Predicting construction productivity with machine learning approaches

Machine learning (ML) is a purpose technology already starting to transform the global economy and has the potential to transform the construction industry with the use of data-driven solutions to improve the way projects are delivered. Unrealistic productivity predictions cause increased delivery cost and time. This study shows the application of supervised ML algorithms on a database including 1,977 productivity measures that were used to train, test, and validate the approach. Deep neural network (DNN), k-nearest neighbours (KNN), support vector machine (SVM), logistic regression, and Bayesian networks are used for predicting productivity by using a subjective measure (compatibility of personality), together with external and site conditions and other workforce characteristics. A case study of a masonry project is discussed to analyse and predict task productivity

Recent results on magnetic plasma turbulence

Magnetic plasma turbulence is observed over a broad range of scales in the solar wind. We discuss the results of high-resolution numerical simulations of magnetohydrodynamic (MHD) turbulence that models plasma motion at large scales and the results of numerical simulations of kinetic-Alfvén turbulence that models plasma motion at small, sub-proton scales. The simulations, with numerical resolutions up to 20483 mesh points in the MHD case and 5123 points in kinetic-Alfvén case and statistics accumulated over 30 to 150 eddy turnover times, constitute, to the best of our knowledge, the largest statistical sample of steadily driven three dimensional MHD and kinetic-Alfvén turbulence to date.This work was supported by the NSF/DoE partnership grant NSF-ATM-1003451 at the University of New Hampshire, the NSF sponsored Center for Magnetic Self-Organization in Laboratory and Astrophysical Plasmas at the University of Chicago and the University of Wisconsin - Madison, the US DoE awards DE-FG02- 07ER54932, DE-SC0003888, DE-SC0001794, the NSF grants PHY-0903872 and AGS-1003451, and the DoE INCITE 2010 Award. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, supported by the DoE Office of Science under contract DE-AC02-06CH11357. The studies were also supported by advanced computing resources provided by the NSF XSEDE allocation TG-PHY110016 at the National Institute for Computational Sciences and the PADS resource (NSF grant OCI-0821678) at the Computation Institute, a joint institute of Argonne National Laboratory and the University of Chicago

A model of plasma heating by large-scale flow

PublishedIn this work, we study the process of energy dissipation triggered by a slow large-scale motion of a magnetized conducting fluid. Our consideration is motivated by the problem of heating the solar corona, which is believed to be governed by fast reconnection events set off by the slow motion of magnetic field lines anchored in the photospheric plasma. To elucidate the physics governing the disruption of the imposed laminar motion and the energy transfer to small scales, we propose a simplified model where the large-scale motion of magnetic field lines is prescribed not at the footpoints but rather imposed volumetrically. As a result, the problem can be treated numerically with an efficient, highly accurate spectral method, allowing us to use a resolution and statistical ensemble exceeding those of the previous work. We find that, even though the large-scale deformations are slow, they eventually lead to reconnection events that drive a turbulent state at smaller scales. The small-scale turbulence displays many of the universal features of field-guided magnetohydrodynamic turbulence like a well-developed inertial range spectrum. Based on these observations, we construct a phenomenological model that gives the scalings of the amplitude of the fluctuations and the energy-dissipation rate as functions of the input parameters. We find good agreement between the numerical results and the predictions of the model.This research was supported by the NSF Center for Magnetic Self-Organization in Laboratory and Astrophysical Plasmas at the University of Chicago, by the US DOE award no. DE-SC0003888, by the NASA grant no. NNX11AE12G, and by the National Science Foundation under grant no. NSF PHY11-25915 and no. AGS-1261659. SB and JM appreciate the hospitality and support of the Kavli Institute for Theoretical Physics, University of California, Santa Barbara, where part of this work was conducted. Simulations were performed at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin under the NSF-Teragrid Projects TG-AST140015 & TG-PHY120042 and by the National Institute for Computational Sciences

The development of the enterprising motivation in tourism students. A comparative analysis between grade and postgraduate students

Increasing the number of entrepreneurs and the quality of the entrepreneurship, it is the key thing because its positive influences over the economic activity. For this reason, it turns out essential to understand the factors that determine this phenomenon. This paper develops a model that includes those factors which allows acting on the enterprising intention of the students in the field of tourism. It has been decided on a theoretical approach based on the basics of the intentional theory from a perspective of higher education. A survey with a sample of 122 tourism students has been used ? including both graduates and students. Our analysis suggests that curricular and extracurricular activities have a different effect in the intentions, attitudes and capacities for the business? project development. On the other hand, our results show a weak impact of these activities in the business? competences

Canonical quantization of non-commutative holonomies in 2+1 loop quantum gravity

In this work we investigate the canonical quantization of 2+1 gravity with cosmological constant $\Lambda>0$ in the canonical framework of loop quantum gravity. The unconstrained phase space of gravity in 2+1 dimensions is coordinatized by an SU(2) connection $A$ and the canonically conjugate triad field $e$. A natural regularization of the constraints of 2+1 gravity can be defined in terms of the holonomies of $A+=A + \sqrt\Lambda e$. As a first step towards the quantization of these constraints we study the canonical quantization of the holonomy of the connection $A_{\lambda}=A+\lambda e$ on the kinematical Hilbert space of loop quantum gravity. The holonomy operator associated to a given path acts non trivially on spin network links that are transversal to the path (a crossing). We provide an explicit construction of the quantum holonomy operator. In particular, we exhibit a close relationship between the action of the quantum holonomy at a crossing and Kauffman's q-deformed crossing identity. The crucial difference is that (being an operator acting on the kinematical Hilbert space of LQG) the result is completely described in terms of standard SU(2) spin network states (in contrast to q-deformed spin networks in Kauffman's identity). We discuss the possible implications of our result.Comment: 19 pages, references added. Published versio

Multiplatform serum metabolic phenotyping combined with pathway mapping to identify biochemical differences in smokers

Aim: Determining perturbed biochemical functions associated with tobacco smoking should be helpful for establishing causal relationships between exposure and adverse events. Results: A multiplatform comparison of serum of smokers (n = 55) and never-smokers (n = 57) using nuclear magnetic resonance spectroscopy, UPLC–MS and statistical modeling revealed clustering of the classes, distinguished by metabolic biomarkers. The identified metabolites were subjected to metabolic pathway enrichment, modeling adverse biological events using available databases. Perturbation of metabolites involved in chronic obstructive pulmonary disease, cardiovascular diseases and cancer were identified and discussed. Conclusion: Combining multiplatform metabolic phenotyping with knowledge-based mapping gives mechanistic insights into disease development, which can be applied to next-generation tobacco and nicotine products for comparative risk assessment

Nanotechnology in dentistry: prevention, diagnosis, and therapy

Ensanya Ali Abou Neel,1–3 Laurent Bozec,3 Roman A Perez,4,5 Hae-Won Kim,4–6 Jonathan C Knowles3,5 1Division of Biomaterials, Operative Dentistry Department, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia; 2Biomaterials Department, Faculty of Dentistry, Tanta University, Tanta, Egypt; 3UCL Eastman Dental Institute, Biomaterials and Tissue Engineering, London, UK; 4Institute of Tissue Regenerative Engineering (ITREN), 5Department of Nanobiomedical Science and BK21 Plus NBM Global Research Center for Regenerative Medicine, 6Department of Biomaterials Science, College of Dentistry, Dankook University, Cheonan, Republic of Korea Abstract: Nanotechnology has rapidly expanded into all areas of science; it offers significant alternative ways to solve scientific and medical questions and problems. In dentistry, nanotechnology has been exploited in the development of restorative materials with some significant success. This review discusses nanointerfaces that could compromise the longevity of dental restorations, and how nanotechnolgy has been employed to modify them for providing long-term successful restorations. It also focuses on some challenging areas in dentistry, eg, oral biofilm and cancers, and how nanotechnology overcomes these challenges. The recent advances in nanodentistry and innovations in oral health-related diagnostic, preventive, and therapeutic methods required to maintain and obtain perfect oral health, have been discussed. The recent advances in nanotechnology could hold promise in bringing a paradigm shift in dental field. Although there are numerous complex therapies being developed to treat many diseases, their clinical use requires careful consideration of the expense of synthesis and implementation. Keywords: nanotechnology, nanointerfaces, biofilm-related oral diseases, tissue engineering, drug delivery, toxicit

Production of the Superconducting Matching Quadrupoles for the LHC Insertions

The LHC insertions are equipped with individually powered superconducting quadrupole assemblies comprising several quadrupole magnets and orbit correctors, and range in length from 5.3 m to 11.3 m. Following the initial experience in the assembly of the pre-series cold masses, the production has advanced well and about half of the total of 82 units has been produced at CERN. In this paper we present the experience gained in steering the cold mass production, in particular with respect to the alignment requirements. We also report on the field quality and other measurements made for assuring the quality of the quadrupoles