Grafting of poly(ethylene terephthalate) fibers with methacrylic acid using benzoyl peroxide

The graft copolymerization of methacrylic acid onto poly(ethylene terephthalate) fibers, by the aid of benzoyl peroxide, have been investigated. The graft yield increased up to 85°C, and then decreased with the further increase in temperature. The maximum graft yield was obtained at benzoyl peroxide concentration of 4.0 × 10^?3 mol/L. The increase in the concentration of monomer was found to increase the graft yield. The change in the graft yield was followed by the experiments carried out using different water/solvent mixtures. Also, the change in the properties of polye (thylene terephthalate) fibers grafted with methacrylic acid such as moisture regain, density, and diameter were investigated.     

Oxidation of low concentrations of hydrogen sulfide over activated carbon

The oxidation of low concentrations of hydrogen sulfide with air over activated carbon was studied over the temperature range 24-200°C using both fixed and fluid bed reactors. The predominant reaction, H₂S + ½ O_a → H₂O + S, was found to have an order of 0.5 with respect of H₂S concentration. Activity of the catalyst decreased as the amount of sulfur deposited on it increased. Indirect evidence suggests that adsorption of water by the carbon also decreases its activity as a catalyst at lower temperatures. Values of the activation energy and the frequency factor were determined for various sulfur loadings using the fixed bed reaction system. Regeneration of the carbon loaded with sulfur was studied at temperatures between 150 and 500°C using steam as a carrier gas. Bright yellow sulfur was recovered. The regenerated carbon was shown to have its original activity.     

Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.     

Spatio–Temporal Transform Based Video Hashing

Identification and verification of a video clip via its fingerprint find applications in video browsing, database search and security. For this purpose, the video sequence must be collapsed into a short fingerprint using a robust hash function based on signal processing operations. We propose two robust hash algorithms for video based both on the discrete cosine transform (DCT), one on the classical basis set and the other on a novel randomized basis set (RBT). The robustness and randomness properties of the proposed hash functions are investigated in detail. It is found that these hash functions are resistant to signal processing and transmission impairments, and therefore can be instrumental in building database search, broadcast monitoring and watermarking applications for video. The DCT hash is more robust, but lacks security aspect, as it is easy to find different video clips with the same hash value. The RBT based hash, being secret key based, does not allow this and is more secure at the cost of a slight loss in the receiver operating curves     

The effects of xanthan and guar gums on staling of gluten-free rice cakes baked in different ovens

The objective of this study was to determine the effects of different concentrations of xanthan and guar gums and their blends on staling of gluten-free rice cakes baked in microwave-infrared combination oven (MW–IR) and to compare the cakes with conventionally baked ones. Gums were added at concentrations of 0.3% and 1.0%. For preparation of gum blend, 0.5% xanthan gum was mixed with 0.5% guar gum. In order to understand the staling behaviour of cakes, cakes were stored at 22 ± 2 °C for 120 h. Xanthan-guar gum blend decreased hardness, weight loss, retrogradation enthalpy and the change in setback viscosity values of cakes during storage for both types of ovens as compared to control formulation. It was found that oven type was a significant factor in affecting staling parameters of gluten-free cakes.     

Friction and Wear Characteristics of Haynes 25, 188, and 214 Superalloys Against Hastelloy X up to 540 <Emphasis Type="Bold">°</Emphasis>C

As demand for more power increases, compression ratios, and operating temperatures keep rising. High speeds combined with high temperatures make turbomachinery sealing applications even more challenging. In order to confirm sufficient service life material pairs should be tested under conditions similar to engine operating conditions. This study presents high temperature friction and wear characteristics of cobalt/nickel superalloys, Haynes 25 (51Co–10Ni–20Cr–15W), Haynes 188 (39Co–22Ni–22Cr–14W), and Haynes 214 (75Ni–16Cr–3Fe–0.5Mn) sheets when rubbed against Hastelloy X (47Ni–22Cr–18Fe–9Mo) pins. Tests are conducted at 25, 200, 400, and 540 °C with a validated custom design linear reciprocating tribometer. Sliding speed and sliding distance are 1 Hz and 1.2 km, respectively. Friction coefficients are calculated with friction force data acquired from a load cell. Wear coefficients are calculated through weight loss measurements. Results indicate that Haynes 25 (H25) has the lowest friction coefficients at all test temperatures. Above 400 °C, H25 and Haynes 188 (H188) exhibit the best wear resistance. Protective cobalt oxide layers are formed on the H25 and H188 at 540 °C in addition to nickel, chrome, and tungsten oxides. Although, it has better oxidation resistance, Haynes 214 has relatively higher wear rates than other tested materials especially at low temperatures. However, its wear performance improves beyond 200 °C.     

Comprehensibility of universal healthcare symbols for wayfinding in healthcare facilities

Healthcare facilities are often complex and overwhelming for visitors, and wayfinding in healthcare facilities can be challenging. As there is an increasing number of global citizens who travel to seek medical care in another country, it is critical to make wayfinding easy for visitors who are not familiar with the language in a foreign country. Among many wayfinding aids, symbols are helpful for those visitors who have limited ability to understand written language. This study tested universal healthcare symbols in the United States, South Korea, and Turkey to compare the comprehension of symbols cross-country and identify predictors of the correct comprehension. To explore statistically significant relationships between symbol comprehension and countries, Pearson's Chi-square tests, logistic regression, and ANOVA were conducted. The test results showed that ten symbols among 14 tested have significant relationship with countries. Results of this study demonstrate that symbol comprehension can be varied significantly in different countries.     

Artificial neural network and regression models for performance prediction of abrasive waterjet in rock cutting

An experimental study is carried out for modeling the rock cutting performance of abrasive waterjet. Kerf angle (KA) is considered as a performance criteria and modeled using artificial neural network (ANN) and regression analysis based on operating variables. Three operating variables, including traverse speed, standoff distance, and abrasive mass flow rate, are studied for obtaining different results for the KA. Data belonging to the trials are used for construction of ANN and regression models. The developed models are then tested using a test data set which is not utilized during construction of models. Additionally, the regression model is validated using various statistical approaches. The results of regression analysis are also used to determine the significant operating variables affecting the KA. Furthermore, the performances of derived models are compared for showing the accuracy levels in prediction of the KA. As a result, it is concluded that both ANN and regression models can give adequate prediction for the KA with an acceptable accuracy level. The compared results reveal also that the corresponding ANN model is more reliable than the regression model. On the other hand, the standoff distance and traverse speed are statistically determined as dominant operating variables on the KA, respectively.