Novel ZA27/B4C/Graphite Hybrid Nanocomposite-Bearing Materials with Enhanced Wear and Corrosion Resistance

Metallurgical and Materials Transactions A - In this study, nanosized B4C and graphite-reinforced ZA27 matrix hybrid nanocomposites were produced with mechanical milling followed by hot pressing....     

Theoretical and experimental investigation of effects of failures on the tensile endurance of elevator ropes

Elevators have an important place in human life just after uprising demands in high rise buildings, skyscraper and luxury residence concepts have surged. Steel wire ropes provide permanent working safety and reliability in the elevator installations. Due to this reason steel wire ropes are one of the vital components of elevators. In this study, effects of discard criteria such as wire breaks, abrasive wear and corrosion where extents of those failures are defined by ISO 4344 standard on the tensile endurance of 6×19 Seale and 8×19 Warrington elevator ropes have been investigated theoretically and experimentally. Tensile endurances of rope samples that have different extents of failure have been determined by using tensile testing machine and comparison is made by undamaged samples for each rope separately. In addition, stress and strain values occurred on the elevator ropes investigated have also been presented. The most critical failure type has been found to be 24 wire breaks for 6×19 Seale rope with fiber core where lifting capacity reduced by 25.99 %. The most critical failure type has been found to be abrasive wear which is 0.1 mm in depth along 100 mm length for 8×19 Warrington rope with independent wire rope core where lifting capacity reduced by 7.93 %.     

Kinetically Stable Oxide Overlayers on Mo3P Nanoparticles Enabling Lithium–Air Batteries with Low Overpotentials and Long Cycle Life

The main drawbacks of today's state-of-the-art lithium–air (Li–air) batteries are their low energy efficiency and limited cycle life due to the lack of earth-abundant cathode catalysts that can drive both oxygen reduction and evolution reactions (ORR and OER) at high rates at thermodynamic potentials. Here, inexpensive trimolybdenum phosphide (Mo₃P) nanoparticles with an exceptional activity—ORR and OER current densities of 7.21 and 6.85 mA cm⁻² at 2.0 and 4.2 V versus Li/Li⁺, respectively—in an oxygen-saturated non-aqueous electrolyte are reported. The Tafel plots indicate remarkably low charge transfer resistance—Tafel slopes of 35 and 38 mV dec⁻¹ for ORR and OER, respectively—resulting in the lowest ORR overpotential of 4.0 mV and OER overpotential of 5.1 mV reported to date. Using this catalyst, a Li–air battery cell with low discharge and charge overpotentials of 80 and 270 mV, respectively, and high energy efficiency of 90.2% in the first cycle is demonstrated. A long cycle life of 1200 is also achieved for this cell. Density functional theory calculations of ORR and OER on Mo₃P (110) reveal that an oxide overlayer formed on the surface gives rise to the observed high ORR and OER electrocatalytic activity and small discharge/charge overpotentials.     

Model-based ideal testing of hardware description language (HDL) programs

Software and Systems Modeling - An ideal test is supposed to show not only the presence of bugs but also their absence. Based on the Fundamental Test Theory of Goodenough and Gerhart (IEEE Trans...     

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.

     

The Effects of Thermal Cycles on the Impact Fatigue Properties of Thermoplastic Matrix Composites

The effects of thermal cycles on the impact fatigue properties of unidirectional carbon fibre reinforced polyetherimide (PEI) matrix composites were investigated. During the thermal cycles, samples were immersed into boiling water (100 °C) and subsequently to ice water (0 °C), 50, 200 and 500 times. The changes in viscoelastic properties of the composites were investigated by means of dynamic mechanical thermal analyzer (DMTA). At the second step, thermal cycled composites were subjected to repeated impact loadings, with different impact energies. Instrumented impact test results were presented as a function of force, energy, deformation during the experiments. The scanning electron microscope (SEM) studies were done in order to understand the morphology of fractured samples after impact fatigue loading. The number of thermal cycles and applied impact energy of the hammer are found to have a great importance on the fracture morphology of repeatedly impacted material, as expected.