Parametric Surfaces for Augmented Architecture representation

Augmented Reality (AR) represents a growing communication channel, responding to the need to expand reality with additional information, offering easy and engaging access to digital data. AR for architectural representation allows a simple interaction with 3D models, facilitating spatial understanding of complex volumes and topological relationships between parts, overcoming some limitations related to Virtual Reality. In the last decade different developments in the pipeline process have seen a significant advancement in technological and algorithmic aspects, paying less attention to 3D modeling generation. For this, the article explores the construction of basic geometries for 3D model’s generation, highlighting the relationship between geometry and topology, basic for a consistent normal distribution. Moreover, a critical evaluation about corrective paths of existing 3D models is presented, analysing a complex architectural case study, the virtual model of Villa del Verginese, an emblematic example for topological emerged problems. The final aim of the paper is to refocus attention on 3D model construction, suggesting some "good practices" useful for preventing, minimizing or correcting topological problems, extending the accessibility of AR to people engaged in architectural representation

A novel multidimensional model for the OLAP on documents : modeling, generation and implementation

International audienceAs the amount of textual information grows explosively in various kinds of business systems, it becomes more and more essential to analyze both structured data and unstructured textual data simultaneously. However information contained in non structured data (documents and so on) is only partially used in business intelligence (BI). Indeed On-Line Analytical Processing (OLAP) cubes which are the main support of BI analysis in decision support systems have focused on structured data. This is the reason why OLAP is being extended to unstructured textual data. In this paper we introduce the innovative “Diamond” multidimensional model that will serve as a basis for semantic OLAP on XML documents and then we describe the meta modeling, generation and implementation of a the Diamond multidimensional model

Long-term assessment of power capacity incentives by modeling generation investment dynamics under irreversibility and uncertainty

In actual energy-only markets, the high volatility of power prices affects the expected returns of generators. When dealing with irreversibility under uncertainty, deferring decisions to commit in new power plants, waiting for better information, is therefore a rational approach. Theoretical and empirical evidence suggests that such investment pattern determines the occurrence of construction cycles, which strongly compromise supply security. In order to supplement generators´ revenues, several remuneration mechanisms have been devised over past years. Along this line, this work addresses the long-run dynamics of capacity adequacy and market efficiency with both a price-based and a quantity-based capacity remuneration policy. For that purpose, a recently-developed, stochastic simulation model is used as a benchmark. Hence, the optimal postponement of generation investment decisions is integrated into a long-run power market model by formulating the decision-making problem in the framework of Real Options Analysis. Results suggest that policymakers may exchange supply security (effectiveness) for energy prices to be paid by consumers (efficiency) when designing and implementing capacity remuneration mechanisms. By doing so, this article contributes to the ongoing debate regarding the design of incentive policies and efficient power markets by considering the microeconomics of investors? decision-making under irreversibility and uncertainty.Fil: Rios Festner, Daniel. Universidad Nacional de Asunción; ParaguayFil: Blanco, Gerardo. Universidad Nacional de Asunción; ParaguayFil: Olsina, Fernando Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentin

Comparative Analysis of 3D Human modeling generation using 3D Structured Light Scanning and Machine Learning generation

Integration of 3D model production from a single 2D RGB picture using machine learning into mainstream human 3D modelling requires significant evaluation data with an appropriate reference value relative to structured light approach scanning. The purpose of this work is to bridge the gap between the structured light technique and the machine learning algorithm generation method through a comparative analysis based on qualitative criteria such as accuracy and efficiency. The subsequent research was undertaken in two parts. Phase 1 centered on the experimental setup of the data collecting approach utilizing several scanning techniques on the sample model in a controlled setting, whereas phase 2 focuses on the analysis of the subsequent data to determine functional equivalency. The most significant finding of the comparison study is the practicality of the PIFuHD machine learning algorithm with respect to the Artec EVA scanner in terms of efficiency and equivalent precision. Despite the advancements in Machine learning algorithm for generative 3D modeling, major improvements are necessary to achieve functional parity with the structured light scanning approach in terms of accuracy

The Future: Machine learning

Learning is a process or activity of understanding and improving one’s then it may be human or machineability to perform a operations efficiently. Machine learning is one of sub-field of computer science, which enables computers to learn and analyze thinks without being or writing explicitly programmed. It basically evolved from AI(artificial intelligence) via pattern recognition and computational learning theory. It also explores the area of algorithms, which can make high end predictions on data. Currently it deployed in a wide range of computing tasks, where designing efficient algorithms and programs becomes rather difficult, such as email spam filtering, optical character recognition, search engine improvement etc.Advance machine learning will basically concentrate on modeling, generation, and prediction of multiple inter-dependent variables

On the properties of Circular-Beams

Circular-Beams were introduced as a very general solution of the paraxial wave equation carrying Orbital Angular Momentum. Here we study their properties, by looking at their normalization and their expansion in terms of Laguerre-Gauss modes. We also study their far-field divergence and, for particular cases of the beam parameters, their possible experimental generation.Comment: 5 page

Dynamic and multi-pharmacophore modeling for designing polo-box domain inhibitors.

The polo-like kinase 1 (Plk1) is a critical regulator of cell division that is overexpressed in many types of tumors. Thus, a strategy in the treatment of cancer has been to target the kinase activity (ATPase domain) or substrate-binding domain (Polo-box Domain, PBD) of Plk1. However, only few synthetic small molecules have been identified that target the Plk1-PBD. Here, we have applied an integrative approach that combines pharmacophore modeling, molecular docking, virtual screening, and in vitro testing to discover novel Plk1-PBD inhibitors. Nine Plk1-PBD crystal structures were used to generate structure-based hypotheses. A common pharmacophore model (Hypo1) composed of five chemical features was selected from the 9 structure-based hypotheses and used for virtual screening of a drug-like database consisting of 159,757 compounds to identify novel Plk1-PBD inhibitors. The virtual screening technique revealed 9,327 compounds with a maximum fit value of 3 or greater, which were selected and subjected to molecular docking analyses. This approach yielded 93 compounds that made good interactions with critical residues within the Plk1-PBD active site. The testing of these 93 compounds in vitro for their ability to inhibit the Plk1-PBD, showed that many of these compounds had Plk1-PBD inhibitory activity and that compound Chemistry_28272 was the most potent Plk1-PBD inhibitor. Thus Chemistry_28272 and the other top compounds are novel Plk1-PBD inhibitors and could be used for the development of cancer therapeutics