Combined Effect of Textured Patterns and Graphene Flake Additives on Tribological Behavior under Boundary Lubrication

A ball-on-plate wear test was employed to investigate the effectiveness of graphene (GP) nanoparticles dispersed in a synthetic-oil-based lubricant in reducing wear. The effect by area ratio of elliptically shaped dimple textures and elevated temperatures were also explored. Pure PAO4 based oil and a mixture of this oil with 0.01 wt% GP were compared as lubricants. At pit area ratio of 5%, GP-base oil effectively reduced friction and wear, especially at 60 and 100°C. Under pure PAO4 oil lubrication, the untextured surfaces gained low friction coefficients (COFs) and wear rates under 60 and 100°C. With increasing laser—texture area ratio, the COF and wear rate decreased at 25 and 150°C but increased at 60 and 100°C. Under the GP-based oil lubrication, the textured surface with 5% area ratio achieved the lowest COF among those of the area ratios tested at all test temperatures. Meanwhile, the textured surface with 20% area ratio obtained the highest COF among those of the area ratios. With the joint action of GP and texture, the textured surface with 10% area ratio exhibited the best anti-wear performance among all of the textured surfaces at all test temperatures.     

Baicalein Inhibits Staphylococcus aureus Biofilm Formation and the Quorum Sensing System In Vitro

Biofilm formed by Staphylococcus aureus significantly enhances antibiotic resistance by inhibiting the penetration of antibiotics, resulting in an increasingly serious situation. This study aimed to assess whether baicalein can prevent Staphylococcus aureus biofilm formation and whether it may have synergistic bactericidal effects with antibiotics in vitro. To do this, we used a clinically isolated strain of Staphylococcus aureus 17546 (t037) for biofilm formation. Virulence factors were detected following treatment with baicalein, and the molecular mechanism of its antibiofilm activity was studied. Plate counting, crystal violet staining, and fluorescence microscopy revealed that 32 μg/mL and 64 μg/mL baicalein clearly inhibited 3- and 7-day biofilm formation in vitro. Moreover, colony forming unit count, confocal laser scanning microscopy, and scanning electron microscopy showed that vancomycin (VCM) and baicalein generally enhanced destruction of biofilms, while VCM alone did not. Western blotting and real-time quantitative polymerase chain reaction analyses (RTQ-PCR) confirmed that baicalein treatment reduced staphylococcal enterotoxin A (SEA) and α-hemolysin (hla) levels. Most strikingly, real-time qualitative polymerase chain reaction data demonstrated that 32 μg/mL and 64 μg/mL baicalein downregulated the quorum-sensing system regulators agrA, RNAIII, and sarA, and gene expression of ica, but 16 μg/mL baicalein had no effect. In summary, baicalein inhibited Staphylococcus aureus biofilm formation, destroyed biofilms, increased the permeability of vancomycin, reduced the production of staphylococcal enterotoxin A and α-hemolysin, and inhibited the quorum sensing system. These results support baicalein as a novel drug candidate and an effective treatment strategy for Staphylococcus aureus biofilm-associated infections.     

Inertia weight control strategies for particle swarm optimization

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.     

A taxonomic revision of <Emphasis Type="Italic">Bursera</Emphasis> subgen. <Emphasis Type="Italic">Bursera</Emphasis> in the Greater Antilles and the Bahamas,including a new species from Cuba

A revision of Bursera in the Greater Antilles and the Bahamas has confirmed that Commiphora does not occur in the region and that all but one species in the region belong to Bursera subgen. Bursera. Here we describe a new species, Bursera yaterensis; B. nashii is synonymized with B. glauca and B. ovata is synonymized with B. trinitensis; and we return five species from Commiphora to Bursera. A dichotomous key is provided using mostly vegetative characters due to the frequent lack of adequate reproductive material and the relative uniformity of most floral and fruit characters.