Effects of Adhesive Composition on Green Joining of CePO_4-ZrO_2 Ceramics

1　Introduction Among structural advanced ceramics, zirconia isconsidered as an important ceramic material for its excel lent high temperature strength, resistance to wear and oxi dation.In order to widen its applications,it is essential todevelop re…     

Surface-modified alumina nanoparticles-filled aramid fiber-reinforced epoxy nanocomposites: preparation and mechanical properties

Iranian Polymer Journal - Surface of Al2O3 nanoparticles was modified with a silane coupling agent, and aramid fiber-reinforced epoxy nanocomposites were produced using these particles. Three...     

Mesoscopic structure and swelling properties of crosslinked polyethylene glycol in water

In this paper, we present our efforts in modeling and simulation of polyethylene glycol crosslinked with an isocyanate tHDI. The polymer, by its nature, is hydrophilic and has strong hydrogen bond interactions with water. The simulations are performed at coarse-grained scale by using a dissipative particle dynamics (DPD) simulation method. The effect of hydrogen bond between water and polymer beads on the structure of the crosslinked hydrophilic polymer structure is studied. The polymer is observed to phase separate with water in the absence of hydrogen bonds in DPD simulations. In the reverse case, where hydrogen bonds are explicitly included in DPD simulations, polymer mixes with water. This behavior is investigated by plotting the density profiles. Moreover, the volumetric swelling behavior in mixtures with different water contents is estimated from simulations and extrapolated by a polynomial fit to compare with experiments. It is observed that the predicted swelling ratio is in good agreement with the experimental measurements.     

The effect of recycling number on the mechanical,chemical, thermal,and rheological properties of PBT/PC/ABS ternary blends: With and without glass‐fiber

The recycling possibilities of poly(butylene terephthalate)/polycarbonate/acrylonitrile–butadiene–styrene (PBT/PC/ABS) ternary blend with and without glass‐fiber content were investigated using repeated injection molding process. In this study, PBT/PC/ABS ternary blends were reprocessed at five times and the results were presented after each recycling process. The recycling possibility of PBT/PC/ABS ternary blend was evaluated by measuring the mechanical, chemical, thermal, and rheological properties. Mechanical properties were determined by the tensile strength, yield strength, strain at break, elastic modulus, impact strength, flexural strength, and flexural modulus. Chemical and thermal properties were evaluated by Fourier transform infrared spectroscopy, differential scanning calorimetry, thermal gravimetric analysis, and scanning electron microscopy. Rheological properties of the ternary blends were studied by melt flow index measurement. From the results, it was found that mechanical properties of recycled composites were better than virgin PBT/PC/ABS ternary blends. POLYM. COMPOS., 35:2074–2084, 2014. © 2014 Society of Plastics Engineers     

Graft copolymers from commercial chlorinated polypropylene via Cu(0)‐mediated atom transfer radical polymerization

Commercially available chlorinated polypropylene has been used as a macroinitiator for the Cu(0)‐mediated atom transfer radical polymerization of methyl methacrylate and tert‐butyl acrylate to obtain well‐defined graft copolymers. The relatively narrow molecular weight distribution in the graft copolymers and linear kinetic plots indicated the controlled nature of the copolymerization reactions. Both Fourier transform infrared and ¹H NMR studies confirmed that the graft reactions had taken place successfully. After graft copolymer formation, tert‐butyl groups of poly(tert‐butyl acrylate) side chains were completely converted into poly(acrylic acid) chains to afford corresponding amphiphilic graft copolymers. © 2016 Society of Chemical Industry     

In situ image processing capabilities of ARM-based micro-controllers

Dealing with visual data is the key for environmental monitoring tasks in Wireless Multimedia Sensor Networks (WMSNs). Tasks such as object detection, recognition, and/or tracking do require extracting and using the right information from the inherently large amount of visual data. The widely accepted solution of legacy WSNs, transmitting the acquired data to a central base station for further processing, would render a WMSN totally useless because of the unacceptable use of bandwidth and energy. Therefore, we consider the in situ processing as a viable solution for WMSNs. However, processing power and memory capacity restrictions of existing multimedia sensor nodes along with their power consumption are the limiting factors for wide-spread use of in situ processing. Nevertheless, recent technological improvements and introduction of the new ARM cores encourage us to evaluate the image processing capabilities of ARM7/ARM9/ARM11 based micro-controllers for in situ processing in WMSNs. In this work, we first discussed the architectural design differences among the various ARM cores. Then we classified image processing algorithms into three categories. Then, we evaluated the performance of each microcontroller by running a set of basic image processing algorithms necessary for object detection, recognition, and/or tracking. The test results show that ARM11 runs up to 6–30 times faster than ARM9 and ARM7, respectively. Besides, ARM11 consumes up to 5–7 times less energy than ARM9 and ARM7 for the same type of operations.     

Analyzing business competition by using fuzzy TOPSIS method: An example of Turkish domestic airline industry

The article uses fuzzy TOPSIS multi-methodological approach in the Turkish domestic airline industry. It starts by describing exceedingly complex nature of competition in the sector. Then, it deals with the constituent parts of the research methodology and the eclectic approach itself. The implementation of fuzzy TOPSIS method in the Turkish domestic airline industry reveals the ranking of major air carriers in light of key success variables in the sector. The article also provides an evaluation of empirical findings of fuzzy TOPSIS method from a managerial perspective.     

Design of a Commercial Hybrid VTOL UAV System

For the last four decades Unmanned Air Vehicles (UAVs) have been extensively used for military operations that include tracking, surveillance, active engagement with weapons and airborne data acquisition. UAVs are also in demand commercially due to their advantages in comparison to manned vehicles. These advantages include lower manufacturing and operating costs, flexibility in configuration depending on customer request and not risking the pilot on demanding missions. Even though civilian UAVs currently constitute 3 % of the UAV market, it is estimated that their numbers will reach up to 10 % of the UAV market within the next 5 years. Most of the civilian UAV applications require UAVs that are capable of doing a wide range of different and complementary operations within a composite mission. These operations include taking off and landing from limited runway space, while traversing the operation region in considerable cruise speed for mobile tracking applications. This is in addition to being able traverse in low cruise speeds or being able to hover for stationary measurement and tracking. All of these complementary and but different operational capabilities point to a hybrid unmanned vehicle concept, namely the Vertical Take-Off and Landing (VTOL) UAVs. In addition, the desired UAV system needs to be cost-efficient while providing easy payload conversion for different civilian applications. In this paper, we review the preliminary design process of such a capable civilian UAV system, namely the TURAC VTOL UAV. TURAC UAV is aimed to have both vertical take-off and landing and Conventional Take-off and Landing (CTOL) capability. TURAC interchangeable payload pod and detachable wing (with potential different size variants) provides capability to perform different mission types, including long endurance and high cruise speed operations. In addition, the TURAC concept is to have two different variants. The TURAC A variant is an eco-friendly and low-noise fully electrical platform which includes 2 tilt electric motors in the front, and a fixed electric motor and ducted fan in the rear, where as the TURAC B variant is envisioned to use high energy density fuel cells for extended hovering time. In this paper, we provide the TURAC UAV’s iterative design and trade-off studies which also include detailed aerodynamic and structural configuration analysis. For the aerodynamic analysis, an in-house software including graphical user interface has been developed to calculate the aerodynamic forces and moments by using the Vortex Lattice Method (VLM). Computational Fluid Dynamics (CFD) studies are performed to determine the aerodynamic effects for various configurations For structural analysis, a Finite Element Model (FEM) of the TURAC has been prepared and its modal analysis is carried out. Maximum displacements and maximal principal stresses are calculated and used for streamlining a weight efficient fuselage design. Prototypes have been built to show success of the design at both hover and forward flight regime. In this paper, we also provide the flight management and autopilot architecture of the TURAC. The testing of the controller performance has been initiated with the prototype of TURAC. Current work focuses on the building of the full fight test prototype of the TURAC UAV and aerodynamic modeling of the transition flight.     

Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features

One of the most important processes in the diagnosis of breast cancer, which is the leading mortality rate in women, is the detection of the mitosis stage at the cellular level. In literature, many studies have been proposed on the computer-aided diagnosis (CAD) system for detecting mitotic cells in breast cancer histopathological images. In this study, comparative evaluation of conventional and deep learning based feature extraction methods for automatic detection of mitosis in histopathological images are focused. While various handcrafted features are extracted with textural/spatial, statistical and shape-based methods in conventional approach, the convolutional neural network structure proposed on the deep learning approach aims to create an architecture that extracts the features of small cellular structures such as mitotic cells. Mitosis detection/counting is an important process that helps us assess how aggressive or malignant the cancer’s spread is. In the proposed study, approximately 180,000 non-mitotic and 748 mitotic cells are extracted for the evaluations. It is obvious that the classification stage cannot be performed properly due to the imbalanced numbers of mitotic and non-mitotic cells extracted from histopathological images. Hence, the random under-sampling boosting (RUSBoost) method is exploited to overcome this problem. The proposed framework is tested on mitosis detection in breast cancer histopathological images dataset provided from the International Conference on Pattern Recognition (ICPR) 2014 contest. In the results obtained with the deep learning approach, 79.42% recall, 96.78% precision and 86.97% F-measure values are achieved more successfully than handcrafted methods. A client/server-based framework has also been developed as a secondary decision support system for use by pathologists in hospitals. Thus, it is aimed that pathologists will be able to detect mitotic cells in various histopathological images more easily through necessary interfaces.

     

Extending boosting for large scale spoken language understanding

We propose three methods for extending the Boosting family of classifiers motivated by the real-life problems we have encountered. First, we propose a semisupervised learning method for exploiting the unlabeled data in Boosting. We then present a novel classification model adaptation method. The goal of adaptation is optimizing an existing model for a new target application, which is similar to the previous one but may have different classes or class distributions. Finally, we present an efficient and effective cost-sensitive classification method that extends Boosting to allow for weighted classes. We evaluated these methods for call classification in the AT&;T VoiceTone^® spoken language understanding system. Our results indicate that it is possible to obtain the same classification performance by using 30% less labeled data when the unlabeled data is utilized through semisupervised learning. Using model adaptation we can achieve the same classification accuracy using less than half of the labeled data from the new application. Finally, we present significant improvements in the “important” (i.e., higher weighted) classes without a significant loss in overall performance using the proposed cost-sensitive classification method.