L'élision du /d/ intervocalique en espagnol soutenu à Grenade:facteurs linguistiques

En este artículo se analiza el estado en que se encuentra el proceso de elisión de /d/ intervocálica en un una comunidad de habla andaluza (Granada, España). Se intenta dilucidar si la pérdida de la sonora dental sigue unas pautas de funcionamiento similares a las registradas en la antigüedad; es decir, si está controlada por factores de carácter morfológico y léxico. Asimismo, nuestros datos apuntan a dos formas diferentes de propagarse el proceso: de forma regular, en los morfemas de las palabras, y según la teoría de la “difusión léxica”, cuando el debilitamiento tiene lugar en los lexemas. El análisis arroja unos índices de elisión moderados (23,1%), similares a los registrados en otras comunidades andaluzas, pero que se sitúan muy por encima de lo observado en el resto del mundo hispánico

Distance Range Queries in SpatialHadoop

Efficient processing of Distance Range Queries (DRQs) is of great importance in spatial databases due to the wide area of applications. This type of spatial query is characterized by a distance range over one or two datasets. The most representative and known DRQs are the ε Distance Range Query (εDRQ) and the ε Distance Range Join Query (εDRJQ). Given the increasing volume of spatial data, it is difficult to perform a DRQ on a centralized machine efficiently. Moreover, the εDRJQ is an expensive spatial operation, since it can be considered a combination of the εDR and the spatial join queries. For this reason, this paper addresses the problem of computing DRQs on big spatial datasets in SpatialHadoop, an extension of Hadoop that supports spatial operations efficiently, and proposes new algorithms in SpatialHadoop to perform efficient parallel DRQs on large-scale spatial datasets. We have evaluated the performance of the proposed algorithms in several situations with big synthetic and real-world datasets. The experiments have demonstrated the efficiency and scalability of our proposal

Enhancing SpatialHadoop with Closest Pair Queries

Given two datasets P and Q, the K Closest Pair Query (KCPQ) finds the K closest pairs of objects from P ×Q. It is an operation widely adopted by many spatial and GIS applications. As a combination of the K Nearest Neighbor (KNN) and the spatial join queries, KCPQ is an expensive operation. Given the increasing volume of spatial data, it is difficult to perform a KCPQ on a centralized machine efficiently. For this reason, this paper addresses the problem of computing the KCPQ on big spatial datasets in SpatialHadoop, an extension of Hadoop that supports spatial operations efficiently, and proposes a novel algorithm in SpatialHadoop to perform efficient parallel KCPQ on large-scale spatial datasets. We have evaluated the performance of the algorithm in several situations with big synthetic and real-world datasets. The experiments have demonstrated the efficiency and scalability of our proposal

Efficient Large-scale Distance-Based Join Queries in SpatialHadoop

Efficient processing of Distance-Based Join Queries (DBJQs) in spatial databases is of paramount importance in many application domains. The most representative and known DBJQs are the K Closest Pairs Query (KCPQ) and the ε Distance Join Query (εDJQ). These types of join queries are characterized by a number of desired pairs (K) or a distance threshold (ε) between the components of the pairs in the final result, over two spatial datasets. Both are expensive operations, since two spatial datasets are combined with additional constraints. Given the increasing volume of spatial data originating from multiple sources and stored in distributed servers, it is not always efficient to perform DBJQs on a centralized server. For this reason, this paper addresses the problem of computing DBJQs on big spatial datasets in SpatialHadoop, an extension of Hadoop that supports efficient processing of spatial queries in a cloud-based setting. We propose novel algorithms, based on plane-sweep, to perform efficient parallel DBJQs on large-scale spatial datasets in Spatial Hadoop. We evaluate the performance of the proposed algorithms in several situations with large real-world as well as synthetic datasets. The experiments demonstrate the efficiency and scalability of our proposed methodologies

Single-Ion Magnet and Photoluminescence Properties of Lanthanide(III) Coordination Polymers Based on Pyrimidine-4,6-Dicarboxylate

Herein, we report the magnetic and photoluminescence characterization of coordination polymers (CP) built from the combination of lanthanide(III) ions, pyrimidine-4,6-dicarboxylate (pmdc) ligand and a co-ligand with formula {[Dy(µ-pmdc)(µ-ox)0.5(H2O)3 ]·2H2O}n (1-Dy), {[Dy(µ3 - pmdc)(µ-ox)0.5(H2O)2 ] ~2.33H2O}n (2-Dy), {[Dy2 (µ3 -pmdc)(µ4 -pmdc)(µ-ox)(H2O)3 ]·5H2O}n (3-Dy), {[Ln(µ3 -pmdc)(µ-ox)0.5(H2O)2 ]·H2O}n (where Ln(III) = Nd (4-Nd), Sm (4-Sm), Eu (4-Eu) and Dy (4-Dy)) and {[Dy(µ4 -pmdc)(NO3 )(H2O)]·H2O}n (5-Dy). It must be noted the presence of oxalate anion acting as ditopic co-ligand in compounds 1-Dy, 2-Dy, 3-Dy and 4-Ln, whereas in 5-Dy the nitrate anion plays the role of terminal co-ligand. Direct current measurements carried out for the dysprosium-based CPs reveal almost negligible interactions between Dy3+ ions within the crystal structure, which is confirmed by computed values of the exchange parameters J. In addition, alternating current measurements show field-induced single-molecule magnet (SMM) behavior in compounds 1-Dy, 2-Dy, 4-Dy and 5-Dy, whereas slight-frequency dependence is also observed in 3-Dy. Solid state emission spectra performed at room temperature for those compounds emitting in visible region confirm the occurrence of significant ligand-to-lanthanide charge transfer in view of the strong characteristic emissions for all lanthanide ions. Emission decay curves were also recorded to estimate the emission lifetimes for the reported compounds, in addition to the absolute quantum yields. Among them, the high quantum yield of 25.0% measured for 4-Eu is to be highlighted as a representative example of the good emissive properties of the materials.Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE) (PGC2018-102052-A-C22, PGC2018-102052-B-C21, PID2019-108028GBC21)Junta de Andalucía (FQM-394)Gobierno Vasco/Eusko Jaurlaritza (IT1005-16, IT1291-19)University of the Basque Country (UPV/EHU) (GIU 17/13, GIU17/50

A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitals

This paper proposes a Markov chain model to describe the spread of a single bacterial species in a hospital ward where patients may be free of bacteria or may carry bacterial strains that are either sensitive or resistant to antimicrobial agents. The aim is to determine the probability law of the exact reproduction number Rexact,0 which is here defined as the random number of secondary infections generated by those patients who are accommodated in a predetermined bed before a patient who is free of bacteria is accommodated in this bed for the first time. Specifically, we decompose the exact reproduction number Rexact,0 into two contributions allowing us to distinguish between infections due to the sensitive and the resistant bacterial strains. Our methodology is mainly based on structured Markov chains and the use of related matrix-analytic methods.Depto. de Estadística e Investigación OperativaFac. de Ciencias MatemáticasFALSEMinisterio de Ciencia e InnovaciónFundação para a Ciência e a Tecnologia (Portugal)unpu

Improving Distance-Join Query Processing with Voronoi-Diagram based Partitioning in SpatialHadoop

SpatialHadoop is an extended MapReduce framework supporting global indexing techniques that partition spatial datasets across several machines and improve spatial query processing performance compared to traditional Hadoop systems. SpatialHadoop supports several spatial operations (e.g., Nearest Neighbor search, range query, spatial intersection join, etc.) and seven spatial partitioning techniques (Grid, Quadtree, STR, STR+, -d tree, Z-curve and Hilbert-curve). Distance-Join Queries (DJQs), like the Nearest Neighbors Join Query (NNJQ) and Closest Pairs Query (CPQ), are common operations used in numerous spatial applications. DJQs are costly operations, since they combine spatial joins with distance-based search. Data partitioning improves the management of large datasets and speeds up query performance. Therefore, performing DJQs efficiently with new partitioning methods in SpatialHadoop is a challenging task. In this paper, a new data partitioning technique based on Voronoi-Diagrams is designed and implemented in SpatialHadoop. Moreover, improved NNJQ and CPQ MapReduce algorithms, using the new partitioning mechanism, are also designed and developed for SpatialHadoop. Finally, the results of an extensive set of experiments with real-world datasets are presented, demonstrating that the new partitioning technique and the improved DJQ MapReduce algorithms are efficient, scalable and robust in SpatialHadoop

A Lamellar Zn-Based Coordination Polymer Showing Increasing Photoluminescence upon Dehydration

The present study reports on a 2D lamellar coordination polymer (CP) of {[Zn(µ3-pmdc)(H2O)]·H2O}n formula (pmdc = pyrimidine-4,6-dicarboxylate). This CP is synthesized under an appropriate acid-base reaction between the gently mortared reagents in the solid state through a solvent-free procedure that avoids the presence of concomitant byproducts. The X-ray crystal structure reveals the occurrence of Zn2 entities connected through carboxylate groups of pmdc, which behave as triconnected nodes, giving rise to six-membered ring-based layers that are piled up through hydrogen bonding interactions. In addition to a routine physico-chemical characterization, the thermal evolution of the compound has been studied by combining thermogravimetric and thermodiffractometric data. The photoluminescence properties are characterized in the solid state and the processes governing the spectra are described using time-dependent density-functional theory (TD-DFT) with two different approaches employing different program packages. The emissive capacity of the material is further analyzed according to the dehydration and decreasing temperature of the polycrystalline sampleThis research was funded by Gobierno Vasco/Eusko Jaurlaritza (IT1755-22, IT1722-22 and IT1500-22) and Junta de Andalucía (B-FQM-734-UGR20, ProyExcel_00386 and FQM-394)

A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitals

Ordinary differential equation (ODE) models used in mathematical epidemiology assume explicitly or implicitly large populations. For the study of infections in a hospital this is an extremely restrictive assumption as typically a hospital ward has a few dozen, or even fewer, patients. This work reframes a well-known model used in the study of the spread of antibiotic-resistant bacteria in hospitals, to consider the pathogen transmission dynamics in small populations. In this vein, this paper proposes a Markov chain model to describe the spread of a single bacterial species in a hospital ward where patients may be free of bacteria or may carry bacterial strains that are either sensitive or resistant to antimicrobial agents. We determine the probability law of the \emph{exact} reproduction number ${\cal R}_{exact,0}$, which is here defined as the random number of secondary infections generated by those patients who are accommodated in a predetermined bed before a patient who is free of bacteria is accommodated in this bed for the first time. Specifically, we decompose the exact reproduction number ${\cal R}_{exact,0}$ into two contributions allowing us to distinguish between infections due to the sensitive and the resistant bacterial strains. Our methodology is mainly based on structured Markov chains and the use of related matrix-analytic methods. This guarantees the compatibility of the new, finite-population model, with large population models present in the literature and takes full advantage, in its mathematical analysis, of the intrinsic stochasticity.Comment: 30 pages, 9 figure

Efficient Distance Join Query Processing in Distributed Spatial Data Management Systems

Due to the ubiquitous use of spatial data applications and the large amounts of such data these applications use, the processing of large-scale distance joins in distributed systems is becoming increasingly popular. Distance Join Queries (DJQs) are important and frequently used operations in numerous applications, including data mining, multimedia and spatial databases. DJQs (e.g., k Nearest Neighbor Join Query, k Closest Pair Query, ε Distance Join Query, etc.) are costly operations, since they involve both the join and distance-based search, and performing DJQs efficiently is a challenging task. Recent Big Data developments have motivated the emergence of novel technologies for distributed processing of large-scale spatial data in clusters of computers, leading to Distributed Spatial Data Management Systems (DSDMSs). Distributed cluster-based computing systems can be classified as Hadoop-based or Spark-based systems. Based on this classification, in this paper, we compare two of the most recent and leading DSDMSs, SpatialHadoop and LocationSpark, by evaluating the performance of several existing and newly proposed parallel and distributed DJQ algorithms under various settings with large spatial real-world datasets. A general conclusion arising from the execution of the distributed DJQ algorithms studied is that, while SpatialHadoop is a robust and efficient system when large spatial datasets are joined (since it is built on top of the mature Hadoop platform), LocationSpark is the clear winner in total execution time efficiency when medium spatial datasets are combined (due to in-memory processing provided by Spark). However, LocationSpark requires higher memory allocation when large spatial datasets are involved in DJQs (even more so when k and ε are large). Finally, this detailed performance study has demonstrated that the new distributed DJQ algorithms we have proposed are efficient, robust and scalable with respect to different parameters, such as dataset sizes, k, ε and number of computing nodes