A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

Influence of pressure and dwell time on pressure-assisted sintering of calcium cobaltite

Calcium cobaltite Ca₃Co₄O₉, abbreviated Co349, is a promising thermoelectric material for high-temperature applications in air. Its anisotropic properties can be assigned to polycrystalline parts by texturing. Tape casting and pressure-assisted sintering (PAS) are a possible future way for a cost-effective mass-production of thermoelectric generators. This study examines the influence of pressure and dwell time during PAS at 900°C of tape-cast Co349 on texture and thermoelectric properties. Tape casting aligns lentoid Co349. PAS results in a textured Co349 microstructure with the thermoelectrically favorable ab-direction perpendicular to the pressing direction. By pressure variation during sintering, the microstructure of Co349 can be tailored either toward a maximum figure of merit as required for energy harvesting or toward a maximum power factor as required for energy harvesting. Moderate pressure of 2.5 MPa results in 25% porosity and a textured microstructure with a figure of merit of 0.13 at 700°C, two times higher than the dry-pressed, pressureless-sintered reference. A pressure of 7.5 MPa leads to 94% density and a high power factor of 326 µW/mK² at 800°C, which is 11 times higher than the dry-pressed reference (30 MPa) from the same powder.     

Nanocomposite powders of hydroxyapatite-graphene oxide for biological applications

The repair of bone fractures is a clinical challenge for patients with impaired healing, such as osteoporosis. Currently, different strategies have been developed to design new biomaterials, enhancing their interactions with biological systems and conducting the cellular behavior in the desired direction to help fracture healing. In the present work, hydroxyapatite-graphene oxide (HA-GO) nanocomposites were produced and the morphological and physicochemical influences of the addition of 0.5 wt%, 1.0 wt% and 1.5 wt% of GO to HA were observed. FEG-SEM and TEM analyses of HA-GO nanocomposites showed HA nanoparticles adhered to the surface of the GO sheets, suggesting an effective method to form nanostructured graphene-based biomaterials. As confirmation, physicochemical analyses by Raman, FTIR and TGA demonstrated a strong affinity between HA and GO, according to the increase of concentration from 0.5 wt% to 1.5 wt% GO in the HA-GO nanocomposites. Also, in order to evaluate the HA-GO nanocomposites behavior under biological microenvironment, in vitro bioactivity and indirect cytotoxicity tests were performed. FEG-SEM analyses confirmed the positive results for the bioactivity properties of HA-GO nanocomposite and indirect cytotoxicity demonstrated that even with a decrease in the hDPSCs viability and proliferation, when increasing to 1.5 wt% of GO concentration, high level of cell viability was exhibited by HA-GO nanocomposites. These biological results suggested the 0.5 wt% HA-GO nanocomposite as a potential bioactive bone graft and a promising biomaterial for bone tissue regeneration, when compared to the pure HA.     

Environmentally Friendly Graphene Inks for Touch Screen Sensors

Graphene-based materials have attracted significant attention in many technological fields, but scaling up graphene-based technologies still faces substantial challenges. High-throughput top-down methods generally require hazardous, toxic, and high-boiling-point solvents. Here, an efficient and inexpensive strategy is proposed to produce graphene dispersions by liquid-phase exfoliation (LPE) through a combination of shear-mixing (SM) and tip sonication (TS) techniques, yielding highly concentrated graphene inks compatible with spray coating. The quality of graphene flakes (e.g., lateral size and thickness) and their concentration in the dispersions are compared using different spectroscopic and microscopy techniques. Several approaches (individual SM and TS, and their combination) are tested in three solvents (N-methyl-2-pyrrolidone, dimethylformamide, and cyrene). Interestingly, the combination of SM and TS in cyrene yields high-quality graphene dispersions, overcoming the environmental issues linked to the other two solvents. Starting from the cyrene dispersion, a graphene-based ink is prepared to spray-coat flexible electrodes and assemble a touch screen prototype. The electrodes feature a low sheet resistance (290 Ω □⁻¹) and high optical transmittance (78%), which provide the prototype with a high signal-to-noise ratio (14 dB) and multi-touch functionality (up to four simultaneous touches). These results illustrate a potential pathway toward the integration of LPE-graphene in commercial flexible electronics.     

An automated treatment planning strategy for highly noncoplanar radiotherapy arc trajectories

Radiation therapy is a technology-driven cancer treatment modality that has experienced significant advances over the last decades, due to multidisciplinary contributions that include engineering and computing. Recent technological developments allow the use of noncoplanar volumetric modulated arc therapy (VMAT), one of the most recent photon treatment techniques, in clinical practice. In this work, an automated noncoplanar arc trajectory optimization framework designed in two modular phases is presented. First, a noncoplanar beam angle optimization algorithm is used to obtain a set of noncoplanar irradiation directions. Then, anchored in these directions, an optimization strategy is proposed to compute an optimal arc trajectory. The computational experiments considered a pool of twelve difficult head-and-neck tumor cases. It was possible to observe that, for some of these cases, the optimized noncoplanar arc trajectories led to significant treatment planning quality improvements, when compared with coplanar VMAT treatment plans. Although these experiments were done in a research environment treatment planning software (matRad), the conclusions can be of interest for a clinical setting: automated procedures can simplify the current treatment workflow, produce high-quality treatment plans, making better use of human resources and allowing for unbiased comparisons between different treatment techniques.     

Bibliometric analysis for characterization of oil production in Brazilian territory

Scientometrics - The theme related to the oil production in the Brazilian territory is not limited to the extraction means, distribution and use of this product, and involves political, cultural...     

Changes of the cell surface hydrophobicity of Lactobacillus acidophilus La‐5 in response to pH,temperature and inulin

The main topic of this study was to study how cell surface hydrophobicity (CSH) could change in response to pH, temperature and inulin; Lactobacillus acidophilus La‐5 was used as a model microorganism. pH, temperature, inulin and incubation time (exposure to prebiotic or incubation at pH 4.0 and 9.0) were combined through a full factorial design and a Central Composite Design; the results were analysed using a multifactorial anova (first step) and a stepwise regression (second step). Temperature and pH significantly affected CSH: an increase in the temperature determined a significant increase in CSH, whereas the correlation pH vs. CSH was negative, as an increase in pH caused a significant decrease in CSH. Inulin played a significant role, but its effect could be influenced by temperature, pH and exposure time. This study is the first approach on the effects of some environmental factors on CSH and suggests that the culturing conditions and/or the exposure to some prebiotics could modify it with positive or negative effects.