Sr2+–Mg2+-Doped SiAlON Ceramics

In the present work, multi-cation-doped (Sr²⁺–Mg²⁺) SiAlON ceramics were investigated. MgO and SrO were used in 100:0 and 50:50 molar ratios. The mixture was sintered at 1800° and 1830°C for 1 h in a gas pressure-sintering furnace. The results showed that sintered samples were composed of mainly α- and β-SiAlON phases and small amounts of some Sr-containing phases and SiAlON polytypes. According to Rietveld analysis of X-ray diffraction patterns, Mg is incorporated into the α-SiAlON structure. However, the incorporation of Sr is limited.     

Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange

Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).     

A stochastic neighborhood search approach for airport gate assignment problem

An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own physical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for ?stanbul Atatürk Airport.     

Inference and uncertainty quantification of stochastic gene expression via synthetic models

Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model, to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of non-trivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.     

Output-associative RVM regression for dimensional and continuous emotion prediction

Many problems in machine learning and computer vision consist of predicting multi-dimensional output vectors given a specific set of input features. In many of these problems, there exist inherent temporal and spatial dependencies between the output vectors, as well as repeating output patterns and input–output associations, that can provide more robust and accurate predictors when modeled properly. With this intrinsic motivation, we propose a novel Output-Associative Relevance Vector Machine (OA-RVM) regression framework that augments the traditional RVM regression by being able to learn non-linear input and output dependencies. Instead of depending solely on the input patterns, OA-RVM models output covariances within a predefined temporal window, thus capturing past, current and future context. As a result, output patterns manifested in the training data are captured within a formal probabilistic framework, and subsequently used during inference. As a proof of concept, we target the highly challenging problem of dimensional and continuous prediction of emotions, and evaluate the proposed framework by focusing on the case of multiple nonverbal cues, namely facial expressions, shoulder movements and audio cues. We demonstrate the advantages of the proposed OA-RVM regression by performing subject-independent evaluation using the SAL database that constitutes naturalistic conversational interactions. The experimental results show that OA-RVM regression outperforms the traditional RVM and SVM regression approaches in terms of accuracy of the prediction (evaluated using the Root Mean Squared Error) and structure of the prediction (evaluated using the correlation coefficient), generating more accurate and robust prediction models.     

Chip geometry and cutting force prediction in gear hobbing

This paper presents a tri-dexel geometric engine integrated simulation model for the gear hobbing operation. The process kinematics are modeled and validated using CNC signals from a Liebherr LC500 hobbing machine. Workpiece geometry updating and cutter-workpiece engagement (CWE) calculations are efficiently realized in the tri-dexel engine. 3D force contributions at discretized nodes along the hob's cutting edges are computed considering the localized principal cutting directions, and rake and inclination angles. To measure cutting forces, a rotary dynamometer is successfully adapted and used alongside a Kalman filter to compensate for structural dynamics. The predicted forces agree well with their experimental counterparts.     

时光机器：对话卡斯·卡恩教授

"致密化"是现代城市发展不可回避的趋势。城市新插件与历史环境交融并置将是下一个时代浪潮的重要特征,这将进一步引发关于历史价值与当代性的讨论和思辨。荷兰作为高密度国家典型代表,近十年在建成环境的适应性转型中表现突出。文章通过与荷兰卡恩建筑事务所主持建筑师卡斯·卡恩教授的访谈,深度辨析荷兰建筑师面向复杂历史环境的立场与方法。对话重点聚焦于改造策略、遗产价值与实践经验,以期获得设计介入保护与更新议题的可描摹的研究线索。     

Investigation of Corrosion Behavior of Borided Gear Steels

In this study, corrosion behaviors of GS18NiMoCr36 (GS 18) and GS32NiCrMo6.4 (GS 32) gear steels borided in Ekabor-II powder at the temperature of 950 °C for 2 and 6 h were investigated in a 6 % M HCI acid solution. The boride layer was characterized by optical microscopy, X-ray diffraction technique and the Micro-Vickers hardness tester. X-ray diffraction analysis of boride layers on the surface of the steels revealed the existence of FeB, Fe₂B, CrB and Cr₂B compounds. The thickness of the boride layer increases by increasing boriding time for gear steels. The hardness of the boride compounds formed on the surface of the steels GS 18 and GS 32 ranged from 1,728 to 1,905 HV_0,05 and 1,815 to 2,034 HV_0,05 respectively, whereas Vickers hardness values of the untreated steels GS 18 and GS 32 were 335 HV_0,05 and 411 HV_0,05, respectively. The corrosion resistance of borided gear steels is higher compared with that of unborided steels. The boride layer increased the corrosion resistances of gear steels 4–6-fold.     

Stabilized electrospinning of heat stimuli/in situ crosslinkable nanofibers and their self‐same nanocomposites

We present a strategy for stabilizing the morphological integrity of electrospun polymeric nanofibers by heat stimuli in situ crosslinking. Amorphous polymer nanofibers, such as polystyrene (PS) and its co‐polymers tend to lose their fiber morphology during processing at temperatures above their glass transition temperature (T_g) typically bound to happen in nanocomposite/structural composite applications. As an answer to this problem, incorporation of the crosslinking agents, phthalic anhydride (PA) and tributylamine (TBA), into the electrospinning polymer solution functionalized by glycidylmethacrylate (GMA) copolymerization, namely P(St‐co‐GMA), is demonstrated. Despite the presence of the crosslinker molecules, the electrospinning polymer solution is stable and its viscosity remains unaffected below 60 °C. Crosslinking reaction stands‐by and can be thermally stimulated during post‐processing of the electrospun P(St‐co‐GMA)/PA‐TBA fiber mat at intermediate temperatures (below the T_g). This strategy enables the preservation of the nanofiber morphology during subsequent high temperature processing. The crosslinking event leads to an increase in T_g of the base polymer by 30 °C depending on degree of crosslinking. Crosslinked nanofibers are able to maintain their nanofibrous morphology above the T_g and upon exposure to organic solvents. In situ crosslinking in epoxy matrix is also reported as an example of high temperature demanding application/processing. Finally, a self‐same fibrous nanocomposite is demonstrated by dual electrospinning of P(St‐co‐GMA) and stabilized P(St‐co‐GMA)/PA‐TBA, forming an intermingled nanofibrous mat, followed by a heating cycle. The product is a composite of crosslinked P(St‐co‐GMA)/PA‐TBA fibers fused by P(St‐co‐GMA) matrix. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 44090.