La economía de las tiendas de barrio en ColombiaReseña Las tiendas de barrio, entendidas como microestablecimientos comerciales que no hacen parte de cadenas o franquicias, son uno de los elementos culturales y económicos centrales de los barrios tradicionales en gran parte del mundo. Este libro analiza su existencia y su relevancia bajo diferentes miradas para comprender el papel de la informalidad en las economías emergentes. En primer lugar, se realiza un acercamiento teórico desde la economía y la teoría de sistemas, acompañado de una caracterización estadística de esta clase de negocios. En este sentido se exploran diversas teorías sobre el rol del tendero como emprendedor, desempleado disfrazado, y de su papel como nodo central de la actividad en los barrios. Posteriormente, se analiza la informalidad empresarial en las economías emergentes, haciendo una revisión de la literatura especializada en diversos continentes. De ese aspecto global se pasa a un estudio de caso: el barrio Minuto de Dios en la ciudad de Bogotá, en donde se realiza una caracterización y una intervención a los establecimientos de un barrio diseñado para la clase media. Sobre intervenciones, en particular se explora la transformación digital de los tenderos, un tema que tomó especial relevancia en la época de la crisis generada por la pandemia generada por el coronavirus. Se analizan en detalle los casos de Pereira e Ibagué, donde se caracteriza la respuesta de los establecimientos a la situación.Palabras clave: tiendas de barrio, economía informal, economía minorista, comportamiento del consumidor, economía inclusiva, coronavirus, transformación digital.
This work presents a method for rock porosity prediction from the X-ray computed tomography (CT) logs obtained using a double energy approach, bulk density (RHOB) and photoelectric factor (PEF). The proposed method seeks to correlate the known porosity from the Routine Core Analysis (RCAL) with RHOB and PEF high-resolution logs, as the response of these two measurements depends on the volumetric quantity of different rock materials and of the volume of its porous space. Artificial Neural Networks (ANNs) are trained so they can predict porosity from CT logs at a high resolution (0.625 mm). The ANNs validation and regression plots show that porosity predictions are good. High-resolution porosity models linked to CT images could contribute to enhancing the petrophysics model as they allow a more refined identification of intervals of interest due to the detailed measurement.
Abstract-Space-time causality is one of the fundamental notions of modern physics; however, it is difficult to define in observational physical terms. Intuitively, the fact that a space-time event e = (t, x) can causally influence an eventmeans that what we do in the vicinity of e changes what we observe at e ′ . If we had two copies of the Universe, we could perform some action at e in one copy but not in another copy; if we then observe the difference at e ′ , this would be an indication of causality. However, we only observe one Universe, in which we either perform the action or we do not. At first glance, it may seem that in this case, there is no meaningful way to provide an operational definition of causality. In this paper, we show that such a definition is possible if we use the notions of algorithmic randomness and Kolmogorov complexity. The resulting definition leads to a somewhat unexpected consequence: that space-time causality is a matter of degree. I. DEFINING CAUSALITY IS IMPORTANTSpace-time causality is important. Causality relation between space-time events (i.e., points in space-time) is one of the fundamental notions of physics; see, e.g., [1], [3]. Because of this, many fundamental physical theories describe, among other things, the causality relation between space-time events.According to modern physics, space-time causality relation is non-trivial. In Newton's physics, it was assumed that influences can propagate with an arbitrary speed, constituting, in effect, immediate action-at-a-distance. Under this assumption, an event e = (t, x) occurring at moment t at location x can influence an event e ′ = (t ′ , x ′ ) occurring at moment t ′ at location x ′ if and only if the second event occurs later than the first one, i.e., if and only if t < t ′ . In special relativity, the speeds of all the processes are limited by the speed of light c. In this theory, an event e = (t, x) can influence an event e ′ = (t ′ , x ′ ) if during the time t ′ − t, the faster possible process -light -can cover the distance d(x, x ′ ) between locations x and x ′ , i.e., ifIn the general relativity theory, the space-time is curved, so the corresponding causality relation is even more com-
We show how transformation group ideas can be naturally used to generate efficient algorithms for scientific computations. The general approach is illustrated on the example of determining, from the experimental data, the dissociation constants related to multiple binding sites. We also explain how the general transformation group approach is related to the standard (backpropagation) neural networks; this relation justifies the potential universal applicability of the group-related approach.
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