Propylene Epoxidation: High-Throughput Screening of Supported Metal Catalysts Combinatorially Prepared by Rapid Sol–Gel Method

The gas phase oxidation of propylene using molecular oxygen was studied on a variety of supported metal catalysts. The most promising PO activity was obtained for Cu supported on high surface area SiO₂ and the multimetallic systems exhibit synergistic effects that increased the desired PO yield by several folds for Ag promoted with Cu on SiO₂ after screening a large number of catalysts by a high throughput testing technique.     

Erbium(III)‐doped polyurethaneureas: Novel broadband ultraviolet‐to‐visible converters

Novel, polymeric UV‐to‐visible converters were prepared by doping elastomeric poly(ether‐urethaneurea) copolymers with 5–25% by weight of ErCl₃ 6H₂O, corresponding to Er³⁺ concentrations of 2.19 to 10.86% by weight. When excited in the UV at 355 nm, the doped films generated a very broad, continuous visible luminescence between 400 and 750 nm. Preparation and spectroscopic characterization of the samples are discussed in detail. The color coordinates, color temperature, color rendering index of the samples, and the degree of overlap of their emission bands with the spectral response of the eye were determined. The color rendering index of samples is in the 57–70 range. The sample containing 2.19% by weight of Er³⁺ was found to give the color coordinates closest to the white‐source region and the highest color rendering index. The color temperatures of the samples were in the 5093–5540 K range. Overlap between the emission bands and the spectral response of the eye improved with increasing erbium concentration. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

Topography based design of the T-DAB SFN for a mountainous area

A method for designing a terrestrial digital audio broadcasting single frequency network (T-DAB SFN) for a mountainous region is proposed. A computer database and the digital terrain elevation data are used in designing the network. Two fundamental approaches are compared; first, utilization of the existing transmitter sites is maximized by applying the initial cost constraint; then, the coverage percent of the service is forced to approach to 100% by applying the coverage efficiency constraint. Finally, a T-DAB SFN hybrid design procedure optimizing both constraints is proposed. The three design approaches are illustrated on the coverage of the mountainous Istanbul-Ankara highway. The results are compared using a reference cost look up table. The hybrid approach is shown to optimize the initial cost and the coverage efficiency, and can be applied to similar networking problems     

Effect of stress state on the stress-induced martensitic transformation in polycrystalline Ni-Ti alloy

The effect of stress state on the character and extent of the stress-induced martensitic transformation in polycrystalline Ni-Ti shape memory alloy has been investigated. Utilizing unique experimental equipment, uniaxial and triaxial stress states have been imposed on Ni-Ti specimens and the pseudoelastic transformation strains have been monitored. Comparisons between tests of differing stress states have been performed using effective stress and effective strain quantities; a strain offset method has been utilized to determine the effective stress required for transformation under a given stress state. Results of the tests under different stress states indicate that (1) despite the negative volumetric strain associated with the austenite-to-martensite transformation in Ni-Ti, effective stress for the onset of transformation decreases with increasing hydrostatic stress; (2) effective stressvs effective strain behavior differs greatly under different applied stress states; and (3) austenite in Ni-Ti is fully stable under large values of compressive hydrostatic stress.     

On the oscillatory instability of multiple-parameter systems

The postcritical oscillatory behaviour of an autonomous discrete system under the influence of two independent parameters is studied. Three distinct situations are identified and explored via the intrinsic harmonic balancing technique introduced earlier. In each case, the asymptotic equations of the behaviour surface in parameter-amplitude space are derived explicitly. It is observed that there exists an interesting analogy between this surface and the equilibrium surface associated with static instabilities. Indeed, the phenomenon analyzed here is akin to fold catastrophe. The family of limit cycles associated with the behaviour surface are also obtained in general terms. The results can be used very easily to analyze specific problems, and this has been demonstrated on an illustrative example.     

Process parameters for hydrocooling apricots, plums, and peaches

Freshly harvested apricots, plums, and peaches in crates containing 5–20kg were hydrocooled in flowing water at 1°C and 50mms^-1. The cooling parameters: lag factor, cooling coefficient, and time to half-cool, determined from the exponential decline in the dimensionless ratio of temperature differences between fruit centre and coolant temperature at time t and at zero time, varied somewhat with crate load. Increasing the load from 5 to 20kg increased the time to half-cool apricots by 17% and plums and peaches by 39%.     

Throughput enhanced scheduling (TES) scheme for ultra‐dense networks

This paper proposes two novel packet scheduling schemes, called as throughput enhanced scheduling (TES) and TES plus (TES+), for future ultra‐dense networks. These schemes introduce two novel parameters to the scheduling decision making and reformulate the parameters used by the state‐of‐the‐art schemes. The aim is to have a more balanced weight distribution between delay and throughput‐related parameters at scheduling decisions. Also include a new telecommunications related parameter into scheduling decision making that has not been studied by popular schedulers. The performance of novel schemes is compared with well‐known schemes—proportional fairness (PF), exponential/proportional fairness (EXP/PF), and M‐LWDF. For performance evaluation, five performance metrics—average spectral efficiency and delay, quality of service (QoS) violation ratio, jitter, and Jain's fairness index—are investigated. The simulation results show that proposed schemes can outperform all the compared scheduling schemes.     

BinEHO: a new binary variant based on elephant herding optimization algorithm

One of the new optimization techniques proposed in recent years is elephant herding optimization (EHO) algorithm. Despite its short history, EHO has been used to solve many engineering and real-world problems by attracting researcher attention with its advantages such as efficient global search ability, having fewer control parameters and ease of implementation. However, there is no remarkable binary variant of EHO algorithm in the literature. A new binary approach based on EHO algorithm is proposed in this study. The newer binary variant of EHO named as BinEHO is binarized with preserving the search ability of basic EHO. The main purpose of the study is to present a simple, efficient and robust binary variant which copes with different binary problems. Therefore, the proposed method is tested on three important binary optimization problems, 0–1 knapsack, uncapacitated facility location and wind turbine placement, in order to show its performance and accuracy. In addition, the BinEHO is compared with various binary variants on these problems. Experimental results and comparisons show that the BinEHO algorithm is a robust and efficient tool for binary optimization.

     

Performance prediction of roadheaders using ensemble machine learning techniques

Mechanical excavators are widely used in mining, tunneling and civil engineering projects. There are several types of mechanical excavators, such as a roadheader, tunnel boring machine and impact hammer. This is because these tools can bring productivity to the project quickly, accurately and safely. Among these, roadheaders have some advantages like selective mining, mobility, less over excavation, minimal ground disturbances, elimination of blast vibration, reduced ventilation requirements and initial investment cost. A critical issue in successful roadheader application is the ability to evaluate and predict the machine performance named instantaneous (net) cutting rate. Although there are several prediction methods in the literature, for the prediction of roadheader performance, only a few of them have been developed via artificial neural network techniques. In this study, for this purpose, 333 data sets including uniaxial compressive strength and power on cutting boom, 103 data set including RQD, and 125 data sets including machine weight are accumulated from the literature. This paper focuses on roadheader performance prediction using six different machine learning algorithms and a combination of various machine learning algorithms via ensemble techniques. Algorithms are ZeroR, random forest (RF), Gaussian process, linear regression, logistic regression and multi-layer perceptron (MLP). As a result, MLP and RF give better results than the other algorithms also the best solution achieved was bagging technique on RF and principle component analysis (PCA). The best success rate obtained in this study is 90.2% successful prediction, and it is relatively better than contemporary research.

     

Online anomaly detection for multi‐source VMware using a distributed streaming framework

Anomaly detection refers to the identification of patterns in a dataset that do not conform to expected patterns. Such non‐conformant patterns typically correspond to samples of interest and are assigned to different labels in different domains, such as outliers, anomalies, exceptions, and malware. A daunting challenge is to detect anomalies in rapid voluminous streams of data. This paper presents a novel, generic real‐time distributed anomaly detection framework for multi‐source stream data. As a case study, we investigate anomaly detection for a multi‐source VMware‐based cloud data center, which maintains a large number of virtual machines (VMs). This framework continuously monitors VMware performance stream data related to CPU statistics (e.g., load and usage). It collects data simultaneously from all of the VMs connected to the network and notifies the resource manager to reschedule its CPU resources dynamically when it identifies any abnormal behavior from its collected data. A semi‐supervised clustering technique is used to build a model from benign training data only. During testing, if a data instance deviates significantly from the model, then it is flagged as an anomaly. Effective anomaly detection in this case demands a distributed framework with high throughput and low latency. Distributed streaming frameworks like Apache Storm, Apache Spark, S4, and others are designed for a lower data processing time and a higher throughput than standard centralized frameworks. We have experimentally compared the average processing latency of a tuple during clustering and prediction in both Spark and Storm and demonstrated that Spark processes a tuple much quicker than storm on average. Copyright © 2016 John Wiley & Sons, Ltd.