La morfología de las urban village en la era de la datificación. El ejemplo de Xiasha Village the Shenzhen

Actualmente, los procesos acelerados de urbanización y ocupación del suelo se han convertido en un fenómeno mundial. El crecimiento de las ciudades ha promovido el progreso de la sociedad, pero también ha producido numerosos problemas. En China, la revitalización de las aldeas urbanas (urban village) consecuencia de los procesos de urbanización necesitan encontrar soluciones urgentemente. En la era de Internet, la revolución de los datos nos proporciona información básica, objetiva, instantánea y dinámica. Toda ella puede ser de gran ayuda para estudiar este problema. En este trabajo primero se aborda una introducción y contextualización a la definición de los urban village chinos, y se estudia la historia de las urban village de Shenzhen. Después, se aborda un análisis a escala macroscópico, mesoscópico y microscópico, tratando de sacar partido a los datos disponibles que caracterizan el funcionamiento de dichas villages para hacer una investigación sobre la forma urbana espacial de las aldeas urbanas de Shenzhen. Finalmente, según los resultados del estudio, se proporciona la estrategia de revitalización de los urban village.Currently, urbanization has become a global phenomenon. It pushes the human society to go forward, but meanwhile has produced numerous social problems. In China, the urban village revitalization coming with urbanization needs to be solved urgently. In the internet era, the generous data provides all-sided, objective, instant and dynamic basic data. They can help us to study this problem. This paper firstly explains the definition of Chinese urban villages, and studies the history of Shenzhen¿s urban villages. And then, from the macroscopic, mesoscopic and microscopic levels, using different tools and other new data to make a research on urban spatial form (morphology) of Shenzhen¿s urban villages. Finally, according to the results of the study, the revitalization strategy of urban villages is provided.Chen, W. (2017). La morfología de las urban village en la era de la datificación. El ejemplo de Xiasha Village the Shenzhen. http://hdl.handle.net/10251/113663TFG

Spanning trails containing given edges

A graph G is Eulerian-connected if for any u and v in V ( G ) , G has a spanning ( u , v ) -trail. A graph G is edge-Eulerian-connected if for any e ′ and e ″ in E ( G ) , G has a spanning ( e ′ , e ″ ) -trail. For an integer r ⩾ 0 , a graph is called r -Eulerian-connected if for any X ⊆ E ( G ) with | X | ⩽ r , and for any u , v ∈ V ( G ) , G has a spanning ( u , v ) -trail T such that X ⊆ E ( T ) . The r -edge-Eulerian-connectivity of a graph can be defined similarly. Let θ ( r ) be the minimum value of k such that every k -edge-connected graph is r -Eulerian-connected. Catlin proved that θ ( 0 ) = 4 . We shall show that θ ( r ) = 4 for 0 ⩽ r ⩽ 2 , and θ ( r ) = r + 1 for r ⩾ 3 . Results on r -edge-Eulerian connectivity are also discussed

Threshold Characteristics of Slow-Light Photonic Crystal Lasers

The threshold properties of photonic crystal quantum dot lasers operating in the slow-light regime are investigated experimentally and theoretically. Measurements show that, in contrast to conventional lasers, the threshold gain attains a minimum value for a specific cavity length. The experimental results are explained by an analytical theory for the laser threshold that takes into account the effects of slow-light and random disorder due to unavoidable fabrication imperfections. Longer lasers are found to operate deeper into the slow-light region, leading to a trade-off between slow-light induced reduction of the mirror loss and slow-light enhancement of disorder-induced losses.Comment: 5 pages, 7 figure

Prior knowledge guided active modules identification: an integrated multi-objective approach

BACKGROUND: Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. METHODS: A prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules. RESULTS: Application of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation. CONCLUSIONS: Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance

Active module identification in biological networks

This thesis addresses the problem of active module identification in biological networks. Active module identification is a research topic in network biology that aims to identify regions in network showing striking changes in activity. It is often associated with a given cellular response and expected to reveal dynamic and process-specific information. The key research questions for this thesis are the practical formulations of active module identification problem,the design of effective, efficient and robust algorithms to identify active modules, and the right way to interpret identified active module. This thesis contributes by proposing three different algorithm frameworks to address the research question from three different aspects. It first explores an integrated approach of combining both gene differential expression and differential correlation, formulates it as a multi-objective problem, and solves it on both simulated data and real world data. Then the thesis investigates a novel approach that brings in prior knowledge of biological process, and balances between pure data-driven search and prior information guidance. Finally, the thesis presents a brand new framework of identifying active module and topological communities simultaneously using evolutionary multitasking, accompanied with a series of task-specific algorithm designs and improvements, and provides a new way of integrating topological information to help the interpretation of active module