Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model.

This paper presents the application of remote sensing techniques, digital image analysis and Geographic Information System tools to delineate the degree of landslide hazard and risk areas in the Balik Pulau area in Penang Island, Malaysia. Its causes were analysed through various thematic attribute data layers for the study area. Firstly, landslide locations were identified in the study area from the interpretation of aerial photographs, satellite imageries, field surveys, reports and previous landslide inventories. Topographic, geologic, soil and satellite images were collected and processed using Geographic Information System and image processing tools. There are 12 landslide-inducing parameters considered for the landslide hazard analyses. These parameters are: topographic slope, topographic aspect, plan curvature, distance to drainage and distance to roads, all derived from the topographic database; geology and distance to faults, derived from the geological database; landuse/landcover, derived from Landsat satellite images; soil, derived from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value, derived from SPOT satellite images. In addition, hazard analyses were performed using landslide-occurrence factors with the aid of a statistically based frequency ratio model. Further, landslide risk analysis was carried out using hazard map and socio-economic factors using a geospatial model. This landslide risk map could be used to estimate the risk to population, property and existing infrastructure like transportation networks. Finally, to check the accuracy of the success-rate prediction, the hazard map was validated using the area under curve method. The prediction accuracy of the hazard map was 89%. Based on these results the authors conclude that frequency ratio models can be used to mitigate hazards related to landslides and can aid in land-use planning

Novel Papaverine Metal Complexes with Potential Anticancer Activities

Cancer is one of the leading causes of death worldwide. Although several potential therapeutic agents have been developed to efficiently treat cancer, some side effects can occur simultaneously. Papaverine, a non-narcotic opium alkaloid, is a potential anticancer drug that showed selective antitumor activity in various tumor cells. Recent studies have demonstrated that metal complexes improve the biological activity of the parent bioactive ligands. Based on those facts, herein we describe the synthesis of novel papaverine–vanadium(III), ruthenium(III) and gold(III) metal complexes aiming at enhancing the biological activity of papaverine drug. The structures of the synthesized complexes were characterized by various spectroscopic methods (IR, UV–Vis, NMR, TGA, XRD, SEM). The anticancer activity of synthesized metal complexes was evaluated in vitro against two types of cancer cell lines: human breast cancer MCF-7 cells and hepatocellular carcinoma HepG-2 cells. The results revealed that papaverine-Au(III) complex, among the synthesized complexes, possess potential antimicrobial and anticancer activities. Interestingly, the anticancer activity of papaverine–Au(III) complex against the examined cancer cell lines was higher than that of the papaverine alone, which indicates that Au-metal complexation improved the anticancer activity of the parent drug. Additionally, the Au complex showed anticancer activity against the breast cancer MCF-7 cells better than that of cisplatin. The biocompatibility experiments showed that Au complex is less toxic than the papaverine drug alone with IC50 ≈ 111 µg/mL. These results indicate that papaverine–Au(III) complex is a promising anticancer complex-drug which would make it a suitable candidate for further in vivo investigations.Peer Reviewe

Comparison of some parallel Krylov solvers for large scale groundwater contaminant transport simulations

Some popular iterative solvers for non-symmetric systems arising from the finite-element discretization of three-dimensional groundwater contaminant transport problem are implemented and compared on distributed memory parallel platforms. This paper attempts to determine which solvers are most suitable for the contaminant transport problem under varied conditions for large scale simulations on distributed parallel platforms. The original parallel implementation was targeted for the 1024 node Intel paragon platform using explicit message passing with the NX library. This code was then ported to SGI Power Challenge Array, Convex Exemplar, and Origin 2000 machines using an MPI implementation. The performance of these solvers is studied for increasing problem size, roughness of the coefficients, and selected problem scenarios. These conditions affect the properties of the matrix and hence the difficulty level of the solution process. Performance is analyzed in terms of convergence behavior, overall time, parallel efficiency, and scalability. The solvers that are presented are BiCGSTAB, GMRES, ORTHOMIN, and CGS. A simple diagonal preconditioner is used in this parallel implementation for all the methods. The results indicate that all methods are comparable in performance with BiCGSTAB slightly outperforming the other methods for most problems. The authors achieved very good scalability in all the methods up to 1024 processors of the Intel Paragon XPS/150. They demonstrate scalability by solving 100 time steps of a 40 million element problem in about 5 minutes using either BiCGSTAB or GMRES

Minimal-memory realization of pearl-necklace encoders of general quantum convolutional codes

Quantum convolutional codes, like their classical counterparts, promise to offer higher error correction performance than block codes of equivalent encoding complexity, and are expected to find important applications in reliable quantum communication where a continuous stream of qubits is transmitted. Grassl and Roetteler devised an algorithm to encode a quantum convolutional code with a "pearl-necklace encoder." Despite their theoretical significance as a neat way of representing quantum convolutional codes, they are not well-suited to practical realization. In fact, there is no straightforward way to implement any given pearl-necklace structure. This paper closes the gap between theoretical representation and practical implementation. In our previous work, we presented an efficient algorithm for finding a minimal-memory realization of a pearl-necklace encoder for Calderbank-Shor-Steane (CSS) convolutional codes. This work extends our previous work and presents an algorithm for turning a pearl-necklace encoder for a general (non-CSS) quantum convolutional code into a realizable quantum convolutional encoder. We show that a minimal-memory realization depends on the commutativity relations between the gate strings in the pearl-necklace encoder. We find a realization by means of a weighted graph which details the non-commutative paths through the pearl-necklace. The weight of the longest path in this graph is equal to the minimal amount of memory needed to implement the encoder. The algorithm has a polynomial-time complexity in the number of gate strings in the pearl-necklace encoder.Comment: 16 pages, 5 figures; extends paper arXiv:1004.5179v

New Triazoloquinoxaline Ligand and its Polymeric 1D Silver(I) complex Synthesis, Structure, and Antimicrobial activity

The organic ligand 4-Benzyl-1-(N,N-dimethylamino)-[1,2,4]triazolo[4,3a]quinoxaline 1 (L) and its polymeric silver(I) complex, [Ag2L(NO3)2]n (2), have been synthesized and characterized. The organic ligand 1 crystallizes in the triclinic space group P¯1. The unit cell contains two parallel-stacked molecules. The complex [Ag2L(NO3)2]n (2) crystallizes in the monoclinic space group P21/n. The structure contains two different silver(I) ions. Ag(2) is coordinated by three oxygens (involving two nitrate groups) and to a nitrogen of the triazole ring of 1. These ligands form a strongly distorted tetrahedral, nearly planar coordination sphere. Ag(1) has an approximately tetrahedral geometry. It is bonded to one oxygen of a nitrate anion and a nitrogen of two different L; this aspect giving rise to an infinite chain structure. A final bond to Ag(1) involves the carbon of a phenyl group. It is more weakly bonded to the phenyl carbons on either side of this, so that the Ag(1)-phenyl bonding has aspects of an Ag-allyl bond. Ag(1) and Ag(2) participate in bonding to a common nitrate anion and alternate, the two distinct modes of bridging between them lead to a zig-zag chain structure. In addition to spectroscopic studies, the biological activities of the ligand and of the complex were scanned over a wide range of Gram positive and Gram negative flesh- and bone-eating bacteria. The results are discussed in comparison with well-known antibiotics

On Using Machine Learning to Identify Knowledge in API Reference Documentation

Using API reference documentation like JavaDoc is an integral part of software development. Previous research introduced a grounded taxonomy that organizes API documentation knowledge in 12 types, including knowledge about the Functionality, Structure, and Quality of an API. We study how well modern text classification approaches can automatically identify documentation containing specific knowledge types. We compared conventional machine learning (k-NN and SVM) and deep learning approaches trained on manually annotated Java and .NET API documentation (n = 5,574). When classifying the knowledge types individually (i.e., multiple binary classifiers) the best AUPRC was up to 87%. The deep learning and SVM classifiers seem complementary. For four knowledge types (Concept, Control, Pattern, and Non-Information), SVM clearly outperforms deep learning which, on the other hand, is more accurate for identifying the remaining types. When considering multiple knowledge types at once (i.e., multi-label classification) deep learning outperforms na\"ive baselines and traditional machine learning achieving a MacroAUC up to 79%. We also compared classifiers using embeddings pre-trained on generic text corpora and StackOverflow but did not observe significant improvements. Finally, to assess the generalizability of the classifiers, we re-tested them on a different, unseen Python documentation dataset. Classifiers for Functionality, Concept, Purpose, Pattern, and Directive seem to generalize from Java and .NET to Python documentation. The accuracy related to the remaining types seems API-specific. We discuss our results and how they inform the development of tools for supporting developers sharing and accessing API knowledge. Published article: https://doi.org/10.1145/3338906.333894

Elasticité et porosité de l'os cortical humain : modèles et expériences

A l'échelle millimétrique, l'os cortical est vu comme une matrice minéralisée traversée de pores (canaux de Havers). Nous avons mesuré la porosité et l'élasticité de 21 échantillons (10 donneurs) et nous montrons que la rigidité de la matrice a une influence très faible sur l'élasticité apparente. Ces données permettent pour la première fois une analyse critique des modèles de changement d'échelle (homogénéisation asymptotique, Mori-Tanaka, bornes, etc.). Nous établissons que la densité apparente à l'échelle millimétrique peut être reliée au tenseur d'élasticité anisotrope grâce aux modèles