Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network

In this study, an artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the short-term $x(t+6)$. The performance prediction was evaluated and compared with another studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called {\it stochastic} hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute uncertainties of predictions for noisy Mackey--Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level ($\sigma_{N}$) from 0.01 to 0.1.Comment: 11 pages, 8 figure

Ontology's crossed life cycles

This paper presents the idea that the life cycle of an ontology is highly impacted as a result of the process of reusing it for building another ontology. One of the more important results of the experiment presented is how the different activities to be carried out during the development of a specific ontology may involve performing other types of activities on other ontologies already built or under construction. We identify in that paper new intradependencies between activities carried out inside the same otology and interdependencies between activities carried out in different ontologies. The interrelation between life cycles of several ontologies provokes that integration has to be approached globally rather than as a mere integration of out implementation

Carbon and oxygen abundances from recombination lines in low-metallicity star-forming galaxies. Implications for chemical evolution

We present deep echelle spectrophotometry of the brightest emission-line knots of the star-forming galaxies He 2-10, Mkn 1271, NGC 3125, NGC 5408, POX 4, SDSS J1253-0312, Tol 1457-262, Tol 1924-416 and the HII region Hubble V in the Local Group dwarf irregular galaxy NGC 6822. The data have been taken with the Very Large Telescope Ultraviolet-Visual Echelle Spectrograph in the 3100-10420 {\AA} range. We determine electron densities and temperatures of the ionized gas from several emission-line intensity ratios for all the objects. We derive the ionic abundances of C$^{2+}$ and/or O$^{2+}$ from faint pure recombination lines (RLs) in several of the objects, permitting to derive their C/H and C/O ratios. We have explored the chemical evolution at low metallicities analysing the C/O vs. O/H, C/O vs. N/O and C/N vs. O/H relations for Galactic and extragalactic HII regions and comparing with results for halo stars and DLAs. We find that HII regions in star-forming dwarf galaxies occupy a different locus in the C/O vs. O/H diagram than those belonging to the inner discs of spiral galaxies, indicating their different chemical evolution histories, and that the bulk of C in the most metal-poor extragalactic HII regions should have the same origin than in halo stars. The comparison between the C/O ratios in HII regions and in stars of the Galactic thick and thin discs seems to give arguments to support the merging scenario for the origin of the Galactic thick disc. Finally, we find an apparent coupling between C and N enrichment at the usual metallicities determined for HII regions and that this coupling breaks in very low-metallicity objects.Comment: 27 pages, 12 figures, Accepted for publication in Monthly Notices of the Royal Astronomical Societ

Biodiesel production from palm oil in a fixed-bed-reactor with calcium methoxide catalyst

En el presente trabajo se analizó el comportamiento del metóxido de calcio como catalizador para la producción de biodiesel a partir de aceite de palma. Inicialmente se establecieron condiciones de operación en un reactor por lotes, como temperatura de activación CaO (25°C), temperatura de reacción (60°C) y relación molar metanol/aceite (10). Se describió el montaje y puesta en marcha de un reactor de lecho fijo y flujo continuo a escala laboratorio; a las condiciones establecidas se obtuvieron rendimientos a métilesteres del 20% con un tiempo de residencia de 60 min. También se analizó el uso de cosolventes como el terbutanol y el acetato de etilo para eliminar las limitaciones de transferencia de masa entre las fases e incrementar los rendimientos a metil ésteres. Se encontró que el uso de etil acetato como cosolvente incrementó el rendimiento a metil esteres hasta el 31 %. Abstract In this research, the behavior of calcium methoxide as catalyst for the production of biodiesel from palm oil in a continuous system was studied. Initially, operating conditions such as CaO activation temperature (25°C), reaction temperature (60°C) and molar ratio methanol-oil (10)were established for a batch reactor. Then, the schematic diagram of the experimental setup and its implementation were described for a packed-bed laboratory scale reactor with continuous flow; for this system, yields of methyl esters of 20% with a residence time of 60 min were obtained. The use of co-solvent such as tert-Bbutanol and ethyl acetate in the continuous reaction system was also investigated, aiming to eliminate mass transfer artifacts between the phases and eventually increasing yields to methyl esters. The use of ethyl acetate as co-solvent increased methyl esters yields up to 31 %

AIRPA: An Architecture to Support the Execution and Maintenance of AI-Powered RPA Robots

Robotic Process Automation (RPA) has quickly evolved from automating simple rule-based tasks. Nowadays, RPA is required to mimic more sophisticated human tasks, thus implying its combina tion with Artificial Intelligence (AI) technology, i.e., the so-called intelli gent RPA. Putting together RPA with AI leads to a challenging scenario since (1) it involves professionals from both fields who typically have different skills and backgrounds, and (2) AI models tend to degrade over time which affects the performance of the overall solution. This paper describes the AIRPA project, which addresses these challenges by proposing a software architecture that enables (1) the abstraction of the robot development from the AI development and (2) the monitor, con trol, and maintain intelligent RPA developments to ensure its quality and performance over time. The project has been conducted in the Serv inform context, a Spanish consultancy firm, and the proposed prototype has been validated with reality settings. The initial experiences yield promising results in reducing AHT (Average Handle Time) in processes where AIRPA deployed cognitive robots, which encourages exploring the support of intelligent RPA development.Ministerio de Ciencia, Innovación y Universidades PID2019-105455GB-C31Centro para el Desarrollo Tecnológico Industrial EXP00118029/IDI-20190524Centro para el Desarrollo Tecnológico Industrial P011-19/E0

Performance enhancement of WLAN IEEE 802.11 for asymmetric traffic

Most studies about the performance of IEEE 802.11 consider scenarios of ad-hoc topology and networks where all stations have the same traffic load (symmetric traffic conditions). This paper presents a study of performance parameters of more realistic networks. We focus the attention on WLAN with infrastructure networks, where the traffic distribution is asymmetric. In this case, the traffic load at the access point is much heavier than that at user stations. These studies are more realistic because most nowadays installed WLAN are infrastructure topology type, due to the fact that they are used as access networks. In this case, the access point has to retransmit all incoming traffic to the basic service set and therefore its traffic load is higher. Finally, the paper presents the tuning of the contention window, taken from IEEE 802.11e, used to increase the system performance under asymmetric traffic conditions, and the proposal of an adaptive algorithm to adapt the MAC layer settings to the system traffic load.Peer Reviewe

Performance enhancement of outdoor IEEE 802.11 cellular networks

Most studies about the performance of IEEE 802.11 are limited to a single cell environment. Nevertheless, the idea of designing an outdoor cellular network based on WLAN IEEE 802.11 results very attractive, due to the several advantages that this technology presents: the low cost of the equipment, its operation in unlicensed spectrum and its higher data rates. If we compare the system performance in a cellular environment with its behavior in a single cell environment, we observe that its performance decreases considerably with the growth of the transmission data rate employed and due to co-channel interference. In this paper, we propose some enhancement mechanisms, in order to reduce the interference influence on network performance. Moreover, we study the viability of using sectorised antennas at the access points. We present its performance under different load conditions and compare this behavior with the results obtained in an isolated single cell environment, which has no interference.Peer Reviewe

Eliminating Error in the Chemical Abundance Scale for Extragalactic HII Regions

In an attempt to remove the systematic errors which have plagued the calibration of the HII region abundance sequence, we have theoretically modeled the extragalactic HII region sequence. We then used the theoretical spectra so generated in a double blind experiment to recover the chemical abundances using both the classical electron temperature + ionization correction factor technique, and the technique which depends on the use of strong emission lines (SELs) in the nebular spectrum to estimate the abundance of oxygen. We find a number of systematic trends, and we provide correction formulae which should remove systematic errors in the electron temperature + ionization correction factor technique. We also provide a critical evaluation of the various semi-empirical SEL techniques. Finally, we offer a scheme which should help to eliminate systematic errors in the SEL-derived chemical abundance scale for extragalactic HII regions.Comment: 24 pages, 9 Tables, 13 figures, accepted for publication in MNRAS. Updated considering minor changes during the final edition process and some few missing reference

Searching for promisingly trained artificial neural networks

Assessing the training process of artificial neural networks (ANNs) is vital for enhancing their performance and broadening their applicability. This paper employs the Monte Carlo simulation (MCS) technique, integrated with a stopping criterion, to construct the probability distribution of the learning error of an ANN designed for short-term forecasting. The training and validation processes were conducted multiple times, each time considering a unique random starting point, and the subsequent forecasting error was calculated one step ahead. From this, we ascertained the probability of having obtained all the local optima. Our extensive computational analysis involved training a shallow feedforward neural network (FFNN) using wind power and load demand data from the transmission systems of the Netherlands and Germany. Furthermore, the analysis was expanded to include wind speed prediction using a long short-term memory (LSTM) network at a site in Spain. The improvement gained from the FFNN, which has a high probability of being the global optimum, ranges from 0.7% to 8.6%, depending on the forecasting variable. This solution outperforms the persistent model by between 5.5% and 20.3%. For wind speed predictions using an LSTM, the improvement over an average-trained network stands at 9.5%, and is 6% superior to the persistent approach. These outcomes suggest that the advantages of exhaustive search vary based on the problem being analyzed and the type of network in use. The MCS method we implemented, which estimates the probability of identifying all local optima, can act as a foundational step for other techniques like Bayesian model selection, which assumes that the global optimum is encompassed within the available hypotheses