Population scenarios and policy implications for South Mediterranean countries, 2010-2050

Four population scenarios were derived describing changes in indicators of demographic behaviour should people come to live in different future political-economic contexts. Focus of this policy brief is on expected trends in (1) population growth at regional and national levels, (2) working age populations in view of demographic dividend potential for the economy, and (3) population of elderly persons in view of the future financial burden this group might impose on societies. Results show that different economic-political development scenarios do have large effects on population growth, at least up to 2030). This is due to the socalled population momentum effect in the relatively young age-structures of most SMCs. In the short term, up to 2030, and depending on which economic-political unfolds, SMCs expected to grow from 280 million people to a figure between 362 and 349 million people. Thus, in a period of about 20 years SMC populations are expected to grow with a figure between 69 and 83 million. In that same period, EU27 populations will grow with 21 million only from about 500 to 521 million people. Between 2030 and 2050, additional population growth is foreseen in SMCs, between 48 and 62 million people, while EU27 populations are expected to grow with only 4 million during that period. SMCs appear to vary widely regarding demographic transition profiles so that demographic dividend potentials also vary. For instance, Egypt has considerably demographic dividend potential ahead in the coming decades as working age population shares will rise from 63% (2010) to a peak level of about 68% by 2045. In Turkey though, the working age population share is already high (68%) and near the expected peak level of 69% (by 2025) after which a decline sets in. The window of opportunity -the period when working age population shares rise to peak levels and remain at a high level- is starting to close for Lebanon and Tunisia though levels will remain high up to 2035 after which a decline sets in due to ageing of these populations. Ageing implies an increase of the economic burden to economies as elderly generally do not contribute any longer to economies as they did during their working age years. Old-age dependency ratios, the share of elderly in relation to the working age population, are still low compared to EU27 ratios but will increase after 2035. Should SMCs remain politically, economically and environmentally fragile in the coming decades, these lower dependency ratios will impose a relatively higher social and financial burden to societies than the high dependency ratios in EU countries

Modeling of evolving textures using granulometries

This chapter describes a statistical approach to classification of dynamic texture images, called parallel evolution functions (PEFs). Traditional classification methods predict texture class membership using comparisons with a finite set of predefined texture classes and identify the closest class. However, where texture images arise from a dynamic texture evolving over time, estimation of a time state in a continuous evolutionary process is required instead. The PEF approach does this using regression modeling techniques to predict time state. It is a flexible approach which may be based on any suitable image features. Many textures are well suited to a morphological analysis and the PEF approach uses image texture features derived from a granulometric analysis of the image. The method is illustrated using both simulated images of Boolean processes and real images of corrosion. The PEF approach has particular advantages for training sets containing limited numbers of observations, which is the case in many real world industrial inspection scenarios and for which other methods can fail or perform badly. [41] G.W. Horgan, Mathematical morphology for analysing soil structure from images, European Journal of Soil Science, vol. 49, pp. 161–173, 1998. [42] G.W. Horgan, C.A. Reid and C.A. Glasbey, Biological image processing and enhancement, Image Processing and Analysis, A Practical Approach, R. Baldock and J. Graham, eds., Oxford University Press, Oxford, UK, pp. 37–67, 2000. [43] B.B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the Making, A.K. Peters Ltd., Wellesley, MA, 1995. [44] H. Iversen and T. Lonnestad. An evaluation of stochastic models for analysis and synthesis of gray-scale texture, Pattern Recognition Letters, vol. 15, pp. 575–585, 1994. [45] A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, vol. 24(12), pp. 1167–1186, 1991. [46] T. Jossang and F. Feder, The fractal characterization of rough surfaces, Physica Scripta, vol. T44, pp. 9–14, 1992. [47] A.K. Katsaggelos and T. Chun-Jen, Iterative image restoration, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 208–209, 2000. [48] M. K¨oppen, C.H. Nowack and G. R¨osel, Pareto-morphology for color image processing, Proceedings of SCIA99, 11th Scandinavian Conference on Image Analysis 1, Kangerlussuaq, Greenland, pp. 195–202, 1999. [49] S. Krishnamachari and R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation, IEEE Transactions on Image Processing, vol. 6(2), pp. 251–267, 1997. [50] T. Kurita and N. Otsu, Texture classification by higher order local autocorrelation features, Proceedings of ACCV93, Asian Conference on Computer Vision, Osaka, pp. 175–178, 1993. [51] S.T. Kyvelidis, L. Lykouropoulos and N. Kouloumbi, Digital system for detecting, classifying, and fast retrieving corrosion generated defects, Journal of Coatings Technology, vol. 73(915), pp. 67–73, 2001. [52] Y. Liu, T. Zhao and J. Zhang, Learning multispectral texture features for cervical cancer detection, Proceedings of 2002 IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 169–172, 2002. [53] G. McGunnigle and M.J. Chantler, Modeling deposition of surface texture, Electronics Letters, vol. 37(12), pp. 749–750, 2001. [54] J. McKenzie, S. Marshall, A.J. Gray and E.R. Dougherty, Morphological texture analysis using the texture evolution function, International Journal of Pattern Recognition and Artificial Intelligence, vol. 17(2), pp. 167–185, 2003. [55] J. McKenzie, Classification of dynamically evolving textures using evolution functions, Ph.D. Thesis, University of Strathclyde, UK, 2004. [56] S.G. Mallat, Multiresolution approximations and wavelet orthonormal bases of L2(R), Transactions of the American Mathematical Society, vol. 315, pp. 69–87, 1989. [57] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989. [58] B.S. Manjunath and W.Y. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837–842, 1996. [59] B.S. Manjunath, G.M. Haley and W.Y. Ma, Multiband techniques for texture classification and segmentation, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 367–381, 2000. [60] G. Matheron, Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York, 1975

CAD and creativity: does the computer really help?

We are frequently told by its exponents that computeraided design (CAD) liberates designers and gives them new ways of envisioning their work, but is this really true? CAD in architecture is examined to see to what extent it has enhanced creativity in design. This is partly done by applying a test of creativity advanced by contemporary architect Herman Hertzberger. In this analysis, CAD is found somewhat wanting, and some suggestions are made as to why this might be so

What Is the Integrated Information Theory of Consciousness?

In the first instance, IIT is formulated as a theory of the physical basis of the 'degree' or ‘level’ or ‘amount’ of consciousness in a system. In addition, integrated information theorists have tried to provide a systematic theory of how physical states determine the specific qualitative contents of episodes of consciousness: for instance, an experience as of a red and round thing rather than a green and square thing. I raise a series of questions about the central explanatory target, the 'degree' or ‘level’ or ‘amount’ of consciousness. I suggest it is not at all clear what scientists and philosophers are talking about when they talk about consciousness as gradable. I also raise some questions about the explanation of qualitative content