Numerical verification of the continuous calculation method for tip stress during the driving process of the dynamic penetration test

This paper presents a calculation method for obtaining the continuous variation in stress between the tip and the soil during dynamic penetration tests, particularly in the case of using the Panda 3® penetration testing device. The originality of the method is that the tip stress can be computed continuously throughout the driving process. For each impact of the hammer on the penetrometer, data are recorded by sensors located at the top of the apparatus. Then, the stress at the tip and the displacement of the apparatus are calculated with a method based on the propagation of waves in the device. A three-dimensional numerical model of the penetration test, based on the Panda 3® specifications and using the discrete element method (DEM), is proposed in this paper. The purpose of the simulations is to validate the calculation method by comparing the curves of the tip stress versus the penetration distance obtained in two different ways, the first being the distance directly observed at the tip and the second being the distance calculated from the data recorded at the top of the penetrometer, as with the experimental device. The entire apparatus is represented, including the hammer, the rod, and the tip, and is driven into the model soil. The calculation method is applied, and the results are compared to the actual response of the soil to the driving of the penetrometer directly at the tip, which can be obtained with the numerical model. The responses are found to be very similar, confirming the theoretical framework and its underlying assumptions. This method is applied to dynamic penetration tests and provides the opportunity to obtain mechanical parameters other than the tip resistance from the tests.     

Microstructural development during crystallization firing of a dental-grade nanostructured lithia-zirconia glass-ceramic

The microstructural development during crystallization firing of a commercially-available dental-grade nanostructured lithia-zirconia glass-ceramic (Vita Suprinity® PC) was unraveled using a wide battery of ex-situ and in-situ characterization techniques. It was found that the milling blocks are slightly crystallized glass-ceramics, with a complex chemical composition and consisting of partially de-polymerized glass plus lithium silicate (Li₂SiO₃) nanocrystals. It was also found that during crystallization firing the glassy matrix first reacts with part of the Li₂SiO₃ to form lithium disilicate (Li₂Si₂O₅) at ～810?820 °C, and then lithium orthophosphate (Li₃PO₄) precipitates from the glass. This results in glass-ceramics with abundant nanocrystals embedded in a sparse zirconosilicate glass matrix (containing many other cations subsumed) that, due to its high viscosity, inhibited crystal growth. Therefore, these dental glass-ceramics are not reinforced with zirconia (ZrO₂) crystals unless over-fired above ～890 °C and at the expense of its singular nanostructure. Finally, this study opens doors for optimizing the clinical performance of these dental glass-ceramics via microstructural tailoring.     

The analytic network process for managing inter-enterprise collaboration: A case study in a collaborative enterprise network

In the last years, collaboration among enterprises has gained attention in the business environment as a means to remain competitive. Enterprises that are collaborating look for improving their performance but, in real assessments, they often do not establish efficient frameworks to structure and manage the enterprise association/inter-enterprise performance. In addition, there are many factors that act as barriers to effective collaboration and have to be also properly managed as they impact on the inter-enterprise performance. This paper provides a methodology based on the analytic network process (ANP) to identify and measure, under an integrated approach, both factors and inter-enterprise performance considering their reciprocal impact. With this innovative approach, enterprises will obtain significant information for the decision-making process regarding which are the factors and inter-enterprise performance elements that generate a higher impact and therefore have a high priority within the specific collaborative relationship. Thus, enterprises can focus their efforts on improving those most important factors and performance elements, and consequently, enhancing their competitiveness.     

Application of ergothioneine-rich extract from an edible mushroom Flammulina velutipes for melanosis prevention in shrimp,Penaeus monodon and Litopenaeus vannamei

This report investigated the melanosis-inhibiting properties of crude water soluble extract from the base and fruiting body waste of an edible mushroom (Flammulina velutipes) containing 9.13 ± 2.03 mg/mL 2-thiol-l-histidine-betaine (ergothioneine, ESH). Immersion of live full-grown black tiger shrimp (Penaeus monodon) and Pacific white shrimp (Litopenaeus vannamei) in a 0.5% w/v solution of mushroom extract for 1 h significantly decreased polyphenoloxidase (PPO) activity in shrimp hemolymph and expression of the prophenoloxidase (proPO) gene in hemocytes. Consequently, the development of melanosis in the treated shrimp during ice storage was prevented. Treatment with a 0.05% w/v solution of sodium sulfite and 4-hexyl-1,3-benzenediol had a similar effect. Enzyme assays showed that ESH is a non-competitive inhibitor. It is proposed that ESH possibly interacts directly with Cu²⁺ at the putative binding sites of PPO and proPO, based on copper-chelating activity analyses, thus preventing melanosis in the shrimp. This study indicated that application of ESH-rich F. velutipes mushroom extract from trimming waste is an effective natural alternative to synthetic melanosis-inhibiting agents to prevent postmortem melanosis in shrimp.     

Relating TQM,marketing and business performance: An exploratory study

Total Quality Management (TQM) is perhaps the leading management approach that companies employ to improve their product and service quality with the aim of improving typical measures of business performance (e.g. increased profits, increased market share, reduced costs). However, consumer perception of quality not only results from an evaluation of the intrinsic quality attributes of the product (e.g. performance, reliability, durability) but is also affected by the marketing mix (e.g. price, advertising, warranties) adopted by the company selling the product. This paper offers a first attempt at cross-functional fertilization by examining the relationships between TQM, some marketing mix variables and measures of company performance by exploring the diverse multidisciplinary literature and developing an explanatory framework, which is tested using non-parametric correlation and refined through an empirical study of Spanish manufacturing companies. The results show that the most important TQM dimensions are the system of employee relations and the use of quality management-related design tools. The results also indicate the existence of a relationship amongst price, advertising and warranties, but that these marketing variables were not related to TQM and that further research is required to include a wider definition of marketing strategy.     

Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill

In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are available only after the bar has entered the mill, and therefore they have to be estimated. Estimation of process variables, particularly that of temperature, is of crucial importance for the bar front section to fulfill quality requirements, and the same must be performed in the shortest possible time to preserve heat. Currently, temperature estimation is performed by physical modeling; however, it is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques such as artificial neural networks and fuzzy logic have been proposed. In this article, neural network-based systems, including neural-based Gray-Box models, are applied to estimate scale breaker entry temperature, given its importance, and their performance is compared to that of the physical model used in plant. Several neural systems and several neural-based Gray-Box models are designed and tested with real data. Taking advantage of the flexibility of neural networks for input incorporation, several factors which are believed to have influence on the process are also tested. The systems proposed in this study were proven to have better performance indexes and hence better prediction capabilities than the physical models currently used in plant.     

Occurrence of C16:1 isomers in milk fats from ewes fed with different dietary lipid supplements

The trans as well as the cis C16:1 isomer profiles were established in 43 ewe milk fats supplemented with different dietary lipid sources representative of the variety of unsaturated fatty acids found in nature such as olive, sunflower, linseed and fish oils. Fractionation by silver-ion solid phase extraction facilitated a rapid separation of the trans, cis and saturated FA before gas chromatography analysis took place. C16:1 isomers with a double bond in positions 7, 9 and 13 in the cis group and 8 and 9 in the trans fraction were the most abundant. Dietary lipid supplementation produced a noticeable increase in the total trans C16:1 content and elevated correlations were observed between trans-8 C16:1 and trans-10 C18:1 as well as trans-9 C16:1 and trans-11 C18:1. These results support the idea that altering the trans C18:1 profile affects trans C16:1 isomer composition consistent with the β-oxidation products from the trans C18:1 isomers.