Literatura académica sobre el tema "Graphe massifs"
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Artículos de revistas sobre el tema "Graphe massifs"
Beltek, M. y O. Frolov. "Determination of the influence of the degree of fracturing of the rock mass on the index of reduction of its strength". Collection of Research Papers of the National Mining University 74 (septiembre de 2023): 7–18. http://dx.doi.org/10.33271/crpnmu/74.007.
Texto completoDmitrienko, Vladimir, Nadejda Dmitrienko y Аleksandr Bogomazov. "Impact of “wet conservation” of mining enterprises on constructing buildings of lightweight materials". E3S Web of Conferences 284 (2021): 05013. http://dx.doi.org/10.1051/e3sconf/202128405013.
Texto completoDongak, Dzhamil Aiyr-Sanaaevich, Artysh Valerievich Mongush, Chinchi Buyanovna Mongush y Shydar Orlanovich Chuldum. "A distance study of seasonal dynamics of the vegetation index (NVDI) of the Mongun-Taiga massif vegetation cover". Samara Journal of Science 11, n.º 4 (1 de diciembre de 2022): 22–29. http://dx.doi.org/10.55355/snv2022114103.
Texto completoTer-Martirosyan, Zaven G., Armen Z. Ter-Martirosyan y Yuliya V. Vanina. "Long-term settlement and bearing capacity of foundations adjacent to vertical excavation at various parameters of soil viscosity". Vestnik MGSU, n.º 12 (diciembre de 2022): 1664–76. http://dx.doi.org/10.22227/1997-0935.2022.12.1664-1676.
Texto completoKuzmenko, Eduard, Sergiy Bagriy, Inna Artym y Volodymyr Artym. "GEODYNAMICS". GEODYNAMICS 2(33)2022, n.º 2(33) (2022): 64–74. http://dx.doi.org/10.23939/jgd2022.02.065.
Texto completoSokornov, Anton, Aleksandr Kon'kov, Anatoliy Novikov y Andrey Benin. "Factors Affecting Additional Pressure Distribution from Ground Construction on Subway Tunnels". Proceedings of Petersburg Transport University 19, n.º 2 (22 de junio de 2022): 367–77. http://dx.doi.org/10.20295/1815-588x-2022-19-2-367-377.
Texto completoAlkhdour, Ahmad, Anatolii Radkevych, Oleksii Tiutkin y Nataliia Bondarenko. "Prediction of the stress-strain state of circular workings in a layered massif by scaling". E3S Web of Conferences 168 (2020): 00020. http://dx.doi.org/10.1051/e3sconf/202016800020.
Texto completoVidal, Muriel, Daniel Goujet, Philippe Janvier y Hubert Lardeux. "Micro-restes de Vertébrés du Calcaire de La Grange, Dévonien inférieur (Emsien) du Sud-Est du Massif Armoricain [Fish remains from the La Grange Limestone, Lower Devonian (Emsian), South-Eastern Armorican Massif". Neues Jahrbuch für Geologie und Paläontologie - Abhandlungen 194, n.º 2-3 (21 de diciembre de 1994): 321–41. http://dx.doi.org/10.1127/njgpa/194/1994/321.
Texto completoAtrassi, Fatima E. L., Fabrice Brunet, Mohamed Bouybaouene, Christian Chopin y Gilles Chazot. "Melting textures and microdiamonds preserved in graphite pseudomorphs from the Beni Bousera peridotite massif, Morocco". European Journal of Mineralogy 23, n.º 2 (3 de mayo de 2011): 157–68. http://dx.doi.org/10.1127/0935-1221/2011/0023-2094.
Texto completoSubbotin, Ignat. "Discrete and continuous models in calculating the bearing capacity of soil massifs reinforced with geosynthetics". Construction and Architecture 8, n.º 4 (15 de octubre de 2020): 28–36. http://dx.doi.org/10.29039/2308-0191-2020-8-4-28-36.
Texto completoTesis sobre el tema "Graphe massifs"
Nabti, Chems Eddine. "Subgraph Isomorphism Search In Massive Graph Data". Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1293/document.
Texto completoQuerying graph data is a fundamental problem that witnesses an increasing interest especially for massive structured data where graphs come as a promising alternative to relational databases for big data modeling. However, querying graph data is different and more complex than querying relational table-based data. The main task involved in querying graph data is subgraph isomorphism search which is an NP-complete problem. Subgraph isomorphism search, is an important problem which is involved in various domains such as pattern recognition, social network analysis, biology, etc. It consists to enumerate the subgraphs of a data graph that match a query graph. The most known solutions of this problem are backtracking-based. They explore a large search space which results in a high computational cost when we deal with massive graph data. To reduce time and memory space complexity of subgraph isomorphism search. We propose to use compressed graphs. In our approach, subgraph isomorphism search is achieved on compressed representations of graphs without decompressing them. Graph compression is performed by grouping vertices into super vertices. This concept is known, in graph theory, as modular decomposition. It is used to generate a tree representation of a graph that highlights groups of vertices that have the same neighbors. With this compression we obtain a substantial reduction of the search space and consequently a significant saving in the processing time. We also propose a novel encoding of vertices that simplifies the filtering of the search space. This new mechanism is called compact neighborhood Index (CNI). A CNI distills all the information around a vertex in a single integer. This simple neighborhood encoding reduces the time complexity of vertex filtering from cubic to quadratic which is considerable for big graphs. We propose also an iterative local global filtering algorithm that relies on the characteristics of CNIs to ensure a global pruning of the search space.We evaluated our approaches on several real-word datasets and compared them with the state of the art algorithms
Bletterer, Arnaud. "Une approche basée graphes pour la modélisation et le traitement de nuages de points massifs issus d’acquisitions de LiDARs terrestres". Thesis, Université Côte d'Azur (ComUE), 2018. http://www.theses.fr/2018AZUR4218/document.
Texto completoWith the evolution of 3D acquisition devices, point clouds have now become an essential representation of digitized scenes. Recent systems are able to capture several hundreds of millions of points in a single acquisition. As multiple acquisitions are necessary to capture the geometry of large-scale scenes, a historical site for example, we obtain massive point clouds, i.e., composed of billions of points. In this thesis, we are interested in the structuration and manipulation of point clouds from acquisitions generated by terrestrial LiDARs. From the structure of each acquisition, graphs, each representing the local connectivity of the digitized surface, are constructed. Created graphs are then linked together to obtain a global representation of the captured surface. We show that this structure is particularly adapted to the manipulation of the underlying surface of massive point clouds, even on computers with limited memory. Especially, we show that this structure allow to deal with two problems specific to that kind of data. A first one linked to the resampling of point clouds, by generating distributions of good quality in terms of blue noise thanks to a Poisson disk sampling algorithm. Another one connected to the construction of centroidal Voronoi tessellations, allowing to enhance the quality of generated distributions and to reconstruct triangular meshes
Baudin, Alexis. "Cliques statiques et temporelles : algorithmes d'énumération et de détection de communautés". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS609.
Texto completoGraphs are mathematical objects used to model interactions or connections between entities of various types. A graph can represent, for example, a social network that connects users to each other, a transport network like the metro where stations are connected to each other, or a brain with the billions of interacting neurons it contains. In recent years, the dynamic nature of these structures has been highlighted, as well as the importance of taking into account the temporal evolution of these networks to understand their functioning. While many concepts and algorithms have been developed on graphs to describe static network structures, much remains to be done to formalize and develop relevant algorithms to describe the dynamics of real networks. This thesis aims to better understand how massive graphs are structured in the real world, and to develop tools to extend our understanding to structures that evolve over time. It has been shown that these graphs have particular properties, which distinguish them from theoretical or randomly drawn graphs. Exploiting these properties then enables the design of algorithms to solve certain difficult problems much more quickly on these instances than in the general case. My PhD thesis focuses on cliques, which are groups of elements that are all connected to each other. We study the enumeration of cliques in static and temporal graphs and the detection of communities they enable. The communities of a graph are sets of vertices such that, within a community, the vertices interact strongly with each other, and little with the rest of the graph. Their study helps to understand the structural and functional properties of networks. We are evaluating our algorithms on massive real-world graphs, opening up new perspectives for understanding interactions within these networks. We first work on graphs, without taking into account the temporal component of interactions. We begin by using the clique percolation method of community detection, highlighting its limitations in memory, which prevent it from being applied to graphs that are too massive. By introducing an approximate problem-solving algorithm, we overcome this limitation. Next, we improve the enumeration of maximal cliques in the case of bipartite graphs. These correspond to interactions between groups of vertices of different types, e.g. links between people and viewed content, participation in events, etc. Next, we consider interactions that take place over time, using the link stream formalism. We seek to extend the algorithms presented in the first part, to exploit their advantages in the study of temporal interactions. We provide a new algorithm for enumerating maximal cliques in link streams, which is much more efficient than the state-of-the-art on massive datasets. Finally, we focus on communities in link streams by clique percolation, developing an extension of the method used on graphs. The results show a significant improvement over the state of the art, and we analyze the communities obtained to provide relevant information on the organization of temporal interactions in link streams. My PhD work has provided new insights into the study of massive real-world networks. This shows the importance of exploring the potential of graphs in a real-world context, and could contribute to the emergence of innovative solutions for the complex challenges of our modern society
Hinge, Antoine. "Dessin de graphe distribué par modèle de force : application au Big Data". Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0092/document.
Texto completoGraphs, usually used to model relations between entities, are continually growing mainly because of the internet (social networks for example). Graph visualization (also called drawing) is a fast way of collecting data about a graph. Internet graphs are often stored in a distributed manner, split between several machines interconnected. This thesis aims to develop drawing algorithms to draw very large graphs using the MapReduce paradigm, used for cluster computing. Among graph drawing algorithms, those which rely on a physical model to compute the node placement are generally considered to draw graphs well regardless of the type of graph. We developped two force-directed graph drawing algorithms in the MapReduce paradigm. GDAD, the fist distributed force-directed graph drawing algorithm ever, uses pivots to simplify computations of node interactions. MuGDAD, following GDAD, uses a recursive simplification to draw the original graph, keeping the pivots. We compare these two algorithms with the state of the art to assess their performances
Hernández, Rivas Cecilia Paola. "Managing massive graphs". Tesis, Universidad de Chile, 2014. http://repositorio.uchile.cl/handle/2250/131839.
Texto completoCon la popularidad de la Web y, mas recientemente, el amplio uso de las redes sociales, la necesidad de procesar y encontrar información en grafos muy grandes impone varios desafíos: Cómo procesar grafos muy grandes e cientemente, dado que probablemente son muy grandes para la memoria disponible, o incluso si la memoria es su ciente, realizar un paso sobre el grafo es todavía caro computacionalmente? Cómo almacenar esos grafos e cientemente, para ser archivados, o para ejecutar algoritmos de grafos? Cómo descubrir información relevante tal como componentes densos, comunidades, u otras estructuras? Se han propuesto tres enfoques para manejar grafos grandes. El primero es usar formatos de grafos comprimidos que permiten consultas de navegación básicas directamentee sobre la estructura comprimida, sin la necesidad de descompresión. Esto permite simular cualquier algoritmo de grafo en memoria principal usando mucho menos espacio que la representación plana. Una segunda línea de investigación se focaliza en usar modelos de stream o semi- stream de datos de manera de procesar secuencialmente, idealmente en un paso sobre el disco, usando una cantidad limitada de memoria principal. La tercera línea es el uso de sistemas distribuidos y paralelos donde la memoria es agregada sobre múltiples unidades de procesamiento para procesar el grafo en paralelo. En esta tesis presentamos varios enfoques para manejar grafos grandes (con arcos sin etiquetas) considerando los tres enfoques. Primero, buscamos por patrones que aparecen en grafos de la Web y redes sociales los que podemos representar en forma compacta, en particular mostramos como generalizar algoritmos para encontrar cliques o bicliques para encontrar sub-estructuras densas que comprimen ambas. Segundo, basado en estos subgrafos densos, proponemos esquemas comprimidos que soportan consultas de vecinos directos y reversos, así como otras consultas mas complejas sobre subgrafos densos. Algunas de las contribuciones combinan técnicas del estado del arte mientras otras incluyen representaciones comprimidas novedosas basadas en estructuras de datos compactas. Encontrar subgrafos densos es una tarea que consume tiempo y espacio, así que proporcionamos algoritmos de streaming and algoritmos de memoria externa para descubrir subgrafos densos, asi como también algoritmos distribuidos para construir las estructuras básicas que usamos para las representaciones comprimidas.
Gillet, Noel. "Optimisation de requêtes sur des données massives dans un environnement distribué". Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0553/document.
Texto completoDistributed data store are massively used in the actual context of Big Data. In addition to provide data management features, those systems have to deal with an increasing amount of queries sent by distant users in order to process data mining or data visualization operations. One of the main challenge is to evenly distribute the workload of queries between the nodes which compose these system in order to minimize the treatment time. In this thesis, we tackle the problem of query allocation in a distributed environment. We consider that data are replicated and a query can be handle only by a node storing the concerning data. First, near-optimal algorithmic proposals are given when communications between nodes are asynchronous. We also consider that some nodes can be faulty. Second, we study more deeply the impact of data replication on the query treatement. Particularly, we present an algorithm which manage the data replication based on the demand on these data. Combined with our allocation algorithm, we guaranty a near-optimal allocation. Finally, we focus on the impact of data replication when queries are received as a stream by the system. We make an experimental evaluation using the distributed database Apache Cassandra. The experiments confirm the interest of our algorithmic proposals to improve the query treatement compared to the native allocation scheme in Cassandra
Wang, Guan. "STREAMING HYPERGRAPH PARTITION FOR MASSIVE GRAPHS". Kent State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=kent1385097649.
Texto completoHabi, Abdelmalek. "Search and Aggregation in Big Graphs". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1259/document.
Texto completoRecent years have witnessed a growing renewed interest in the use of graphs as a reliable means for representing and modeling data. Thereby, graphs enable to ensure efficiency in various fields of computer science, especially for massive data where graphs arise as a promising alternative to relational databases for big data modeling. In this regard, querying data graph proves to be a crucial task to explore the knowledge in these datasets. In this dissertation, we investigate two main problems. In the first part we address the problem of detecting patterns in larger graphs, called the top-k graph pattern matching problem. We introduce a new graph pattern matching model named Relaxed Graph Simulation (RGS), to identify significant matches and to avoid the empty-set answer problem. We formalize and study the top-k matching problem based on two classes of functions, relevance and diversity, for ranking the matches according to the RGS model. We also consider the diversified top-k matching problem, and we propose a diversification function to balance relevance and diversity. Moreover, we provide efficient algorithms based on optimization strategies to compute the top-k and the diversified top-k matches according to the proposed model. The proposed approach is optimal in terms of search time and flexible in terms of applicability. The analyze of the time complexity of the proposed algorithms and the extensive experiments on real-life datasets demonstrate both the effectiveness and the efficiency of these approaches. In the second part, we tackle the problem of graph querying using aggregated search paradigm. We consider this problem for particular types of graphs that are trees, and we deal with the query processing in XML documents. Firstly, we give the motivation behind the use of such a paradigm, and we explain the potential benefits compared to traditional querying approaches. Furthermore, we propose a new method for aggregated tree search, based on approximate tree matching algorithm on several tree fragments, that aims to build, the extent possible, a coherent and complete answer by combining several results. The proposed solutions are shown to be efficient in terms of relevance and quality on different real-life datasets
Jiang, Jiaxin. "Efficient frameworks for keyword search on massive graphs". HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/806.
Texto completoLu, Linyuan Lincoln. "Probabilistic methods in massive graphs and internet computing /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2002. http://wwwlib.umi.com/cr/ucsd/fullcit?p3061653.
Texto completoLibros sobre el tema "Graphe massifs"
Bader, David A. Massive Graph Analytics. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003033707.
Texto completoCaproni, Giorgio. Il Girasole. Editado por Giada Baragli. Florence: Firenze University Press, 2017. http://dx.doi.org/10.36253/978-88-6453-495-4.
Texto completoBader, David A. Massive Graph Analytics. Taylor & Francis Group, 2022.
Buscar texto completoBader, David A. Massive Graph Analytics. CRC Press LLC, 2022.
Buscar texto completoBader, David A. Massive Graph Analytics. Taylor & Francis Group, 2022.
Buscar texto completoMassive Graph Analytics. Taylor & Francis Group, 2022.
Buscar texto completoBader, David A. Massive Graph Analytics. Taylor & Francis Group, 2022.
Buscar texto completoStaff, Smart Books Inc y Eric Char. Massive Graph Paper Book. Independently Published, 2018.
Buscar texto completoMassive - Ragnarok. Dark Horse Comics, 2015.
Buscar texto completoVivian, Aaron. Massive Graph Book: 500 Pages of Assorted Graph Paper for Drawing. Independently Published, 2021.
Buscar texto completoCapítulos de libros sobre el tema "Graphe massifs"
Slota, George M., Sivasankaran Rajamanickam y Kamesh Madduri. "Multicore Algorithms for Graph Connectivity Problems". En Massive Graph Analytics, 63–84. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003033707-5.
Texto completoFender, Alex, Brad Rees y Joe Eaton. "RAPIDS cuGraph". En Massive Graph Analytics, 483–93. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003033707-22.
Texto completoPenschuck, Manuel, Ulrik Brandes, Michael Hamann, Sebastian Lamm, Ulrich Meyer, Ilya Safro, Peter Sanders y Christian Schulz. "Recent Advances in Scalable Network Generation1". En Massive Graph Analytics, 333–76. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003033707-16.
Texto completoErb, Stephan, Moritz Kobitzsch, Lawrence Mandow y Peter Sanders. "Multi-Objective Shortest Paths". En Massive Graph Analytics, 35–59. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003033707-3.
Texto completoHasenplaugh, William, Tim Kaler, Tao B. Schardl y Charles E. Leiserson. "Ordering Heuristics for Parallel Graph Coloring". En Massive Graph Analytics, 193–221. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003033707-11.
Texto completoAngriman, Eugenio, Patrick Bisenius, Elisabetta Bergamini y Henning Meyerhenke. "Computing Top-k Closeness Centrality in Fully Dynamic Graphs". En Massive Graph Analytics, 161–92. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003033707-10.
Texto completoSrivastava, Ajitesh, Charalampos Chelmis y Viktor K. Prasanna. "Computational Models for Cascades in Massive Graphs". En Massive Graph Analytics, 377–95. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003033707-17.
Texto completoDu, Zhihui, Oliver Alvarado Rodriguez, Joseph Patchett y David A. Bader. "Interactive Graph Analytics at Scale in Arkouda". En Massive Graph Analytics, 549–89. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003033707-26.
Texto completoSanders, Geoffrey, Roger Pearce, Benjamin W. Priest y Trevor Steil. "Massive-Scale Distributed Triangle Computation and Applications". En Massive Graph Analytics, 127–58. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003033707-8.
Texto completoLeiserson, Charles E. y Tao B. Schardl. "A Work-Efficient Parallel Breadth-First Search Algorithm (or How To Cope With the Nondeterminism of Reducers)". En Massive Graph Analytics, 3–33. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003033707-2.
Texto completoActas de conferencias sobre el tema "Graphe massifs"
Aiello, William, Fan Chung y Linyuan Lu. "A random graph model for massive graphs". En the thirty-second annual ACM symposium. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/335305.335326.
Texto completoGao, Jian, Jiejiang Chen, Minghao Yin, Rong Chen y Yiyuan Wang. "An Exact Algorithm for Maximum k-Plexes in Massive Graphs". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/201.
Texto completoJiang, Hua, Dongming Zhu, Zhichao Xie, Shaowen Yao y Zhang-Hua Fu. "A New Upper Bound Based on Vertex Partitioning for the Maximum K-plex Problem". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/233.
Texto completoHu, Xiaocheng, Yufei Tao y Chin-Wan Chung. "Massive graph triangulation". En the 2013 international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2463676.2463704.
Texto completoWang, Zhengren, Yi Zhou, Chunyu Luo y Mingyu Xiao. "A Fast Maximum k-Plex Algorithm Parameterized by the Degeneracy Gap". En Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/627.
Texto completoEberhart, Aaron, Peter Haase y Wolfgang Schell. "metaphactory for Massive Graphs". En ICPE '23: ACM/SPEC International Conference on Performance Engineering. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3578245.3585330.
Texto completoKing, Valerie, Alex Thomo y Quinton Yong. "Computing (1+epsilon)-Approximate Degeneracy in Sublinear Time". En Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/240.
Texto completoWang, Yiyuan, Shaowei Cai, Jiejiang Chen y Minghao Yin. "A Fast Local Search Algorithm for Minimum Weight Dominating Set Problem on Massive Graphs". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/210.
Texto completoAiello, W., Fan Chung y Linyuan Lu. "Random evolution in massive graphs". En Proceedings 42nd IEEE Symposium on Foundations of Computer Science. IEEE, 2001. http://dx.doi.org/10.1109/sfcs.2001.959927.
Texto completoTabassum, Shazia y João Gama. "Sampling massive streaming call graphs". En SAC 2016: Symposium on Applied Computing. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2851613.2851654.
Texto completoInformes sobre el tema "Graphe massifs"
Wylie, Brian Neil y Kenneth D. Moreland. Massive graph visualization : LDRD final report. Office of Scientific and Technical Information (OSTI), octubre de 2007. http://dx.doi.org/10.2172/921736.
Texto completoMarcus, L. F. y P. Lampietti. Interactive graphic analysis and sequence comparison of host rocks containing stratiform volcanogenic massive sulphide deposits. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128083.
Texto completoKaffenberger, Michelle, Lant Pritchett y Martina Viarengo. Towards a Right to Learn: Concepts and Measurement of Global Education Poverty. Research on Improving Systems of Education (RISE), diciembre de 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/085.
Texto completoPacheco-Ojeda, Luis, Carolina Sáenz-Gómez, Stalin Cañizares-Quisiguiña, Tatiana Borja-Herrera, Juan Carlos Vallejo-Garzón y Sergio Poveda. Function Sparing Conservative Approach of a Low-Grade Chondrosarcoma of the Larynx: Case Report and Literature Review. Science Repository, marzo de 2024. http://dx.doi.org/10.31487/j.scr.2024.01.04.
Texto completoBerkhout, Emilie, Goldy Dharmawan, Amanda Beatty, Daniel Suryadarma y Menno Pradhan. Who Benefits and Loses from Large Changes to Student Composition? Assessing Impacts of Lowering School Admissions Standards in Indonesia. Research on Improving Systems of Education (RISE), abril de 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/094.
Texto completoBerkhout, Emilie, Goldy Dharmawan, Amanda Beatty, Daniel Suryadarma y Menno Pradhan. Who Benefits and Loses from Large Changes to Student Composition? Assessing Impacts of Lowering School Admissions Standards in Indonesia. Research on Improving Systems of Education (RISE), abril de 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/094.
Texto completoGadd, M. G., J. M. Peter y D. Layton-Matthews. Genesis of hyper-enriched black shale Ni-Mo-Zn-Pt-Pd-Re mineralization in the northern Canadian Cordillera. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/328013.
Texto completoMawassi, Munir y Valerian V. Dolja. Role of the viral AlkB homologs in RNA repair. United States Department of Agriculture, junio de 2014. http://dx.doi.org/10.32747/2014.7594396.bard.
Texto completoFontecave, Marc y Candel Sébastien. Quelles perspectives énergétiques pour la biomasse ? Académie des sciences, enero de 2024. http://dx.doi.org/10.62686/1.
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