A comprehensive theory of induction and abstraction, part I

I present a solution to the epistemological or characterisation problem of induction. In part I, Bayesian Confirmation Theory (BCT) is discussed as a good contender for such a solution but with a fundamental explanatory gap (along with other well discussed problems); useful assigned probabilities like priors require substantive degrees of belief about the world. I assert that one does not have such substantive information about the world. Consequently, an explanation is needed for how one can be licensed to act as if one has substantive information about the world when one does not. I sketch the outlines of a solution in part I, showing how it differs from others, with full details to follow in subsequent parts. The solution is pragmatic in sentiment (though differs in specifics to arguments from, for example, William James); the conceptions we use to guide our actions are and should be at least partly determined by preferences. This is cashed out in a reformulation of decision theory motivated by a non-reductive formulation of hypotheses and logic. A distinction emerges between initial assumptions--that can be non-dogmatic--and effective assumptions that can simultaneously be substantive. An explanation is provided for the plausibility arguments used to explain assigned probabilities in BCT. In subsequent parts, logic is constructed from principles independent of language and mind. In particular, propositions are defined to not have form. Probabilities are logical and uniquely determined by assumptions. The problems considered fatal to logical probabilities--Goodman's `grue' problem and the uniqueness of priors problem are dissolved due to the particular formulation of logic used. Other problems such as the zero-prior problem are also solved. A universal theory of (non-linguistic) meaning is developed. Problems with counterfactual conditionals are solved by developing concepts of abstractions and corresponding pictures that make up hypotheses. Spaces of hypotheses and the version of Bayes' theorem that utilises them emerge from first principles. Theoretical virtues for hypotheses emerge from the theory. Explanatory force is explicated. The significance of effective assumptions is partly determined by combinatoric factors relating to the structure of hypotheses. I conjecture that this is the origin of simplicity

An AI-Assisted Design Method for Topology Optimization Without Pre-Optimized Training Data

Topology optimization is widely used by engineers during the initial product development process to get a first possible geometry design. The state-of-the-art is the iterative calculation, which requires both time and computational power. Some newly developed methods use artificial intelligence to accelerate the topology optimization. These require conventionally pre-optimized data and therefore are dependent on the quality and number of available data. This paper proposes an AI-assisted design method for topology optimization, which does not require pre-optimized data. The designs are provided by an artificial neural network, the predictor, on the basis of boundary conditions and degree of filling (the volume percentage filled by material) as input data. In the training phase, geometries generated on the basis of random input data are evaluated with respect to given criteria. The results of those evaluations flow into an objective function which is minimized by adapting the predictor's parameters. After the training is completed, the presented AI-assisted design procedure supplies geometries which are similar to the ones generated by conventional topology optimizers, but requires a small fraction of the computational effort required by those algorithms. We anticipate our paper to be a starting point for AI-based methods that requires data, that is hard to compute or not available

Elevação da taxa de estruturação de concretos para impressão 3D: o efeito de aditivos modificadores de viscosidade

In the last decades, 3D printing has emerged as a promising new paradigm for manufacturing. Even in the civil construction industry, it has gained attention from companies and researchers around the world. Despite of that, the properties of materials applied in the additive manufacturing process are still understudied. One of the challenges is the need to conciliate both high bearing capacity, sparing the need of any confining measures, and the ability of keeping fluidity for enough time, in order to avoid cold joints between the layers. In that scenario, viscosity enhancing admixtures (VEAs) can be a solution, because they are able of promoting flocculation, viscosity gain and yield strength increase, reducing the deposition time in between the layers, which may decrease the formation of cold joints. This research evaluated rheological parameters of four different VEAs and found out that they show potential for increasing the cohesion and buildability of concretes for 3D-printing. The results showed that this effect varies with the type and amount of the admixture adopted and bentonite clay, as a mineral powder material, performed best in comparison to other polymeric VEAs, presenting structuration rates of up to 62% higher than the reference mixture.Nas últimas décadas, a impressão 3D emergiu como um novo e promissor paradigma de fabricação.  Mesmo na indústria da construção civil, ela tem ganhado atenção de empresas e pesquisadores de todo o mundo.  Apesar disso, as propriedades dos materiais aplicados no processo de manufatura aditiva ainda são pouco estudadas.  Um dos desafios está na necessidade de conciliar alta capacidade portante, poupando a necessidade de quaisquer medidas restritivas, com a capacidade de manter a fluidez por tempo suficiente, a fim de evitar juntas frias entre as camadas.  Nesse sentido, os aditivos Modificadores de viscosidade (VMAs) podem ser uma solução, pois são capazes de promover floculação, ganho de viscosidade e aumento da tensão de escoamento, reduzindo o tempo de deposição entre as camadas, o que pode diminuir a formação de juntas frias.  Esta pesquisa avaliou parâmetros reológicos de quatro VMAs diferentes e descobriu que eles apresentam potencial para aumentar a coesão e a construtibilidade de concretos para impressão 3D.  Os resultados mostraram que esse efeito varia com o tipo e a quantidade de aditivo adotado. Particularmente, a argila bentonita, como aditivo mineral em pó, apresentou melhor desempenho em comparação com os VMAs poliméricos, porduzindo taxas de estruturação até 62% superiores à mistura de referência

Accurate and precise viral quantification for rapid vaccine development in- process production monitoring using Radiance® Laser Force Cytology\u3csup\u3eTM

The biopharmaceutical world is evolving rapidly, bringing with it the need for technologies to support this fast-paced and changing environment. Trends in biomanufacturing are moving towards shortened development cycles as companies balance increased productivity requirements with the goal of reducing costs while at the same time ensuring production consistencies are met and batch out of specification (OOS) and failure events are minimized. LumaCyte’s Radiance® instrument using Laser Force Cytology™ (LFC), a combination of advanced optics and microfluidics to rapidly analyze single cells based upon their intrinsic biochemical and biophysical cellular properties and without the need for antibodies or labels. Subtle cellular changes can be precisely captured with Radiance’s automated workflow enabling new capabilities for measuring real-time product quality attributes to support R&D, process development and manufacturing needs across the biopharmaceutical industry. In this poster, LumaCyte demonstrates how tedious infectivity assays such as plaque and TCID50 can be replaced by Radiance’s rapid viral infectivity quantification assay to provide significant shorter time to result (TTR), reduced labor, and improved data quality and consistency. In addition, the bioproduction of vaccines, viral vectors or VLPs can be monitored in real-time, enabling rapid optimization of key processes and increasing process knowledge. As a result, product yield can be increased using the same inputs and the likelihood of OOS events can be reduced. Radiance applications in oncolytic virus research and neutralization assays are presented as well. Overall, LFC delivers faster TTR and improved data quality for vaccine analytics from R&D to manufacturing

A Structure Function Model Recovers the Many Formulations for Air-Water Gas Transfer Velocity

Two ideas regarding the structure of turbulence near a clear air-water interface are used to derive a waterside gas transfer velocity k(L) for sparingly and slightly soluble gases. The first is that k(L) is proportional to the turnover velocity described by the vertical velocity structure function D-ww(r), where r is separation distance between two points. The second is that the scalar exchange between the air-water interface and the waterside turbulence can be suitably described by a length scale proportional to the Batchelor scale l(B) = Sc-1/2, where Sc is the molecular Schmidt number and eta is the Kolmogorov microscale defining the smallest scale of turbulent eddies impacted by fluid viscosity. Using an approximate solution to the von Karman-Howarth equation predicting D-ww(r) in the inertial and viscous regimes, prior formulations for k(L) are recovered including (i) kL = root 2/15Sc(-1/2)v(K), v(K) is the Kolmogorov velocity defined by the Reynolds number v(K)eta/nu = 1 and nu is the kinematic viscosity of water; (ii) surface divergence formulations; (iii) k(L) alpha Sc(-1/2)u(*), where u(*) is the waterside friction velocity; (iv) k(L) alpha Sc-1/2 root g nu/u(*) for Keulegan numbers exceeding a threshold needed for long-wave generation, where the proportionality constant varies with wave age, g is the gravitational acceleration; and (v) k(L) = root 2/15Sc(-1/2) (nu g beta(o)q(o))(1/4) in free convection, where q(o) is the surface heat flux and beta(o) is the thermal expansion of water. The work demonstrates that the aforementioned k(L) formulations can be recovered from a single structure function model derived for locally homogeneous and isotropic turbulence.Peer reviewe

Stroboscopic Laser Diagnostics for Detection of Ordering in One-Dimensional Ion beam

A novel diagnostic method for detecting ordering in one-dimensional ion beams is presented. The ions are excited by a pulsed laser at two different positions along the beam and fluorescence is observed by a group of four photomultipliers. Correlation in fluorescence signals is firm indication that the ion beam has an ordered structure.Comment: 7 pages, REVTEX, fig3 uuencoded, figs 1-2 available upon request from [email protected], to appear in Phys. Rev.