Silicon-doped hydroxyapatite prepared by a thermal technique for hard tissue engineering applications

Hydroxyapatite (HA, Ca₅(PO₄)₃OH) has been extensively used for bone implantation due to its similarity to the mineral component of bone, which makes it strongly osteoconductive. However, HA has low resorbability, and it is difficult to replace by a newly regenerated bone. Si doping can enhance the resorbability of HA by modifying its crystal structure. Here, we developed a simple thermal technique for preparing Si-doped HA from silica (SiO₂) and HA precursors, both of which are inexpensive and commercially available. This method included the physical binding of SiO₂ and HA particles, followed by pressing and sintering the mixture at an elevated temperature, which enhanced the atomic diffusion of Si into HA unit cells. We also evaluated the simulated body fluid (SBF) activity of the Si-doped HA prepared by this technique and showed that it significantly had higher resorbability and mineralizing potential compared to the pure HA. Our experimental design including, the individual precipitation and resorption assays enabled us to explain the mechanism behind the improved activity of Si-doped HA in SBF. This was attributed to the formation of new phases, such as β-tricalcium phosphate (β-TCP) and calcium silicate (Ca₂SiO₄) with higher solubility than HA on the SiO₂-contating HA during the sintering stage. This can provide some guidelines for designing new calcium phosphate-based materials for hard tissue engineering applications.     

Online monitoring of the curing of adhesives with a miniaturised interdigital sensor using impedance spectroscopy

Monitoring adhesives during manufacturing and their lifetime has become increasingly important due to the variety of materials and applications. Impedance spectroscopy is a suitable method for online monitoring of the curing process. We present a miniaturised interdigital structure to monitor the curing process of the adhesives using impedance spectroscopy. Compared to other sensors, our sensor is ultrathin, so that it disappears in the adhesive joint. Therefore, it can remain in the joint and be used for lifetime monitoring. In addition it is suitable for thin adhesive layers due to its fine grid. We demonstrate that the impedance of the sensor embedded in the adhesive gives insight into the curing mechanisms. Therefore monitoring of a dispersion and an epoxy is shown. In addition, the curing cycles can be reliably controlled using this monitoring method. The permittivity of the adhesives is extracted from the impedance measurements, applying analytical models of the electrical field of the interdigital structure.     

Determination of the physical and mechanical properties of iron-based powder materials produced by microwave sintering

Microwave energy is highly efficient for heating and processing different materials. In recent years, this type of heat transfer has been used in sintering process. Rapid and highly efficient heating, time and energy saving, and improved properties of sintered materials are advantages of microwave sintering. In this paper, Fe and Fe-Cu powder compact samples (cylindrical and bone shapes) are sintered both in microwave and electrical tube furnaces. The microwave generator has 2.45 GHz frequency and 1 KW power. Times are selected in the range of 5–25 min for microwave sintering and 5–40 min for electrical heating. The sintering temperature is set at 1120°C. Samples are sintered in the reducing atmosphere of 95% N₂ + 5% H₂ mixture. The density, hardness, and tensile strength of the samples are measured. The results are compared. The results show that the microwave-sintered materials have a finer microstructure. The microwave-sintered materials have 6–8% higher density, 5–10 HV5 higher hardness, and about 10% higher tensile strength than conventionally sintered materials.     

Effects of silymarin on biochemical and oxidative stress markers in end‐stage renal disease patients undergoing peritoneal dialysis

Introduction End‐stage renal disease (ESRD) patients especially those undergoing dialysis are vulnerable to several complications, in particular those related to oxidative stress. Silymarin is an herbal medicine commonly used as an antioxidant in different pathologies. Methods To evaluate the effect of silymarin on biochemical and oxidative stress markers, 50 ESRD patients undergoing peritoneal dialysis were randomly divided into two groups of silymarin (n = 28) and control (n = 22) and received silymarin (140 mg every 8 hours) or placebo for 2 months, respectively. Ferric reducing antioxidant power and total 8‐iso‐prostaglandin F_2α were measured in plasma, while catalase enzyme activity was measured in erythrocytes of both groups before and after treatment. Findings Ferric reducing antioxidant power values after treatment were significantly decreased in silymarin group compared to before treatment values (17.2 ± 2.9 and 15.9 ± 3.1 µM equivalent of quercetin/dL, respectively, P < 0.05). Conversely, catalase levels were increased 17.3% after silymarin consumption, while it was decreased 9.1% in control group. Further, hemoglobin (from 10.94 ± 2.17 to 11.54 ± 2.03 g/dL, P < 0.05) and albumin levels (from 3.48 ± 0.67 to 3.61 ± 0.53 g/dL, P < 0.05) were significantly increased after silymarin administration. Discussion It is concluded that silymarin could be regarded as a supplementary therapy for ESRD patients undergoing peritoneal dialysis in order to reduce complications.     

Gravitational search algorithm–optimized neural misuse detector with selected features by fuzzy grids–based association rules mining

Feature selection is one of the most important techniques for data preprocessing in classification problems. In this paper, fuzzy grids–based association rules mining, as an effective data mining technique, is used for feature selection in misuse detection application in computer networks. The main idea of this algorithm is to find the relationships between items in large datasets so that it detects correlations between inputs of the system and then eliminates the redundant inputs. To classify the attacks, a fuzzy ARTMAP neural network is employed whose training parameters are optimized by gravitational search algorithm. The performance of the proposed system is compared with some other machine learning methods in the same application. Experimental results show that the proposed system, when choosing optimum “feature subset size-adjustment” parameter, performs better in terms of detection rate, false alarm rate, and cost per example in classification problems. In addition, employing the reduced-size feature set results in more than 8.4 percent reduction in computational complexity.     

Developing economic order quantity model for non-instantaneous deteriorating items in vendor-managed inventory (VMI) system

This paper develops an economic order quantity model for non-instantaneous deteriorating items with and without shortages to investigate the performance of the vendor-managed inventory (VMI) system. This model is developed for a two-level supply chain consisting of a single supplier and single retailer with a single non-instantaneous deteriorating item. A numerical example and sensitivity analysis are provided to illustrate how increasing or reducing the related parameters change the optimal values of the decision variables of the two proposed models. The results show that VMI works better and charges lower cost in all conditions.     

Feature extraction using median–mean and feature line embedding

In this article, we propose a feature extraction method based on median–mean and feature line embedding (MMFLE) for the classification of hyperspectral images. In MMFLE, we maximize the class separability using discriminant analysis. Moreover, we remove the negative effect of outliers on the class mean using the median–mean line (MML) measurement and virtually enlarge the training set using the feature line (FL) distance metric. The experimental results on Indian Pines and University of Pavia data sets show the better performance of MMFLE compared to nearest feature line embedding (NFLE), median–mean line discriminant analysis (MMLDA), and some other feature extraction approaches in terms of classification accuracy using a small training set.