The flame propagation characteristics and detonation parameters of ammonia/oxygen in a large-scale horizontal tube: As a carbon-free fuel and hydrogen-energy carrier

As a carbon-free fuel and a hydrogen-energy carrier, ammonia is a potential candidate for future energy utilization. Therefore, in order to promote the application of ammonia in detonation engines and to evaluate the safety of ammonia related industrial process, DDT experiments for ammonia/oxygen mixtures with different ERs were carried out in a large-scale horizontal tube. Moreover, pressure transducers and self-developed temperature sensors were applied to record the overpressure and the instantaneous flame temperature during DDT process. The results show that the DDT process in ammonia/oxygen mixtures contains four stages: Slow propagation stage, Flame and pressure wave acceleration stage, Fast propagation and detonation wave formation stage, Detonation wave self-sustained propagation stage. For stoichiometric ammonia/oxygen mixtures, flame front and the leading shock wave propagate one after another with different velocity, until they closely coupled and propagated together with one steady velocity. At the same time, it is found that an interesting retonation wave propagates backward. The peak overpressure, detonation velocity, and flame temperature of the self-sustained detonation are 2 MPa, 2000 m/s and 3500 K, respectively. With the ER increased from 0.6 to 1.6, the detonation velocities and peak overpressures ranged from 2310 m/s to 2480 m/s and 25.6 bar–28.7 bar, respectively. In addition, the detonation parameters of ammonia were compared with those of methane and hydrogen to evaluate the detonation performance and destructiveness of ammonia.     

Support Vector Machine Regression for project control forecasting

Support Vector Machines are methods that stem from Artificial Intelligence and attempt to learn the relation between data inputs and one or multiple output values. However, the application of these methods has barely been explored in a project control context. In this paper, a forecasting analysis is presented that compares the proposed Support Vector Regression model with the best performing Earned Value and Earned Schedule methods. The parameters of the SVM are tuned using a cross-validation and grid search procedure, after which a large computational experiment is conducted. The results show that the Support Vector Machine Regression outperforms the currently available forecasting methods. Additionally, a robustness experiment has been set up to investigate the performance of the proposed method when the discrepancy between training and test set becomes larger.     

On the fabrication of functional graded 3Y-PSZ/316L materials by SPS: Process optimization and characterization of the obtained products

Dense and crack free six-layered functional graded materials were successfully produced by Spark Plasma Sintering by combining 3 mol% Y₂O₃-partially stabilized ZrO₂ (3Y-PSZ) and 316L stainless steel. All the sintered products consisted of a steel free layer on one side and a cermet composite containing 50 vol% of both constituents on the opposite side. Conversely, the stainless steel concentration in the interlayers was progressively changed following diverse spatial profiles.It was found that the temperature interval from 1080 to 1180 °C required for the full consolidation from the 50 vol% composite layer to the 3Y-PSZ one, respectively, can be reached when adopting a specific die configuration where the cross section was varied from 30 to 28 mm, respectively. Correspondingly, the densification level of each layer, as well as the related hardness and fracture toughness properties, were highly enhanced with respect to the standard cylindrical die. In addition, a significant improvement of the material toughness was obtained when the material concentration exponent was decreased from 2 to 1, whereas this effect tends to vanish when such parameter was further reduced to 0.5.     

Top-k/w publish/subscribe: A publish/subscribe model for continuous top-k processing over data streams

Continuous processing of top-k queries over data streams is a promising technique for alleviating the information overload problem as it distinguishes relevant from irrelevant data stream objects with respect to a given scoring function over time. Thus it enables filtering of irrelevant data objects and delivery of top-k objects relevant to user interests in real-time. We propose a solution for distributed continuous top-k processing based on the publish/subscribe communication paradigm—top-k publish/subscribe over sliding windows (top-k/w publish/subscribe). It identifies k best-ranked objects with respect to a given scoring function over a sliding window of size w, and extends the publish/subscribe communication paradigm by continuous top-k processing algorithms coming from the field of data stream processing.In this paper, we introduce, analyze and evaluate the essential building blocks of distributed top-k/w publish/subscribe systems: first, we present a formal top-k/w publish/subscribe model and compare it to the prevailing Boolean publish/subscribe model. Next, we outline the top-k/w processing tasks performed by publish/subscribe nodes and investigate the properties of supported scoring functions. Furthermore, we explore potential routing strategies for distributed top-k/w publish/subscribe systems. Finally, we experimentally evaluate model properties and provide a comparative study investigating traffic requirements of potential routing strategies.     

Scenario cluster Lagrangean decomposition for risk averse in multistage stochastic optimization

In this work we present a decomposition approach as a mixture of dualization and Lagrangean Relaxation for obtaining strong lower bounds on large-sized multistage stochastic mixed 0–1 programs with a time stochastic dominance risk averse measure. The objective function to minimize is a composite function of the expected cost along the time horizon over the scenarios and the penalization of the expected cost excess on reaching the set of thresholds under consideration, subject to a bound on the expected cost excess for each threshold and a bound on the failure probability of reaching it. The main differences with some other risk averse strategies are presented. The problem is represented by a mixture of the splitting representation up to a given stage, so-called break stage, and the compact representation for the other stages along the time horizon. The dualization of the nonanticipativity constraints for the node-based and risk averse variables up to the break stage and the Lagrangean Relaxation of the cross node constraints of the risk averse strategy result in a model that can be decomposed into a set of independent scenario cluster submodels. Three Lagrangean multipliers updating schemes as the Subgradient method, the Lagrangean Progressive Hedging algorithm and the Dynamic Constrained Cutting Plane are computationally compared. We have observed in the randomly generated instances we have experimented with that the smaller the number of clusters, the stronger the lower bound provided for the original problem (even, frequently, it is the solution value) obtained with an affordable computing time.     

Churn prediction using comprehensible support vector machine: An analytical CRM application

Support vector machine (SVM) is currently state-of-the-art for classification tasks due to its ability to model nonlinearities. However, the main drawback of SVM is that it generates “black box” model, i.e. it does not reveal the knowledge learnt during training in human comprehensible form. The process of converting such opaque models into a transparent model is often regarded as rule extraction. In this paper we proposed a hybrid approach for extracting rules from SVM for customer relationship management (CRM) purposes. The proposed hybrid approach consists of three phases. (i) During first phase; SVM-RFE (SVM-recursive feature elimination) is employed to reduce the feature set. (ii) Dataset with reduced features is then used in the second phase to obtain SVM model and support vectors are extracted. (iii) Rules are then generated using Naive Bayes Tree (NBTree) in the final phase. The dataset analyzed in this research study is about Churn prediction in bank credit card customer (Business Intelligence Cup 2004) and it is highly unbalanced with 93.24% loyal and 6.76% churned customers. Further we employed various standard balancing approaches to balance the data and extracted rules. It is observed from the empirical results that the proposed hybrid outperformed all other techniques tested. As the reduced feature dataset is used, it is also observed that the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. The generated rules act as an early warning expert system to the bank management.     

Capitalizing on the Koran to fuel online violent radicalization: A taxonomy of Koranic references in ISIS’s Dabiq

The current study set out to investigate to what extent ISIS is bolstering its jihadist ideology on a ‘cut-and-paste’ or ‘cherry-picked’ version of Islam in their renowned online propaganda magazine Dabiq. The main objective was to examine in a systematic and quantitative way to what extent ISIS utilizes the Koran in an atomistic, truncated and tailored manner to bolster its religious legitimacy. A total of 15 issues of Dabiq and 700 Koranic references were scrutinized. By means of a quantitative analysis we developed an innovative taxonomy of Koranic chapters and verses (i.e. surahs and ayat, respectively) on the basis of their appearance in Dabiq. Our large-scale data analysis provide consistent empirical evidence for severe decontextualization practices of the Koran in three ways: (1) a thin, Medinan-dominated religious layer, (2) ayah mutilation, and (3) clustered versus exclusive mentions. Limitations and implications for future research, policy makers and CVE initiatives are discussed.     

Improving aircraft safety and reliability by aircraft maintenance technician training

Aircraft maintenance is one of the primary causes or contributing factors in aircraft accidents. It is clear that proper training of Aircraft Maintenance Technicians (AMTs) will avoid failures, reduce maintenance related accidents, improve safety and reliability in aviation and provide recovery of the increasing demand to qualified AMTs for sustainability of the market growth. In this study, European Safety Agency (EASA) based AMT licencing system in EU states (and non-EU states implementing EASA rules) has been analysed and a training model developed in accordance with EASA Part-66 requirements and delivered by e-learning methods has been introduced. The analysis of the licensing process based on EASA regulations showed that this process was based on the candidate's demonstration of knowledge and acquisition of experience. The required experience depends on the training background of the candidate. Field exercise showed that developed e-learning training model, which overcomes the disadvantages of traditional face to face training models, succeeded to improve the attendees' theoretical knowledge level and when combined with the practical trainings given to AMT candidates in maintenance organisations during their experience periods, will be very successful in improving safety and reliability in aviation maintenance operations.     

Fracture toughness of zirconia from a shallow notch produced by ultra-short pulsed laser ablation

Fracture toughness of submicron grain size tetragonal zirconia polycrystals doped with 3 mol% yttria (3Y-TZP) is measured by the single edge V-notch beam (SEVNB) method from a shallow sharp notch produced by ultra-short pulsed femtolaser ablation (UPLA) on the surface of a bending bar. It is shown that the radius of the notch tip achieved is in the submicron range and the damaged volume in front of the notch tip is characterized by using focus ion beam milling and scanning electron microscopy. It consists of a narrow fully microcracked region less than ∼4 μm wide and ∼15 μm deep in front of the notch. If the extension of this region and the length of the notch are used in the determination of the fracture toughness (K_Ic) in the four bending test, the values obtained for submicron grain size 3Y-TZP are in agreement those obtained by using very sharp cracks. It is concluded that the SEVNB testing method with a sharp notch induced by UPLA may be used for K_Ic testing of submicron grain size ceramics.