists of the data-driven identification and adaptation of an algorithmic structure and the associated hyperparameters. To limit the complexity of the metalearning problem, neural architectures with a certain inductive bias towards favorable algorithmic structures can, and should, be used. We generalize our previously introduced Runge-Kutta neural network to a recursively recurrent neural network superstructure for the design of customized iterative algorithms. In contrast to off-the-shelf deep learning approaches, it features a distinct division into modules for generation of information and for the subsequent assembly of this information towards a solution. Local information in the form of a subspace is generated by subordinate, inner, iterations of recurrent function evaluations starting at the current outer iterate. The update to the next outer iterate is computed as a linear combination of these evaluations, reducing the residual in this space, and constitutes the output of the network. We demonstrate that regular training of the weight parameters inside the proposed superstructure on input/output data of various computational problem classes yields iterations similar to Krylov solvers for linear equation systems, Newton--Krylov solvers for nonlinear equation systems, and Runge-Kutta integrators for ordinary differential equations. Due to its modularity, the superstructure can be readily extended with functionalities needed to represent more general classes of iterative algorithms traditionally based on Taylor series expansions. [ABSTRACT FROM AUTHOR]

• A synergy of analytical techniques for the characterization of black crusts on built heritage. • The samples feature typical dendritic black crusts and a Fe-rich aluminosilicate patina. • Three typologies of particles detected are carbonaceous particles from diesel exhausts and domestic boilers, Si-rich particles from vehicular emissions and mineral dust, Ti-rich and Fe-rich particles from coal combustion and domestic heating, respectively. • Vehicular emissions pose a clear fingerprint on the formation of the black crusts in urban environments. • Micro XRF line scans gave insight into the distribution of trace elements in a depth profile of the black crust. Samples of black crust from the Roman-era monument of the Library of Hadrian (Athens) have been collected to perform chemical and mineralogical characterization of the degradation products on built heritage, utilizing a synergy of analytical techniques. The decay features consist of typical black crusts and a Fe-rich aluminosilicate patina. The chemical composition of the crusts, the element distribution along the crust and the characterization of individual embedded particles were combined to determine groups of elements related to specific sources and investigate how they are related to each other. Three main typologies of particles were identified: a) carbonaceous particles from diesel exhausts and domestic boilers, b) Si-rich particles from vehicular emissions and mineral dust, and c) metallic Ti-rich and Fe-rich particles from coal combustion and domestic heating, respectively. Traffic was found to pose a clear fingerprint on the type and chronological development of anthropogenic pollutant deposition in the black crusts formed in this particular urban environment. [ABSTRACT FROM AUTHOR]

Background: Our understanding of the pathophysiology and management of intracranial aneurysms (IAs) continuously advances. This case-control study analyzed the demographics of patients with IAs and the morphological Digital Subtraction Angiography (DSA) characteristics of ruptured and unruptured IAs. Methods: Two patient groups with saccular ruptured and unruptured IAs eligible for coiling were prospectively analyzed during a 3-year period. Patient groups were compared regarding gender, age, arterial vasculature side, anatomical location, diameter, preoperative DSA appearance, aneurysmal and anatomical Circle of Willis variations (CWV) coexistence. Results: One hundred and three patients with ruptured and eighty-six patients with unruptured IAs were studied. Anterior communicating and internal carotid artery IAs were the dominant locations: 42.7 % and 23.3 % in ruptured and 29 % and 41.9 % in unruptured IAs, respectively. The female-to-male ratio was 1.78 in ruptured and 2.44 in unruptured IAs (p =0.317), while the rupture was more frequent in younger patients (p =0.034). Angiographically, smaller diameter (p =0.01), abnormal morphology (p =0.0001), and co-existence of CWV (p =0.016) were reported in ruptured IAs. Location at bifurcation/trifurcation (p =0.487) and the co-existence of additional or mirror IA did not differ significantly (p =0.879). Conclusions: On DSA, ruptured and unruptured IAs differed in size, morphology, and co-existence of CWV; findings that may favor the treatment of specific unruptured IAs. However, a higher level of evidence is needed to include all these factors in the treatment decision process, provide patient-oriented treatment and reliably identify unruptured IAs at greater risk. HIPPOKRATIA 2021, 25 (3):100-107. [ABSTRACT FROM AUTHOR]

We consider what we term existence-constrained semi-infinite programs. They contain a finite number of (upper-level) variables, a regular objective, and semi-infinite existence constraints. These constraints assert that for all (medial-level) variable values from a set of infinite cardinality, there must exist (lower-level) variable values from a second set that satisfy an inequality. Existence-constrained semi-infinite programs are a generalization of regular semi-infinite programs, possess three rather than two levels, and are found in a number of applications. Building on our previous work on the global solution of semi-infinite programs (Djelassi and Mitsos in J Glob Optim 68(2):227–253, 2017), we propose (for the first time) an algorithm for the global solution of existence-constrained semi-infinite programs absent any convexity or concavity assumptions. The algorithm is guaranteed to terminate with a globally optimal solution with guaranteed feasibility under assumptions that are similar to the ones made in the regular semi-infinite case. In particular, it is assumed that host sets are compact, defining functions are continuous, an appropriate global nonlinear programming subsolver is used, and that there exists a Slater point with respect to the semi-infinite existence constraints. A proof of finite termination is provided. Numerical results are provided for the solution of an adjustable robust design problem from the chemical engineering literature. [ABSTRACT FROM AUTHOR]

1-Ethyl-3-methylimidazolium acetate (EMIMAc) is an ionic liquid (IL) often investigated as a solvent, especially in the context of biopolymers and biomass pretreatment. A reduced solvent efficacy occurs upon the addition even of low amounts of water to EMIMAc. Molecular mechanisms have not yet been fully understood. It is expected that the functionality as hydrogen bond donor and acceptor is key for the solvent–solute interactions. In this work, we analyze the solvent efficacy of EMIMAc in terms of hydrogen–deuterium (H/D) exchange at the C(2)-position in mixtures with water or acetic acid added as proton donors. Low-field NMR spectroscopy and deuterated solvents are used for a time-resolved evaluation of H/D exchange reactions. The H/D exchange is also modeled to explore changes in the reaction kinetics as a function of the mixture composition. The significant difference in calculated rate constant values among the concentration regimes shows that the chosen model equations of a possible pseudo-first-order and second-order reaction mechanism including water dissociation do not cover all interaction phenomena that influence the exchange in the individual concentration ranges. However, the modeling also indicates that the investigated interaction of EMIM + and Ac - remains constant for concentrated IL mixtures containing 70 mol % of EMIMAc in water up to diluted mixtures as low as 30 mol % EMIMAc. This exemplifies the change between ions strongly associated in networks in concentrated mixtures suitable for biomass pretreatment and the much less associated anion–cation pairs in diluted mixtures which leads to the decreased efficiency of EMIMAc with increasing water content. [ABSTRACT FROM AUTHOR]

• We develop an adaptive multistage scheme for robust NMPC. • This scheme does not scale directly with the number of uncertain model parameters and handles larger problems. • We demonstrate the approach with an industrially relevant semi-batch polymerization process under parametric model uncertainty. • We also investigate a combination of the presented approach with on-line estimation of uncertain model parameters. • We achieve robust NMPC with high performance and less computational cost. We present a multistage NMPC scheme with adaptive on-line scenario-tree generation. The scenario tree is assembled from predictions of worst-case uncertainty realizations that are identified based on a first-order approximation of the process model. The key property of the presented approach is that the size of the resultant optimal control problems does not scale directly with the number of uncertain model parameters. We demonstrate the applicability of the approach with an industrially relevant semi-batch polymerization process under parametric model uncertainty and noisy, incomplete state measurements. By allowing to account explicitly for estimation errors, the presented approach yields increased robustness when compared to nominal NMPC and a standard multistage NMPC scheme. Moreover, we investigate a combination of the presented approach with on-line estimation of uncertain model parameters alongside approximation of their confidence region to reduce the uncertainty range and consequently mitigate unnecessary conservatism. The results show that adaptation of model and uncertainty range yields considerable economic benefits without impairing the attained level of robustness for the considered process. [ABSTRACT FROM AUTHOR]

Background: Classification of binary data arises naturally in many clinical applications, such as patient risk stratification through ICD codes. One of the key practical challenges in data classification using machine learning is to avoid overfitting. Overfitting in supervised learning primarily occurs when a model learns random variations from noisy labels in training data rather than the underlying patterns. While traditional methods such as regularization and early stopping have demonstrated effectiveness in interpolation tasks, addressing overfitting in the classification of binary data, in which predictions always amount to extrapolation, demands extrapolation-enhanced strategies. One such approach is hybrid mechanistic/data-driven modeling, which integrates prior knowledge on input features into the learning process, enhancing the model's ability to extrapolate. Results: We present NoiseCut, a Python package for noise-tolerant classification of binary data by employing a hybrid modeling approach that leverages solutions of defined max-cut problems. In a comparative analysis conducted on synthetically generated binary datasets, NoiseCut exhibits better overfitting prevention compared to the early stopping technique employed by different supervised machine learning algorithms. The noise tolerance of NoiseCut stems from a dropout strategy that leverages prior knowledge of input features and is further enhanced by the integration of max-cut problems into the learning process. Conclusions: NoiseCut is a Python package for the implementation of hybrid modeling for the classification of binary data. It facilitates the integration of mechanistic knowledge on the input features into learning from data in a structured manner and proves to be a valuable classification tool when the available training data is noisy and/or limited in size. This advantage is especially prominent in medical and biomedical applications where data scarcity and noise are common challenges. The codebase, illustrations, and documentation for NoiseCut are accessible for download at https://pypi.org/project/noisecut/. The implementation detailed in this paper corresponds to the version 0.2.1 release of the software. [ABSTRACT FROM AUTHOR]

Highlights • We compare optimal operational strategies based on varied dynamics. • A concise dynamic model of an hybrid energy system is developed. • The optimal operation depends on the dynamics of the system and of the input data. • Quasi-steady models of dynamic systems should contain adequate ramp constraints. • Storage units are no sufficient justification for quasi-steady state assumptions. Graphical abstract Abstract Since design optimization faces the challenge of solving inherently large optimization problems, the complexity of underlying dynamic systems is often reduced by applying quasi-steady state assumptions. It is thereby indispensable to identify the components whose transient behavior is essential to ensure meaningful results. In this study, we discuss a dynamic model for an illustrative hybrid energy system, which extends the quasi-steady models of Voll et al. (2013). Based on optimal operation with fixed design, we underline the importance of the relationship between the dynamics of the model and of the input data for the adequateness of quasi-steady operation. Our results emphasize the need for suitable ramp constraints in quasi-steady models of dynamic systems within operational optimization. Moreover, the existence of a storage unit is no sufficient justification for quasi-steady state assumptions. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Regression subject to chemical equilibrium constant leads to wrong parameter values. • Wrong parameter values cause erroneous equilibrium conversion in process simulation. • We propose bilevel program for regression that leads to correct parameter values. • Illustrative single-phase dimerization using Margules and NRTL is considered. Abstract For process simulation involving chemical reactions, thermodynamically accurate model parameters for chemical equilibrium are indispensable. We demonstrate that regression subject to the well-known chemical equilibrium constant entails violation of the sufficient conditions for thermodynamic stability. As a consequence, erroneous values of the equilibrium conversion are obtained upon use of the respective parameter values in, e.g., process simulation. In order to alleviate the problem, we propose a methodology for estimating parameters subject to necessary and sufficient criteria for thermodynamic stability. In particular, we propose an extension of Mitsos et al. (2009) to parameter estimation in reactive systems; an upper-level model-experiment mismatch is minimized subject to several lower-level minimization problems (LLP), which ensure the fulfilment of the reactive tangent plane criterion (RTPC) of Smith et al. (1993), along with other desired behavior of the Gibbs free energy. A fictitious single-phase dimerization reaction employing Margules and NRTL as Gibbs free energy models illustrates the thermodynamic guarantees provided by the proposed formulation, when the conventional ones fail. Our formulation conceptually allows any type of phase equilibrium, e.g., VLLE, as well as any type of activity coefficient model. The subproblems of the bilevel algorithm pose challenges to state-of-the-art commercial solvers. [ABSTRACT FROM AUTHOR]

Artificial neural networks are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of optimization problems with artificial neural networks embedded. The proposed method is based on relaxations of algorithms using McCormick relaxations in a reduced space (Mitsos et al. in SIAM J Optim 20(2):573-601, 2009) employing the convex and concave envelopes of the nonlinear activation function. The optimization problem is solved using our in-house deterministic global solver. The performance of the proposed method is shown in four optimization examples: an illustrative function, a fermentation process, a compressor plant and a chemical process. The results show that computational solution time is favorable compared to a state-of-the-art global general-purpose optimization solver. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Combination of known robust dynamic optimization algorithms with eNMPC. • Two alternative approaches for robust feasible control are presented. • Evaluation of presented approaches is executed by an illustrative case study. Abstract The goal of this work is an efficient control scheme for the robust satisfaction of constraints for batch processes under parametric uncertainties. The multi-stage economic nonlinear model-predictive controller (eNMPC) [S. Lucia, et al. J. Process Control, 2013] is combined with two different methods for robust dynamic optimization. The multi-stage NMPC considers a scenario tree, branching at each sampling interval. In this work, the scenarios for the branches are generated with two previously investigated methods of detecting worst-case scenarios. One method is based on discretization of the uncertain parameter set (J. Puschke, et al. Comput. Chem. Eng., Vol. 98, 2017a). With this approach, all worst-case models are considered, including points not lying at the boundary of the uncertainty set. The other method is a heuristic approach (J. Puschke, et al. Comput. Chem. Eng., 2017b), which considers only values at the boundary with a high sensitivity. These methods for robust control are evaluated on the basis of an illustrative case study. The results show that, in contrast to the nominal eNMPC, less or even no constraint violations occur. [ABSTRACT FROM AUTHOR]

Abstract: The particle size of polymer colloids is a key characteristic. It directly affects Raman measurements due to light scattering by particles. This publication exploits the extent to which these spectral changes can be correlated to the particle sizes by using a spectral hard model and data‐driven model. To this end, spectra of aqueous polystyrene nanoparticles are decomposed into pure component spectral models by means of indirect hard modeling (IHM). Each pure component model is characterized by multiple peak‐shaped Voigt profiles. By fitting the model to spectra of polymer colloids, spectral changes due to light scattering by particles are incorporated in the model parameters. The resulting parameter values are used as input for data‐driven partial least squares (PLS) regression to extract particle sizes. The method is applied to Raman spectra of polystyrene nanoparticles of 23–60 nm diameter measured repeatedly at concentrations 0.17–1 wt%. The hybrid model predicts the particle size with R2 = 0.99. In contrast, purely data‐driven PLS regression of the spectral intensities with 3 latent PLS variables results in R2 from 0.78 to 0.83. Resulting PLS scores portray a clear differentiability of Raman scattering and light scattering by particles within IHM model parameters. PLS regression coefficients further allow identification of individual peak parameters that correlate with particle size, which substantiates previous findings on correlation between Raman signal and scattering by polymer colloids. Combining IHM and data‐driven PLS regression demonstrates more accurate prediction of particle size for extended usage of Raman spectroscopy as comprehensive process analytical technology to determine sample concentrations and particle data. [ABSTRACT FROM AUTHOR]

• Power-to-X can store renewable energy and decarbonize the chemical sector. • Power-to-X plants can be operated flexibly, but might need intermediate storage. • PEM water electrolyzers can vary the load up to providing ancillary services. • Hydrogen-to-X processes have slower characteristic times than electrolysis. • Process integration improves efficiencies, but could complicate flexible operation. Hydrogen produced with renewable electricity may be used to synthesize valuable chemical products, thus aiding the decarbonization of the chemical industry. Thanks to their fast dynamics, Polymer Electrolyte Membrane Water Electrolyzers (PEM-WEs) appear promising for coupling with fluctuating power inputs, e.g., from a grid balancing request or standalone renewable power supply. However, the flexibility of downstream synthesis processes under time-variable hydrogen flow rates is still unclear, and expensive hydrogen storage could be needed as buffer. We review the state of the technology with a focus on the potential for flexible operation of the key components of the main Power-to-X processes: hydrogen production via PEM-WEs, carbon capture, nitrogen production, and the synthesis of methane, methanol, syngas, and ammonia. Flexibility concepts of the units are described and the characteristic times for start-up and load variations, ramp rate limits, and operating ranges are collected. Opportunities for heat and mass integration are discussed. [ABSTRACT FROM AUTHOR]

Monitoring chemical processes with optical spectroscopy involves a trade-off between high-frequency sampling to capture process dynamics and good signal-to-noise ratio (SNR). This contribution presents a strategy to maximize sampling frequency while maintaining a high SNR during in-line process monitoring. To achieve this, a novel method to determine the analyte-specific SNR from a single multicomponent spectrum with overlapping peaks based on multivariate Indirect Hard Modeling (IHM) regression is presented. The method is extended to optimize the Raman acquisition time dynamically based on the current SNR. After each measurement, the acquisition time is adjusted to reach the target SNR of a component of interest. The method maximizes the number of individual measurements while sustaining the target SNR despite highly variable signal intensity. The method is applied to monitor polymerization reactions showing that it is suitable for reliably monitoring high-rate reactions of multiple spectrally-similar components. [ABSTRACT FROM AUTHOR]

Current and future challenges of optimization in the process industry are discussed. The gap between academic research and industrial workflow is analyzed. Moreover, issues arising from the shift from conventional fossil fuels as both feedstock and energy source to nonconventional feedstocks (shale gas, tar sands, CO 2 and biomass) and penetration of intermittent renewable energy are discussed. This manuscript focuses mainly on offline model-based optimization of design and operation, including the generation and selection of promising process alternatives for new feedstocks in conceptual design, multi-objective optimization, the estimation of thermodynamic parameters of new intermediates and the optimization of process operation under the volatile availability of the new feedstocks and energy sources. Moreover, a number of opportunities and needs for research and development are identified, including the simultaneous optimization of feedstocks, processes and products and a production able to process a variety of feedstocks and to utilize energy when it is cheap. [ABSTRACT FROM AUTHOR]

We comment on work by Ruszczyński et al., who extended the regression method of Cunico et al. (Fluid Phase Equilibria, 2014) and proposed certain Gibbs free energy models for binary liquid-liquid equilibria. We find that their regression method considers only necessary criteria for stability, and we derive constraints to be imposed on the thermodynamic parameters during regression such that the criteria become sufficient and the models thus become numerically easy to use. We respond to the comments by Glass and Mitsos regarding our approach to validation of binary liquid-liquid equilibrium data. [ABSTRACT FROM AUTHOR]

Several discrimination criteria have been proposed to select the correct root in the presence of multiple real roots to a cubic equation of state. Herein, a criterion which exploits the well-known method of Cardan to solve cubic equations is presented. The criterion is straightforward, continuous and covers different root scenarios. As a reference, minimization of Gibbs free energy is formulated for const. ( P , T ). From its KKT conditions, it follows that, in case a phase vanishes, relaxation of cubic equality is an alternative to relaxation of root discrimination as proposed by Kamath et al. (2010) . The proposed criterion and those from literature satisfy at most a necessary condition, while minimization of Gibbs free energy obviously is a necessary and sufficient condition for thermodynamic stability of the selected root. In certain cases, formulations from literature are found to exclude valid solutions, whereas the proposed criterion overcomes this deficiency. [ABSTRACT FROM AUTHOR]

Thoracic outlet syndrome (TOS) is a constellation of signs and symptoms caused by compression of the neurovascular structures in the thoracic outlet. TOS may be classified as either neurogenic TOS (NTOS) or vascular TOS: venous TOS (VTOS) or arterial TOS (ATOS), depending on the specific structure being affected. The basis for the surgical treatment of TOS is resection of the first rib, and it may be combined with scalenectomy or cervical rib resection. Herein, we describe a case of arterial thoracic outlet syndrome which was successfully treated with totally endoscopic video-assisted thoracoscopic surgery (VATS) first rib resection. [ABSTRACT FROM AUTHOR]

A hybrid optimization methodology is proposed for the working fluid selection of an organic Rankine cycle (ORC). First, a stochastic global solver is used to select the chemical species of the working fluid; then, a deterministic global solver is used to optimize the composition. The first step is a mixed integer nonlinear programing (MINLP) and the second a nonlinear program (NLP). The methodology is applied to the recovery of cryogenic energy during the evaporation of liquefied natural gas (LNG), which is a promising way to produce electricity with relatively high efficiency from the large amount of otherwise wasted heat. Seawater or low-grade heat sources can be used as heat source. Multicomponent working fluids have advantages compared to pure fluids because of the nonisothermal evaporation of LNG. Herein, a ternary mixture is considered. A mixture of CF 4 , CHF 3 , n-pentane is identified as an optimum ternary working fluid. It produces about 1.1 MJ/kmol LNG with a simple organic Rankine cycle using seawater as heat source. The optimization results are quantitatively compared with the literature in terms of power generation and are shown to have substantially higher electricity generation. [ABSTRACT FROM AUTHOR]

Optimal management and the role of surgery in multimodality treatment for N2 disease nonsmall cell lung cancer (NSCLC) are controversial. In this review, we focus on the possible role of pneumonectomy as a justified procedure in patients with persistent N2 disease following induction therapy. We have conducted an OVID PubMedbased search including manuscripts published in English for relevant studies. The interpretation of these trials highlights the lack of clarity and consistency in our management and leaves areas of controversy. There are no Level 1 data to support either performing or not performing pneumonectomy in this setting. The majority of the literature reviewed stresses the high risk of mortality and morbidity following pneumonectomy as a part of a trimodality approach to Stage IIIA/N2 NSCLC disease. However, selected highvolume institutions do follow this strategy with the level of risk seemingly justifying it for a highly selected group of patients, and this approach to Stage III/N2 NSCLC can be offered safely with acceptable mortality. Patient selection, response rate to induction therapy, and R0 resection are crucial for survival in experienced centers. [ABSTRACT FROM AUTHOR]

Schwannoma is a benign encapsulated tumor of the nerve sheath. Amongst other sites, it develops in the posterior mediastinum in the costovertebral sulcus. We herein present a case of a 68-year-old woman with an incidental finding of a subcarinal mass. Radiological and histopathological studies were suggestive of schwannoma. Therefore, the mass was completely resected through a right thoracotomy, and a definite histopathological diagnosis was established. Although the subcarinal area is a rare site for this tumor to appear, the schwannoma should be considered as part of the differential diagnosis of lesions in the subcarinal region. Treatment of choice is the nerve-sparing surgical excision of the mass with excellent prognosis. A review of the literature on this topic was performed. [ABSTRACT FROM AUTHOR]

• Global flowsheet optimization with pinch analysis embedded. • Analyzing existing and new nonsmooth, smooth, and mixed-integer formulations. • Comparing solution time of deterministic global optimizers in four case studies. • Nonsmooth formulations work well for non-isothermal streams. • For most solvers, mixed-integer formulations work best for isothermal streams. Heat integration can be considered in flowsheet optimization by including the minimum utility demand from pinch analysis in the objective and constraints. This often results in better process performance than conducting these steps separately. However, it comes with increased computational cost, especially for global optimization. This cost depends both on the problem formulation and on the solver. In this work, we compare several existing and new smooth, nonsmooth, and mixed-integer formulations. Furthermore, we test different choices of optimization variables and constraints reaching from full- to reduced-space formulations. In the reduced-space formulations, heat integration can be included with few, one, or even zero additional variables beyond the pure flowsheet optimization problem. For the considered case studies, this can significantly reduce the solution time of various global solvers, in particular for our open-source solver MAiNGO. Depending on the case study and solver, either nonsmooth or mixed-integer formulations are the fastest to solve. [ABSTRACT FROM AUTHOR]

An integrated model is developed for a hybrid solar-binary geothermal system. The system consists of an existing ORC (organic Rankine cycle) utilizing a low-temperature geothermal brine and a solar trough system. The hybrid system is based on the new parallel configuration developed by Ghasemi et al. (2014) which performs better than combined individual systems. The operation of the hybrid system is optimized maximizing the net power output and evaluated for representative days of year 2013. The design of the solar trough system is based on the objective of maximizing net power of the hybrid system at nominal conditions. This design results in a 7% solar share of net power output. The optimized constant-flow and variable-flow solar configurations lead to 5.5% and 6.3% boost in the net power output compared to the optimized geothermal ORC, respectively. The LEC (levelized electricity cost) is used as an economic performance metric to analyze the hybrid system. The analysis shows that LEC can be decreased by 2% for the hybrid compared to a stand-alone geothermal system. An optimization of the stand-alone geothermal ORC results in 8% reduction in LCE suggesting as a better option compared to the hybridization of the geothermal system at this time. [ABSTRACT FROM AUTHOR]

A pressurized oxycoal combustion process is optimized for variable thermal loading (100%-30%). The steam expansion line behavior is accurately represented based on manufacturer data. Simulations with the nominal design and nominal operation are then performed with variable loads to determine the level of performance decrease if no optimization is performed. Finally, optimization of operation for a fixed design, and simultaneous optimization of design and operation are performed. The design optimization for a specific load does not include the redesign of the turbines to a process specifically designed for this load. However, the design variables of the turbine expansion line, namely the extraction bleeds, are considered. At each load, the performance of the process designed for nominal load is compared to the maximum possible performance obtained when designing the process for that specific load. Thanks to the thermal recovery section, the process exhibits ideal flexibility to load variations (not accounting for efficiency variations in the air separation unit, hence assuming oxygen storage), unlike Rankine cycles without pressurized recovery. Consequently, there is no need to optimize for an expected distribution of load operations. Finally, the process maintains supercritical working-fluid conditions over larger ranges of thermal loads. [ABSTRACT FROM AUTHOR]

Abstract: A split concept for boilers and heat recovery steam generators (HRSG), where flue gas recycling is required for controlling the maximal temperature, is proposed for reducing the heat exchange area and/or the recycling power requirements. The concept is demonstrated in the context of an HRSG of a pressurized oxy-coal combustion process, where the hot flue gas entering the HRSG is diluted by recycled flue gas to comply with the temperature constraint. The split concept proposes splitting the hot flue gas prior to dilution, and introducing the splitted fraction, with or without a secondary recycling stream, at an intermediate point in the HRSG. As a result, the split allows for lower recycling power requirements (lower diluent flowrate) and a smaller heat exchange area because the average temperature difference between the hot and cold streams in the heat exchanger is increased. Multi-objective optimization, for area and power requirements, is performed and the Pareto front is constructed. Results include a reduction by 37% without a change in the compensation power requirements, or a decrease in the power requirements by 18% (corresponding to 0.15 percentage points in cycle efficiency increase) while simultaneously reducing the area by 12%. [Copyright &y& Elsevier]

Abstract: A methodology is proposed to identify improved thermal-based desalination structures. It is based on the notion of superstructure, allowing for the simultaneous representation of numerous feed, brine and vapor routing schemes. By adjusting the flow routings, the superstructure is capable of representing the common thermal desalination structures, as well as an extremely large number of alternate structures, some of which might exhibit advantageous behavior. The superstructure is built around a repeating unit which is a generalization of an effect in a multi-effect distillation system (MED) and a stage in a multi-stage flash system (MSF). The superstructure is proposed as an improved tool for the structural optimization of thermal desalination systems, whereby the optimal selection of components making up the final system, the optimal routing of the vapors as well as the optimal operating conditions are all variables simultaneously determined during the optimization problem. The proposed methodology is applicable to both stand-alone desalination plants and dual purpose (water and power) plants wherein the heat source to the desalination plant is fixed. It can be extended to also consider hybrid thermal–mechanical desalination structures, as well as dual purpose plants where the interface of power cycle and desalination is also optimized for. [Copyright &y& Elsevier]

Abstract: A multi-objective structural optimization of integrated thermal desalination and thermal compression systems is performed, whereby the performance ratio of the structures is maximized while the specific area requirements are minimized. With the aid of the superstructure developed in part I, herein the thermal compression of vapor streams produced in intermediate multi-effect distillation (MED) effects as opposed to the common practice of compressing vapors produced in the last effect, is examined. The study concludes that intermediate vapor compression results in significant reductions in area requirements, as well as significant increases in maximum distillate production capacities. Moreover, the optimal location of vapor extraction is heavily dependent on the exact distillate production requirement in question. Two novel configuration forms are informed by the optimization. The first is an integrated MED–TVC+MED+MSF system, while the second is an integrated MED–TVC+MSF system. [Copyright &y& Elsevier]

Abstract: The performance of a regenerative solid TES (thermal energy storage) system with enhancement heat transfer structures is analyzed. Thermal energy is transferred from a hot heat transfer fluid to the storage unit core elements during charge, and from the core elements to the cold heat transfer fluid during discharge. Herein, concrete as the solid storage material, nitrate solar salt as the heat transfer fluid, and aluminum plates as the heat transfer structures are considered. The discharge process from uniform initial temperature is studied with different configurations (pure concrete and concrete enhanced by transfer structures), operation strategies (laminar versus turbulent flow regimes), and dimensions. The results show a significant decrease in the cost of the TES system when heat transfer structures are added, as well as higher discharge efficiency and lower discharge time period. The amount of solar salt needed for this configuration is also decreased by the use of the heat transfer structures and is five time less than that of a two-tank system. [Copyright &y& Elsevier]

Abstract: Simultaneous multi-variable gradient-based optimization with multi-start is performed on a 300 MWe wet-recycling pressurized oxy-coal combustion process with carbon capture and sequestration, subject to uncertainty in fuel, ambient conditions, and other input specifications. Two forms of flue gas thermal recovery are studied, a surface heat exchanger and a direct contact separation column. Optimization enables ideal flexibility in the processes: when changing the coal utilized, the performance is not compromised compared to the optimum performance of a process specifically designed for that coal. Similarly, the processes are immune to other uncertainties like ambient conditions, air flow, slurry water flow, atomizer stream flow and the oxidizer stream oxygen purity. Consequently, stochastic programming is shown to be unnecessary. Close to optimum design, the processes are also shown to be insensitive towards design variables such as the areas of the feedwater heaters. Recently proposed thermodynamic criteria are used as embedded design specifications in the optimization process, rendering it faster and more robust. [Copyright &y& Elsevier]

Abstract: In this article, an analysis of solar reforming as the solar integration method for a hybrid solar-fossil fuel cycle is presented. The solar reformer is integrated with a solar tower and steam reforming of methane is studied. The tower reformer system is integrated with a standard combined cycle, and the design and operation of the hybrid cycle are optimized for highest annual work output for a fixed fuel input and solar collector area. A heuristic two step procedure is used for the optimization: first, both the design and operation of the plant is optimized for every hour and then in the second step, the design is fixed at the average value determined by the first step and the operation of the plant is again optimized for every hour. The optimization results indicate that the tower reforming integration method is a promising integration option in that this type of hybrid cycle yields high incremental solar efficiencies compared to alternatives. Moreover, the analyzed hybrid cycle has a higher efficiency for a fixed CO2 emissions compared to a linear combination of solar only and fossil fuel only cycles of comparable complexity. [Copyright &y& Elsevier]

A literature review of hybrid solar-fossil fuel power generation is given with an emphasis on system integration and evaluation. Hybrid systems are defined as those which use so-lar energy and fuel simultaneously, thus excluding the viable alternative of solar thermal plants which use fossil fuels as backup. The review is divided into three main sections: petformance metrics, the different concentrated solar receiver technologies and their operating conditions, and the different hybridization schemes. In addition, a new linear combination metric for analysis of hybrid systems, which considers trade-off of different metrics at the fleet level, is presented. This metric is also compared to alternative metrics from multi-objective optimization. Some previous work only evaluates the hybrid cycle at a certain point in time, which can be misleading as this evaluation would not take into account certain aspects of hybrid cycle, such as fluctuating solar supply. Furthermore, almost all previous work designs the hybrid solar-fossil fuel systems for a certain point in time and then evaluates the performance of the system for an entire year. By not taking into account fluctuating solar supply and selling price of electricity in the design of the system, the best possible annual performance of the hybrid cycle may not be reached. [ABSTRACT FROM AUTHOR]

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: I) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms. [ABSTRACT FROM AUTHOR]

Abstract: A double-pinch criterion for closed feedwater heaters (FWH) of regenerative Rankine cycles is presented. The FWHs are modeled as counter-current heat exchangers. Thus, two potential pinch positions in the FWH exist: (i) at the exit of the bleed (drain) and (ii) at the onset of condensation. For a given heat duty in the FWH, feed inlet temperature and flowrate, the extraction flowrate and pressure should be chosen to achieve the same minimal approach temperature at the two potential pinch points. An analytical proof is given for a fixed pinch value for the case that the drain enters the condenser, based on weak assumptions. Additionally, the criterion is numerically demonstrated for fixed pinch value and for fixed heat exchanger area using the most common configurations: drain to condenser, drain to deaerator, and drain cascaded to next FWH. A similar criterion is developed for the case that the drain is pumped (upwards or downwards) and mixed with the feedwater. The double pinch criterion simplifies the optimization procedure and results in significant efficiency increase for fixed heat exchanger area. For numerical reasons it is advisable to use the pressure as the optimization variable and calculate the heat duty and mass flowrate. [Copyright &y& Elsevier]

An algorithm is proposed for the global solution of semi-infinite programs (SIPs) without convexity assumptions. It terminates finitely with a guaranteed feasible point, and a certificate of ϵ  f -optimality. The only assumptions are continuity of the functions and existence of an ϵ  f -optimal SIP-Slater point. The lower and upper bounds are obtained by the solution of two nonconvex nonlinear programs (NLP) each, thus shifting the nonconvexity to the global NLP solver. The main contribution of this article is a new procedure for the generation of feasible points. These are obtained by a restriction of the constraints right-hand side by ϵ g  > 0 and a successively finer discretization of the parameter set. A suitable decreasing sequence of ϵ g  → 0 leads to convergence of the generated upper bound. The converging lower bound is obtained by successively tighter discretization, following the principle of Blankenship and Falk [Infinitely constrained optimization problems, J. Optim. Theory Appl. 19 (1976), pp. 261–281]. A proof of convergence is given along with a prototype implementation in general algebraic modelling system. The algorithm is extended to problems with multiple constraints as well as mixed-integer problems. Literature and new problems are solved numerically. The algorithm is extremely simple to implement, but nevertheless very efficient compared to existing methods for nonconvex lower level programs. [ABSTRACT FROM PUBLISHER]

Abstract: Oxy-fuel combustion, particularly using an integrated oxygen ion transport membrane (ITM), is a thermodynamically attractive concept that seeks to mitigate the penalties associated with CO2 capture from power plants. Oxygen separation in an ITM system consists of many distinct physical processes, ranging from complex electrochemical and thermo-chemical reactions, to conventional heat and mass transfer. The dependence of ITM performance on power cycle operating conditions and system integration schemes must be captured in order to conduct meaningful process flow and optimization studies where multiple degrees of freedom are considered. An axially spatially-distributed, quasi two-dimensional model is developed based on fundamental conservation equations, semi-empirical oxygen transport equations obtained from the literature, and simplified fuel oxidation kinetic mechanisms. Aspects of reactor engineering such as geometric structure, flow configuration and the relationship between oxygen transport, fuel conversion and pressure drop are explored. Emphasis is placed on model robustness, modularity, and low computational expense in order to evaluate the myriad of ITM possibilities within a power cycle simulation quickly and accurately. Overall, the model seeks to bridge the gap between detailed CFD studies and overly-simplified black-box models found in ITM-power cycle analyses, and provides a tool for the analysis and design of ITM systems. [Copyright &y& Elsevier]

Abstract: In order to design ITM-based oxy-combustion power cycles, various reactor engineering analyses must be conducted with detailed modeling and simulation. An intermediate-fidelity ITM model is used to explore the dependence of ITM performance on reactor geometric structure, flow configuration, operating conditions, membrane material properties, and uncertainty in key modeling assumptions, such as the dominant fuel conversion mechanism. Many operational constraints are presented that are usually overlooked by black-box modeling strategies, and the implications of these constraints are explored. Further, a comparison is made between reactive and separation-only ITMs to assess the relative merits and disadvantages of each. The results show that although a reactive ITM significantly improves the partial pressure driving force, practical reactor engineering considerations indicate that this concept is not superior to counter-current separation-only ITMs, mainly because of the stringent temperature limitations of the membrane material. A Second Law assessment of certain ITM configurations is performed to evaluate the potential of ITM technology to reduce the air separation penalty, and to provide insight for effective systems-level integration. Overall, the results of our analyses capture the essential characteristics of ITM air separation systems for power cycles, and enable detailed systems-level studies to be performed. [Copyright &y& Elsevier]

Abstract: This article applies nonlinear programming techniques to optimize humidification–dehumidification (HD) desalination cycles for operating conditions that result in maximum gained output ratio (GOR). Closed air open water as well as open air open water cycles, each with either an air or a water heater, were considered in this analysis. Numerical optimization resulted in a substantial increase in GOR for all four cycle types compared to previous best-case conditions found using heuristic studies. The GOR of the cycles was found to decrease with increasing component terminal temperature difference (TTD). In addition, different cycles perform best at different temperature differences. Optimization also revealed that some counterintuitive design configurations can result in superior performance under the appropriate operating conditions. [Copyright &y& Elsevier]

Background: Signalling pathways are the cornerstone on understanding cell function and predicting cell behavior. Recently, logical models of canonical pathways have been optimised with high-throughput phosphoproteomic data to construct cell-type specific pathways. However, less is known on how signalling pathways can be linked to a cellular response such as cell growth, death, cytokine secretion, or transcriptional activity. Results: In this work, we measure the signalling activity (phosphorylation levels) and phenotypic behavior (cytokine secretion) of normal and cancer hepatocytes treated with a combination of cytokines and inhibitors. Using the two datasets, we construct "extended" pathways that integrate intracellular activity with cellular responses using a hybrid logical/data-driven computational approach. Boolean logic is used whenever a priori knowledge is accessible (i.e., construction of canonical pathways), whereas a data-driven approach is used for linking cellular behavior to signalling activity via non-canonical edges. The extended pathway is subsequently optimised to fit signalling and behavioural data using an Integer Linear Programming formulation. As a result, we are able to construct maps of primary and transformed hepatocytes downstream of 7 receptors that are capable of explaining the secretion of 22 cytokines. Conclusions: We developed a method for constructing extended pathways that start at the receptor level and via a complex intracellular signalling pathway identify those mechanisms that drive cellular behaviour. Our results constitute a proof-of-principle for construction of "extended pathways" that are capable of linking pathway activity to diverse responses such as growth, death, differentiation, gene expression, or cytokine secretion. [ABSTRACT FROM AUTHOR]

Abstract: The operation of seawater reverse osmosis (SWRO) plants is systematically optimized with the objective to reduce the electricity charges, which constitute the largest portion of the operation costs. Variable operating conditions and a time-of-use electricity tariff rate schedule are considered. A well-established model is extended to model a reverse osmosis (RO) system equipped with highly efficient pressure exchangers (PXs) and variable frequency drives (VFDs). All major performance factors are considered, including membrane''s permeability, concentration polarization, temperature effects, membrane fouling, and efficiency dependency of the VFD on the loading conditions. The proposed formulation minimizes the electricity cost per day, given a desired daily production rate. The problem is formulated as a mixed-integer nonlinear program (MINLP), allowing for periods without operation. The time domain (24h) is discretized into 0.5h increments, and a pseudo steady-state assumption is used. The optimal operation plan resulting in maximum electricity cost-savings is established with standard global optimization tools. The results show significant electricity and production cost-saving potentials. More savings in electricity can be achieved by oversizing the plant, thus allowing shutting-off at periods of high electricity cost. The cost-saving potentials are particularly promising in electricity markets in which the electricity rate schedule is widely distributed. [Copyright &y& Elsevier]

Understanding the mechanisms of cell function and drug action is a major endeavor in the pharmaceutical industry. Drug effects are governed by the intrinsic properties of the drug (i.e., selectivity and potency) and the specific signaling transduction network of the host (i.e., normal vs. diseased cells). Here, we describe an unbiased, phosphoproteomic-based approach to identify drug effects by monitoring drug-induced topology alterations. With our proposed method, drug effects are investigated under diverse stimulations of the signaling network. Starting with a generic pathway made of logical gates, we build a cell-type specific map by constraining it to fit 13 key phopshoprotein signals under 55 experimental conditions. Fitting is performed via an Integer Linear Program (ILP) formulation and solution by standard ILP solvers; a procedure that drastically outperforms previous fitting schemes. Then, knowing the cell's topology, we monitor the same key phosphoprotein signals under the presence of drug and we re-optimize the specific map to reveal drug-induced topology alterations. To prove our case, we make a topology for the hepatocytic cell-line HepG2 and we evaluate the effects of 4 drugs: 3 selective inhibitors for the Epidermal Growth Factor Receptor (EGFR) and a non-selective drug. We confirm effects easily predictable from the drugs' main target (i.e., EGFR inhibitors blocks the EGFR pathway) but we also uncover unanticipated effects due to either drug promiscuity or the cell's specific topology. An interesting finding is that the selective EGFR inhibitor Gefitinib inhibits signaling downstream the Interleukin-1alpha (IL1α) pathway; an effect that cannot be extracted from binding affinity-based approaches. Our method represents an unbiased approach to identify drug effects on small to medium size pathways which is scalable to larger topologies with any type of signaling interventions (small molecules, RNAi, etc). The method can reveal drug effects on pathways, the cornerstone for identifying mechanisms of drug's efficacy. [ABSTRACT FROM AUTHOR]

Theory and implementation for the global optimization of a wide class of algorithms is presented via convex/affine relaxations. The basis for the proposed relaxations is the systematic construction of subgradients for the convex relaxations of factorable functions by McCormick [Math. Prog., 10 (1976), pp. 147-175]. Similar to the convex relaxation, the subgradient propagation relies on the recursive application of a few rules, namely, the calculation of subgradients for addition, multiplication, and composition operations. Subgradients at interior points can be calculated for any factorable function for which a McCormick relaxation exists, provided that subgradients are known for the relaxations of the univariate intrinsic functions. For boundary points, additional assumptions are necessary. An automated implementation based on operator overloading is presented, and the calculation of bounds based on affine relaxation is demonstrated for illustrative examples. Two numerical examples for the global optimization of algorithms are presented. In both examples a parameter estimation problem with embedded differential equations is considered. The solution of the differential equations is approximated by algorithms with a fixed number of iterations. [ABSTRACT FROM AUTHOR]

Abstract: Excess Gibbs free energy models contain parameters which for a given mixture are estimated from measurements of phase-splits. Local composition models are very flexible and can accurately predict complex phase behavior. However, in many cases it has been reported that use of local composition models leads to prediction of more phase splits or more phases than measured, modeling homogeneous azeotropes as heterogeneous, etc. Here, a formulation is proposed that addresses these limitations of current parameter estimation methods. The formulation is based on a bilevel program, i.e., an optimization problem embedded in another one. Minimizing the error between model predictions and measurements gives the upper-level program. The lower-level programs are given by the minimization of the Gibbs free energy, or equivalently the satisfaction of the Gibbs tangent plane criterion. Each of the experiments is cast as a separate lower-level program. Additional requirements on the phase behavior of the system, such as enforcing the correct number of phase splits and the correct number of phases in each phase split, are similarly formulated as additional lower-level programs. Global optimization techniques are necessary even to obtain a feasible point since the lower-level programs are nonconvex. The proposed formulation is applied to problems from the literature, in which inappropriate fitting of the parameters of the non-random two-liquid (NRTL) model to experimental data has been reported to result in significant model errors, such as the prediction of an additional spurious phase split. The discussion is restricted to binary mixtures, however, the formulation can in principle be applied also to multicomponent mixtures. [Copyright &y& Elsevier]

Abstract: A known challenge for the simulation of reacting flow systems is that detailed chemical mechanisms contain hundreds to thousands of species and thousands of reactions, leading to high CPU requirements despite the use of state-of-the-art solvers. For specific conditions of interest (temperature, pressure, and composition), smaller mechanisms can predict the chemistry relatively accurately. One possibility for obtaining such mechanisms is species elimination from a detailed mechanism. Here, an automatic method for kinetic model reduction by simultaneous reaction and species elimination is proposed, based on an integer linear program (ILP) formulation. The solution of the ILP is an optimally reduced kinetic mechanism that reproduces the predictions of a reference mechanism within prespecified tolerances for finitely many reference points in the state space. The method is applied to generate optimally reduced models for isobaric, adiabatic homogeneous combustion. Case studies are presented for the combustion of n-heptane. Comparisons between the full and reduced models are shown and the tradeoff between species and reaction elimination is discussed. Tolerances in the ILP formulation control the error introduced by the model reduction. For increasing acceptable error, more species and/or reactions are eliminated. A method of quantifying this tradeoff between approximation error and reduction achieved is proposed, based on multiobjective optimization, and demonstrated in a case study. The effect of variable initial conditions is investigated. The mechanisms generated achieve significant reduction in the CPU requirement and can accurately predict the trajectories of the state variables (species mass fractions and temperature), as well as other metrics of interest, such as ignition time delay. [Copyright &y& Elsevier]

Selective amygdalohippocampectomy (SelAH) is now widely used in the surgical treatment of patients with medically intractable medial temporal lobe epilepsy (MTLE). We present our single-centre experience of a uniform series of drug-resistant MTLE patients treated exclusively with SelAH. A retrospective study of 50 adult patients (25 males, 25 females, mean age 34 years) suffering from medically intractable MTLE was conducted. All patients has been investigated under the same protocol and operated by SelAH performed by one surgeon. Immediate and long-term follow-up was also available and is presented. Forty-two patients (84%) became seizure free (Engel's I); eight of them (16%) were classified as Engel's II; four (8%) as Engel's III; and one as Engel's IV (2%). In 42 of them the final result has been achieved after a single operation, while in the rest eight after a second or third surgical procedure. The mean follow-up period was 6.2 years. Two permanent complications (4%) with no treatment-related mortality have been detected. SelAH performed in well selected cases, where concordance between electrophysiological and MR findings exists, can be a safe and effective surgical treatment method for medically intractable MTLE. [ABSTRACT FROM AUTHOR]

The successful long-term outcome of microvascular decompression for trigeminal neuralgia is largely dependent on the maintenance of the isolation between the trigeminal nerve and the offending vessel, avoiding also the development of scar tissue around the nerve. We propose an alternative technique to achieve this target by “hanging” the offending vessel from the overlying tentorium using a strip of autologous tissue without interposing any foreign material. [ABSTRACT FROM AUTHOR]

This paper proposes a novel computational methodology for modelling the haemodynamic effects of endovascular coil embolization for cerebral aneurysms. We employ high-resolution 3-D angiographic data to reconstruct the intracranial geometry and we model the coiled part of the aneurysm as a porous medium, with porosity decreasing as coils are inserted. The actual dimensions of the coils employed are used to determine the characteristics of the porous medium. Simulation results for saccular aneurysms from the anterior communicating and middle cerebral arteries show that insertion of coils rapidly changes intraaneurysmal blood flow and causes reduction in mural pressure and blood velocity up to stagnation, providing favorable conditions for thrombus formation and obliteration of the aneurysm. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) is a numerical technique that is used for studying haemodynamic parameters in cerebral aneurysms. As it is now possible to represent an anatomically accurate intracranial aneurysm in a computational model, we have attempted to simulate its endosaccular occlusion with coils and demonstrate the haemodynamic changes induced. This is the first attempt to use this particular porous medium-based method for coiling simulation in a CFD model, to our knowledge. Datasets from a rotational 3-D digital subtraction angiogram of a recently ruptured anterior communicating aneurysm were converted into a 3-D geometric model and the discretized data were processed using the computational technique developed. Coiling embolisation simulation was achieved by impediment of flow through a porous medium with characteristics following a series of embolisation coils. Haemodynamic parameters studied were: pressure distribution on the vessel wall, blood velocity and blood flow patterns. Significant haemodynamic changes were detected after deployment of the first coil. Similar, but less dramatic changes occurred during subsequent stages of coiling. The blood flow patterns became less vortical in the aneurysm sac as velocity decreased to stagnation and the wall pressure at the fundus was gradually reduced. Furthermore, the haemodynamic characteristics developed at the area of the neck remnant could form the basis for assessing the likelihood of delayed coil compaction and aneurysm regrowth. Appropriate computational techniques show great promise in simulating the haemodynamic behaviour of the various stages in coil embolisation and may be a potentially valuable tool in interventional planning and procedural decision-making. [ABSTRACT FROM AUTHOR]

Finite formulations are presented for the calculation of lower and upper bounds on the optimal solution value of semi-infinite programs (SIPs) involving smooth, potentially nonconvex objective function and constraints. The lower bounding problem is obtained by a formulation that combines the first- and second-order KKT necessary conditions of the lower-level problem with a discretization of the parameter set. The resulting mathematical program with equilibrium constraints (MPEC) is a relaxation of the original SIP and furnishes valid lower bounds. If the parameter set is subdivided, the optimal solution value of the lower bounding problem converges to the optimal solution value of the SIP. The upper bounding problem is based on convex and linear relaxations of the lower-level problem resulting in a restriction of the SIP. If the parameter set is subdivided, the constructed relaxations converge to the original lower-level program. The existence of SIP Slater points ensures convergence of the upper bounding problems to the optimal solution value of the SIP. Several alternatives for the upper bounding problem are presented and discussed. Numerical results are presented for a number of test problems from the literature. [ABSTRACT FROM AUTHOR]

Idiopathic ruptured aneurysms of distal cerebellar arteries (DCAAs) are rare, and their endovascular therapy (EVT) has as yet not been extensively reported. They are usually assumed to result from local arterial wall disruption rather than infection, unlike distal supratentorial artery aneurysms. This study was performed to audit their frequency, potential aetiology and results of EVT. Using strict inclusion criteria and a database of 1715 EVT patients, we identified ten idiopathic ruptured DCAAs (0.6%) over a 13-year period (1993–2006). The series comprised six males and four females with mean age of 64 years and solitary aneurysms located on posterior inferior cerebellar artery (five patients), anterior inferior cerebellar artery (three patients) and superior cerebellar artery (two patients). Nine aneurysms were fusiform and were treated by endovascular parent artery occlusion, and one was saccular and treated by endosaccular packing. Endovascular therapy was performed with coils in seven cases, n-butyl-2-cyanoacrylate (NBCA) in two cases and with both in one case. Primary EVT was successful in eight patients. One patient died following a procedure-related re-bleeding and one patient required re-treatment after failed endosaccular packing. Nine patients made good or excellent clinical recoveries (modified Rankin Scale 2 or less). Focal cerebellar infarctions were seen on computed tomography images after EVT in three patients, only one of whom was symptomatic with transient dysmetria, which resolved completely during follow up. No aneurysm recanalisation was detected on late follow-up imaging up to 24 months. Ruptured DCAAs are rare. The majority are fusiform in shape and their aetiology remains uncertain. Endovascular treatment is feasible and effective. It usually requires parent artery occlusion. [ABSTRACT FROM AUTHOR]

An overview of a comprehensive methodology for the design of microfabricated fuel cell systems for portable power generation is presented. The methodology is based on a decomposition into three levels of modeling detail:  (i) system-level models for process synthesis, (ii) intermediate-fidelity models for optimization of sizes and operation, and (iii) detailed computational fluid dynamics (CFD) models for geometry improvement. Process synthesis, heat integration, and layout considerations are addressed simultaneously through the use of lumped algebraic models, general enough to be independent of detailed design choices, such as reactor configuration and catalyst choice. At the intermediate-fidelity level, space-distributed models are used, which allow optimization of unit sizes and operation for a given process structure without the need to specify a detailed geometry. Finally, the use of detailed CFD facilitates geometrical improvements as well as the derivation and validation of modeling assumptions that are used in the system-level and intermediate-fidelity models. [ABSTRACT FROM AUTHOR]