Improvement of flexural bond strength of zirconia-resin cement by surface patterning using sub-nanosecond UV laser

Zirconia is a dental material that shows excellent biocompatibility and high strength in clinical applications. This study aims to evaluate the effects of ultrafast laser applications. The surface nanostructures were classified into three groups. Group 1 was generated using the burst mode, with three different distances between dots: 52 µm (Group 1a), 104 µm (Group 1b), and 156 µm (Group 1c). Group 2 was processed using the scanning mode configuration, with a set of parallel lines. Group 3 was also processed using this scanning configuration creating a set of square-shaped patterning. Group 4 was the control group. After the surface treatments, a pair of zirconia specimens was bonded end to end with resin cement. Flexural bond strength (FBS) test was applied in a universal test machine. Multiple comparisons were performed using a one-way analysis of variance and the Tukey's HSD test. All the samples that were treated with the laser showed higher FBS values than the untreated surface. Using the burst mode, preformed circular-shaped surface on an angle of 90⁰ at 52 µm distance (Group 1a) showed the highest FBS values among all groups (p < .05). Groups 2 and 3 had significantly higher values than 1b and 1c.     

Interpenetrating polymer network/functionalized-reduced graphene oxide nanocomposite: As an advanced functional material

In this report, smart polyurethane-polystyrene interpenetrating polymer network (IPN)-based nanocomposites were fabricated using simultaneous polymerization technique with different doses of functionalized reduced graphene oxide (f-RGO). RGO was functionalized with monoglyceride of sunflower oil in the presence of toluene diisocyanate. Successful functionalization of RGO was supported by Fourier-transform infrared spectroscopy, X-Ray diffraction, and transmission electron microscopy analyses. Most interestingly, the fabricated IPN-based nanocomposites showed significant enhancements in mechanical (tensile strength: 165%; elongation at break: 198%; toughness: 340%) and thermal (thermally stable up to 262°C) properties upon incorporation of 1 weight% of f-RGO. Moreover, the fabricated nanocomposites exhibited outstanding chemical resistance, self-cleaning behavior through surface hydrophobicity (static contact angle: 125.6–136.5°), multi-stimuli responsive shape memory effect (100% recovery within 33–44 s by microwave and 265–308 s by sunlight) and thermally actuated artificial muscle-like behavior. Therefore, the studied smart nanocomposites with the aforementioned properties hold significant potential for possible advanced applications.     

Comparative Analysis of Coding and Non-Coding Features within Insect Tolerance Loci in Wheat with Their Homologs in Cereal Genomes

Food insecurity and malnutrition have reached critical levels with increased human population, climate fluctuations, water shortage; therefore, higher-yielding crops are in the spotlight of numerous studies. Abiotic factors affect the yield of staple food crops; among all, wheat stem sawfly (Cephus cinctus Norton) and orange wheat blossom midge (Sitodiplosis mosellana) are two of the most economically and agronomically harmful insect pests which cause yield loss in cereals, especially in wheat in North America. There is no effective strategy for suppressing this pest damage yet, and only the plants with intrinsic tolerance mechanisms such as solid stem phenotypes for WSS and antixenosis and/or antibiosis mechanisms for OWBM can limit damage. A major QTL and a causal gene for WSS resistance were previously identified in wheat, and 3 major QTLs and a causal gene for OWBM resistance. Here, we present a comparative analysis of coding and non-coding features of these loci of wheat across important cereal crops, barley, rye, oat, and rice. This research paves the way for our cloning and editing of additional WSS and OWBM tolerance gene(s), proteins, and metabolites.     

Hydrogen storage capability of carbon nanotube Be@C120

PM3 (RHF) type semiempirical quantum chemical calculations have been carried out on (nH₂+Be)@C₁₂₀ systems where C₁₂₀ is a capped tube and n15. The results indicate that all these systems are stable but endothermic in nature. (7H₂+Be)@C₁₂₀ system has the lowest heat of formation value.     

An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System,Support Vector Machine and Artificial Neural Network Models

Sediment transport in streams and rivers takes two forms as suspended load and bed load. Suspended load comprises sand + silt + clay-sized particles that are held in suspension due to the turbulence and will only settle when the stream velocity decreases, such as when the streambed becomes flatter, or the streamflow into a pond or lake. The sources of the suspended sediments are the sediments transported from the river basin by runoff or wind and the eroded sediments of the river bed and banks. Suspended-sediment load is a key indicator for assessing the effect of land use changes, water quality studies and engineering practices in watercourses. Measuring suspended sediment in streams is real sampling and the collection process is both complex and expensive. In recent years, artificial intelligence methods have been used as a predictor for hydrological phenomenon namely to estimate the amount of suspended sediment. In this paper the abilities of Support Vector Machine (SVM), Artificial Neural Networks (ANNs) and Adaptive Network Based Fuzzy Inference System (ANFIS) models among the artificial intelligence methods have been investigated to estimate the suspended sediment load (SSL) in Ispir Bridge gauging station on Coruh River (station number: 2316). Coruh River is located in the northern east part of Turkey and it is one of the world”s the fastest, the deepest and the largest rivers of the Coruh Basin. In this study, in order to estimate the suspended sediment load, different combinations of the streamflow and the SSL were used as the model inputs. Its results accuracy was compared with the results of conventional correlation coefficient analysis between input and output variables and the best combination was identified. Finally, in order to predict SSL, the SVM, ANFIS and various ANNs models were used. The reliability of SVM, ANFIS and ANN models were determined based on performance criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Efficiency Coefficient (EC) and Determination Coefficient (R²).     

Biodegradable and thermostable synthetic hyperbranched poly(urethane-urea)s as advanced surface coating materials

Aliphatic hyperbranched poly(urethane-urea)s with different weight percentages of branch generating moiety were synthesized by a one pot A₂ + BC₂ approach. Isophorone diisocyanate was used as the A₂ type monomer, while a tri-functional dihydroxyamine compound synthesized from ?-caprolactam and diethanol amine acted as the BC₂ monomer. Evidence supporting the hyperbranched structure of the synthesized poly(urethane-urea) was obtained from ¹H NMR spectra. FTIR study confirmed the nature and extent of hydrogen bonding present in this novel macromolecule. A Gaussian band fitting procedure of the IR band at amide-I region showed that the extent of hydrogen bonding increases with the increase of weight percentage of the tri-functional compound. The tensile strength, elongation at break, impact resistance, scratch hardness and gloss followed an increasing trend with the same. The thermal degradation of the hyperbranched poly(urethane-urea) was found to be dependent on the weight percentage of the BC₂ type moiety. The kinetics of thermal degradation studied by the Ozawa method showed that the activation energy required for thermal degradation of hyperbranched polymer is higher than its linear polyurethane analog. The synthesized polymer was found to be biodegradable by Pseudomonas aeruginosa bacteria. The study showed superiority of the hyperbranched structure over the linear one. Thus the results indicated the potential usage of the studied hyperbranched poly(urethane-urea) as an advanced surface coating material.     

Locational analysis for regionalization of Turkish Red Crescent blood services

After a series of earthquakes in 1999, Turkish Red Crescent (TRC) has engaged in a restructuring for all of its activities, including the blood services. Our study on the blood management system had been started as part of this initiative to restructure the blood services and improve both their effectiveness and efficiency. In the current system of TRC, not much consideration has been given to how the locational decisions affect the performance of blood centers, stations and mobile units. In recent years, however, there has been much discussion regarding the regionalization of the blood management system in Turkey. In this study, we develop several mathematical models to solve the location–allocation decision problems in regionalization of blood services. We report our computational results, obtained by using real data, for TRC blood services.     

Extraction of voltage harmonics using multi-layer perceptron neural network

This paper presents a harmonic extraction algorithm using artificial neural networks for Dynamic Voltage Restorers (DVRs). The suggested algorithm employs a feed forward Multi Layer Perceptron (MLP) Neural Network with error back propagation learning to effectively track and extract the 3rd and 5th voltage harmonics. For this purpose, two different MLP neural network structures are constructed and their performances compared. The effects of hidden layer, supervisors and learning rate are also presented. The proposed MLP Neural Network algorithm is trained and tested in MATLAB program environment. The results show that MLP neural network enable to extract each harmonic effectively.     

Interpretation of MR images using self-organizing maps and knowledge-based expert systems

A new image segmentation system is presented to automatically segment and label brain magnetic resonance (MR) images to show normal and abnormal brain tissues using self-organizing maps (SOM) and knowledge-based expert systems. Elements of a feature vector are formed by image intensities, first-order features, texture features extracted from gray-level co-occurrence matrix and multiscale features. This feature vector is used as an input to the SOM. SOM is used to over segment images and a knowledge-based expert system is used to join and label the segments. Spatial distributions of segments extracted from the SOM are also considered as well as gray level properties. Segments are labeled as background, skull, white matter, gray matter, cerebrospinal fluid (CSF) and suspicious regions.     

Neutron dose transmission measurements for several new concrete samples including colemanite

We have prepared four baryte and four concrete samples having respectively 0%, 5%, 10% and 15% colemanite concentrations. Neutron dose transmission measurements have been done by using a source of mono-energetic neutrons (E_eff = 4.5 MeV–²⁴¹Am–Be). It has been shown that when colemanite percentages of the samples increase, neutron dose transmission values for the samples decrease. It is thus possible to enhance the neutron shielding property of baryte and ordinary concrete by adding different proportions of colemanite.