794 research outputs found
Optimal utility and probability functions for agents with finite computational precision
When making economic choices, such as those between goods or gambles, humans act as if their internal representation of the value and probability of a prospect is distorted away from its true value. These distortions give rise to decisions which apparently fail to maximize reward, and preferences that reverse without reason. Why would humans have evolved to encode value and probability in a distorted fashion, in the face of selective pressure for reward-maximizing choices? Here, we show that under the simple assumption that humans make decisions with finite computational precision––in other words, that decisions are irreducibly corrupted by noise––the distortions of value and probability displayed by humans are approximately optimal in that they maximize reward and minimize uncertainty. In two empirical studies, we manipulate factors that change the reward-maximizing form of distortion, and find that in each case, humans adapt optimally to the manipulation. This work suggests an answer to the longstanding question of why humans make “irrational” economic choices
Canyoles archaeological survey project. Resultados preliminares
En aquest treball presentem els primers resultats d’un intens projecte de prospeccions sistemà tiques dut a terme a la part occidental de la comarca de la Costera (València). Amb la informació obtinguda, es fa una primera aproximació a l’evolució del poblament prehistòric de la zona. Es presta una atenció especial als moments corresponents a la introducció i desenvolupament de les economies productores.
Paraules clau: La Costera. Prospeccions sistemĂ tiques. Poblament. Paisatge. NeolĂtic-Edat del Bronze.In this paper we present the preliminary findings of an extensive survey conducted in the western portion of the La Costera region of Valencia, Spain. This project explores patterns of prehistoric settlement and land use through time with an emphasis on the first farming populations and subsequent cultural developments until the Bronze Age. We discuss the results of this research within a broader regional context.
Key Words: La Costera. Sistematic survey. Settlement. Landscape. Neolithic-Bronze Age.En este trabajo se presentan los primeros resultados de un intenso proyecto de prospecciones sistemáticas en la zona occidental de la comarca de La Costera (València). A travĂ©s de la informaciĂłn obtenida, se realiza una primera presentaciĂłn de la evoluciĂłn del poblamiento prehistĂłrico en la zona. Especial atenciĂłn se presta a los momentos correspondientes a la introducciĂłn y desarrollo de las economĂas productoras.
Palabras Clave: La Costera. Prospecciones sistemáticas. Poblamiento. Paisaje. NeolĂtico-Edad del Bronce
Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables
Collective variables (CVs) are low-dimensional representations of the state of a complex system, which help us rationalize molecular conformations and sample free energy landscapes with molecular dynamics simulations. Given their importance, there is need for systematic methods that effectively identify CVs for complex systems. In recent years, nonlinear manifold learning has shown its ability to automatically characterize molecular collective behavior. Unfortunately, these methods fail to provide a differentiable function mapping high-dimensional configurations to their low-dimensional representation, as required in enhanced sampling methods. We introduce a methodology that, starting from an ensemble representative of molecular flexibility, builds smooth and nonlinear data-driven collective variables (SandCV) from the output of nonlinear manifold learning algorithms. We demonstrate the method with a standard benchmark molecule, alanine dipeptide, and show how it can be non-intrusively combined with off-the-shelf enhanced sampling methods, here the adaptive biasing force method. We illustrate how enhanced sampling simulations with SandCV can explore regions that were poorly sampled in the original molecular ensemble. We further explore the transferability of SandCV from a simpler system, alanine dipeptide in vacuum, to a more complex system, alanine dipeptide in explicit water.Peer ReviewedPostprint (published version
Application assessment of UV-vis and NIR spectroscopy for the quantification of fuel dilution problems on used engine oils
[EN] Fuel dilution in engine oil is a frequent problem in internal combustion engines (ICE); its main consequence is the reduction of the oil viscosity, decreasing lubrication film strength, and causing a negative impact on friction and wear. The standard and more precise methods for assessing fuel content in oil are based on chromatographic analysis (e.g., ASTM D3524, ASTM D7593), requiring high-cost equipment and highly qualified personnel. This work performed a study to propose an alternative method for quantifying diesel fuel dilution in used engine oil by UVÂżvis and NIR spectroscopy. The samples for the study were prepared from used oil from six different vehicles with various mileages. According to the results obtained in this study, NIR spectroscopy proved to be the most suitable method for the quantification of diesel fuel in used engine oils. Furthermore, the use of NIR spectroscopy combined with multivariate calibration methods could predict the fuel concentration of the samples used for validating the model. The best predictive model for the quantification was obtained by Partial Least Squares Regression, which achieved a Root Mean Squared Error of prediction of 0.436% and a coefficient of determination of 0.9435. In comparison, the parameters for Principal Component Regression were 1.049% and 0.8441, respectively.Acknowledgments This work has been partially supported by grant PID2020-119691RB-100 funded by MCIN/AEI/10.13039/501100011033.Macian Martinez, V.; Tormos, B.; GarcĂa-Barberá, A.; Balaguer-Reyes, A. (2023). Application assessment of UV-vis and NIR spectroscopy for the quantification of fuel dilution problems on used engine oils. Fuel. 333. https://doi.org/10.1016/j.fuel.2022.12635033
Can we identify non-stationary dynamics of trial-to-trial variability?"
Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings
Assessment of microbial community structure changes by amplified ribosomal DNA restriction analysis (ARDRA)
Amplified ribosomal DNA restriction analysis (ARDRA) is a simple method based on restriction endonuclease digestion of the amplified bacterial 16S rDNA. In this study we have evaluated the suitability of this method to detect differences in activated sludge bacterial communities fed on domestic or industrial wastewater, and subject to different operational conditions. The ability of ARDRA to detect these differences has been tested in modified Ludzack-Ettinger (MLE) configurations. Samples from three activated sludge wastewater treatment plants (WWTPs) with the MLE configuration were collected for both oxic and anoxic reactors, and ARDRA patterns using double enzyme digestions AluI+MspI were obtained. A matrix of Dice similarity coefficients was calculated and used to compare these restriction patterns. Differences in the community structure due to influent characteristics and temperature could be observed, but not between the oxic and anoxic reactors of each of the three MLE configurations. Other possible applications of ARDRA for detecting and monitoring changes in activated sludge systems are also discussed
A stable fish reporter cell line to study estrogen receptor transactivation by environmental (xeno)estrogens.
International audienceCross-species differences between human and fish estrogen receptor (ER) binding by environmental chemicals have been reported. To study ER transactivation in a fish cellular context, we stably co-transfected the PLHC-1 fish hepatoma cell line with a rainbow trout estrogen receptor (rtER) and the luciferase reporter gene driven by an estrogen response element (ERE). This new cell model, called PELN-rtER (for PLHC-1-ERE-Luciferase-Neomycin), responded to 17beta-estradiol (E2) in a both concentration- and temperature-dependent manner, as well as to environmental ER ligands from different chemical classes: natural and synthetic estrogens, zearalenone metabolites, genistein, alkyphenoles and benzophenone derivatives. The comparison with other in vitro models, i.e. human reporter cell lines (HELN-rtER, MELN) and vitellogenin induction in primary cultures of rainbow trout hepatocytes, showed an overall higher sensitivity of the human cells for a majority of ligands, except for benzophenone derivatives which were active at similar or lower concentrations in fish cells, suggesting species-specificity for these substances. Correlation analyses suggest that the fish cell line is closer to the trout hepatocyte than to the human cell context, and could serve as a relevant mechanistic tool to study ER activation in fish hepatic cellular context
Realtime calibration of the A4 electromagnetic lead fluoride calorimeter
Sufficient energy resolution is the key issue for the calorimetry in particle
and nuclear physics. The calorimeter of the A4 parity violation experiment at
MAMI is a segmented calorimeter where the energy of an event is determined by
summing the signals of neighbouring channels. In this case the precise matching
of the individual modules is crucial to obtain a good energy resolution. We
have developped a calibration procedure for our total absorbing electromagnetic
calorimeter which consists of 1022 lead fluoride (PbF_2) crystals. This
procedure reconstructs the the single-module contributions to the events by
solving a linear system of equations, involving the inversion of a 1022 x
1022-matrix. The system has shown its functionality at beam energies between
300 and 1500 MeV and represents a new and fast method to keep the calorimeter
permanently in a well-calibrated state
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