Minimum Spouting Velocity of Draft Tube Conical Spouted Beds Using the Neural Network Approach

A multilayer perceptron with back‐propagation learning algorithm is developed to predict the minimum spouting velocity (u_ms) in draft tube conical spouted beds. Six dimensionless variables involving ten essential geometric and operating parameters of the beds were taken as model inputs. To compare the model results with both experimental data and those predicted by the limited existing empirical equations, the root mean square error and the mean relative error are utilized. Although there is a complex relationship between the input variables and u_ms, and despite the huge number of data available, the steps of training and testing show good agreement with the corresponding experimental values. This demonstrates that an artificial neural network is a useful approach to predict u_ms, especially when the relationship between the geometric and operating parameters and u_ms is complex and difficult to define.     

Comparative study on the corrosion and wear behavior of plasma-sprayed vs. high velocity oxygen fuel-sprayed Al8Si20BN ceramic coatings

Corrosion and wear are common problems encountered in the oil and gas industry. These entail the gradual destruction of materials by mechanical action on the opposite surface, and the chemical and/or electrochemical reaction with their environment. In this research, Al8Si20BN ceramic powder with specific properties against corrosion and wear was selected, and it was sprayed with high velocity oxygen fuel (HVOF) and plasma spray methods onto carbon steel substrates. The coatings were characterized with respect to phase composition, microstructure, microhardness and adhesion strength. Their wear behavior was inspected by applying 5, 10, 15 and 20?N loads by pin-on-disc machine, after which the results of both methods were compared. According to the results, the HVOF-coated models were more durable than the plasma-coated models under different loads in the same condition. In addition, the corrosion deterioration of the coated specimens in both brine (3.5% NaCl) and fossil oil were tested for one month (30 days). Potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) assessment in 3.5% NaCl solution indicated that the HVOF-sprayed specimens had better corrosion protection than the plasma-sprayed specimens. Generally, the HVOF technique facilitated more durable coats with greater corrosion and tribological resistance compared to the plasma coating technique.     

Microstructural development during spark plasma sintering of ZrB2–SiC–Ti composite

The present study investigates the effect of Ti addition on the microstructure development and phase evolution during spark plasma sintering of ZrB₂–SiC ceramic composite. A ZrB₂–20?vol% SiC sample with 15?wt% Ti was prepared by high-energy milling and spark plasma sintering at 2000?°C for 7?min under 50?MPa. The X-ray diffraction test, microstructural studies and thermodynamic assessments indicated the in-situ formation of several compounds due to the chemical reactions of Ti with ZrB₂ and SiC. The Ti additive was completely consumed during the sintering process and converted to the ceramic compounds of TiC, TiB and TiSi₂. In addition, another refractory phase of ZrC was also formed as a result of sidelong reaction of ZrB₂ and SiC with the Ti additive.     

Dysregulated Metabolic Pathways in Subjects with Obesity and Metabolic Syndrome

Background: Obesity coexists with variable features of metabolic syndrome, which is associated with dysregulated metabolic pathways. We assessed potential associations between serum metabolites and features of metabolic syndrome in Arabic subjects with obesity. Methods: We analyzed a dataset of 39 subjects with obesity only (OBO, n = 18) age-matched to subjects with obesity and metabolic syndrome (OBM, n = 21). We measured 1069 serum metabolites and correlated them to clinical features. Results: A total of 83 metabolites, mostly lipids, were significantly different (p < 0.05) between the two groups. Among lipids, 22 sphingomyelins were decreased in OBM compared to OBO. Among non-lipids, quinolinate, kynurenine, and tryptophan were also decreased in OBM compared to OBO. Sphingomyelin is negatively correlated with glucose, HbA1C, insulin, and triglycerides but positively correlated with HDL, LDL, and cholesterol. Differentially enriched pathways include lysine degradation, amino sugar and nucleotide sugar metabolism, arginine and proline metabolism, fructose and mannose metabolism, and galactose metabolism. Conclusions: Metabolites and pathways associated with chronic inflammation are differentially expressed in subjects with obesity and metabolic syndrome compared to subjects with obesity but without the clinical features of metabolic syndrome.     

Bi-objective heuristics for multi-item freights distribution planning problem in crossdocking networks

Crossdocking is a distribution strategy in which distribution centers operate as transfer points to harmonize the continuous physical flow through supply chain with the least storage. The coordination of this system in a network of crossdocks with multitype and time-restricted pickups and deliveries requires a complex planning. This paper addresses the distribution planning problem of crossdocking network, considering transshipment possibility among crossdocks and tardiness permission for some pickups. The problem is formulated as a bi-objective integer-programming model minimizing total transportation and holding costs and total tardiness. An innovative heuristic procedure to construct an effective initial solution and three improving algorithms based on appropriate meta-heuristics (variable neighborhood search, tabu search, and simulated annealing) is developed to solve the problem. Experimental results demonstrate the potency of proposed algorithms achieving remarkable results.     

Mammographic mass segmentation using multichannel and multiscale fully convolutional networks

Breast cancer is one of the leading causes of death among women worldwide. Mammographic mass segmentation is an important task in mammogram analysis. This process, however, poses a prominent challenge considering that masses can be obscured in images and appear with irregular shapes and low image contrast. In this study, a multichannel, multiscale fully convolutional network is proposed and evaluated for mass segmentation in mammograms. To reduce the impact of surrounding unrelated structures, preprocessed images with a salient mass appearance are obtained as the second input channel of the network. Furthermore, to jointly conduct fine boundary delineation and global mass localization, we incorporate more crucial context information by learning multiscale features from different resolution levels. The performance of our segmentation approach is compared with that of several traditional and deep-learning-based methods on the popular DDSM and INbreast datasets. The evaluation indices consist of the Dice similarity coefficient, area overlap measure, area undersegmentation measure, area oversegmentation measure, and Hausdorff distance. The mean values of the Dice similarity coefficient and Hausdorff distance of our proposed segmentation method are 0.915 ± 0.031 and 6.257 ± 3.380, respectively, on DDSM and 0.918 ± 0.038 and 2.572 ± 0.956, respectively, on INbreast, which are superior to those of the existing methods. The experimental results verify that our proposed multichannel, multiscale fully convolutional network can reliably segment masses in mammograms.     

Stretchable strain sensors based on polyaniline/thermoplastic polyurethane blends

Polymer Bulletin - Thermoplastic polyurethane/polyaniline-based stretchable strain sensors were prepared via in situ polymerization of aniline in the TPU solution in the form of thin films. The...     

Optimisation of pressurised liquid extraction for antioxidative polyphenolic compound from Momordica charantia using response surface methodology

Pressurised liquid extraction (PLE) of antioxidant compounds from bitter gourd fruits (Momordica charantia) in aqueous ethanolic solvent was investigated using response surface methodology at laboratory scale to understand key impact of extraction variables. Extraction efficiency was optimised by measuring the yield of extraction, total phenolic content (TPC), total flavonoid content (TFC), ferric reducing/antioxidant power assay (FRAP) and radical scavenging activity (RSA). The optimal extraction conditions were reached at 80% ethanol concentration, 10‐min extraction time and at 160 °C. Under these extraction conditions, values of TPC (5.40 ± 0.30 g GAE per 100 g), TFC (1.50 ± 0.10 g QE per 100 g), FRAP (778.55 ± 10 μmol eq Fe (II) g^?1), yield (178.50 ± 5.50 mg g^?1 dc) and RSA (75.50 ± 4.50%) were achieved. Furthermore, statistical analysis revealed that antioxidative attributes of bitter gourd extract were strongly and positively correlated with extraction temperature and ethanol concentration rather than processing time. This study illustrated that PLE has the potential to extract antioxidant compounds from tropical fruit vegetables in an accelerated manner. Furthermore, influential parameters affecting the process could be optimised for further industrial intake.