Two-step sinter-crystallization of K2O–CaO–P2O5–SiO2 (45S5-K) bioactive glass

Bioactive glasses and glass-ceramics (GCs) effectively regenerate bone tissue, however most GCs show improved mechanical properties. In this work, we developed and tested a rarely studied bioactive glass composition (24.4K₂O-26.9CaO-46.1SiO₂-2.6P₂O₅ mol%, identified as 45S5-K) with different particle sizes and heating rates to obtain a sintered GC that combines good fracture strength, low elastic modulus, and bioactivity. We analyzed the influence of the sintering processing conditions in the elastic modulus, Vickers microhardness, density, and crystal phase formation in the GC. The best GC shows improved properties compared with its parent glass. This glass achieves a good densification degree with a two-step viscous flow sintering approach and the resulting GC shows as high bioactivity as that of the standard 45S5 Bioglass®. Furthermore, the GC elastic modulus (56 GPa) is relatively low, minimizing stress shielding. Therefore, we unveiled the glass sintering behavior with concurrent crystallization of this complex bioactive glass composition and developed a potential GC for bone regeneration.     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Evaluation of Atmospheric and Vacuum Residues Using Molecular Distillation and Optimization

The term atmospheric residue describes the material at the bottom of the atmospheric distillation tower having a lower boiling point limit of about 340°C; the term vacuum residue (heavy petroleum fractions) refers to the bottom of the vacuum distillation, which has an atmospheric equivalent boiling point (AEBP) above 540°C. In this work, the objective is to evaluate the behavior of different kinds of Brazilian atmospheric and vacuum residues using molecular distillation. The Falling Film Molecular Distillator was used. For the results obtained through this process, a significant range of temperature can be explored avoiding the thermal decomposition of the material. So these results are very important to the refinery decisions and improvements. The Experimental Factorial Design results showed that the temperature has more influence on the process than the feed flow rate, when a higher percentage of distillate is required.     

Probing the thermal decomposition process of layered double hydroxides through in situ <Superscript>57</Superscript>Fe Mössbauer and in situ X-ray diffraction experiments

The thermal decomposition properties of Mg–Fe hydrotalcites were studied through in situ ⁵⁷Fe Mössbauer spectroscopy and in situ X-ray diffraction. Abrupt changes in the quadrupolar splitting measured in the Mössbauer spectra revealed a phase transition from the starting lamellar structure to a new crystalline arrangement. By analyzing the Mössbauer parameters we show that the material is highly disordered in the 300–400 °C temperature range. This hypothesis is confirmed by the X-ray results whose diffractograms indicated the collapse of the lamellar structure and the formation of a solid solution.     

On the stabilizing behavior of zirconia: A Combined experimental and theoretical study

Reactive zirconia powder was synthesized by the complexation of zirconium metal from zirconium hydroxide using a solution of 8-hydroxiquinoline. The kinetics of zirconia crystallization was followed by X-ray diffraction, scanning electron microscopy and surface area measured by the nitrogen adsorption/desorption technique. The results indicated that zirconia with a surface area as high as 100 m²/g can be obtained by this method after calcination at 500°C. Zirconia presents three polymorphic phases (monoclinic, tetragonal and cubic), which are reversibly interconversible. The cluster model Zr₄O₈ and Zr₄O₇ ⁺² was used for a theoretical study of the stabilization process. The ab initio RHF method was employed with the Gaussian94 program and the total energies and the energy gap of the different phases were calculated and compared with the experimental energy gap. The theoretical results show good reproducibility of the energy gap for zirconia.     

An alternating digital tree (ADT) algorithm for 3D geometric searching and intersection problems

A searching algorithm is presented for determining which members of a set of n points in an N dimensional space lie inside a prescribed space subregion. The algorithm is then extended to handle finite size objects as well as points. In this form it is capable of solving problems such as that of finding the objects from a given set which intersect with a prescribed object. The suitability of the algorithm is demonstrated for the problem of three dimensional unstructured mesh generation using the advancing front method.     

Optimal management of logistic activities in multi-site environments

The new emerging area of Enterprise Wide Optimization (EWO) has focused the attention in effectively solving the combined production/distribution scheduling problem. The importance of logistic activities performed in multi-site environments comes from the relative magnitude of the associated transportation costs and the good chance of getting large savings on such expenses. This paper first develops an exact MILP mathematical formulation for the multiple vehicle time-window-constrained pickup and delivery (MVPDPTW) problem. The approach is able to account for many-to-many transportation requests, pure pickup and delivery tasks, heterogeneous vehicles and multiple depots. Optimal solutions for a variety of benchmark problems with cluster/random distributions of pickup and delivery locations and limited sizes in terms of customer requests and vehicles have been discovered. However, the computational cost exponentially grows with the number of requests. For large-scale m-PDPTW problems, a local search improvement algorithm steadily providing a better solution through two evolutionary steps is also presented. A neighborhood structure around the starting solution is generated by first allowing multiple request exchanges among nearby trips and then permitting the reordering of nodes on every individual route. If a better set of routes is found, both steps are repeated until no improved solution is discovered. Compact MILP mathematical formulations for both sub-problems have been developed and solved through an efficient branch-and-bound algorithm. A significant number of large-scale m-PDPTW benchmark problems, some of them including up to 100 transportation requests, were successfully solved in reasonable CPU times.     

2,4,6‐Trichlorophenol and phenol removal in methanogenic and partially‐aerated methanogenic conditions in a fluidized bed bioreactor

A fluidized bed bioreactor (FBBR) was operated for more than 575 days to remove 2,4,6‐trichlorophenol (TCP) and phenol (Phe) from a synthetic toxic wastewater containing 80 mg L^?1 of TCP and 20 mg L^?1 of Phe under two regimes: Methanogenic (M) and Partially‐Aerated Methanogenic (PAM). The mesophilic, laboratory‐scale FBBR consisted of a glass column (3 L capacity) loaded with 1 L of 1 mm diameter granular activated carbon colonized by an anaerobic consortium. Sucrose (1 g COD L^?1) was used as co‐substrate in the two conditions. The hydraulic residence time was kept constant at 1 day. Both conditions showed similar TCP and Phe removal (99.9 + %); nevertheless, in the Methanogenic regime, the accumulation of 4‐chlorophenol (4CP) up to 16 mg L^?1 and phenol up to 4 mg L^?1 was observed, whereas in PAM conditions 4CP and other intermediates were not detected. The specific methanogenic activity of biomass decreased from 1.01 ± 0.14 in M conditions to 0.19 ± 0.06 mmolCH₄ h^?1 gTKN^?1 in PAM conditions whereas the specific oxygen uptake rate increased from 0.039 ± 0.008 in M conditions to 0.054 ± 0.012 mmolO₂ h^?1 gTKN^?1, which suggested the co‐existence of both methanogenic archaea and aerobic bacteria in the undefined consortium. The advantage of the PAM condition over the M regime is that it provides for the thorough removal of less‐substituted chlorophenols produced by the reductive dehalogenation of TCP rather than the removal of the parent compound itself. Copyright © 2005 Society of Chemical Industry