The effect of manganese oxide on the sinterability of hydroxyapatite

The sinterability of manganese oxide (MnO2) doped hydroxyapatite (HA) ranging from 0.05 to 1 wt% was investigated. Green samples were prepared and sintered in air at temperatures ranging from 1000 to 1400 °C. Sintered bodies were characterized to determine the phase stability, grain size, bulk density, hardness, fracture toughness and Young's modulus. XRD analysis revealed that the HA phase stability was not disrupted throughout the sintering regime employed. In general, samples containing less than 0.5 wt% MnO2 and when sintered at lower temperatures exhibited higher mechanical properties than the undoped HA. The study revealed that all the MnO2-doped HA achieved >99% relative density when sintered at 1100–1250 °C as compared to the undoped HA which could only attained highest value of 98.9% at 1150 °C. The addition of 0.05 wt% MnO2 was found to be most beneficial as the samples exhibited the highest hardness of 7.58 GPa and fracture toughness of 1.65 MPam1/2 as compared to 5.72 GPa and 1.22 MPam1/2 for the undoped HA when sintered at 1000 °C. Additionally, it was found that the MnO2-doped samples attained E values above 110 GPa when sintered at temperature as low as 1000 °C if compared to 1050 °C for the undoped HA

Less is More: Micro-expression Recognition from Video using Apex Frame

Despite recent interest and advances in facial micro-expression research, there is still plenty room for improvement in terms of micro-expression recognition. Conventional feature extraction approaches for micro-expression video consider either the whole video sequence or a part of it, for representation. However, with the high-speed video capture of micro-expressions (100-200 fps), are all frames necessary to provide a sufficiently meaningful representation? Is the luxury of data a bane to accurate recognition? A novel proposition is presented in this paper, whereby we utilize only two images per video: the apex frame and the onset frame. The apex frame of a video contains the highest intensity of expression changes among all frames, while the onset is the perfect choice of a reference frame with neutral expression. A new feature extractor, Bi-Weighted Oriented Optical Flow (Bi-WOOF) is proposed to encode essential expressiveness of the apex frame. We evaluated the proposed method on five micro-expression databases: CAS(ME)$^2$, CASME II, SMIC-HS, SMIC-NIR and SMIC-VIS. Our experiments lend credence to our hypothesis, with our proposed technique achieving a state-of-the-art F1-score recognition performance of 61% and 62% in the high frame rate CASME II and SMIC-HS databases respectively.Comment: 14 pages double-column, author affiliations updated, acknowledgment of grant support adde

Learning algorithms for multi-class pattern classification and problems associated with on-line handwritten character recognition

On-line handwritten alphanumeric character recognition system and learning algorithm for multiclass pattern classificatio

TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition

This work tackles the face recognition task on images captured using thermal camera sensors which can operate in the non-light environment. While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD). This is partly due to the much smaller amount number of thermal imagery data collected compared to the VLD data. Unfortunately, direct application of the existing very strong face recognition models trained using VLD data into the thermal imagery data will not produce a satisfactory performance. This is due to the existence of the domain gap between the thermal and VLD images. To this end, we propose a Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is able to transform thermal face images into their corresponding VLD images whilst maintaining identity information which is sufficient enough for the existing VLD face recognition models to perform recognition. Some examples are presented in Figure 1. Unlike the previous methods, our proposed TV-GAN uses an explicit closed-set face recognition loss to regularize the discriminator network training. This information will then be conveyed into the generator network in the forms of gradient loss. In the experiment, we show that by using this additional explicit regularization for the discriminator network, the TV-GAN is able to preserve more identity information when translating a thermal image of a person which is not seen before by the TV-GAN

Metabolism of ticagrelor in patients with acute coronary syndromes.

© The Author(s) 2018Ticagrelor is a state-of-the-art antiplatelet agent used for the treatment of patients with acute coronary syndromes (ACS). Unlike remaining oral P2Y12 receptor inhibitors ticagrelor does not require metabolic activation to exert its antiplatelet action. Still, ticagrelor is extensively metabolized by hepatic CYP3A enzymes, and AR-C124910XX is its only active metabolite. A post hoc analysis of patient-level (n = 117) pharmacokinetic data pooled from two prospective studies was performed to identify clinical characteristics affecting the degree of AR-C124910XX formation during the first six hours after 180 mg ticagrelor loading dose in the setting of ACS. Both linear and multiple regression analyses indicated that ACS patients presenting with ST-elevation myocardial infarction or suffering from diabetes mellitus are more likely to have decreased rate of ticagrelor metabolism during the acute phase of ACS. Administration of morphine during ACS was found to negatively influence transformation of ticagrelor into AR-C124910XX when assessed with linear regression analysis, but not with multiple regression analysis. On the other hand, smoking appears to increase the degree of ticagrelor transformation in ACS patients. Mechanisms underlying our findings and their clinical significance warrant further research.Peer reviewedFinal Published versio

Peningkatan Kinerja Toyota Avanza 1.5 Dengan Penambahan Supercharger Elektrik

Di era mobilitas ini, perkembangan teknologi kendaraan bermotor terutama mobil terus berkembang. Peningkatan kinerja Toyota avanza 1.5 dengan menggunakan supercharger konvensional yang berfungsi memasukan udara ke ruang bakar yang biasa dilakukan untuk meningkatkan kinerja mesin menggunkan tenaga dari putaran mesin. Sedangkan supercharger elektrik bekerja seperti halnya supercharger konvensional dengan menggunakan sumber tenaga penggerak motor listrik DC 12 V yang diambil dari aki mobil tanpa memebebani mesin. Dengan adanya penambahan supercharger elektrik pada Toyota avanza terjadi penigkatan torsi dan daya. Namum secara persen tidak signifikan tetapi torsi dan daya meningkat pada rpm 1500-3500

Optimal Cache-Oblivious Mesh Layouts

A mesh is a graph that divides physical space into regularly-shaped regions. Meshes computations form the basis of many applications, e.g. finite-element methods, image rendering, and collision detection. In one important mesh primitive, called a mesh update, each mesh vertex stores a value and repeatedly updates this value based on the values stored in all neighboring vertices. The performance of a mesh update depends on the layout of the mesh in memory. This paper shows how to find a memory layout that guarantees that the mesh update has asymptotically optimal memory performance for any set of memory parameters. Such a memory layout is called cache-oblivious. Formally, for a $d$-dimensional mesh $G$, block size $B$, and cache size $M$ (where $M=\Omega(B^d)$), the mesh update of $G$ uses $O(1+|G|/B)$ memory transfers. The paper also shows how the mesh-update performance degrades for smaller caches, where $M=o(B^d)$. The paper then gives two algorithms for finding cache-oblivious mesh layouts. The first layout algorithm runs in time $O(|G|\log^2|G|)$ both in expectation and with high probability on a RAM. It uses $O(1+|G|\log^2(|G|/M)/B)$ memory transfers in expectation and $O(1+(|G|/B)(\log^2(|G|/M) + \log|G|))$ memory transfers with high probability in the cache-oblivious and disk-access machine (DAM) models. The layout is obtained by finding a fully balanced decomposition tree of $G$ and then performing an in-order traversal of the leaves of the tree. The second algorithm runs faster by almost a $\log|G|/\log\log|G|$ factor in all three memory models, both in expectation and with high probability. The layout obtained by finding a relax-balanced decomposition tree of $G$ and then performing an in-order traversal of the leaves of the tree

Housing Bubbles: A Tale of Two Cities

postprin

Performance enhancement of single-chamber sediment-microbial fuel cell with variation in cathode surface area

This study investigates the impact of cathode surface area on single chamber sediment-microbial fuel cell (S-MFC). A fixed graphite anode surface area of 0.000471m2 has been used on four S-MFCs coupled with four carbon fiber cloth cathode electrodes with variation of surface area. Pond sediment has been used as the anode medium that inoculated with acetate as substrate to ramp up the amount of electrochemical-active bacteria (EAB). The S-MFCs has been operated and monitored for 120 hours using Arduino based data logger. The outcomes of this observation period have indicated the S-MFC with larger cathode surface area (0.01m2) possess smaller internal resistance (123.96±2.68 Ω) and thus performed significantly better than other S-MFC with the smaller cathode surface area, resulting with average voltage and current of 0.598±0.008V and 4.827±0.124mA respectively, where a maximum power density of 2.867mW with a coulombic efficiency of 64.63% was achieved. Successful performance increase suggests enlargement of the cathode area could be the alternative to reduce the internal resistance in traditional MFCs for electricity generation