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Highlights • The optimal power share between GT and SOFC in a hybrid system was analyzed. • SOFC voltage degradation and a mitigation strategy were considered. • Genetic algorithm was used to optimize the plant internal rate of return. • The proposed strategy to reduce degradation effect resulted in 50–50 optimal share. • Local analysis of degradation effects in the SOFC was performed. Abstract The coupling of a pressurized solid oxide fuel cell (SOFC) and a gas turbine has been proven to result in extremely high efficiency and reduced emissions. The presence of the gas turbine can improve system durability compared to a standalone SOFC, because the turbomachinery can supply additional power as the fuel cell degrades to meet the power request. Since performance degradation is an obstacles to SOFC systems commercialization, the optimization of the hybrid system to mitigate SOFC degradation effects is of great interest. In this work, an optimization approach was used to innovatively study the effect of gas turbine size on system durability for a 400 kW fuel cell stack. A larger turbine allowed a bigger reduction in SOFC power before replacing the stack, but increased the initial capital investment and decreased the initial turbine efficiency. Thus, the power ratio between SOFC and gas turbine significantly influenced system economic results. [ABSTRACT FROM AUTHOR]

This brief deals with the problem of mathematically formalizing hardware circuits’ vulnerability to side-channel attacks. We investigate whether spectral analysis is a useful analytical tool for this purpose by building a mathematically sound theory of the vulnerability phenomenon. This research was originally motivated by the need for deeper, more formal knowledge around vulnerable nonlinear circuits. However, while building this new theoretical framework, we discovered that it can consistently integrate known results about linear ones as well. Eventually, we found it adequate to formally model side-channel leakage in several significant scenarios. In particular, we have been able to find the vulnerability perimeter of a known cryptographic primitive (i.e., Keccak [1] ) and thus tackle the analysis of vulnerability when signal glitches are present. We believe the conceptual framework we propose will be useful for researchers and practitioners in the field of applied cryptography and side-channel attacks. [ABSTRACT FROM AUTHOR]

The performance of a solid oxide fuel cell (SOFC) is subject to inherent uncertainty in operational and geometrical parameters, which can cause performance variability and affect system reliability. Operating conditions such as current demand, cell temperature and fuel utilization play an important role on the degradation mechanisms, which affect typical SOFCs. In previous work, a deterministic empirical degradation model of a SOFC was developed as a function of such operating conditions. By the nature of experimental data and regression fitting, this model was not deterministic. The aim of this work is to evaluate the impact of the uncertainties in the degradation model through a stochastic analysis. In particular, the Response Sensitivity Analysis (RSA), an approximate stochastic method based on Taylor series expansion, is applied to a standalone SOFC model and a fuel cell hybrid system model both subjected to cell degradation. The attention is principally focused on the impact on the fuel cell lifetime. To provide an indication of degradation effect and resulting lifetime uncertainty on economic performance, a cursory economic analysis is performed. [ABSTRACT FROM AUTHOR]

The hybridization of Solid Oxide Fuel Cell (SOFC) and gas turbine technologies provides an increase in system efficiency and economic performance. The latter aspect is significantly affected by fuel cell degradation, due to several mechanisms. However, hybrid systems allow different control strategies to minimize degradation effects on system performance and their impact on economic feasibility. A real-time distributed model of a SOFC was used to simulate fuel cell degradation in the cases of a standalone stack and a hybrid configuration, in the latter of which the numerical model is normally coupled with the hybrid system hardware components of the National Energy Technology Laboratory (NETL) hyper facility. The results showed how in a hybrid system it is possible, with an appropriate strategy, to maintain constant voltage even if the cell is degrading, reducing degradation rate during time. At constant power demand, fuel cell life could be significantly extended using the operating strategies allowed by coupling with a turbine (an order of magnitude longer than a standalone fuel cell), maintaining high system efficiency despite fuel cell degradation. [ABSTRACT FROM AUTHOR]

Solid oxide fuel cells are characterized by very high efficiency, low emissions level, and large fuel flexibility. Unfortunately, their elevated costs and relatively short lifetimes reduce the economic feasibility of these technologies at the present time. Several mechanisms contribute to degrade fuel cell performance during time, and the study of these degradation modes and potential mitigation actions is critical to ensure the durability of the fuel cell and their long-term stability. In this work, localized degradation of a solid oxide fuel cell is modeled in real-time and its effects on various cell parameters are analyzed. Profile distributions of overpotential, temperature, heat generation, and temperature gradients in the stack are investigated during degradation. Several causes of failure could occur in the fuel cell if no proper control actions are applied. A local analysis of critical parameters conducted shows where the issues are and how they could be mitigated in order to extend the life of the cell. [ABSTRACT FROM AUTHOR]

Despite the high efficiency and flexibility of fuel cells, which make them an attractive technology for the future energy generation, their economic competitiveness is still penalized by their short lifetime, due to multiple degradation phenomena. As a matter of fact, electrochemical performance of solid oxide fuel cells (SOFCs) is reduced because of different degradation mechanisms, which depend on operating conditions, fuel and air contaminants, impurities in materials, and others. In this work, a real-time, one dimensional (1D) model of a SOFC is used to simulate the effects of voltage degradation in the cell. Different mechanisms are summarized in a simple empirical expression that relates degradation rate to cell operating parameters (current density, fuel utilization and temperature), on a localized basis. Profile distributions of different variables during cell degradation are analyzed. In particular, the effect of degradation on current density, temperature, and total resistance of the cell are investigated. An analysis of localized degradation effects shows how different parts of the cell degrade at a different time rate, and how the various profiles are redistributed along the cell as consequence of different degradation rates. [ABSTRACT FROM AUTHOR]

Fuel cell gas turbine hybrids present significant challenges in terms of system control because of the coupling of different time-scale phenomena. Hence, the importance of studying the integrated system dynamics is critical. With the aim of safe operability and efficiency optimization, the cold air bypass valve was considered an important actuator since it affects several key parameters and can be very effective in controlling compressor surge. Two different tests were conducted using a cyber-physical approach. The Hybrid Performance (HyPer) facility couples gas turbine equipment with a cyber physical solid oxide fuel cell in which the hardware is driven by a numerical fuel cell model operating in real time. The tests were performed moving the cold air valve from the nominal position of 40% with a step of 15% up and down, while the system was in open loop, i.e. no control on turbine speed or inlet temperature. The effect of the valve change on the system was analyzed and transfer functions were developed for several important variables such as cathode mass flow, total pressure drop and surge margin. Transfer functions can show the response time of different system variables, and are used to characterize the dynamic response of the integrated system. Opening the valve resulted in an immediate positive impact on pressure drop and surge margin. A valve change also significantly affected fuel cell temperature, demonstrating that the cold air bypass can be used for thermal management of the cell. [ABSTRACT FROM AUTHOR]

This paper presents OSCAR, an optimization methodology exploiting spatial correlation of multicore design spaces. This paper builds upon the observation that power consumption and performance metrics of spatially close design configurations (or points) are statistically correlated. We propose to exploit the correlation by using a response surface model (RSM), i.e., a closed-form expression suitable for predicting the quality of nonsimulated design points. This model is useful during the design space exploration (DSE) phase to quickly converge to the Pareto set of the multiobjective problem without executing lengthy simulations. To this end, we introduce a multiobjective optimization heuristic which iteratively updates and queries the RSM to identify the design points with the highest expected improvement. The RSM allows to consolidate the Pareto set by reducing the number of simulations required, thus speeding up the exploration process. We compare the proposed heuristic with state-of-the-art approaches [conventional, RSM-based, and structured design of experiments (DoEs)]. Experimental results show that OSCAR is a faster heuristic with respect to state-of-the-art techniques such as response-surface Pareto iterative refinement ReSPIR and nondominated-sorting genetic algorithm NSGA-II. In fact, OSCAR used a lower number of simulations to produce a similar solution, i.e., an average of 150 simulations instead of 320 simulations (NSGA-II) and 178 simulations (ReSPIR). When the number of design points is fixed to an average of 300, OSCAR achieves less than 0.6% in terms of average distance with respect to the reference solution while NSGA-II achieves 3.4%. Reported results also show that OSCAR can significantly improve structured DoE approaches by slightly increasing the number of experiments. [ABSTRACT FROM PUBLISHER]

Background and purpose: The phenomenon of dementia amongst migrants and ethnic minorities represents an emerging concern for European healthcare systems, posing additional challenges in terms of clinical approach, access to care and resource utilization. The aim of the present study was to estimate the cases of dementia amongst immigrant older subjects living in Europe and in each European country. Methods: The estimated cases of dementia amongst older (i.e. 65+) migrants living in the European Union (EU‐28) and European Free Trade Association member states were calculated by multiplying the number of migrants (obtained through the data provided by Eurostat) with the age‐ and sex‐specific prevalence rates (derived by a recent meta‐analysis). Results: Overall, 6 507 360 older migrants lived in Europe in 2017. In addition, 1 204 671 migrants were registered in Germany in 2010. Nearly 475 000 dementia cases (329 028 women, 147 410 men) were estimated in this population by applying age‐ and sex‐specific prevalence rates. When considering each European country, the number of estimated cases ranged from 108 (Iceland) to 119 161 (France). In parallel, the proportion of dementia cases occurring in migrants ranged from 0.9% (Czech Republic) to 51.2% (Liechtenstein). Conclusions: The issue of dementia in migrants and ethnic minorities is emerging but already relevant for European healthcare systems. The magnitude of this phenomenon and its complexities reinforce the need for coordinated initiatives both at a national and continental level. These epidemiological data should ideally be integrated with those coming from 'real world' services in order to better calibrate these actions. [ABSTRACT FROM AUTHOR]

Background and purpose: There is now a wide consensus at recognizing social and economic circumstances as main determinants of an individual's health status. Nevertheless, characteristics relating to socioeconomic status (SES) are poorly described in research reports. The aim of the present review was to verify whether the SES of participants is adequately reported in interventional studies targeting Alzheimer's disease (AD), and to explore the impact of SES proxy measures on the efficacy of the considered medications. Methods: A systematic review of available randomized controlled trials (RCTs) on the currently marketed drugs for AD (i.e. cholinesterase inhibitors and memantine) was conducted by performing a structured search on PubMed and the Cochrane databases. The following indicators of SES were considered in the retained studies: (i) educational level, (ii) lifetime job category, (iii) income and (iv) wealth. The study quality was assessed using the Cochrane Risk of Bias Tool for Randomized Controlled Trials. Results: A total of 48 articles were finally selected. Overall, only eight RCTs reported data concerning the four considered SES indicators. Indeed, only information pertaining to the educational level of participants was provided. Only one RCT (n = 60) performed ad hoc, secondary analyses accounting for the SES of participating subjects. Conclusions: The research and clinical relevance of SES has mistakenly been overlooked by the vast majority of RCTs on AD. A greater effort should be made to collect and report data on those SES indicators that may significantly affect the clinical manifestations and trajectories of patients with cognitive disturbances. [ABSTRACT FROM AUTHOR]

Nowadays, owing to unpredictable changes of the environment and workload variation, optimally running multiple applications in terms of quality, performance and power consumption on embedded multi-core platforms is a huge challenge. A lightweight run-time manager, linked with an automated design-time exploration and incorporated in the host processor of the platform, is required to dynamically and efficiently configure the applications according to the available platform resources (e.g. processing elements, memories, communication bandwidth), for minimising the cost (e.g. power consumption), while satisfying the constraints (e.g. deadlines). This study presents a flow linking a design-time design space explorer, coupled with platform simulators at two abstraction levels, with a fast and lightweight priority-based heuristic integrated in the run-time manager to select near-optimal application configurations. To illustrate its feasibility and the very low complexity of the run-time selection, the proposed flow is used to manage the processors and clock frequencies of a multiple-stream MPEG4 encoder chip dedicated to automotive cognitive safety applications. [ABSTRACT FROM AUTHOR]

Abstract: The paper introduces a dynamic branch prediction scheme suitable for energy-aware Very Long Instruction Word (VLIW) processors. The proposed technique is based on a compiler hint mechanism to filter the accesses to the branch predictor blocks. We define a configurable hint instruction which anticipates some static information about the upcoming branch to reduce the hardware involved in the prediction, thus, the energy consumption. To analyze the effectiveness of the proposed low-power branch prediction scheme, we combined it with some well-known dynamic branch prediction techniques suitable for VLIW processors. The analyzed branch predictors are characterized by simple hardware implementations, matching the low-power characteristics of the target VLIW processors. Experimental results have been carried out on Lx, an industrial 4-issue VLIW architecture. [Copyright &y& Elsevier]