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In this paper, we apply recent findings from urban scaling theory to evaluate how it could be applied to a one-dimensional archetypal city. Our focus is on how the simplicity of a one-dimensional model can provide intuitive insights that might be obscured in more complex, multidimensional models. With this instructive model, it is possible to visualize that if the population's accessibility decreases more slowly than the increase in distance, an agglomeration effect occurs. This leads to increasing returns to scale when considering interactions between people or economies of scale in the case of amenities that serve the city. These findings highlight a key characteristic of complex systems: interacting parts that together exhibit properties that are not evident when considered individually. The results presented here are derived from both numerical simulations and theoretical analysis. Although one-dimensionality is used here as a simplification, many natural (e.g., Su{\l}oszowa in Poland) and artificial cities (e.g., The Line in Saudi Arabia) worldwide are shaped this way.

We demonstrate that the original fundamental allometry alone cannot accurately describe the relationship between urban area and population size. Instead, building height is a third factor that interplays with area and population. To illustrate this, we propose a straightforward model based on the idea that city area is the result of people's desire to live close to one another while also having sufficient living space. This leads to a more general form of fundamental allometry (relating area, population, and building height). Our argument is supported by empirical data from different countries.

Understanding how size influences the internal characteristics of a system is a crucial concern across various fields. Concepts like scale invariance, universalities, and fractals are fundamental to this inquiry and find application in biology, physics, and particularly urbanism. Size profoundly impacts how cities develop and function economically and socially. For example, what are the pros and cons of residing in larger cities? Is life really more expensive or less safe in larger cities? Or do they really offer more opportunities and generally higher incomes than smaller ones? To address such inquiries, we utilize theoretical tools from scaling theory, enabling a quantitative description of how a system's behavior changes across different scales, from micro to macro. Drawing parallels with research in biology and spatial economics, this chapter explores recent discoveries, ongoing progress, and unanswered questions regarding urban scaling., Comment: Compendium of Urban Complexity

Many physical, biological, and social systems exhibit emergent properties that arise from the interactions between their components (cells). In this study, we systematically treat every-pair interactions (a) that exhibit power-law dependence on the Euclidean distance and (b) act in structures that can be characterized using fractal geometry. We analytically derive the mean interaction field of the cells and find that (i) in a long-range interaction regime, the mean interaction field increases following a power law with the size of the system, (ii) in a short-range interaction regime, the field saturates, and (iii) in the intermediate range it follows a logarithmic behaviour. To validate our analytical solution, we perform numerical simulations. In the case of short-range interactions, we observe that discreteness significantly impacts the continuum approximation used in the derivation, leading to incorrect asymptotic behaviour in this regime. To address this issue, we propose an expansion that substantially improves the accuracy of the analytical expression. Furthermore, our results motivate us to explore a framework for estimating the fractal dimension of unknown structures. This approach offers an alternative to established methods such as box-counting or sandbox methods. Overall, we believe that our analytical work will have broad applicability in systems where every-pair interactions play a crucial role. The insights gained from this study can contribute to a better understanding of various complex systems and facilitate more accurate modelling and analysis in a wide range of disciplines.

The group of large aquatic mammals has representatives being the largest living beings on earth, surpassing the weight and size of dinosaurs. In this paper, we present some empirical evidence and a mathematical model to argue that fat accumulation in marine mammals triggers a series of metabolic events that result in these animals' increased size. Our study starts by analysing 43 ontogenetic trajectories of species of different types and sizes. For instance, the analyses include organisms with asymptotic mass from 27g (Taiwan field mouse) to $2.10^{7}$g (grey whale). The available data allows us to determine all available species' ontogenetic parameters (catabolism and anabolism constant, scaling exponent and asymptotic mass). The analyses of those data show a minimisation of catabolism and scaling exponent in marine mammals compared to other species analysed. We present a possible explanation for this, arguing that the large proportion of adipose tissue in these animals can cause this minimisation. That is because adipocytes have different scaling properties in comparison to non-adipose (typical) cells, expressed in reduced energetic demand and lower metabolism. The conclusion is that when we have an animal with a relatively large amount of adipose tissue, as is the case of aquatic mammals, the cellular metabolic rate decreases compared to other animals with the same mass but with proportionally smaller fat tissue. A final consequence of this cause-effect process is the increase of the asymptotic mass of these mammals.

The quest for a theory of cities that could offer a quantitative and systematic approach to manage cities is at the top priority, given the challenges humanity faces due to the increasing urbanization and densification of cities. If such a theory is feasible, then its formulation must be in a mathematical way. As a contribution to organizing the mathematical ideas that deal with such a systematic way of understanding urban phenomena, we present this material, concentrating on one important aspect of what recently has been called the new science of cities. In this paper, we review the main mathematical models present in the literature that aim at explaining the origin and emergence of urban scaling. We intend to present the models, identify similarities and connections between them, and find situations in which different models lead to the same output. In addition, we report situations in which some ideas initially introduced in a particular model can also be introduced in another model, generating more diversification and increasing the scope of the models. The models treated in this paper explain urban scaling from different premises: from gravity ideas, passing through densification ideas and cites' geometry, to a hierarchical organization and socio-network properties. We also investigate scenarios in which these different fundamental ideas could be interpreted as similar -- where the similarity is likely but not obvious. Furthermore, in what concerns the gravity idea, we propose a general framework that includes all gravity models analyzed as a particular case.

We propose hypotheses describing the empirical finding of an association between the exponents of urban GDP scaling and Zipf's law for cities. These hypotheses represent various combinations of directional or reciprocal causal links between the two phenomena and include inter- and intra-city processes. Future theories and models can be motivated with and categorized according to these hypotheses. This paper intends to stimulate the discussion around the processes behind these phenomena and pave the way to a Unified Urban Theory., Comment: 2 pages, 1 figure

This paper investigates the role of size in biological organisms. More specifically, how the energy demand, expressed by the metabolic rate, changes according to the mass of an organism. Empirical evidence suggests a power-law relation between mass and metabolic rate, namely allometric law. For vascular organisms, the exponent $\beta$ of this power-law is smaller than one, which implies scaling economy; that is, the greater the organism is, the lesser energy per cell it demands. However, the numerical value of this exponent is a theme of an extensive debate and a central issue in comparative physiology. It is presented in this work some empirical data and a detailed discussion about the most successful theories to explain these issues. A historical perspective is also shown, beginning with the first empirical insights in the sec. 19 about scaling properties in biology, passing through the two more important theories that explain the scaling properties quantitatively. Firstly, the Rubner model, that consider organism surface area and heat dissipation to derive $\beta = 2/3$. Secondly, the West-Brown-Enquist theory, that explains such scaling properties as a consequence of the hierarchical and fractal nutrient distribution network, deriving $\beta = 3/4$.

In this article we presented a brief study of the main network models with growth and preferential attachment. Such models are interesting because they present several characteristics of real systems. We started with the classical model proposed by Barabasi and Albert: nodes are added to the network connecting preferably to other nodes that are more connected. We also presented models that consider more representative elements from social perspectives, such as the homophily between the vertices or the fitness that each node has to build connections. Furthermore, we showed a version of these models including the Euclidean distance between the nodes as a preferential attachment rule. Our objective is to investigate the basic properties of these networks as distribution of connectivity, degree correlation, shortest path, cluster coefficient and how these characteristics are affected by the preferential attachment rules. Finally, we also provided a comparison of these synthetic networks with real ones. We found that characteristics as homophily, fitness and geographic distance are significant preferential attachment rules to modeling real networks. These rules can change the degree distribution form of these synthetic network models and make them more suitable to model real networks.

Given that a group of cities follows a scaling law connecting urban population with socio-economic or infrastructural metrics (transversal scaling), should we expect that each city would follow the same behavior over time (longitudinal scaling)? This assumption has important policy implications, although rigorous empirical tests have been so far hindered by the lack of suitable data. Here, we advance the debate by looking into the temporal evolution of the scaling laws for 5507 municipalities in Brazil. We focus on the relationship between population size and two urban variables, GDP and water network length, analyzing the time evolution of the system of cities as well as their individual trajectory. We find that longitudinal (individual) scaling exponents are city-specific, but they are distributed around an average value that approaches to the transversal scaling exponent when the data are decomposed to eliminate external factors, and when we only consider cities with a sufficiently large growth rate. Such results give support to the idea that the longitudinal dynamics is a micro-scaling version of the transversal dynamics of the entire urban system. Finally, we propose a mathematical framework that connects the microscopic level to global behavior, and, in all analyzed cases, we find good agreement between theoretical prediction and empirical evidence., Comment: Feedback and comments are welcome

The comprehension of tumor growth is a intriguing subject for scientists. New researches has been constantly required to better understand the complexity of this phenomenon. In this paper, we pursue a physical description that account for some experimental facts involving avascular tumor growth. We have proposed an explanation of some phenomenological (macroscopic) aspects of tumor, as the spatial form and the way it growths, from a individual-level (microscopic) formulation. The model proposed here is based on a simple principle: competitive interaction between the cells dependent on their mutual distances. As a result, we reproduce many empirical evidences observed in real tumors, as exponential growth in their early stages followed by a power law growth. The model also reproduces the fractal space distribution of tumor cells and the universal behavior presented in animals and tumor growth, conform reported by West, Guiot {\it et. al.}\cite{West2001,Guiot2003}. The results suggest that the universal similarity between tumor and animal growth comes from the fact that both are described by the same growth equation - the Bertalanffy-Richards model - even they does not necessarily share the same biophysical properties.

Power Grids and other delivery networks has been attracted some attention by the network literature last decades. Despite the Power Grids dynamics has been controlled by computer systems and human operators, the static features of this type of network can be studied and analyzed. The topology of the Brazilian Power Grid (BPG) was studied in this work. We obtained the spatial structure of the BPG from the ONS (electric systems national operator), consisting of high-voltage transmission lines, generating stations and substations. The local low-voltage substations and local power delivery as well the dynamic features of the network were neglected. We analyze the complex network of the BPG and identify the main topological information, such as the mean degree, the degree distribution, the network size and the clustering coefficient to caracterize the complex network. We also detected the critical locations on the network and, therefore, the more susceptible points to lead to a cascading failure and even to a blackouts. Surprisely, due to the characteristic of the topology and physical structure of the network, we show that the BPG is resilient against random failures, since the random removal of links does not affect significantly the size of the largest cluster. We observe that when a fraction of the links are randomly removed, the network may disintegrates into smaller and disconnected parts, however, the power grid largest component remains connected. We believe that the even a static study of the network topology can help to identify the critical situations and also prevent failures and possible blackouts on the network., Comment: 7 pages, 3 figures

In this work, some phenomenological models, those that are based only on the population information (macroscopic level), are deduced in an intuitive way. These models, as for instance Verhulst, Gompertz and Bertalanffy models, are posted in such a manner that all the parameters involved have physical interpretation. A model based on the interaction (distance dependent) between the individuals (microscopic level) is also presented. This microscopic model reachs the phenomenological models presented as particular cases. In this approach, the Verhulst model represents the situation in which all the individuals interact in the same way, regardless of the distance between them. That means Verhulst model is a kind of mean field model. The other phenomenological models are reaching from the microscopic model according to two quantities: i) the relation between the way that the interaction decays with the distance; and ii) the dimension of the spatial structure formed by the population. This microscopic model allows understanding population growth by first principles, because it predicts that some phenomenological models can be seen as a consequence of the same individual level kind of interaction. In this sense, the microscopic model that will be discussed here paves the way to finding universal patterns that are common to all types of growth, even in systems of very different nature.