The Evolution of Dust Opacity in Galaxies

(Abridged) We investigate the evolution of the opacity of galaxies as a function of redshift, using simple assumptions about the metal and dust enrichment of the gas and the distribution of dust in galaxies. We use an iterative procedure to reconstruct the intrinsic Star Formation Rate (SFR) density of galaxies with redshift, by applying dust obscuration corrections to the observed UV emission. The iterative procedure converges to multiple solutions for the intrinsic SFR density, divided into two basic classes. The first class of solutions predicts relatively large UV attenuation at high redshift, with A(1500 A)=1.9 mag at z~3, and smaller attenuations at z<1, with A(2800 A)=1.25 mag. The SFR density of this set of solutions is constant for z>~1.2 and declines for z<1.2; it resembles in shape the ``monolithic collapse'' scenario for star formation. The second class of solutions predicts relatively low UV attenuations at high redshift, with A(1500 A)=0.75 mag at z~3, and larger attenuations at z<1, with A(2800 A)=1.50 mag. The SFR density in this case has a peak at z~1.2. The advantages and shortcomings of both classes are analyzed in the light of available observational constraints, including the opacity of galaxies at 0<z<1 and the intensity and spectral energy distribution of the cosmic infrared background from the COBE DIRBE and FIRAS data. We conclude that both classes of models are acceptable within the current uncertainties, but the ``monolithic collapse'' class matches the available observations better than the other one. We also investigate the dependence of our solutions on the different model assumptions.Comment: 54 pages, includes 1 embedded postscript Table and 22 embedded postscript Figures, Latex, uses AAS Latex macro. Accepted for publication in the Astrophysical Journa

Potentials of artificial intelligence in construction management

rtificial intelligence (AI) approaches have been developed since the upcoming of Information Technologies beginning in the 1950s. With rising computing power, the discussion of AI usefulness has been refuelled by new powerful algorithms and, in particular, the availability of the internet as a vast resource of unstructured data. This gives hope to construction management in particular, since construction projects are recently becoming larger and more complex, i.e. encompassing more and more participants focusing on diverging interests while the given frames of time and budget are getting tighter. Finally, construction management is used to establish an efficient organisation of all these issues and able to predict the result with a high degree of precision and certainty. This could be accomplished by the human mind when projects were smaller, but with the recent development human mind is clearly pushed to its limits. On this background, the possible support of AI to organisational tasks needs to be investigated on a theoretical level prior to developing tools. This paper is the extended version of the article ‘Artificial Intelligence in Construction Management – a Perspective’, presented at the Creative Construction Conference 2019 where the algorithmic and entropic scope of AI is investigated in the context of construction management. However, efficient organisation is about restructuring systems into a set of well-separated subsystems, where human intelligence is required to bring in mainly two higher principles which AI fails to provide: the ability to prioritise and creativity allowing for new approaches not derived from given data. This paper additionally focuses on the aspect of in-situ coordination. This service is an aspect of organisation which is not separable and can therefore only be treated as self-determined subsystem, located outside of hierarchical control. At this point algorithms of AI need to be investigated not so much as to substitute human mind but to provide significant suppor

Proceedings of the ECCS 2005 satellite workshop: embracing complexity in design - Paris 17 November 2005

Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr). Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr)

The Riddle of a Human Being: A Human Singularity of Co-evolutionary Processes

The theory of self-organization of complex systems studies laws of sustainable co-evolutionary development of structures having different speeds of development as well as laws of assembling of a complex evolutionary whole from parts when some elements of ldquo;memoryrdquo; (the biological memory, i.e. DNA, the memory of culture, i.e. the cultural and historical traditions, etc.) must be included. The theory reveals general rules of nonlinear synthesis of complex evolutionary structures. The most important and paradoxical consequences of the holistic view, including an approach to solving the riddle of human personality, are as follows: 1) the explanation why and under what conditions a part (a human) can be more complex than a whole (society); 2) in order to reconstruct society it is necessary to change an individual but not by cutting off the supposed undesirable past, since a human being as a microcosm is the synthesis of all previous stages of evolution, and as a result of repression of, it would seem, the wild past one can extinguish a ldquo;divine sparkrdquo; in his soul; 3) in the physical sense, singularity denotes a moment of instability, phase transition; one can talk about the human singularity of co-evolutionary processes, since in such a moment of instability individual actions of a human can play a key role in determining a channel of further development as well as in appearance of a new pattern of collective behavior in society; 4) as the models of nonlinear dynamics, elaborated at the Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences in Moscow, show, there is a possibility of a direct influence of the future and even a touch of an infinitely remote future in certain evolutionary regimes and under rigorously definite conditions, more over, it turns out that such a possibility exists only for a human (admittedly, through a specific state of being inherent to him ndash; the sleep without dreams) but not for the human society

Galaxy Formation Theory

We review the current theory of how galaxies form within the cosmological framework provided by the cold dark matter paradigm for structure formation. Beginning with the pre-galactic evolution of baryonic material we describe the analytical and numerical understanding of how baryons condense into galaxies, what determines the structure of those galaxies and how internal and external processes (including star formation, merging, active galactic nuclei etc.) determine their gross properties and evolution. Throughout, we highlight successes and failings of current galaxy formation theory. We include a review of computational implementations of galaxy formation theory and assess their ability to provide reliable modelling of this complex phenomenon. We finish with a discussion of several "hot topics" in contemporary galaxy formation theory and assess future directions for this field.Comment: 58 pages, to appear in Physics Reports. This version includes minor corrections and a handful of additional reference

An ALMA study of the Orion Integral Filament : I. Evidence for narrow fibers in a massive cloud

© 2018 ESO. Reproduced with permission from Astronomy & Astrophysics. Content in the UH Research Archive is made available for personal research, educational, and non-commercial purposes only. Unless otherwise stated, all content is protected by copyright, and in the absence of an open license, permissions for further re-use should be sought from the publisher, the author, or other copyright holder.Aim. We have investigated the gas organization within the paradigmatic Integral Shape Filament (ISF) in Orion in order to decipher whether or not all filaments are bundles of fibers. Methods. We combined two new ALMA Cycle 3 mosaics with previous IRAM 30m observations to produce a high-dynamic range N 2H + (1-0) emission map of the ISF tracing its high-density material and velocity structure down to scales of 0.009 pc (or ~2000 AU). Results. From the analysis of the gas kinematics, we identify a total of 55 dense fibers in the central region of the ISF. Independently of their location in the cloud, these fibers are characterized by transonic internal motions, lengths of ~0.15 pc, and masses per unit length close to those expected in hydrostatic equilibrium. The ISF fibers are spatially organized forming a dense bundle with multiple hub-like associations likely shaped by the local gravitational potential. Within this complex network, the ISF fibers show a compact radial emission profile with a median FWHM of 0.035 pc systematically narrower than the previously proposed universal 0.1 pc filament width. Conclusions. Our ALMA observations reveal complex bundles of fibers in the ISF, suggesting strong similarities between the internal substructure of this massive filament and previously studied lower-mass objects. The fibers show identical dynamic properties in both low- and high-mass regions, and their widespread detection in nearby clouds suggests a preferred organizational mechanism of gas in which the physical fiber dimensions (width and length) are self-regulated depending on their intrinsic gas density. Combining these results with previous works in Musca, Taurus, and Perseus, we identify a systematic increase of the surface density of fibers as a function of the total mass per-unit-length in filamentary clouds. Based on this empirical correlation, we propose a unified star-formation scenario where the observed differences between low- and high-mass clouds, and the origin of clusters, emerge naturally from the initial concentration of fibers.Peer reviewedFinal Published versio

Small scale problems of the Λ\LambdaCDM model: a short review

The $\Lambda$CDM model, or concordance cosmology, as it is often called, is a paradigm at its maturity. It is clearly able to describe the universe at large scale, even if some issues remain open, such as the cosmological constant problem , the small-scale problems in galaxy formation, or the unexplained anomalies in the CMB. $\Lambda$CDM clearly shows difficulty at small scales, which could be related to our scant understanding, from the nature of dark matter to that of gravity; or to the role of baryon physics, which is not well understood and implemented in simulation codes or in semi-analytic models. At this stage, it is of fundamental importance to understand whether the problems encountered by the $\Lambda$DCM model are a sign of its limits or a sign of our failures in getting the finer details right. In the present paper, we will review the small-scale problems of the $\Lambda$CDM model, and we will discuss the proposed solutions and to what extent they are able to give us a theory accurately describing the phenomena in the complete range of scale of the observed universe.Comment: 48pp 19 figs, invited review, accepted by Galaxie

Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity

The clustering of data into physically meaningful subsets often requires assumptions regarding the number, size, or shape of the subgroups. Here, we present a new method, simultaneous coherent structure coloring (sCSC), which accomplishes the task of unsupervised clustering without a priori guidance regarding the underlying structure of the data. sCSC performs a sequence of binary splittings on the dataset such that the most dissimilar data points are required to be in separate clusters. To achieve this, we obtain a set of orthogonal coordinates along which dissimilarity in the dataset is maximized from a generalized eigenvalue problem based on the pairwise dissimilarity between the data points to be clustered. This sequence of bifurcations produces a binary tree representation of the system, from which the number of clusters in the data and their interrelationships naturally emerge. To illustrate the effectiveness of the method in the absence of a priori assumptions, we apply it to three exemplary problems in fluid dynamics. Then, we illustrate its capacity for interpretability using a high-dimensional protein folding simulation dataset. While we restrict our examples to dynamical physical systems in this work, we anticipate straightforward translation to other fields where existing analysis tools require ad hoc assumptions on the data structure, lack the interpretability of the present method, or in which the underlying processes are less accessible, such as genomics and neuroscience

Galaxy Formation Spanning Cosmic History

Over the past several decades, galaxy formation theory has met with significant successes. In order to test current theories thoroughly we require predictions for as yet unprobed regimes. To this end, we describe a new implementation of the Galform semi-analytic model of galaxy formation. Our motivation is the success of the model described by Bower et al. in explaining many aspects of galaxy formation. Despite this success, the Bower et al. model fails to match some observational constraints and certain aspects of its physical implementation are not as realistic as we would like. The model described in this work includes substantially updated physics, taking into account developments in our understanding over the past decade, and removes certain limiting assumptions made by this (and most other) semi-analytic models. This allows it to be exploited reliably in high-redshift and low mass regimes. Furthermore, we have performed an exhaustive search of model parameter space to find a particular set of model parameters which produce results in good agreement with a wide range of observational data (luminosity functions, galaxy sizes and dynamics, clustering, colours, metal content) over a wide range of redshifts. This model represents a solid basis on which to perform calculations of galaxy formation in as yet unprobed regimes.Comment: MNRAS accepted. Extended version (with additional figures and details of implementation) is available at http://www.galform.or