Maximum Robustness Research Articles

Novel Approaches for a Time and Cost Efficient Activation of Polymer Electrolyte Membrane Fuel Cell Stacks

Towards a CO2 free energy supply, hydrogen plays an important role as a chemical energy storage capable of storing renewable energy in large dimensions. In this context, fuel cells, such as e.g. polymer electrolyte membrane fuel cells (PEMFC) are going to evolve to a key technology for the conversion of the chemically stored energy back to usable electric energy. Therefore, after decades of fuel cell research, the phase of industrialization of fuel cells has begun recently to provide fuel cells in large quantity for a variety of applications, such as e.g. to power heavy duty vehicles, ships and even airplanes or as stationary power plants.For PEMFC mass production, the optimization of the manufacturing process of fuel cells is important to decrease the manufacturing time and costs. One of the most time and cost consuming steps during the production of fuel cells is the first time activation. During the so-called break-in, the fuel cell is operated for the first time in order to activate the membrane electrode assembly (MEA). During break-in, the proton conductivity of the membrane is established and the catalyst is activated, but also chemical residues of the production of the fuel cell components were removed. This procedure is necessary for a fuel cell stack to provide its maximum power output and maximum robustness of all single cells. Furthermore, the successful activation of the PEMFC strongly affects the overall lifetime of a fuel cell stack.To date, a typical break-in of a fuel cell stack takes in between 2 and 8 hours of time, in which a considerable amount of hydrogen and air is required. Thereby, the stack is commonly operated at a high relative humidity doing several cycles of current ramps from minimum to medium or high load interrupted by shutdowns, whereby the first current ramp is usually performed very slowly. Besides the media consumption, the rather long state-of-the-art break-in time calls for a high degree of parallelization of break-in stations at each production line to reach a reasonable output of fuel cell stacks, which is related to high investment costs. An optimization of the break-in process in respect to the necessary time and media consumption significantly reduces the production costs of a PEMFC stack. In this work, several new approaches to activate PEMFCs were introduced in order to decrease the duration and media consumption of the break-in process. Thereby, the common break-in doing several current ramps was replaced by more efficient and promising approaches, such as e.g., hydrogen starvation events or hydrogen pumping. This break-in experiments were performed in short stacks of small scale as well as automotive scale. Finally, the influence of each break-in variation on the performance and durability of the PEMFC was investigated.

Fishy forensics: FT-NIR and machine learning based authentication of Mediterranean anchovies (Engraulis encrasicolus)

Seafood authentication and traceability is a challenging issue owing to its complex supply chain and fishing in international waters. NIR spectroscopy has been successfully used to authenticate food of animal and plant origin. In this study, FT-NIR was used to discriminate between Mediterranean anchovies (Engraulis encrasicolus) fished from Adriatic, Balearic, and Tyrrhenian Sea. The spectra were prepared using the standard normal variate (SNV) and the Savitzky-Golay 1st derivative, 2nd order (SG), as well as both together. The model was built, after outlier removal, with linear-support vector machine (L-SVM), polynomial-SVM (P-SVM), k-nearest neighbor (k-NN) and Random Forest (RF). Spectral preprocessing improved model classification accuracy for all algorithms. The data could not be put into groups by linear algorithms like L-SVM and k-NN because the NIR spectra were not linear and had many columns. Non-linear algorithms, P-SVM and RF when coupled with SG+SNV, successfully produced models with maximum robustness. P-SVM and RF models had 100 % accuracy in training set and 95.7 % and 95.5 % accuracy in testing set, respectively and 95.2 and 95.1 accuracy in cross-validation set.

Digital PCR threshold robustness analysis and optimization using dipcensR.

Digital polymerase chain reaction (dPCR) is a best-in-class molecular biology technique for the accurate and precise quantification of nucleic acids. The recent maturation of dPCR technology allows the quantification of up to thousands of targeted nucleic acids per instrument per day. A key step in the dPCR data analysis workflow is the classification of partitions into two classes based on their partition intensities: partitions either containing or lacking target nucleic acids of interest. Much effort has been invested in the design and tailoring of automated dPCR partition classification procedures, and such procedures will be increasingly important as the technology ventures into high-throughput applications. However, automated partition classification is not fail-safe, and evaluation of its accuracy ishighly advised. This accuracy evaluation is a manual endeavor and is becoming a bottleneck for high-throughput dPCR applications. Here, we introduce dipcensR, the first data-analysis procedure that automates the assessment of any linear partition classifier's partition classification accuracy, offering potentially substantial efficiency gains. dipcensR is based on a robustness evaluation of said partition classification and flags classifications with low robustness as needing review. Additionally, dipcensR's robustness analysis underpins (optional) automatic optimization of partition classification to achieve maximal robustness. A freely available R implementation supports dipcensR's use.

Principle of Information Increase: An Operational Perspective on Information Gain in the Foundations of Quantum Theory

A measurement performed on a quantum system is an act of gaining information about its state. However, in the foundations of quantum theory, the concept of information is multiply defined, particularly in the area of quantum reconstruction, and its conceptual foundations remain surprisingly under-explored. In this paper, we investigate the gain of information in quantum measurements from an operational viewpoint in the special case of a two-outcome probabilistic source. We show that the continuous extension of the Shannon entropy naturally admits two distinct measures of information gain, differential information gain and relative information gain, and that these have radically different characteristics. In particular, while differential information gain can increase or decrease as additional data are acquired, relative information gain consistently grows and, moreover, exhibits asymptotic indifference to the data or choice of Bayesian prior. In order to make a principled choice between these measures, we articulate a Principle of Information Increase, which incorporates a proposal due to Summhammer that more data from measurements leads to more knowledge about the system, and also takes into consideration black swan events. This principle favours differential information gain as the more relevant metric and guides the selection of priors for these information measures. Finally, we show that, of the symmetric beta distribution priors, the Jeffreys binomial prior is the prior that ensures maximal robustness of information gain for the particular data sequence obtained in a run of experiments.

Gaussian elimination for flexible systems of linear inclusions

Flexible systems are linear systems of inclusions in which the elements of the coefficient matrix are external numbers in the sense of nonstandard analysis. External numbers represent real numbers with small, individual error terms. Using Gaussian elimination, a flexible system can be put into a row-echelon form with increasing error terms on the right-hand side. Then parameters are assigned to the error terms and the resulting system is solved by common methods of linear algebra. The solution set may have indeterminacy not only in terms of linear spaces, but also of modules. We determine maximal robustness for flexible systems.

Ideational robustness of economic ideas in action: the case of European Union economic governance through a decade of crisis

Abstract Is it possible to develop a robust crisis management response in a system where governance is characterized by coercive power and adversarial bargaining rather than the diversity, inclusion, and openness highlighted by extant scholarship as conducive factors for robustness? Using two instances of crisis in the European Union—the Eurozone crisis (2010‒2015) and COVID-19 pandemic (2020‒2022)—the paper argues that how actors reinterpret existing rules and institutions offers an important source of robustness in crisis management. Based on the employment of a disaggregation of robustness into degrees of robustness, as well as the concepts of ideational and institutional power, we show how actors can counter the coercive power of dominant coalitions and open up for rule adaptation through reinterpretations of existing rules that, at least in the short term, can solidify the functioning of existing institutions faced by turbulence. In the context of the Eurozone crisis, ideational and institutional power thus enabled a moderately robust response without treaty reform. In the case of the pandemic, it was possible to convince (particularly German) policymakers of the need to employ new ideas about common debt. This meant less need to employ ideational and institutional power by other actors, leading to significantly more effective crisis management than in the Eurozone crisis, what the paper terms maximal robustness.

On Hurwitz stability for families of polynomials

AbstractThe robustness of a linear system in the view of parametric variations requires a stability analysis of a family of polynomials. If the parameters vary in a compact set , then obtaining necessary and sufficient conditions to determine stability of the family is one of the most important tasks in the field of robust control. Three interesting classes of families arise when is a diamond, a box or a ball of dimension . These families will be denoted by , , and , respectively. In this article, a study is presented to contribute to the understanding of Hurwitz stability of families of polynomials . As a result of this study and the use of classical results found in the literature, it is shown the existence of an extremal polynomial whose stability determines the stability of the entire family . In this case comes from minimizing determinants and in some cases coincides with a Kharitonov's polynomial. Thus another extremal property of Kharitonov's polynomials has been found. To illustrate our approach, it is applied to families such as , , and with . The study is also used to obtain the maximum robustness of the parameters of a polynomial. To exemplify the proposed results, first, a family is taken from the literature to compare and corroborate the effectiveness and the advantage of our perspective. Followed by two examples where the maximum robustness of the parameters of polynomials of degree 3 and 4 are obtained. Lastly, a family is proposed whose extreme polynomial is not necessarily a Kharitonov's polynomial. Finally, a family is used to exemplify that if the boundary of is given by a polynomial equation in several variables, the number of candidates to be an extremal polynomial is finite.

The Risk of Expected Utility Under Parameter Uncertainty

We derive analytical expressions for the risk of an investor’s expected utility under parameter uncertainty. In particular, our analysis focuses on characterizing the out-of-sample utility variance of three portfolios: the classic mean-variance portfolio, the minimum-variance portfolio, and a shrinkage portfolio that combines both. We then use our analytical expressions to study a robustness measure that balances out-of-sample utility mean and volatility. We show that neither the sample mean-variance portfolio nor the sample minimum-variance portfolio exhibits maximal robustness individually, and one needs to combine both to optimize portfolio robustness. Accordingly, we introduce a robust shrinkage portfolio that delivers an optimal tradeoff between out-of-sample utility mean and volatility and is more resilient to estimation errors. Our results highlight the importance of considering out-of-sample performance risk in designing and evaluating investment strategies and stochastic discount factor models. This paper was accepted by Kay Giesecke, finance. Funding: N. Lassance gratefully acknowledges financial support by the Fonds de la Recherche Scientifique [Grant J.0115.22]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00178 .

Risk-aware two-stage stochastic short-term planning of a hybrid multi-microgrid integrated with an all-in-one vehicle station and end-user cooperation

The interconnection between different energy networks considerably improves their technical and economical aspects; however, it increases the complexity of decisions. This paper optimizes the short-term planning of a hybrid multi-microgrid integrated with an all-in-one vehicle station composed of charging facilities, battery swapping slots, parking lots and hydrogen and gasoline refueling parts. In order to achieve the optimal operation in such a complex system and consider the influence of day-ahead and real-time decisions under uncertainties, a risk-aware two-stage stochastic programming is suggested in the presented study. In this regard, fluctuations of wind and solar units and different types of electrical and thermal loads and markets are modeled using various scenarios and their risk is measured by the downside risk method. In addition, the impact of end-user cooperation is analyzed and a sensitivity analysis is implemented. The simulations demonstrate that the proposed model can successfully specify the behavior of all components under a risk-averse strategy with maximum robustness, where the profit is decreased by 8.4 % to achieve an expected risk in profit equal to 0 $. The results also validate that although the all-in-one station can act as a bulk storage, a multi-directional energy injection is required to provide its high demand.

Lyapunov-Based Control Strategy for a Single-Input Dual-Output Three-Level DC/DC Converter

This paper proposes a robust control strategy using direct Lyapunov strategy (DLS) for a single-input dual-output three-level dc-dc converter (SIDO-TLC) while the output loads and voltage references are disturbed. The first proceeding is to concurrently focus on the current and voltage dynamics of the converter buck and boost segments for exerting the related variable errors to the DLS-based function. Accordingly, through a comprehensive analysis based on the outcome of this proceeding, the PI controller coefficients allotted to the current reference design process are enabled to reach their maximum robustness capabilities against any output voltage variation. Moreover, the converter reference current tracking-based assessment leads to several smooth hyperbolic curves with their unique geometric properties. Consequently, the alteration trend of these curves facilitates the Lyapunov coefficients to be adjusted commensurate with the dynamic operation of the converter while converging to zero steady-state errors. Using a TMS320F28335 digital signal processor (DSP), disparate experimental results are conducted on the SIDO-TLC to verify the accurate and robust performance of the proposed control strategy.

Contextuality with vanishing coherence and maximal robustness to dephasing

Contextuality with vanishing coherence and maximal robustness to dephasing

Maximum mutational robustness in genotype-phenotype maps follows a self-similar blancmange-like curve.

Phenotype robustness, defined as the average mutational robustness of all the genotypes that map to a given phenotype, plays a key role in facilitating neutral exploration of novel phenotypic variation by an evolving population. By applying results from coding theory, we prove that the maximum phenotype robustness occurs when genotypes are organized as bricklayer's graphs, so-called because they resemble the way in which a bricklayer would fill in a Hamming graph. The value of the maximal robustness is given by a fractal continuous everywhere but differentiable nowhere sums-of-digits function from number theory. Interestingly, genotype-phenotype maps for RNA secondary structure and the hydrophobic-polar (HP) model for protein folding can exhibit phenotype robustness that exactly attains this upper bound. By exploiting properties of the sums-of-digits function, we prove a lower bound on the deviation of the maximum robustness of phenotypes with multiple neutral components from the bricklayer's graph bound, and show that RNA secondary structure phenotypes obey this bound. Finally, we show how robustness changes when phenotypes are coarse-grained and derive a formula and associated bounds for the transition probabilities between such phenotypes.

High-Capacity Double-Sided Square-Mesh-Type Chipless RFID Tags

This paper presents a novel methodology for designing high-capacity frequency domain chipless RFID tags based on the backscattering principle. The tag consists of multiple square open-loop resonators loaded with a varying number of square metallic patches to form a mesh structure. Thus, in contrast to conventional designs, the overall physical size of each resonator remains fixed and does not change with respect to its operating resonant frequency. The RCS response of the proposed tag can be easily manipulated by varying the loading factor of each resonator. The tag size is further miniaturized by placing resonators on both sides of the substrate used. The tag configuration with resonators arranged in the form of a single row (4 × 1) printed on both sides of the substrate is finally chosen for maximum robustness and efficiency. The frequency shift coding (FSC) technique is used to encode more than one bit per resonator by using segments within sub-bands. The proposed tag encodes 16-bit data in a frequency band from 5.9 to 10.5 GHz and has a very high code density of 23.51 bits/cm2 and a spectral efficiency of 3.47 bits/GHz. The design methodology is novel and leads to a very efficient chipless RFID tag that can be used in a variety of high-data-capacity applications.

Are there limits to robustness? Exploring tools from regenerative economics for a balanced transition towards a circular EU27

The first step for transforming the current linear and degenerative socio-economic systems into ones that are circular and regenerative is to understand how they grow and develop. Here, we explore whether there are limits to robustness of a socio-economic system as the result of a linear metabolic structure, and how those limits could theoretically be affected by its transition to a circular economy. First, we study how the circular use of materials and the economic openness of the EU27 would affect the value of its circularity rate (as defined by Eurostat), theoretically. Then, given that the circularity rate does not capture regenerative aspects, we develop a conceptual framework based on regenerative economics and on indicators from ascendency analysis and ecological network analysis. We use this framework to assess a theoretical future case where the EU27 manages to successfully transition to a CE within its given linear material flow metabolism. The results show that there are limits to robustness, and which do not necessarily correspond to a maximum circularity rate. None of the 45 scenarios assessed can theoretically lead to the maximum robustness observed in natural ecosystems, including those which maximized the circularity rate. Interestingly, the highest possible robustness value is obtained at a circularity rate of about 33% as a combination of a material recovery rate of 30% and of a material export rate of 10%. Scenarios of higher circularity rate (as the result of higher export rates and/or higher material recovery rates) seem to lead to brittle networks. Other indicators from regenerative economics are also discussed. Furthermore, the results show that even if substantial steps are taken by the EU27 towards a circular economy, 100% circularity rate seems to be unlikely. This analysis highlights that the use of tools from regenerative economics can assist policy makers and researchers to account for and to monitor network properties such as those of resilience and robustness, during strategic planning activities for a transition to a regenerative circular economy. • Linear socio-metabolic structures have limits to their robustness. • Achieving 100% circularity rate in EU27's material flow metabolism is unrealistic. • High material recovery and export rates increase the brittleness of linear networks. • Network structure conceptualization matters in regenerative economics. • Circularity transitions should be considered in tandem with regenerative economics.

Quantum Optimal Control: Practical Aspects and Diverse Methods

Quantum controls realize the unitary or nonunitary operations employed in quantum computers, quantum simulators, quantum communications, and other quantum information devices. They implement the desired quantum dynamics with the help of electric, magnetic, or electromagnetic control fields. Quantum optimal control (QOC) deals with designing an optimal control field modulation that most precisely implements a desired quantum operation with minimum energy consumption and maximum robustness against hardware imperfections as well as external noise. Over the last 2 decades, numerous QOC methods have been proposed. They include asymptotic methods, direct search, gradient methods, variational methods, machine learning methods, etc. In this review, we shall introduce the basic ideas of QOC, discuss practical challenges, and then take an overview of the diverse QOC methods.

Cubic nonlinear squeezing and its decoherence.

Squeezed states of the harmonic oscillator are a common resource in applications of quantum technology. If the noise is suppressed in a nonlinear combination of quadrature operators below threshold for all possible up-to-quadratic Hamiltonians, the quantum states are non-Gaussian and we refer to the noise reduction as nonlinear squeezing. Non-Gaussian aspects of quantum states are often more vulnerable to decoherence due to imperfections appearing in realistic experimental implementations. Therefore, a stability of nonlinear squeezing is essential. We analyze the behavior of quantum states with cubic nonlinear squeezing under loss and dephasing. The properties of nonlinear squeezed states depend on their initial parameters which can be optimized and adjusted to achieve the maximal robustness for the potential applications.

Scalable and Robust Algorithms for Task-Based Coordination From High-Level Specifications (ScRATCHeS)

Many existing approaches for coordinating heterogeneous teams of robots either consider small numbers of agents, are application-specific, or do not adequately address common real-world requirements, e.g., strict deadlines or intertask dependencies. We introduce scalable and robust algorithms for task-based coordination from high-level specifications (ScRATCHeS) to coordinate such teams. We define a specification language, capability temporal logic, to describe rich, temporal properties involving tasks requiring the participation of multiple agents with multiple capabilities, e.g., sensors or end effectors. Arbitrary missions and team dynamics are jointly encoded as constraints in a mixed integer linear program, and solved efficiently using commercial off-the-shelf solvers. ScRATCHeS optionally allows optimization for maximal robustness to agent attrition at the penalty of increased computation time. We include an online replanning algorithm that adjusts the plan after an agent has dropped out. The flexible specification language, fast solution time, and optional robustness of ScRATCHeS provide a first step toward a multipurpose on-the-fly planning tool for tasking large teams of agents with multiple capabilities enacting missions with multiple tasks. We present randomized computational experiments to characterize scalability and hardware demonstrations to illustrate the applicability of our methods.

Relationship between long-term recreational video gaming and visual processing

Studies reported that previous experience or training with video games can lead to improvements on perceptual and sensory processing. However, the results are scarce and often contradictory. An explanation may be related to differences in procedures, training, types of games and samples used in the studies. Here, we investigate whether or not video game players (VGPs; expert gamers) and non-video game players (NVGPs; nonexpert gamers) would perform differently on a visual task. We recruited 30 VPGs and 30 NVGPs aged 18 to 36 years (mean age = 22.29 years; SD = 4.28 years). No differences were observed for any of the spatial frequencies between the groups (p > 0.05). Bayesian analyses were also carried out considering maximum robustness to avoid bias. Our results indicated that the results were minimally influenced, or were not influenced at all, by the recreational practice of action video games. This study highlights the existing gaps regarding the influence of recreational video games on visual processing. We trust our results are relevant and may open doors for future studies using different approaches.

Optimal and two-step adaptive quantum detector tomography

Quantum detector tomography is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, we design optimal probe states for detector estimation based on the minimum upper bound of the mean squared error (UMSE) and the maximum robustness. We establish the minimum UMSE and the minimum condition number for quantum detectors and provide concrete examples that can achieve optimal detector tomography. In order to enhance the estimation precision, we also propose a two-step adaptive detector tomography algorithm to optimize the probe states adaptively based on a modified fidelity index. We present a sufficient condition on when the estimation error of our two-step strategy scales inversely proportional to the number of state copies. Moreover, the superposition of coherent states is used as probe states for quantum detector tomography and the estimation error is analyzed. Numerical results demonstrate the effectiveness of both the proposed optimal and adaptive quantum detector tomography methods.

Switchable DNA-Encoded Chemical Library: Interconversion between Double- and Single-Stranded DNA Formats.

DNA-encoded chemical libraries (DEL) have attracted substantial attention due to the infinite possibility for hit discovery in both pharmaceutical companies and academia. The encoding method is the initial step of DEL construction and one of the cornerstones of DEL applications. Classified by the DNA format, the existing DEL encoding strategies were categorized into single-stranded DNA-based strategies and double-stranded DNA-based strategies. The two DEL formats have their unique advantages but are usually incompatible with each other. To address this issue, we propose the concept of interconversion between double- and single-stranded DEL based on the "reversible covalent headpiece (RCHP)" design, which combines maximum robustness of synthesis with extraordinary flexibility of applications in distinct setups. Future opportunities in this field are also proposed to advance DEL technology to a comprehensive drug discovery platform.