Company failure prediction in the construction industry

This paper proposes a new model to predict company failure in the construction industry. The model includes three major innovative aspects. The use of strategic variables reflecting the key specificities of construction companies, which are critical to explain company failure. The use of data mining techniques, i.e. support vector machine to predict company failure. The use of two different sampling methods (random undersampling and random oversampling with replacement) to balance class distributions. The model proposed was empirically tested using all Portuguese contractors that operated in 2009. It is concluded that support vector machine, with random oversampling and including strategic variables, is a very robust tool to predict company failure in the context of the construction industry. In particular, this model outperforms the results obtained with logistic regression.     

Effects of contacts on the electrical characterization of amorphous chalcogenide alloys

The comparison of the electrical parameters in amorphous chalcogenide, obtained using three different methods (electrical impedance, d.c. and the four-point resistivity measurements), has shown that impedance measurement is the most appropriate because it is able to separate the contributions of the bulk and contacts. For that reason, impedance measurements provide the real values for the electrical parameters of the material. In addition, the impedance measurements were able to detect the initial stages of the amorphous-to-crystalline phase transition.     

NASA Langley’s approach to the Sandia’s structural dynamics challenge problem

The objective of this challenge is to develop a data-based probabilistic model of uncertainty to predict the acceleration response of subsystems (payloads) by themselves and while coupled to a primary (target) system. Although deterministic analyses of this type are routinely performed and representative of issues faced in real-world system design and integration, there are still several key technical challenges that must be addressed when analyzing the uncertainties of interconnected systems. For example, one key technical challenge is related to the fact that there is limited data on the target configurations. Also, while multiple data sets from experiments conducted at the subsystem level are provided, samples sizes are not sufficient to compute high confidence statistics. Moreover, in this challenge problem, additional constraints, in the form of ground rules, have been added. One such constraint is that mathematical models of the subsystem are limited to linear approximations of the nonlinear physics of the problem at hand. Also, participants are constrained to use these subsystem models and the multiple data sets to make predictions about the target system response under completely different forcing functions.Initially, our approach involved the screening of several different methods to arrive at the three presented herein. The first one is based on a transformation of the structural dynamic data in the modal domain to an orthogonal space where the mean and covariance of the data are matched. The other two approaches worked solutions in physical space where the uncertain parameter set is made of masses, stiffnessess, and damping coefficients; one matches the confidence intervals of low order moments of the statistics via optimization while the second one uses a Kernel density estimation approach. The paper will touch on the approaches, lessons learned, validation metrics and their comparison, data quantity restriction, and assumptions/limitations of each approach.     

Knowledge-Sharing Issues in Experimental Software Engineering

Recently the awareness of the importance of replicating studies has been growing in the empirical software engineering community. The results of any one study cannot simply be extrapolated to all environments because there are many uncontrollable sources of variation between different environments. In our work, we have reasoned that the availability of laboratory packages for experiments can encourage better replications and complementary studies. However, even with effectively specified laboratory packages, transfer of experimental know-how can still be difficult. In this paper, we discuss the collaboration structures we have been using in the Readers’ Project, a bilateral project supported by the Brazilian and American national science agencies that is investigating replications and transfer of experimental know-how issues. In particular, we discuss how these structures map to the Nonaka–Takeuchi knowledge sharing model, a well-known paradigm used in the knowledge management literature. We describe an instantiation of the Nonaka–Takeuchi Model for software engineering experimentation, establishing a framework for discussing knowledge sharing issues related to experimental software engineering. We use two replications to illustrate some of the knowledge sharing issues we have faced and discuss the mechanisms we are using to tackle those issues in Readers’ Project.     

Large scale additive manufacturing of eco-composites

The evolution of additive manufacturing processes is enabling the production of parts with improved dimensional accuracy, mechanical, physical and chemical properties [1]. New materials also contribute to this trend, and in this scope, eco-composites, materials with environmental and ecological advantages, which include natural polymers, have been acquiring increased relevance [2]. The purpose of this study is to develop composite material parts manufactured from recycled thermoplastics and natural fibres, in this case, wood residues. Additive manufacturing (fused deposition modelling) will be accomplished using a robot combined with extrusion unit. The objective is to access the influence of the main manufacturing parameters, such as temperature, distance between layers or deposition speed, on the final part characteristics, especially dimensional accuracy. Reverse engineering and several material analysis techniques will be employed to achieve this goal.     

Economic feasibility for acquisition of efficient refrigerators in Brazil

Since 1993, the Brazilian National Program of Electricity Conservation has been developing the PROCEL Label Program, oriented towards helping consumers to buy more efficient home appliances. In this context, an energy savings of 1379 GWh and a reduction of 197 MW in the Brazilian demand in 2007 are estimated as resulting from efficiency labeling in refrigerators and freezers. This paper aims to evaluate the economic feasibility of purchasing labeled refrigerators instead of inefficient ones, from the consumer’s point of view, considering actual market conditions and buying in cash or financing. The evaluation of energy saving was done for 22 different models of refrigerators and the economy was calculated considering the retail price in the Brazilian market and two actual electricity tariffs, taking into account, respectively, high and low household electricity consumption. The effect of ambient temperature on refrigerator performance was evaluated in two conditions: according to the Brazilian standard for performance tests (32 °C) and using the average temperature of Southern Brazil (18 °C), the mildest region. The benefit in buying labeled refrigerators was evaluated using the Internal Rate of Return and the Payback Time for a cash flow during the appliance life, estimated as 16 years. The results indicate that for cash purchase, for any electricity tariff and for both ambient temperatures studied, the consumers are economically benefited buying labeled refrigerators instead of less efficient models. For credit purchases, in the evaluated conditions, high-tariff consumer typically gains selecting labeled refrigerator, on the other hand, for low-tariff consumer and in colder areas, it is economically advisable to buy less efficient appliances. Sensitivity analysis of energy tariff and financing conditions are presented.     

The gene for autosomal dominant cerebellar ataxia type II is located in a 5-cM region in 3p12-p13: genetic and physical mapping of the SCA7 locus

Two families with autosomal dominant cerebellar ataxia with pigmentary macular dystrophy (ADCA type II) were investigated. Analysis of 23 parent-child couples demonstrated the existence of marked anticipation, greater in paternal than in maternal transmissions, with earlier age at onset and a more rapid clinical course in successive generations. Clinical analysis revealed the presence of a great variability in age at onset, initial symptom, and associated signs, confirming the characteristic clinical heterogeneity of ADCA type II. The gene for ADCA type II previously was mapped to the spinocerebellar ataxia 7 (SCA7) locus on chromosome 3p12-p21.1. Linkage analysis of the two new families of different geographic origin confirmed the characteristic genetic homogeneity of ADCA type II, distinguishing it from ADCA type I. Haplotype analysis permitted refinement of the SCA7 region to the 5-cM interval between markers D3S1312 and D3S1600 on chromosome 3p12-p13. Eighteen sequence-tagged sites were used for the construction of an integrated map of the candidate region, based on a YACs contig. The entire candidate region is contained in a single nonchimeric YAC of 660 kb. The probable involvement of a CAG trinucleotide expansion, suggested by previous studies, should greatly facilitate the identification of the gene for ADCA type II.     

Desorption isotherms and thermodynamic properties of sweet corn cultivars (Zea mays L.)

The desorption isotherms and thermodynamic properties of two cultivars of sweet corn were obtained during the drying process of these products. The isotherms were determined by a dynamic method for various temperature and humidity conditions. Equilibrium moisture content (X_eq) data were correlated by the Guggenheim–Anderson–de Boer model and an artificial neural network (ANN) model. These models were fitted to the experimental data. The X_eq for corn grain increased with an increase in the relative humidity at fixed temperature and decreased with an increase in temperature at a constant relative humidity. The experimental data were analysed by a thermodynamic approach to obtain the isosteric heat of desorption (ΔH), differential entropy (ΔS), activation energy (E_a) and Gibbs free energy (ΔG). The ΔH and ΔS increased with a decrease in moisture content, while ΔG decreased exponentially with an increase in X_eq. The Arrhenius equation was used to obtain E_a values, with Supersweet corn having higher E_a.