No abstract

In this study a total of 220 isolates of lactic acid bacteria (LAB) recovered from 10 types of Brazilian artisanal cheeses marketed in 4 main regions of Brazil were evaluated regarding their safety and ability to produce diacetyl (a precursor of aromatic compounds), exopolysaccharides (EPS; from different sugar sources), and antagonistic activity against Listeria monocytogenes and Staphylococcus aureus. The results indicated that 131 isolates (59.6%) were classified as strong (40.5%) and moderate (19.1%) diacetyl producers; 28 isolates (12.7%) stood out due to their remarkable production of EPS from different sugars, including sucrose (3.2%), fructose (2.3%), lactose (2.3%), and glucose (6%). Furthermore, 94.1% and 95.9% of isolates presented antagonistic activity against S. aureus and L. monocytogenes, respectively, even though only 27 isolates (12.3%) exhibited positive results in the bacteriocin production test. None of the isolates tested presented hemolytic activity, and 117 were classified as safe, due to their intrinsic resistance to a maximum of 4 different antibiotics. The data obtained for assessment of antibiogram profile and technological potential (moderate and high production of diacetyl, EPS, and bacteriocins) were submitted to a multiple correspondence analysis to correlate them with the cheese of isolation. Regarding the antimicrobial profile of LAB strains, it was possible to verify an association between isolates from Minas artisanal cheeses from Araxá and resistance to tetracycline; Minas artisanal cheeses from Serro and resistance to erythromycin; Coalho and Minas artisanal cheese from Cerrado and resistance to penicillin; and isolates from Serrano and Colonial cheeses with clindamycin and ceftazidime resistance. Although the susceptibility of strains to these antibiotics was considered high (71.8-80.5%), these data may be related to the horizontal transfer of genes in the production chain of these cheeses. Results of multiple correspondence analysis also showed that isolates with antagonistic activity were mostly isolated from Manteiga, Colonial, and Coalho cheeses. The isolates with high or moderate EPS-producer ability from sucrose, glucose, and fructose were mainly associated with Minas artisanal cheeses from Cerrado. In contrast, isolates with high or moderate EPS-producer ability from lactose were isolated from Serrano, Minas artisanal cheeses from Canastra, and Campo das Vertentes microregions. Finally, isolates from Minas artisanal cheeses (from Araxá microregion), Coalho, and Caipira cheeses were associated with moderate/high diacetyl production. To the best of the authors' knowledge, this study provides, for the first time, data indicating that the dominant technological, biopreservative, and safety properties of LAB isolates can be correlated with the type of Brazilian artisanal cheeses, which denotes its singularity. This knowledge is of utmost relevance for the development of starter or adjunct cultures with tailored properties.

In a real-time hybrid simulation, a transfer system is used to enforce the interface interaction between computational and physical substructures. A model-based, multilayer nonlinear control system is developed to accommodate extensive performance variations and uncertainties in a physical substructure. The aim of this work is to extend the application of real-time hybrid simulation to investigating failure, nonlinearity, and nonstationary behavior. This Self-tuning Robust Control System (SRCSys) consists of two layers: robustness and adaptation. The robustness layer synthesizes a nonlinear control law such that the closed-loop dynamics perform as intended under a broad range of parametric and nonparametric uncertainties. Sliding mode control is employed as the control scheme in this layer. Then, the adaptation layer reduces uncertainties at run time through slow and controlled learning of the control plant. The tracking performance of the SRCSys is evaluated in two experiments that have highly uncertain physical specimens.

No abstract

No abstract

No abstract

No abstract

Mobile bridges have been used for a broad range of applications including military transportation or disaster restoration. Because mobile bridges are rapidly deployed under a wide variety of conditions, often remaining in place for just minutes to hours, and have irregular usage patterns, a detailed record of usage history is important for ensuring structural safety. To facilitate usage data collection in mobile bridges, a new acceleration-based vehicle classification technique is proposed to automatically identify the class of each vehicle. Herein we present a new technique that is based on the premise that each class of vehicles produces distinctive dynamic patterns while crossing this mobile bridge, and those patterns can be extracted from the system's acceleration responses. Measured acceleration signals are converted to time-frequency images to extract twodimensional patterns. The Viola-Jones object detection algorithm is applied here to extract and classify those patterns. The effectiveness of the technique is investigated and demonstrated using laboratory and full-scale mobile bridges by simulating realistic scenarios.