Age and productivity as determinants of firm survival over the industry life cycle

This paper contributes to fill the gap between the literature on the determinants of firm survival and the empirical works on the industry life cycle (ILC). Using a representative sample of Spanish firms with 10 or more employees over the period 1993–2009, the role played by firm age and productivity in firm survival is empirically analysed across three stages of the life cycle of forty-seven 3-digit manufacturing sectors. In the ‘early’ stage of the ILC, firm age is negatively correlated with hazard rates while firm productivity is not. Firm productivity is associated with lower hazard in the ‘mature’ stage of the ILC, when competition is primarily efficiency-driven, while firm age does not play a significant role for firm survival. In the ‘intermediate’ stage, both age and productivity play a role in reducing firms’ hazard rates.     

An optimization method for learning statistical classifiers in structural reliability

Monte Carlo simulation is a general and robust method for structural reliability analysis, affected by the serious efficiency problem consisting in the need of computing the limit state function a very large number of times. In order to reduce this computational effort the use of several kinds of solver surrogates has been proposed in the recent past. Proposals include the Response Surface Method (RSM), Neural Networks (NN), Support Vector Machines (SVM) and several other methods developed in the burgeoning field of Statistical Learning (SL). Many of these techniques can be employed either for function approximation (regression approach) or for pattern recognition (classification approach). This paper concerns the use of these devices for discriminating samples into safe and failure classes using the classification approach, because it constitutes the core of Monte Carlo simulation as applied to reliability analysis as such. Due to the flexibility of most SL methods, a critical step in their use is the generation of the learning population, as it affects the generalization capacity of the surrogate. To this end it is first demonstrated that the optimal population from the information viewpoint lies around in the vicinity of the limit state function. Next, an optimization method assuring a small as well as highly informative learning population is proposed on this basis. It consists in generating a small initial quasi-random population using Sobol sequence for triggering a Particle Swarm Optimization (PSO) performed over an iteration-dependent cost function defined in terms of the limit state function. The method is evaluated using SVM classifiers, but it can be readily applied also to other statistical classification techniques because the distinctive feature of the SVM, i.e. the margin band, is not actively used in the algorithm. The results show that the method yields results for the probability of failure that are in very close agreement with Monte Carlo simulation performed on the original limit state function and requiring a small number of learning samples.     

Anticoagulant, Antioxidant and Antitumor Activities of Heterofucans from the Seaweed Dictyopteris delicatula

In the present study, six families of sulfated polysaccharides were obtained from seaweed Dictyopteris delicatula by proteolytic digestion, followed by acetone fractionation and molecular sieving on Sephadex G-100. Chemical analyses demonstrated that all polysaccharides contain heterofucans composed mainly of fucose, xylose, glucose, galactose, uronic acid, and sulfate. The fucans F0.5v and F0.7v at 1.0 mg/mL showed high ferric chelating activity (～45%), whereas fucans F1.3v (0.5 mg/mL) showed considerable reducing power, about 53.2% of the activity of vitamin C. The fucan F1.5v presented the most prominent anticoagulant activity. The best antiproliferative activity was found with fucans F1.3v and F0.7v. However, F1.3v activity was much higher than F0.7v inhibiting almost 100% of HeLa cell proliferation. These fucans have been selected for further studies on structural characterization as well as in vivo experiments, which are already in progress.     

Effect of Temperature on the Viscosity of Honey

Influence of temperature on the viscosity of honeys was analysed in the present work. The viscosity value decreased with the increase of temperature. The effect caused by temperature in the range studied was more important in the low range of temperature, whereas at high temperature, the viscosity showed less variation. Water activity and glass transition temperature was determined to relate these characteristics. Four experimental viscosity models were checked using the experimental data to correlate the influence of temperature upon honey viscosity.     

Error modeling for an untethered ultra-wideband system for construction indoor asset tracking

This paper analyzes the performance of an ultra-wideband (UWB) wireless network system for mobile asset tracking at a dynamic construction site. Static and dynamic error tests were performed on a commercial UWB system in different building spaces including open/closed spaces, steel/wood framed construction sites, and a closed office area. All tests were carried out with an untethered UWB configuration for more flexible deployment of the UWB system at construction sites. Statistical approaches including regression analysis, outlier detection, and Kalman filtering were used to build an error model. The research found that each site has a unique pattern of producing errors caused by various types of interference, e.g., electromagnetic interference, multi-path propagation, fading and scattering of signals. Approximately 25% of the errors were reduced by using the proposed error modeling process. The paper concludes that a statistically developed error model process can significantly reduce random errors and improve position accuracy for indoor mobile asset tracking applications in construction.     

Stress–strain relationship in axial compression for concrete using recycled saturated coarse aggregate

In this work the stress–strain curve of the recycled concretes was determined by replacing different percentages of the natural coarse aggregate with recycled coarse aggregate (20%, 50% and 100%). The results made it possible to establish the differences between the stress–strain relationship of a conventional concrete and this relationship for a recycled concrete depending on the percentage of replacement. In this way, it was found that the longitudinal strain of the recycled concretes increases with the percentage of recycled coarse aggregate used.Finally, using the experimental results, an analytical expression of the stress–strain curve accounting for the percentage of replacement was developed. The verification of the proposed model equation was done by comparing it to the experimental data. The results show that the proposed model equation satisfactorily describes the effect of the recycled aggregate on the stress–strain curve.