Depletion of the high quality ores around the world has forced ferronickel producers to extract metal values from low-grade ore bodies with significant amounts of impurities. Under this condition, maintaining alloy quality is of utmost importance for the smelters; however still, accessibility of a reliable sulphide capacity model for FeNi refining processes is an issue. Many of the current models, such as those incorporating optical basicity, have proven to be erroneous and unreliable for wide ranges of composition and temperature. These models are typically developed and tested without a proper validation method thus allowing for great correlations which may not fare well with the introduction of new data. Models built from fundamental thermodynamic data perform much better in predicting sulphide capacities but are not only complicated to formulate but also too complicated to be used by operators on a day to day basis as multitude of inputs are needed. Hence, development of a reliable model based on fundamentals, which can also be directly used by plant operators is very much demanded by the industry. In the current study, an artificial neural network (ANN) approach has been used to predict sulphide capacities of slag compositions in the CaO-SiO 2 -Al 2 O 3 -MgO system with an objective to be used in ferronickel refining processes. The resulting models are evaluated on: 1) coefficient of multiple determination (R 2 ), 2) correlation strength (r), 3) root mean square error (RMSE) and 4) computation speed. The ANN based model has shown to be superior in predicting sulphide capacities to current models.

Our previous genome-wide association study (GWAS) in a Hong Kong Southern Chinese population with extreme bone mineral density (BMD) scores revealed suggestive association with MPP7, which ranked second after JAG1 as a candidate gene for BMD. To follow-up this suggestive signal, we replicated the top single-nucleotide polymorphism rs4317882 of MPP7 in three additional independent Asian-descent samples (n= 2684). The association of rs4317882 reached the genome-wide significance in the meta-analysis of all available subjects (P(meta)= 4.58 × 10(-8), n= 4204). Site heterogeneity was observed, with a larger effect on spine than hip BMD. Further functional studies in a zebrafish model revealed that vertebral bone mass was lower in an mpp7 knock-down model compared with the wide-type (P= 9.64 × 10(-4), n= 21). In addition, MPP7 was found to have constitutive expression in human bone-derived cells during osteogenesis. Immunostaining of murine MC3T3-E1 cells revealed that the Mpp7 protein is localized in the plasma membrane and intracytoplasmic compartment of osteoblasts. In an assessment of the function of identified variants, an electrophoretic mobility shift assay demonstrated the binding of transcriptional factor GATA2 to the risk allele 'A' but not the 'G' allele of rs4317882. An mRNA expression study in human peripheral blood mononuclear cells confirmed that the low BMD-related allele 'A' of rs4317882 was associated with lower MPP7 expression (P= 9.07 × 10(-3), n= 135). Our data suggest a genetic and functional association of MPP7 with BMD variation.

In the present study, 35MnVS experimental steels containing nanoparticles are manufactured using a vacuum induction furnace (VIF), as well as a vacuum induction levitation furnace (VILF). The differences on the utilization ratio of nanoparticles (URN), inclusion characteristics, and steel microstructure between the original steel and experimental steels are compared. The results reveal that the steel processed with a VILF has a higher URN that helps to form a finer inclusion size range. There is a critical size for each inclusion, and only when the inclusion size is the critical value, the inclusions can efficiently induce acicular ferrites (AF). Among the three steels, only the inclusion size range of the VILF steel is less than the critical size which is between %2.2 and %5.2 m. Therefore, the inclusions in VILF steel have a relatively stronger ability on inducing AF, and that is revealed by its microstructure showing large proportions of AF.