Polypropylene (PP) was blended with polycaprolactone (PCL) and nanoclay (NC) in a twin-screw extruder (TSE) using a traditional extrusion process and a sub-critical gas-assisted process (SGAP). SGAP is a new and facile processing method that injects compressed gas (C[O.sub.2] or [N.sub.2]) at low pressures (~10 bars) into the barrel of the extruder to induce rapid and repetitive foaming and resolubilization as the melt travels through regions of high pressure and low pressure. Bubble expansion during foaming introduces an equibiaxial elongational flow not otherwise generated in TSE, adding to the total stress the polymer matrix can exert to break up nanoparticle agglomerates and reduce the droplet size of secondary polymers in blends. Impact, morphology, and X-ray diffraction (XRD) properties confirmed a smaller PCL phase droplet size and an increase in dispersion of the NC when SGAP was used. Standard small amplitude oscillatory (SAOS) rheological tests for the storage modulus G' were not sensitive enough to discern the difference between the traditionally extruded samples and the SGAP samples. However, the zero-strain non-linearity parameter, [Q.sub.0], determined by the Fourier-Transform rheology, was able to distinguish the enhanced dispersive and mixing capabilities of SGAP. Practical implications of SGAP and Fourier-Transform (FT) rheology are also discussed in this paper.
Technology application, Coca-Cola Co. (Atlanta, Georgia) -- Production management, Coca-Cola Co. (Atlanta, Georgia) -- Technology application, Machinery -- Production management, Machinery -- Technology application, Magneto-electric machines -- Production management, Magneto-electric machines -- Technology application, Automation -- Technology application, Mechanization -- Technology application, Control systems -- Production management, Control systems -- Technology application, Plastic containers -- Production management, Plastic containers -- Technology application, Soft drink industry -- Production management, and Soft drink industry -- Technology application
Coca-Cola FEMSA, the largest public bottler of Coca-Cola products in the world, relies heavily on advanced automation to maintain production efficiency. Rather than being produced and then shipped [...]
High-throughput technologies for genomics, transcriptomics, proteomics, and metabolomics, and integrative analysis of these data, enable new, systems-level insights into disease pathogenesis. Mitochondrial diseases are an excellent target for hypothesis-generating omics approaches, as the disease group is mechanistically exceptionally complex. Although the genetic background in mitochondrial diseases is in either the nuclear or the mitochondrial genome, the typical downstream effect is dysfunction of the mitochondrial respiratory chain. However, the clinical manifestations show unprecedented variability, including either systemic or tissue-specific effects across multiple organ systems, with mild to severe symptoms, and occurring at any age. So far, the omics approaches have provided mechanistic understanding of tissue-specificity and potential treatment options for mitochondrial diseases, such as metabolome remodeling. However, no curative treatments exist, suggesting that novel approaches are needed. In this Review, we discuss omics approaches and discoveries with the potential to elucidate mechanisms of and therapies for mitochondrial diseases.
Bulletin of the American Meteorological Society. Dec, 2019, Vol. 100 Issue 12, p2423, 11 p.
Technology application, Environmental monitoring -- Technology application, Earth sciences research -- Analysis, Information systems -- Usage, and Electronic data processing -- Methods
ABSTRACT The current heterogeneity of the existing global collection of measuring assets, satellite and surface based, is a major obstacle to creating a truly integrated, globally uniform information [...]
Condition monitoring and fault diagnosis play the most important role in industrial applications. The gearbox system is an essential component of mechanical system in fault identification and classification domains. In this paper, we propose a new technique which is based on the Fast-Kurtogram method and Self Organizing Map (SOM) neural network to automatically diagnose two localized gear tooth faults: a pitting and a crack. These faults could have very different diagnostics; however, the existing diagnostic techniques only indicate the presence of local tooth faults without being able to differentiate between a pitting and a crack. With the aim to automatically diagnose these two faults, a dynamic model of an electromechanical system which is a simple stage gearbox with and without defect driven by a three phase induction machine is proposed, which makes it possible to simulate the effect of pitting and crack faults on the induction stator current signal. The simulated motor current signal is then analyzed by using a Fast-Kurtogram method. Self-organizing map (SOM) neural network is subsequently used to develop an automatic diagnostic system. This method is suitable for differentiating between a pitting and a crack fault. Keywords: fast kurtogram, gear faults detection, MCSA, signal analysis, self-organizing map (SOM) neural network, fault classification.