Application of artificial neural networks to the prediction of tunnel boring machine penetration rate

Rate of penetration of a Tunnel Boring Machine(TBM)in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project.This paper presents the results of a study into the application of an Artificial Neural Network(ANN)technique for modeling the penetration rate of tunnel boring machines.A database,including actual,measured TBM penetration rates,uniaxial compressive strengths of the rock,the distance between planes of weakness in the rock mass and rock quality designation was established.Data collected from three different TBM projects(the Queens Water Tunnel,USA,the Karaj-Tehran water transfer tunnel,Iran,and the Gilgel Gibe Ⅱ hydroelectric project,Ethiopia).A five-layer ANN was found to be optimum,with an architecture of three neurons in the input layer,9,7 and 3 neurons in the first,second and third hidden layers,respectively,and one neuron in the output layer.The correlation coefficient determined for penetration rate predicted by the ANN was 0.94.     

Biogenic amines in Iranian white brine cheese: modelling and optimisation of processing factors

The simultaneous effects of processing factors such as ripening time (25–75 days), ripening temperature (4–14 °C) and brine concentration (10–13%) on biogenic amines content, proteolysis and sensory score of Iranian white brine cheese were studied, in 12 cheeses. Response surface methodology (RSM) was used to minimise biogenic amines content. At low level of ripening time, biogenic amines content decreased with increasing levels of brine concentration but at high level of ripening time, brine concentration had inverse effect. Ripening time showed quadratic effect on biogenic amines content. Based on biogenic amines content and sensory score, the optimum conditions were 13% brine and ripening at 9–14 °C for 43–65 days.     

Isolation of lactic acid bacteria from Lighvan cheese, a semihard cheese made from raw sheep milk in Iran

The lactic acid bacteria contributing to Lighvan cheese ripening during the different stages of production were investigated. Isolated strains from different culture media were identified phenotypically to species and subspecies level. In total, 413 strains were isolated from raw milk, 1-day-old cheese and fully ripened cheese. The most abundant species belonged to Enterococcus faecium (87 isolates), Lactococcus lactis ssp. lactis (68 isolates), Enterococcus faecalis (55 isolates) and Lactobacillus plantarum (48 isolates). E. faecium, Lc. lactis and Lb. plantarum were the predominantly isolated strains from ripened cheese. Therefore, they may contribute considerably to the aroma and flavour development of Lighvan cheese.     

Proteolysis in ultra-filtered and conventional Iranian white cheese during ripening

Conventional and ultra-filtered (UF) Iranian white cheeses were made with almost identical gross chemical composition and the extent and characteristics of proteolysis were studied during ripening. UF cheeses exhibited a lower rate of development of pH 4.6-soluble nitrogen than conventional cheeses. The rates of degradation of α_s1-casein and particularly β-casein were lower in UF cheeses than in conventional cheeses. Plasmin activity was lower in UF cheeses than that in conventional cheese, whereas coagulant activity was higher in the former. Noticeable qualitative and quantitative differences were observed in reverse-phase high performance liquid chromatography (RP-HPLC) peptide profiles between UF and conventional white cheeses and chemometric analysis of peak height data distributed the cheeses into two separate groups. The levels of free amino acids in UF cheeses were lower than in conventional cheeses. Lower peptide degradation and production of amino acids suggested slower ripening, which may have been associated with the weak aroma development characteristic of UF cheeses.     

Setup cost reduction in an inventory model with finite-range stochastic lead times

Traditionally, upon solution, independent demand inventory models result in the determination of a closed form for the economic lot size. Generally, this is obtained from the result that holding costs and setup costs are constant and equal at the optimum. However, the experience of the Japanese indicates that this need not be the case. Specifically, setup cost may be reduced by investing in reduced setup times resulting in smaller lot sizes and increased flexibility. Various authors have investigated the impact of such investment on classical lot sizing formulas which has resulted in the derivation of modified relationships. A common assumption of this research has been that demand and lead time are deterministic. This paper extends this previous work by considering the more realistic case of investing in decreasing setup costs where lead time is stochastic. Closed form relationships for optimal lot size, optimal setup cost, optimal total cost, etc. are derived. Numerical results are presented for cases where lead times follow uniform and normal distributions. Sensitivity analysis is performed to indicate under what conditions investment is warranted.     

Impact of native Lactobacillus paracasei subsp. paracasei and Pediococcus spp. as adjunct cultures on sensory quality of Iranian white brined cheese

Paired wild‐type cultures consisting of a Lactobacillus paracasei subsp. paracasei (three strains) or Pediococcus pentosaceus (one strain) and a Pediococcus inopinatus (five strains) were used as adjunct cultures in the production of Iranian white brined cheese. After 8 weeks of ripening, adjunct‐treated cheeses produced by L. paracasei subsp. paracasei and P. inopinatus received significantly higher scores for flavour/taste, aroma, texture and overall preference than those produced by P. pentosaceus and P. inopinatus as well as the control cheese (P < 0.05). In conclusion, a greater improvement of sensory quality of cheeses was strongly associated with the presence of L. paracasei subsp. paracasei rather than pediococci.