Safety effects of low-cost engineering measures. An observational study in a Portuguese multilane road

Single carriageway multilane roads are not, in general, a very safe type of road, mainly because of the high number of seriously injured victims in head-on collisions, when compared with dual carriageway multilane roads, with a median barrier.     

Proposal of visual strength grading rules for Uruguayan pine timber

A previous database with bending and density data of 261 structural size specimens of currently produced Uruguayan slash pine and loblolly pine timber was adjusted to meet testing and characterization requirements of European standards. Visual parameters and mechanical properties for each specimen were evaluated and their relationship was statistically analyzed. Results suggested that all specimens can be grouped in one visual grade with engineered properties similar to those of European C14 strength class. Modulus of elasticity and characteristic bending strength were the defining properties for class assignation.     

A deep learning approach for the forensic evaluation of sexual assault

Despite the existence of patterns able to discriminate between consensual and non-consensual intercourse, the relevance of genital lesions in the corroboration of a legal rape complaint is currently under debate in many countries. The testimony of the physicians when assessing these lesions has been questioned in court due to several factors (e.g., a lack of comprehensive knowledge of lesions, wide spectrum of background area, among others). Therefore, it is relevant to provide automated tools to support the decision process in an objective manner. In this work, we evaluate the performance of state-of-the-art deep learning architectures for the forensic assessment of sexual assault. We propose a deep architecture and learning strategy to tackle the class imbalance on deep learning using ranking. The proposed methodologies achieved the best results when compared with handcrafted feature engineering and with other deep architectures.     

A methodology for screening of microalgae as a decision making tool for energy and green chemical process applications

The increasing interest for biotechnological use of microalgae demands a methodology for selection of species suitable to support the development of technologies based on the use of such non-conventional renewable raw material, i.e., green industrial applications. The vast and expanding collection of experimental data on both cell growth and biomass composition available in the literature can be used to reduce the cost of the experimental investigations required to support process engineering and optimization. Selecting the appropriate organism requires extracting useful information from such data, a cumbersome task since various multidisciplinary factors must be considered. This paper presents a computer-aided methodology for selecting appropriate algal species given an energy or green chemical process application employing microalgae as a renewable raw material. The approach is “system oriented”, based on biomass composition and chemical processing of the biomass downstream of the CO₂ biofixation and harvesting operations. Quantitative performance results are supported by professional process simulation. Besides comparison of a set of species performances, the proposed methodology also allows the discrimination among distinct algal compositions resulting from different growth conditions for a given species. Furthermore, three categories of screening metrics are proposed to be maximized by the decision making procedure in order to elicit the relevant information. To demonstrate the potential of the proposed methodology, a databank of both biochemical and elemental compositions of microalgal biomass was used in three green applications: Assessment of biomass heating value; production of syngas by gasification of the biomass; and production of Bio-H₂. Within the accuracy of the databank employed to illustrate the procedure, the methodology selected Botryococcus braunii and Isochrysis galbana as potential promising candidates, for the three examined applications.     

Extraction and characterization of highly purified collagen from bovine pericardium for potential bioengineering applications

Bovine pericardium is widely used as a raw material in bioengineering as a source of collagen, a fundamental structural molecule. The physical, chemical, and biocompatibility characteristics of these natural fibers enable their broad use in several areas of the health sciences. For these applications, it is important to obtain collagen of the highest possible purity. The lack of a method to produce these pure biocompatible materials using simple and economically feasible techniques presents a major challenge to their production on an industrial scale. This study aimed to extract, purify, and characterize the type I collagen protein originating from bovine pericardium, considered to be an abundant tissue resource. The pericardium tissue was collected from male animals at slaughter age. Pieces of bovine pericardium were enzymatically digested, followed by a novel protocol developed for protein purification using ion-exchange chromatography. The material was extensively characterized by electrophoresis, scanning electron microscopy, energy dispersive X-ray spectroscopy, and infrared spectroscopy. The results showed a purified material with morphological properties and chemical functionalities compatible with type I collagen and similar to a highly purified commercial collagen. Thus, an innovative and relatively simple processing method was developed to extract and purify type I collagen from bovine tissue with potential applications as a biomaterial for regenerative tissue engineering.     

Preliminary Characterization of Novel Extra-cellular Lipase from Penicillium crustosum Under Solid-State Fermentation and its Potential Application for Triglycerides Hydrolysis

Current studies about lipase production involve the use of agro-industrial residues and newly isolated microorganisms aimed at increasing economic attractiveness of the process. Based on these aspects, the main objective of this work is to perform the partial characterization of enzymatic extracts produced by a newly isolated Penicillium crustosum in solid-state fermentation. Lipase extract presented optimal temperature and pH of 37?°C and 9?C10, respectively. The concentrated enzymatic extract showed more stability at 25?°C and pH?7. The enzymes kept 100% of their enzymatic activity until 60?days of storage at 4 and ?10?°C. The stability under calcium salts indicated that the hydrolytic activity presented decay with the increase of calcium concentration. The specificity under several substrates indicated good enzyme activities in triglycerides from C4 to C18.     

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from ?0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.     

Motivations for the IT Professional Turnaway Intention: A Delphi Approach

ABSTRACT

In this paper, we use the Delphi method to identify the main motivations that have led IT professionals to intend to move to another area. We found that the pursuit of career growth and the quest for acquiring new experiences and abilities have a marked impact on the IT professional turnaway intention. Moreover, female IT professionals and IT professionals oriented by the career advancement paradigm have a higher propensity to abandon the IT area.     

Modeling the leadership – project performance relation: radial basis function,Gaussian and Kriging methods as alternatives to linear regression

The purpose of this paper is to analyze alternative forecasting methods that produce results at least similar to or better than linear regression (MLR) that can be used in the modeling of social systems. While organizations may be considered as typically non-linear systems, the common feature of most models found in literature continues to be the use of linear regression techniques. From a case study, advanced statistical methods of Gaussian and Kriging are evaluated, as well as an artificial intelligence (AI) tool, the radial basis function (RBF). The results show the best performance of the suggested methods compared to MLR, especially RBF, because of its uniform prediction behavior throughout all ranges of evaluation. These techniques, although somewhat unconventional in social systems modeling, present a potential contribution in increasing the accuracy and precision of the predictions allowing a more accurate assessment of the impact of certain strategies on the project performance to be made before the allocation of material, human and financial resources.