RLZAP: Relative Lempel-Ziv with Adaptive Pointers

Relative Lempel-Ziv (RLZ) is a popular algorithm for compressing databases of genomes from individuals of the same species when fast random access is desired. With Kuruppu et al.'s (SPIRE 2010) original implementation, a reference genome is selected and then the other genomes are greedily parsed into phrases exactly matching substrings of the reference. Deorowicz and Grabowski (Bioinformatics, 2011) pointed out that letting each phrase end with a mismatch character usually gives better compression because many of the differences between individuals' genomes are single-nucleotide substitutions. Ferrada et al. (SPIRE 2014) then pointed out that also using relative pointers and run-length compressing them usually gives even better compression. In this paper we generalize Ferrada et al.'s idea to handle well also short insertions, deletions and multi-character substitutions. We show experimentally that our generalization achieves better compression than Ferrada et al.'s implementation with comparable random-access times

Potential applications of artificial intelligence in computer-based management systems for mixed waste incinerator facility operation

The Department of Energy/Oak Ridge Field Office (DOE/OR) operates a mixed waste incinerator facility at the Oak Ridge K-25 Site, designed for the thermal treatment of incinerable liquid, sludge, and solid waste regulated under the Toxic Substances Control Act (TSCA) and the Resource Conversion and Recovery Act (RCRA). Operation of the TSCA Incinerator is highly constrained as a result of the regulatory, institutional, technical, and resource availability requirements. This presents an opportunity for applying computer technology as a technical resource for mixed waste incinerator operation to facilitate promoting and sustaining a continuous performance improvement process while demonstrating compliance. This paper describes mixed waste incinerator facility performance-oriented tasks that could be assisted by Artificial Intelligence (AI) and the requirements for AI tools that would implement these algorithms in a computer-based system. 4 figs., 1 tab

Propuesta de modelo para estimar el estado de carga de baterías de LiFePO4 para un vehículo eléctrico

CIES2020 - XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia SolarRESUMEN: El dimensionamiento de un sistema de almacenamiento juega un papel indispensable a la hora de proporcionar una fuente de energía constante. Sin embargo, estos sistemas luchan con la inestabilidad de los parámetros resistivos entre celda y celda, que finalmente logran variar la previsión de diseño. Este trabajo contribuye con una metodología de modelado y simulación para un sistema eléctrico de un vehículo eléctrico utilizando la plataforma Matlab-Simulink. El objetivo es estimar el estado de carga y, por tanto, la autonomía del sistema de tracción eléctrica. Se estudian las condiciones tanto internas como externas en las que funciona un EV y se construye un prototipo de EV para validar los resultados de las simulaciones. El modelo computacional propone el uso de polinomios característicos de las curvas de trabajo de un motor eléctrico y de los componentes RC del circuito equivalente de una batería. Se modelan las ecuaciones matemáticas que representan las fuerzas a las que está sometido el tren de tracción y se obtiene la corriente que se extrae del sistema de almacenamiento ().ABSTRACT: Sizing a storage system plays an indispensable role in providing a constant energy source. However, these systems struggle with the instability of resistive parameters between cell and cell, which finally manages to vary the design forecast. This work contributes with a modeling and simulation methodology for an EV power system using the Matlab-Simulink platform. The aim is to estimate the state of charge and, therefore, the autonomy of the electrical traction system. Both internal and external conditions in which an EV works are studied and an EV prototype is built to validate the results of the simulations. The computational model proposes the use of characteristic polynomials of the work curves of an electric motor and of the RC components of the equivalent circuit of battery. The mathematical equations that represent the forces to which the traction train is subjected are modeled and the current that is extracted from the storage system ().info:eu-repo/semantics/publishedVersio

A Modeling Framework to Assess Strategies Alignment based on Collaborative Network Emotions

[DE] The Collaborative Networks (CN) discipline has been largely studied in last decades, addressing different problems and proposing solutions for the robust establishment of collaborative processes, within the enterprises willing to collaborate. The main aim of CN research is, therefore, to generate approaches that enable creating effective relationships in the long term, to achieve stable and agile alliances. The concept of alignment among the CN partners has been considered since the beginning of CN research. Nevertheless, novel perspectives of study in CN, such as the consideration of collaborative emotional states, within the CN, have been introduced in recent years. This paper connects the research area of strategies alignment and the CN emotion models. Accordingly, a modelling framework to assess strategies alignment considering the emotional environment within the CN is proposed. The modelling framework allows representing how the enterprises emotions affect in the selection and alignment of formulated enterprises¿ strategiesAndres, B.; Ferrada, F.; Poler, R.; Camarinha-Matos, L. (2018). A Modeling Framework to Assess Strategies Alignment based on Collaborative Network Emotions. IFIP Advances in Information and Communication Technology. 534:349-361. https://doi.org/10.1007/978-3-319-99127-6_30S349361534Camarinha-Matos, L.M.: Collaborative networks in industry and the role of PRO-VE. Int. J. Prod. Manag. Eng. 2(2), 53–57 (2014)Andres, B., Poler, R.: Models, guidelines and tools for the integration of collaborative processes in non-hierarchical manufacturing networks: a review. Int. J. Comput. Integr. Manuf. 2(29), 166–201 (2016)Bititci, U., Martinez, V., Albores, P., Parung, J.: Creating and managing value in collaborative networks. Int. J. Phys. Distrib. Logist. Manag. 34(3/4), 251–268 (2004)Carbo, B.: Align the organization for improved supply chain performance. ASCET Proj. 2, 244–447 (2002)Macedo, P., Camarinha-Matos, L.: Value systems alignment analysis in collaborative networked organizations management. Appl. Sci. 7(12), 123 (2017)Andres, B., Poler, R.: A decision support system for the collaborative selection of strategies in enterprise networks. Decis. Support Syst. 91, 113–123 (2016)Andres, B., Macedo, P., Camarinha-Matos, L.M., Poler, R.: Achieving coherence between strategies and value systems in collaborative networks. In: Camarinha-Matos, L.M., Afsarmanesh, H. (eds.) PRO-VE 2014. IFIP AICT, vol. 434, pp. 261–272. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44745-1_26Ferrada, F., Camarinha-Matos, L.M.: A system dynamics and agent-based approach to model emotions in collaborative networks. In: Camarinha-Matos, L.M., Parreira-Rocha, M., Ramezani, J. (eds.) DoCEIS 2017. IFIP AICT, vol. 499, pp. 29–43. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56077-9_3Campuzano, F., Mula, J.: Supply Chain Simulation. A System Dynamics Approach for Improving Performance. Springer, London (2011). https://doi.org/10.1007/978-0-85729-719-8Camarinha-Matos, L.M., Afsarmanesh, H.: Collaborative networks: a new scientific discipline. J. Intell. Manuf. 16(4–5), 439–452 (2005)Vicsek, T.: Complexity: the bigger picture. Nature 418(6894), 131 (2002)Sterman, J., Richardson, G., Davidsen, P.: Modelling the estimation of petroleum resources in the United States. Technol. Forecast. Soc. Chang. 33(3), 219–249 (1998)Vlachos, D., Georgiadis, P., Iakovou, E.: A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains. Comput. Oper. Res. 34(2), 367–394 (2007)Campuzano-Bolarín, F., Mula, J., Peidro, D.: An extension to fuzzy estimations and system dynamics for improving supply chains. Int. J. Prod. 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Rev. 3, 281–296 (2000)Lyneisa, J.M.: System dynamics for market forecasting and structural analysis. Syst. Dyn. Rev. 16(1), 3–25 (2000)Borshchev, A., Filippov, A.: From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. In: The 22nd International Conference of the System Dynamics Society (2004)Ferrada, F.: C-EMO: A Modeling Framework for Collaborative Network Emotions Doctoral dissertation, Nova University of Lisbon, Portugal (2017). https://run.unl.pt/handle/10362/26857Scherer, K.R.: Emotions are emergent processes: they require a dynamic computational architecture. Rev. Philos. Trans. R. Soc. Biol. Sci. 364(1535), 3459–3474 (2009

Cognitive impairment induced by delta9-tetrahydrocannabinol occurs through heteromers between cannabinoid CB1 and serotonin 5-HT2A receptors

Delta-9-tetrahydrocannabinol (THC), the main psychoactive compound of marijuana, induces numerous undesirable effects, including memory impairments, anxiety, and dependence. Conversely, THC also has potentially therapeutic effects, including analgesia, muscle relaxation, and neuroprotection. However, the mechanisms that dissociate these responses are still not known. Using mice lacking the serotonin receptor 5-HT2A, we revealed that the analgesic and amnesic effects of THC are independent of each other: while amnesia induced by THC disappears in the mutant mice, THC can still promote analgesia in these animals. In subsequent molecular studies, we showed that in specific brain regions involved in memory formation, the receptors for THC and the 5-HT2A receptors work together by physically interacting with each other. Experimentally interfering with this interaction prevented the memory deficits induced by THC, but not its analgesic properties. Our results highlight a novel mechanism by which the beneficial analgesic properties of THC can be dissociated from its cognitive side effects

Four Distances between Pairs of Amino Acids Provide a Precise Description of their Interaction

The three-dimensional structures of proteins are stabilized by the interactions between amino acid residues. Here we report a method where four distances are calculated between any two side chains to provide an exact spatial definition of their bonds. The data were binned into a four-dimensional grid and compared to a random model, from which the preference for specific four-distances was calculated. A clear relation between the quality of the experimental data and the tightness of the distance distribution was observed, with crystal structure data providing far tighter distance distributions than NMR data. Since the four-distance data have higher information content than classical bond descriptions, we were able to identify many unique inter-residue features not found previously in proteins. For example, we found that the side chains of Arg, Glu, Val and Leu are not symmetrical in respect to the interactions of their head groups. The described method may be developed into a function, which computationally models accurately protein structures

On the differences in the vertical distribution of modeled aerosol optical depth over the southeastern Atlantic

The southeastern Atlantic is home to an expansive smoke aerosol plume overlying a large cloud deck for approximately a third of the year. The aerosol plume is mainly attributed to the extensive biomass burning activities that occur in southern Africa. Current Earth system models (ESMs) reveal significant differences in their estimates of regional aerosol radiative effects over this region. Such large differences partially stem from uncertainties in the vertical distribution of aerosols in the troposphere. These uncertainties translate into different aerosol optical depths (AODs) in the planetary boundary layer (PBL) and the free troposphere (FT). This study examines differences of AOD fraction in the FT and AOD differences among ESMs (WRF-CAM5, WRF-FINN, GEOS-Chem, EAM-E3SM, ALADIN, GEOS-FP, and MERRA-2) and aircraft-based measurements from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign. Models frequently define the PBL as the well-mixed surface-based layer, but this definition misses the upper parts of decoupled PBLs, in which most low-level clouds occur. To account for the presence of decoupled boundary layers in the models, the height of maximum vertical gradient of specific humidity profiles from each model is used to define PBL heights. Results indicate that the monthly mean contribution of AOD in the FT to the total-column AOD ranges from 44 % to 74 % in September 2016 and from 54 % to 71 % in August 2017 within the region bounded by 25∘ S–0∘ N–S and 15∘ W–15∘ E (excluding land) among the ESMs. ALADIN and GEOS-Chem show similar aerosol plume patterns to a derived above-cloud aerosol product from the Moderate Resolution Imaging Spectroradiometer (MODIS) during September 2016, but none of the models show a similar above-cloud plume pattern to MODIS in August 2017. Using the second-generation High Spectral Resolution Lidar (HSRL-2) to derive an aircraft-based constraint on the AOD and the fractional AOD, we found that WRF-CAM5 produces 40 % less AOD than those from the HSRL-2 measurements, but it performs well at separating AOD fraction between the FT and the PBL. AOD fractions in the FT for GEOS-Chem and EAM-E3SM are, respectively, 10 % and 15 % lower than the AOD fractions from the HSRL-2. Their similar mean AODs reflect a cancellation of high and low AOD biases. Compared with aircraft-based observations, GEOS-FP, MERRA-2, and ALADIN produce 24 %–36 % less AOD and tend to misplace more aerosols in the PBL. The models generally underestimate AODs for measured AODs that are above 0.8, indicating their limitations at reproducing high AODs. The differences in the absolute AOD, FT AOD, and the vertical apportioning of AOD in different models highlight the need to continue improving the accuracy of modeled AOD distributions. These differences affect the sign and magnitude of the net aerosol radiative forcing, especially when aerosols are in contact with clouds.</p