Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Effect of particle morphology on the mechanical and thermo-mechanical behavior of polymer composites

Fiber reinforced polymer composites have been used in many applications, such as in automobile, aerospace and naval industries, due basically to their high strength-to-weight and modulus-to-weight, among other properties. Even though particles are usually not able to lead to the level of reinforcement of fibers, particle reinforced polymer composites have been proposed for many new applications due to their low cost, easy fabrication and isotropic properties. In this work, polymer composites were prepared by incorporating glass particles of different morphologies on poly(aryl sulfones) matrices. Particles with aspect ratios equal to 1, 2.5 and 10 were used. The prepared composites were characterized using electron microscopy and thermal analysis. Mechanical properties of the composites were evaluated using a four-point bending test. The thermo-mechanical behavior of the obtained composites was also investigated. The results showed that the morphology of the particles alter significantly the mechanical properties of composites. Particles with larger values of aspect ratio led to large elastic modulus but low levels of strain at failure. This result was explained by modeling the thermo-mechanical behavior of the composites using a viscoelastic model. Parameters of the model, obtained from a Cole-Cole type of plot, demonstrated that interactions at the polymer-reinforcing agent interface were higher for composites with large aspect ratio particles. Higher levels of interactions at interfaces can lead to higher degrees of stress transfer and, consequently, to composites with large elastic modulus, as experimentally observed.

Adenovirus-mediated siRNA targeting TNF-α and overexpression of bone morphogenetic protein-2 promotes early osteoblast differentiation on a cell model of Ti particle-induced inflammatory response in vitro

Wear particles are phagocytosed by macrophages and other inflammatory cells, resulting in cellular activation and release of proinflammatory factors, which cause periprosthetic osteolysis and subsequent aseptic loosening, the most common causes of total joint arthroplasty failure. During this pathological process, tumor necrosis factor-alpha (TNF-α) plays an important role in wear-particle-induced osteolysis. In this study, recombination adenovirus (Ad) vectors carrying both target genes [TNF-α small interfering RNA (TNF-α-siRNA) and bone morphogenetic protein 2 (BMP-2)] were synthesized and transfected into RAW264.7 macrophages and pro-osteoblastic MC3T3-E1 cells, respectively. The target gene BMP-2, expressed on pro-osteoblastic MC3T3-E1 cells and silenced by the TNF-α gene on cells, was treated with titanium (Ti) particles that were assessed by real-time PCR and Western blot. We showed that recombinant adenovirus (Ad-siTNFα-BMP-2) can induce osteoblast differentiation when treated with conditioned medium (CM) containing RAW264.7 macrophages challenged with a combination of Ti particles and Ad-siTNFα-BMP-2 (Ti-ad CM) assessed by alkaline phosphatase activity. The receptor activator of nuclear factor-κB ligand was downregulated in pro-osteoblastic MC3T3-E1 cells treated with Ti-ad CM in comparison with conditioned medium of RAW264.7 macrophages challenged with Ti particles (Ti CM). We suggest that Ad-siTNFα-BMP-2 induced osteoblast differentiation and inhibited osteoclastogenesis on a cell model of a Ti particle-induced inflammatory response, which may provide a novel approach for the treatment of periprosthetic osteolysis.

Particle size and cholesterol content of a mayonnaise formulated by OSA-modified potato starch

Egg yolk was partially replaced (0, 25, 50, 75, and 100%) with octenyl succinic anhydride (OSA)-modified potato starch in a reduced-fat mayonnaise formulation to curtail the problems associated with high cholesterol and induced allergic reactions. The physicochemical properties included parameters such as: pH, fat content, and emulsion stability of the formulations analyzed. The samples with 75% and 100% egg yolk substitute showed the maximum emulsion stability (>95% after two of months storage), and they were selected according to cholesterol content, particle size distributions, dynamic rheological properties, microstructure, and sensory characteristic. A significant reduction (84-97%) in the cholesterol content was observed in the selected samples. Particle size analysis showed that by increasing the amount of OSA starch, the oil droplets with the peak size of 70 µm engulfed by this compound became larger. The rheological tests elucidated that in the absence of egg yolk, OSA starch may not result in a final product with consistent texture and that the best ratio of the two emulsifiers (OSA starch/egg yolk) to produce stable reduced-fat, low cholesterol mayonnaise is 75/25. The microscopic images confirmed the formation of a stable cohesive layer of starch surrounding the oil droplets emulsified in the samples selected.