Factors controlling tropospheric O3, OH, NOx, and SO2 over the tropical Pacific during PEM-Tropics B

Observations over the tropical Pacific during the Pacific Exploratory Mission (PEM)-Tropics B experiment (March-April 1999) are analyzed. Concentrations of CO and long-lived nonmethane hydrocarbons in the region are significantly enhanced due to transport of pollutants from northern industrial continents. This pollutant import also enhances moderately O3 concentrations but not NOx concentrations. It therefore tends to depress OH concentrations over the tropical Pacific. These effects contrast to the large enhancements of O3 and NOx concentrations and the moderate increase of OH concentrations due to biomass burning outflow during the PEM-Tropics A experiment (September-October 1996). Observed CH3I concentrations, as in PEM-Tropics A, indicate that convective mass outflux in the middle and upper troposphere is largely independent of altitude over the tropical Pacific. Constraining a one-dimensiohal model with CH3I observations yields a 10-day timescale for convective turnover of the free troposphere, a factor of 2 faster than during PEM-Tropics A. Model simulated HO2, CH2O, H2O2, and CH3OOH concentrations are generally in agreement with observations. However, simulated OH concentrations are lower (∼25%) than observations above 6 km. Whereas models tend to overestimate previous field measurements, simulated HNO3 concentrations during PEM-Tropics B are too low (a factor of 2-4 below 6 km) compared to observations. Budget analyses indicate that chemical production of O3 accounts for only 50% of chemical loss; significant transport of O3 into the region appears to take place within the tropics. Convective transport of CH3OOH enhances the production of HOx and O3 in the upper troposphere, but this effect is offset by HOx loss due to the scavenging of H2O2. Convective transport and scavenging of reactive nitrogen species imply a necessary source of 0.4-1 Tg yr-1 of NOx in the free troposphere (above 4 km) over the tropics. A large fraction of the source could be from marine lightning. Oxidation of DMS transported by convection from the boundary layer could explain the observed free tropospheric SO2 concentrations over the tropical Pacific. This source of DMS due to convection, however, would imply in the model free tropospheric concentrations much higher than observed. The model overestimate cannot be reconciled using recent kinetics measurements of the DMS-OH adduct reaction at low pressures and temperatures and may reflect enhanced OH oxidation of DMS during convection. Copyright 2001 by the American Geophysical Union

Seasonal differences in the photochemistry of the South Pacific: A comparison of observations and model results from PEM-Tropics A and B

A time-dependent photochemical box model is used to examine the photochemistry of the equatorial and southern subtropical Pacific troposphere with aircraft data obtained during two distinct seasons: the Pacific Exploratory Mission-Tropics A (PEM-Tropics A) field campaign in September and October of 1996 and the Pacific Exploratory Mission-Tropics B (PEM-Tropics B) campaign in March and April of 1999. Model-predicted values were compared to observations for selected species (e.g., NO2, OH, HO2) with generally good agreement. Predicted values of HO2 were larger than those observed in the upper troposphere, in contrast to previous studies which show a general underprediction of HO2 at upper altitudes. Some characteristics of the budgets of HOx, NOx, and peroxides are discussed. The integrated net tendency for O3 is negative over the remote Pacific during both seasons, with gross formation equal to no more than half of the gross destruction. This suggests that a continual supply of O3 into the Pacific region throughout the year must exist in order to maintain O3 levels. Integrated net tendencies for equatorial O3 showed a seasonality, with a net loss of 1.06×1011 molecules cm-2 s-1 during PEM-Tropics B (March) increasing by 50% to 1.60×1011 molecules cm-2 s-1 during PEM-Tropics A (September). The seasonality over the southern subtropical Pacific was somewhat lower, with losses of 1.21×1011 molecules cm-2 s-1 during PEM-Tropics B (March) increasing by 25% to 1.51×1011 molecules cm-2 s-1 during PEM-Tropics A (September). While the larger net losses during PEM-Tropics A were primarily driven by higher concentrations of O3, the ability of the subtropical atmosphere to destroy O3 was ∼30% less effective during the PEM-Tropics A (September) campaign due to a drier atmosphere and higher overhead O3 column amounts. Copyright 2001 by the American Geophysical Union

Evolutionary-game-based dynamical tuning for multi-objective model predictive control

Model predictive control (MPC) is one of the most used optimization-based control strategies for large-scale systems, since this strategy allows to consider a large number of states and multi-objective cost functions in a straightforward way. One of the main issues in the design of multi-objective MPC controllers, which is the tuning of the weights associated to each objective in the cost function, is treated in this work. All the possible combinations of weights within the cost function affect the optimal result in a given Pareto front. Furthermore, when the system has time-varying parameters, e.g., periodic disturbances, the appropriate weight tuning might also vary over time. Moreover, taking into account the computational burden and the selected sampling time in the MPC controller design, the computation time to find a suitable tuning is limited. In this regard, the development of strategies to perform a dynamical tuning in function of the system conditions potentially improves the closed-loop performance. In order to adapt in a dynamical way the weights in the MPC multi-objective cost function, an evolutionary-game approach is proposed. This approach allows to vary the prioritization weights in the proper direction taking as a reference a desired region within the Pareto front. The proper direction for the prioritization is computed by only using the current system values, i.e., the current optimal control action and the measurement of the current states, which establish the system cost function over a certain point in the Pareto front. Finally, some simulations of a multi-objective MPC for a real multi-variable case study show a comparison between the system performance obtained with static and dynamical tuning.Peer ReviewedPostprint (author's final draft

Dynamic capacity provision for wireless sensors connectivity: A profit optimization approach

[EN] We model a wireless sensors' connectivity scenario mathematically and analyze it using capacity provision mechanisms, with the objective of maximizing the profits of a network operator. The scenario has several sensors' clusters with each one having one sink node, which uploads the sensing data gathered in the cluster through the wireless connectivity of a network operator. The scenario is analyzed both as a static game and as a dynamic game, each one with two stages, using game theory. The sinks' behavior is characterized with a utility function related to the mean service time and the price paid to the operator for the service. The objective of the operator is to maximize its profits by optimizing the network capacity. In the static game, the sinks' subscription decision is modeled using a population game. In the dynamic game, the sinks' behavior is modeled using an evolutionary game and the replicator dynamic, while the operator optimal capacity is obtained solving an optimal control problem. The scenario is shown feasible from an economic point of view. In addition, the dynamic capacity provision optimization is shown as a valid mechanism for maximizing the operator profits, as well as a useful tool to analyze evolving scenarios. Finally, the dynamic analysis opens the possibility to study more complex scenarios using the differential game extension.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy and Competitiveness through project TIN2013-47272-C2-1-R; AEI/FEDER, UE through project TEC2017-85830-C2-1-P; and co-supported by the European Social Fund BES-2014-068998.Sanchis-Cano, Á.; Guijarro, L.; Condoluci, M. (2018). 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Human Decision-Making in Multi-Agent Systems

In order to avoid suboptimal collective behaviors and resolve social dilemmas, researchers have tried to understand how humans make decisions when interacting with other humans or smart machines and carried out theoretical and experimental studies aimed at influencing decision-making dynamics in large populations. We identify the key challenges and open issues in the related research, list a few popular models with the corresponding results, and point out future research directions

The Value of Information in Selfish Routing

Path selection by selfish agents has traditionally been studied by comparing social optima and equilibria in the Wardrop model, i.e., by investigating the Price of Anarchy in selfish routing. In this work, we refine and extend the traditional selfish-routing model in order to answer questions that arise in emerging path-aware Internet architectures. The model enables us to characterize the impact of different degrees of congestion information that users possess. Furthermore, it allows us to analytically quantify the impact of selfish routing, not only on users, but also on network operators. Based on our model, we show that the cost of selfish routing depends on the network topology, the perspective (users versus network operators), and the information that users have. Surprisingly, we show analytically and empirically that less information tends to lower the Price of Anarchy, almost to the optimum. Our results hence suggest that selfish routing has modest social cost even without the dissemination of path-load information.Comment: 27th International Colloquium on Structural Information and Communication Complexity (SIROCCO 2020

Robustness assessment of the ‘cooperation under resource pressure’ (CURP) model: Insights on resource availability and sharing practices among hunter-gatherers

A well-known challenge in archaeological research is the exploration of the social mechanisms that hunter-gatherers may have implemented throughout history to deal with changes in resource availability. The agent-based model (ABM) ‘cooperation under resource pressure’ (CURP) was conceived to explore food stress episodes in societies lacking a food preservation technology. It was particularly aimed at understanding how cooperative behaviours in the form of food sharing practices emerge, increase and may become the prevailing strategy in relation to changes in resource availability and expectancy of reciprocity. CURP’s main outcome is the identification of three regimes of behaviour depending on the stress level. In this work, the model’s robustness to the original selection mechanism (random tournament) is assessed, as different dynamics can lead to different persistent regimes. For that purpose, three other selection mechanisms are implemented and evaluated, to identify the prevailing states of the system. Results show that the three regimes are robust irrespective of the analysed dynamics. We consequently examine in more detail the long-term archaeological implications that these results may have.Spanish Ministry of Economy and Competitiveness (former Ministry of Science and Innovation): SimulPast Project (CSD2010- 00034 CONSOLIDER-INGENIO 2010), HAR2009-06996 and CULM Project (HAR2016- 77672-P); from the Argentine National Scientific and Technical Research Council (CONICET): Project PIP-0706; from the Wenner-Gren Foundation for Anthropological Research: Project GR7846; from the project H2020 FET OPEN RIA IBSEN/662725 and from the European Social Fund as one of the authors is the recipient of a predoctoral grant from the Department of Education of Junta de Castilla y León (Spain)

On Maximum Weight Clique Algorithms, and How They Are Evaluated

Maximum weight clique and maximum weight independent set solvers are often benchmarked using maximum clique problem instances, with weights allocated to vertices by taking the vertex number mod 200 plus 1. For constraint programming approaches, this rule has clear implications, favouring weight-based rather than degree-based heuristics. We show that similar implications hold for dedicated algorithms, and that additionally, weight distributions affect whether certain inference rules are cost-effective. We look at other families of benchmark instances for the maximum weight clique problem, coming from winner determination problems, graph colouring, and error-correcting codes, and introduce two new families of instances, based upon kidney exchange and the Research Excellence Framework. In each case the weights carry much more interesting structure, and do not in any way resemble the 200 rule. We make these instances available in the hopes of improving the quality of future experiments