New-generation biocompatible Ti-based metallic glass ribbons for flexible implants

We introduce five new biocompatible Ti-based metallic glass (MG) compositions with different metalloid and soft metal content for a synergistic improvement in corrosion properties. Without any potentially harmful elements such as Cu, Ni or Be, these novel alloys can eliminate the risk of inflammatory reaction when utilized for permanent medical implants. Excluding Cu, Ni or Be, which are essential for Ti-based bulk MG production, on the other hand, confines the glass-forming ability of novel alloys to a moderate level. In this study, toxic-element free MG alloys with significant metalloid (Si–Ge–B, 15–18 at.%) and minor soft element (Sn, 2–5 at.%) additions are produced in ribbon form using conventional single-roller melt spinning technique. Their glass-forming abilities and their structural and thermal properties are comparatively investigated using X-ray diffraction (XRD), synchrotron XRD and differential scanning calorimetry. Their corrosion resistance is ascertained in a biological solution to analyze their biocorrosion properties and compare them with other Ti-based bulk MGs along with energy dispersive X-ray. Ti60Zr20Si8Ge7B3Sn2 and Ti50Zr30Si8Ge7B3Sn2 MG ribbons present a higher pitting potential and passivation domain compared with other Ti-based MG alloys tested in similar conditions. Human mesenchymal stem cell metabolic activity and cytocompatibility tests confirm their outstanding cytocompatibility, outperforming Ti-Al6-V4

New hybrid nano additives for thermoplastic compounding: CVD grown carbon fiber on graphene

Nano additives have unique characteristics widely used in high technology applications due to their ultrahigh mechanical and thermal properties. They are not preferred in price sensitive sectors especially in automotive applications because of their high cost. On the other hand, there is a growing interest to use graphene as a reinforcing agent in composite production. At this point, graphene platelet (GNP) produced from the recycle source was used as a template for carbon nanofiber production by using chemical vapor deposition (CVD) technique to overcome commercialization harrier. This bicomponent and novel structure is a good candidate to be used as a reinforcing agent in compound formulations. This produced hybrid additive was dispersed in thennoplastic resin by thennokinetic mixer to get homogeneous dispersion and provide strong interfacial interactions. In the current work, the outstanding properties of graphene with carbon fibers were combined into one type structure. With the further research, the number of graphene layer were adjusted in this hybrid structure to bring a new insight in graphene and its composite applications. After the fabrication of graphene and carbon fiber-based reinforcements with different graphene sources, mechanically and thermally improved Polyamide 6.6 were developed at very low loadings by a thermokinetic high shear mixer. This developed technology will utilize an innovation to produce advanced thermoplastic prepregs including graphene and its hybrid additives with high mechanical properties and increased recycling degree by decreasing manufacturing costs

Smart textiles for healthcare and medicine applications (WG1): state-of-the art report, CONTEXT Project

The aim of this document is to provide information on the state-of-the-art related to the topics covered by each working group within the CONTEXT project. It provides information on materials and technologies used to develop smart textiles with targeted performance, general applications of smart textiles in the field, case-studies on the use of smart textiles, opportunities for smart textiles considering the needs of each field, trends on the development of smart textiles in terms of market and technical expectations. This paper gives an overview of the potential of smart textiles for healthcare & medicine, ongoing developments, state-of-the-art products and future developments

Influence of socioeconomic factors on pregnancy outcome in women with structural heart disease

OBJECTIVE: Cardiac disease is the leading cause of indirect maternal mortality. The aim of this study was to analyse to what extent socioeconomic factors influence the outcome of pregnancy in women with heart disease.  METHODS: The Registry of Pregnancy and Cardiac disease is a global prospective registry. For this analysis, countries that enrolled ≥10 patients were included. A combined cardiac endpoint included maternal cardiac death, arrhythmia requiring treatment, heart failure, thromboembolic event, aortic dissection, endocarditis, acute coronary syndrome, hospitalisation for cardiac reason or intervention. Associations between patient characteristics, country characteristics (income inequality expressed as Gini coefficient, health expenditure, schooling, gross domestic product, birth rate and hospital beds) and cardiac endpoints were checked in a three-level model (patient-centre-country).  RESULTS: A total of 30 countries enrolled 2924 patients from 89 centres. At least one endpoint occurred in 645 women (22.1%). Maternal age, New York Heart Association classification and modified WHO risk classification were associated with the combined endpoint and explained 37% of variance in outcome. Gini coefficient and country-specific birth rate explained an additional 4%. There were large differences between the individual countries, but the need for multilevel modelling to account for these differences disappeared after adjustment for patient characteristics, Gini and country-specific birth rate.  CONCLUSION: While there are definite interregional differences in pregnancy outcome in women with cardiac disease, these differences seem to be mainly driven by individual patient characteristics. Adjustment for country characteristics refined the results to a limited extent, but maternal condition seems to be the main determinant of outcome

Effect of sitagliptin on cardiovascular outcomes in type 2 diabetes

BACKGROUND: Data are lacking on the long-term effect on cardiovascular events of adding sitagliptin, a dipeptidyl peptidase 4 inhibitor, to usual care in patients with type 2 diabetes and cardiovascular disease. METHODS: In this randomized, double-blind study, we assigned 14,671 patients to add either sitagliptin or placebo to their existing therapy. Open-label use of antihyperglycemic therapy was encouraged as required, aimed at reaching individually appropriate glycemic targets in all patients. To determine whether sitagliptin was noninferior to placebo, we used a relative risk of 1.3 as the marginal upper boundary. The primary cardiovascular outcome was a composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina. RESULTS: During a median follow-up of 3.0 years, there was a small difference in glycated hemoglobin levels (least-squares mean difference for sitagliptin vs. placebo, -0.29 percentage points; 95% confidence interval [CI], -0.32 to -0.27). Overall, the primary outcome occurred in 839 patients in the sitagliptin group (11.4%; 4.06 per 100 person-years) and 851 patients in the placebo group (11.6%; 4.17 per 100 person-years). Sitagliptin was noninferior to placebo for the primary composite cardiovascular outcome (hazard ratio, 0.98; 95% CI, 0.88 to 1.09; P<0.001). Rates of hospitalization for heart failure did not differ between the two groups (hazard ratio, 1.00; 95% CI, 0.83 to 1.20; P = 0.98). There were no significant between-group differences in rates of acute pancreatitis (P = 0.07) or pancreatic cancer (P = 0.32). CONCLUSIONS: Among patients with type 2 diabetes and established cardiovascular disease, adding sitagliptin to usual care did not appear to increase the risk of major adverse cardiovascular events, hospitalization for heart failure, or other adverse events

Computational Homogenization of Architectured Materials

Architectured materials involve geometrically engineered distributions of microstructural phases at a scale comparable to the scale of the component, thus calling for new models in order to determine the effective properties of materials. The present chapter aims at providing such models, in the case of mechanical properties. As a matter of fact, one engineering challenge is to predict the effective properties of such materials; computational homogenization using finite element analysis is a powerful tool to do so. Homogenized behavior of architectured materials can thus be used in large structural computations, hence enabling the dissemination of architectured materials in the industry. Furthermore, computational homogenization is the basis for computational topology optimization which will give rise to the next generation of architectured materials. This chapter covers the computational homogenization of periodic architectured materials in elasticity and plasticity, as well as the homogenization and representativity of random architectured materials

Classification of power quality events signals with pattern recognition methods by using Hilbert transform and genetic algorithms [Güç kalitesi bozulma sinyallerinin hilbert dönüşümü ve genetik algoritmalar kullanilarak örüntü tanima yöntemleri ile siniflandirilmasi]

Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780In this study, instantaneous envelope, phase and frequency series are obtained by Hilbert transform for Power Quality (PQ-Power Quality) disturbances signals. Rms, Thd, energy, entropy and statistical properties are applied to these series. With the wrapper feature selection approach, a set of features is obtained that has a small number of feature subset and a high performance from 36 features. Genetic Algorithm (GA) is used as a search algorithm and the classifier algorithm is K nearest neighborhood (KNN). Support Vector Machines (SVM) for selected features are also used in the classification step. The learning algorithm is obtained as KNN, the model performance that classifies PQ classes with 99.07%. The number of feature sets is 8. In addition, performance under noisy data is also tested to show that the generated model has a generalized structure. © 2018 IEEE