The urban sprawl dynamics: does a neural network understand the spatial logic better than a cellular automata?

Cellular Automata are usually considered the most efficient technology to understand the spatial logic of urban dynamics: they are inherently spatial, they are simple and computationally efficient and are able to represent a wide range of pattern and situations. Nevertheless the implementation of a CA requires the formulation of explicit spatial rules which represents the greatest limit of this approach. Whatever rich and complex the rules are, they don`t are able to capture satisfactorily the variety of the real processes. Recent developments in natural algorithms, and particularly in Artificial Neural Networks (ANN), allow to reverse the approach by learning the rules and the behaviours in urban land use dynamics directly from the Data Base, following a bottom-up process. The basic problem is to discover how and in to what extent the land use change of each cell i at time t+1 is determined by the neighbouring conditions (CA assumptions) or by other social, environmental, territorial features (i.e. political maps, planning rules) which where holding at the previous time t. Once the NN has learned the rules, it is able to predict the changes at time t+2 and following. In this paper we show and discuss the prediction capability of different architectures of supervised and unsupervised ANN. The Case study and Data Base concern the land use dynamics, between two temporal thresholds, in the South metropolitan area of Milan. The records have been randomly split in two sets which have been alternatively used in Training and in Testing phase in each ANN. The different ANNs performances have been evaluated with Statistical Functions. Finally, for the prediction, we have used the average of the prediction values of the 10 ANNs, and tested the results through the usual Statistical Functions.

Capacités morphogènes des cellules d’éponges dissociées

The reconstitution of functional sponges from aggregates, some built from non-fractionated suspensions, others from purified archaeocytes, has been studied using electron microscopy in process of time.The reorganization of aggregates made from complete suspensions mainly consists in a gathering of cells keeping their initial differentiation into functional structures. During restructuration, cellular debris resulting from dissociation and surnumerary healthy cells are phagocytized by archaeocytes.The evolution of archaeocyte aggregates points out the totipotency of these cells, since they appear to be able to differentiate into all sponge cell types. Nevertheless, the anomalies appearing during the sponge reconstitution, which mainly consist in a cell type population ratio desequilibrium, suggest that some morphogenetic regulation mechanisms are lost

Returning the 'social' to social work:Recommitting to social development in an age of neoliberalism

In the context of globalisation, privatisation and liberalisation there is a tendency to marketise and monetise essential services. Erstwhile fundamental services that were considered to be life saving are being marketed and sold. Education soon followed the same trend; unsurprisingly the profession of social work also is being subjected to the treatment of the markets in an uncharacteristic manner. Social work and social welfare are being regarded as marketable services. This has lead to an exclusivist approach which is fundamentally different from the tenets of the profession. This paper explores the way social work is transforming under economic liberalisation as a response to this trend

The risk evaluation in urban sustainability: a threshold methodology and a neral network investigation

Urban sustainability is a dynamic process that lies on positive interactions among three different urban sub-systems: social, economic and physical, where social well-being coexists with economic development and environmental quality. Nevertheless in the existing cities this utopian scenario does not appear. Aestethic quality of natural and built environment is often associated to marginality and poverty, labor market variety and urban efficiency coexist with pollution, criminality and high settlement costs. In the conurbation of Milan those sub-systems combines themselves differently to form the core, the periphery and the large metropolitan area. The interactions among the over-mentioned systems are complex and unforeseable and seem to present the opportunity for a scientific investigation: based on Neural Network approach. The aim of this study is to investigate the underlying relationships among the three sub-systems, by a set of social, economic and physical attributes of the conurbation of Milan, and to verify if this underlying structure reproduces the heterogeneity of urban realities and allows distinguishing part of the town with different assets or drawbacks in sustainability. The Data Base (DB), composed by 80 indicators and 144 areal units of the city of Milan, has been processed by Self-Reflexive Neural Networks (SRNN). These Networks are an useful instrument of investigation and analogic questioning of the Data Base. Once the SRNN has learned the structure of the weights from the DB, by querying the network with the maximization or minimization of specific groups of attributes, is possible to read the related properties and to rank the cities performing this urban profile

The urban sprawl dynamics: does a neural network understand the spatial logic better than a cellular automata?

Cellular Automata are usually considered the most efficient technology to understand the spatial logic of urban dynamics: they are inherently spatial, they are simple and computationally efficient and are able to represent a wide range of pattern and situations. Nevertheless the implementation of a CA requires the formulation of explicit spatial rules which represents the greatest limit of this approach. Whatever rich and complex the rules are, they don`t are able to capture satisfactorily the variety of the real processes. Recent developments in natural algorithms, and particularly in Artificial Neural Networks (ANN), allow to reverse the approach by learning the rules and the behaviours in urban land use dynamics directly from the Data Base, following a bottom-up process. The basic problem is to discover how and in to what extent the land use change of each cell i at time t+1 is determined by the neighbouring conditions (CA assumptions) or by other social, environmental, territorial features (i.e. political maps, planning rules) which where holding at the previous time t. Once the NN has learned the rules, it is able to predict the changes at time t+2 and following. In this paper we show and discuss the prediction capability of different architectures of supervised and unsupervised ANN. The Case study and Data Base concern the land use dynamics, between two temporal thresholds, in the South metropolitan area of Milan. The records have been randomly split in two sets which have been alternatively used in Training and in Testing phase in each ANN. The different ANNs performances have been evaluated with Statistical Functions. Finally, for the prediction, we have used the average of the prediction values of the 10 ANNs, and tested the results through the usual Statistical Functions

Isolation and characterization of few-layer black phosphorus

Isolation and characterization of mechanically exfoliated black phosphorus flakes with a thickness down to two single-layers is presented. A modification of the mechanical exfoliation method, which provides higher yield of atomically thin flakes than conventional mechanical exfoliation, has been developed. We present general guidelines to determine the number of layers using optical microscopy, Raman spectroscopy and transmission electron microscopy in a fast and reliable way. Moreover, we demonstrate that the exfoliated flakes are highly crystalline and that they are stable even in free-standing form through Raman spectroscopy and transmission electron microscopy measurements. A strong thickness dependence of the band structure is found by density functional theory calculations. The exciton binding energy, within an effective mass approximation, is also calculated for different number of layers. Our computational results for the optical gap are consistent with preliminary photoluminescence results on thin flakes. Finally, we study the environmental stability of black phosphorus flakes finding that the flakes are very hydrophilic and that long term exposure to air moisture etches black phosphorus away. Nonetheless, we demonstrate that the aging of the flakes is slow enough to allow fabrication of field-effect transistors with strong ambipolar behavior. Density functional theory calculations also give us insight into the water-induced changes of the structural and electronic properties of black phosphorus.Comment: 11 main figures, 7 supporting figure