A decision support model for construction cash flow management

The excessive level of construction business failures and their association with financial difficulties has placed financial management in the forefront of many business imperatives. This has highlighted the importance of cash flow forecasting and management that has given rise to the development of several forecasting models. The traditional approach to the use of project financial models has been largely a project-oriented perspective. However, the dominating role of “project economics” in shaping “corporate economics” tends to place the corporate strategy at the mercy of the projects. This article approaches the concept of cash flow forecasting and management from a fresh perspective. Here, the use of forecasting models is extended beyond their traditional role as a guideline for monitoring and control of progress. They are regarded as tools for driving the project in the direction of corporate goals. The work is based on the premise that the main parties could negotiate the terms and attempt to complement their priorities. As part of this approach, a model is proposed for forecasting and management of project cash flow. The mathematical component of the model integrates three modules: an exponential and two fourth-degree polynomials. The model generates a forecast by potentially combining the outcome of data analysis with the experience and knowledge of the forecaster/organization. In light of corporate objectives, the generated forecast is then manipulated and replaced by a range of favorable but realistic cash flow profiles. Finally, through a negotiation with other parties, a compromised favorable cash flow is achieved. This article will describe the novel way the model is used as a decision support tool. Although the structure of the model and its mathematical components are described in detail, the data processing and analysis parts are briefly described and referenced accordingly. The viability of the model and the approach are demonstrated by means of a scenario

Multi-waveband Emission Maps of Blazars

We are leading a comprehensive multi-waveband monitoring program of 34 gamma-ray bright blazars designed to locate the emission regions of blazars from radio to gamma-ray frequencies. The "maps" are anchored by sequences of images in both total and polarized intensity obtained with the VLBA at an angular resolution of ~ 0.1 milliarcseconds. The time-variable linear polarization at radio to optical wavelengths and radio to gamma-ray light curves allow us to specify the locations of flares relative to bright stationary features seen in the images and to infer the geometry of the magnetic field in different regions of the jet. Our data reveal that some flares occur simultaneously at different wavebands and others are only seen at some of the frequencies. The flares are often triggered by a superluminal knot passing through the stationary "core" on the VLBA images. Other flares occur upstream or even parsecs downstream of the core.Comment: 5 pages, including 2 figures; to be published in Journal of Astrophysics and Astronomy, as part of proceedings of the meeting "Multiwavelength Variability of Blazars" held in Guangzhou, China, in September 201

A Special Homotopy Continuation Method For A Class of Polynomial Systems

A special homotopy continuation method, as a combination of the polyhedral homotopy and the linear product homotopy, is proposed for computing all the isolated solutions to a special class of polynomial systems. The root number bound of this method is between the total degree bound and the mixed volume bound and can be easily computed. The new algorithm has been implemented as a program called LPH using C++. Our experiments show its efficiency compared to the polyhedral or other homotopies on such systems. As an application, the algorithm can be used to find witness points on each connected component of a real variety

Granuloma multifocal de células de langherhans (Doença de Hand-Schuller-Christian): relato de caso e revisão de literatura.

Trabalho de Conclusão de Curso - Universidade Federal de Santa Catarina, Centro de Ciências da Saúde, Departamento de Pediatria, Curso de Medicina, Florianópolis, 199

An Improvement Study of the Decomposition-based Algorithm Global WASF-GA for Evolutionary Multiobjective Optimization

The convergence and the diversity of the decompositionbased evolutionary algorithm Global WASF-GA (GWASF-GA) relies on a set of weight vectors that determine the search directions for new non-dominated solutions in the objective space. Although using weight vectors whose search directions are widely distributed may lead to a well-diversified approximation of the Pareto front (PF), this may not be enough to obtain a good approximation for complicated PFs (discontinuous, non-convex, etc.). Thus, we propose to dynamically adjust the weight vectors once GWASF-GA has been run for a certain number of generations. This adjustment is aimed at re-calculating some of the weight vectors, so that search directions pointing to overcrowded regions of the PF are redirected toward parts with a lack of solutions that may be hard to be approximated. We test different parameters settings of the dynamic adjustment in optimization problems with three, five, and six objectives, concluding that GWASF-GA performs better when adjusting the weight vectors dynamically than without applying the adjustment.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The Gauss Law: A Tale

The Gauss law plays a basic role in gauge theories, enforcing gauge invariance and creating edge states and superselection sectors. This article surveys these aspects of the Gauss law in QED, QCD and nonlinear $G/H$ models. It is argued that nonabelian superselection rules are spontaneously broken. That is the case with $SU(3)$ of colour which is spontaneously broken to $U(1)\times U(1)$. Nonlinear $G/H$ models are reformulated as gauge theories and the existence of edge states and superselection sectors in these models is also established.Comment: Published version. References adde

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

The surface accessibility of α-bungarotoxin monitored by a novel paramagnetic probe

The surface accessibility of {alpha}-bungarotoxin has been investigated by using Gd2L7, a newly designed paramagnetic NMR probe. Signal attenuations induced by Gd2L7 on {alpha}-bungarotoxin C{alpha}H peaks of 1H-13C HSQC spectra have been analyzed and compared with the ones previously obtained in the presence of GdDTPA-BMA. In spite of the different molecular size and shape, for the two probes a common pathway of approach to the {alpha}-bungarotoxin surface can be observed with an equally enhanced access of both GdDTPA-BMA and Gd2L7 towards the protein surface side where the binding site is located. Molecular dynamics simulations suggest that protein backbone flexibility and surface hydration contribute to the observed preferential approach of both gadolinium complexes specifically to the part of the {alpha}-bungarotoxin surface which is involved in the interaction with its physiological target, the nicotinic acetylcholine receptor