Drop of Pressure Behavior of Open-Cell Aluminum Foams at High Pressure Flow

Open-cell aluminum foams were produced by the replication technique in three different pore sizes, ranging from 0.71 to 4.75 mm. The manufactured specimens were physically characterized, determining their porosity, relative density, pores per inch and interconnection windows density. A new experimental design is proposed in order to assess the drop of pressure behavior resulting from the injection of gasoline additive at increasing high pressure intervals, ranging from 200 to 25,000 psi, reproducing the tests at room temperature and 200 °C. The regime governing the flow through the investigated samples was determined as a function of flowrate and the foams physical properties. The structural capacity of open-cell Al foams to conduct highly pressurized flow was evaluated by means of compression tests. It was found that at room temperature, the drop of pressure behavior is strongly associated to physical parameters, whilst at 200 °C, dimensional and geometrical properties are negligible. In addition, in this investigation, it is presumed that the studied foams have the structural capacity to conduct fluids at critical conditions of pressure and temperature.

Optimization of parameters affecting dealcoholization of anthocyanin extract by liquid emulsion membrane using response surface methodology

Colour is one of most important properties of foods and beverages and is a basis for their identification and acceptability. Anthocyanin from red cabbage was extracted using 50 % ethanol. The extract was dealcoholized by Liquid Emlusion Membrane technology (LEM). Parafin oil was used as a solvent, lecithin was used as a surfactant and water as stripping medium. Response surface methodology (RSM) was used to design the experiments. A total of 30 experiments were conducted in accordance with central composite rotatable design. Design expert 8 was used to design the experiments. % extraction of alcohol in each case was determined. A suitable model was fitted to experimental data by regression analysis (R-square=0.93). Response surface plot were analysed and optimum parameters for dealcoholization were found to be speed=365.44 rpm, time=18.62 min, concentration of lecithin=2.84 %, feed to emulsion ratio=3.05. A maximum dealcoholisation of 18.63 % was observed under these conditions

The effects of applying filters on EEG signals for classifying developers’ code comprehension

EEG signals are a relevant indicator for measuring aspects related to human factors in Software Engineering. EEG is used in software engineering to train machine learning techniques for a wide range of applications, including classifying task difficulty, and developers’ level of experience. The EEG signal contains noise such as abnormal readings, electrical interference, and eye movements, which are usually not of interest to the analysis, and therefore contribute to the lack of precision of the machine learning techniques. However, research in software engineering has not evidenced the effectiveness when applying these filters on EEG signals. The objective of this work is to analyze the effectiveness of filters on EEG signals in the software engineering context. As literature did not focus on the classification of developers’ code comprehension, this study focuses on the analysis of the effectiveness of applying EEG filters for training a machine learning technique to classify developers' code comprehension. A Random Forest (RF) machine learning technique was trained with filtered EEG signals to classify the developers' code comprehension. This study also trained another random forest classifier with unfiltered EEG data. Both models were trained using 10-fold cross-validation. This work measures the classifiers' effectiveness using the f-measure metric. This work used the t-test, Wilcoxon, and U Mann Whitney to analyze the difference in the effectiveness measures (f-measure) between the classifier trained with filtered EEG and the classifier trained with unfiltered EEG. The tests pointed out that there is a significant difference after applying EEG filters to classify developers' code comprehension with the random forest classifier. The conclusion is that the use of EEG filters significantly improves the effectivity to classify code comprehension using the random forest technique.

Improved reconstruction methodology of clinical electron energy spectra based on Tikhonov regularization and generalized simulated annealing

Electron beam radiotherapy is the most widespread treatment modality todeal with superficial cancers. In electron radiotherapy, the energy spectrum isimportant for electron beam modelling and accurate dose calculation. Since thepercentage depth-dose (PDD) is a function of the beam’s energy, the reconstruction of the spectrum from the depth-dose curve represents an inverse problem.Thus, the energy spectrum can be related to the depth-dose by means of anappropriate mathematical model as the Fredholm equation of the first kind.Since the Fredholm equation of the first kind is ill-posed, some regularizationmethod has to be used to achieve a useful solution. In this work the Tikhonovregularization function was solved by the generalized simulated annealing optimization method. The accuracy of the reconstruction was verified by thegamma index passing rate criterion applied to the simulated PDD curves forthe reconstructed spectra compared to experimental PDD curves. Results showa good coincidence between the experimental and simulated depth-dose curvesaccording to the gamma passing rate better than 95% for 1% dose difference(DD)/1 mm distance to agreement (DTA) criteria. Moreover, the results showimprovement from previous works not only in accuracy but also in calculationtime. In general, the proposed method can help in the accuracy of dosimetryprocedures, treatment planning and quality control in radiotherapy.

A Mapping Analysis of Maintenance in Industry 4.0

This paper analyses and reviews the most important literature papers relating to the evolution of maintenance in Industry 4.0 and its applications. The topic's importance is stated by n increasing number of publications in this field, which suggested a systematic literature review. The proliferation of hardware devices in the workplace, such as smartphones and tablets, has caused engineers to develop the industrial sector 's maintenance world. This review aims to classify the literature published from 2015 to early 2020 to identify the major benefits and areas where it obtained them. This study surveys the latest approaches and emerging trends in maintenance management strategies commonly used in the era of Industry 4.0. It discusses the state-of-the-art of Industry 4.0 technology and the associated use of manufacturing and maintenance management. The data collection was obtained by conducting a systematic search of the literature.

Dense monocular Simultaneous Localization and Mapping by direct surfel optimization

This work presents a novel approach for monocular dense Simultaneous Localization and Mapping. The surface to be estimated is represented as a piecewise planar surface, defined as a group of surfels each having as parameters its position and normal. These parameters are then directly estimated from the raw camera pixels measurements, by a Gauss-Newton iterative process. The representation of the surface as a group of surfels has several advantages. It allows the recovery of robust and accurate pixel depths, without the need to use a computationally demanding depth regularization schema. This has the further advantage of avoiding the use of a physically unlikely surface smoothness prior. New surfels can be correctly initialized from the information present in nearby surfels, avoiding also the need to use an expensive initialization routine commonly needed in Gauss-Newton methods. The method was written in the GLSL shading language, allowing the usage of GPU thus achieving real-time. The method was tested against several datasets, showing both its depth and normal estimation correctness, and its scene reconstruction quality. The results presented here showcase the usefulness of the more physically grounded piecewise planar scene depth prior, instead of the more commonly pixel depth independence and smoothness prior.

Combination of Morphology, Wavelet and Convex Hull Features in Classification of Patchouli Varieties with Imbalance Data using Artificial Neural Network

Patchouli plants are main raw materials for essential oils in Indonesia. Patchouli leaves have a very varied physical form based on the area planted, making it difficult to recognize the variety. This condition makes it difficult for farmers to recognize these varieties and they need experts’ advice. As there are few experts in this field, a technology for identifying the types of patchouli varieties is required. In this study, the identification model is constructed using a combination of leaf morphological features, texture features extracted with Wavelet and shape features extracted with convex hull. The results of feature extraction are used as input data for training of classification algorithms. The effectiveness of the input features is tested using three classification methods in class artificial neural network algorithms: (1) feedforward neural networks with backpropagation algorithm for training, (2) learning vector quantization (LVQ), (3) extreme learning machine (ELM). Synthetic minority over-sampling technique (SMOTE) is applied to solve the problem of class imbalance in the patchouli variety dataset. The results of the patchouli variety identification system by combining these three features indicate the level of recognition with an average accuracy of 72.61%, accuracy with the combination of these three features is higher when compared to using only morphological features (58.68%) or using only Wavelet features (59.03 %) or both (67.25%). In this study also showed that the use of SMOTE in imbalance data increases the accuracy with the highest average accuracy of 88.56%.

Discrete Time Multi-servers Loss Systems and Stochastic Approximation with Delayed Groups of Customers

The stochastic approximation procedure with delayed groups of delayed customers is investigated. The Robbins-Monro stochastic approximation procedure is adjusted to be usable in the presence of delayed groups of delayed customers. Two loss systems are introduced to get an accurate description of the proposed procedure. Each customer comes after fixed time-intervals with the stage of the following customer is accurate according to the outcome of the preceding one, where the serving time of a customer is assumed to be discrete random variable. Some applications of the procedure are given where the analysis of their results is obtained. The analysis shows that efficiencies of the procedure can be increased by minimizing the number of customers of a group irrespective of their service times that may take maximum values. Efficiencies depend on the maximum service time of the customer and on the number of customers of the group. The most important result is that efficiencies of the procedure are increased by increasing the service time distributions as well as service times of customers .This new situation can be applied to increase the number of served customers where the number of served groups will also be increased. The results obtained seem to be acceptable. In general, our proposal can be utilized to other stochastic approximation procedures to increase the production in many fields such as medicine, computer sciences, industry, and applied sciences.

REST API Composition for Effective Testing the Cloud

Cloud offers many ready-made REST services for the end users. This offer realizes the service composition through implementation somewhere on internet based on Service Level Agreement (SLA). For ensuring this SLA, a software testing is a useful means for attesting a non-functional requirement that guarantees quality assurance from end user's perspective. However, test engineer experiences only what goes in and out through an interface that contains a high level behaviors separated from its underlying details. Testing with these behaviors become an issue for classical testing procedures. So, REST API through composition is an alternative new promising approach for modeling behaviors with parameters against the cloud. This new approach helps to devise test effectiveness in terms of REST based behavior-driven implementation. It aims to understand functional behaviors through API methods based on input domain modeling (IDM) on the standard keyboard pattern. By making an effective REST design the test engineer sends complete test inputs to its API directly on application, and gets test responses from the infrastructure. We consider NEMo mobility API specification to design an IDM, which represents pattern match of mobility search URL API path scope. With this scope, sample mobility REST API service compositions are used. Then, the test assertions are implemented to validate each path resource to test the components and the end-to-end integration on the specified service.

Multi-Objective Optimization of Aluminium Powder-mixed EDM process using GRA and TOPSIS Technique based on Fuzzy AHP approach

Electric discharge machining is an advanced machining technique. Spark is initiated between the tool and work piece interface which has a gap between them. Low material removal rate as well as low surface finish is a major concern of this process. Therefore, Powder mixed electric discharge machining is developed. In PMEDM process, powders like silicon, aluminium, chromium, manganese, etc. are circulated along with dielectric fluid in a particular proportion. In this present study, aluminium powder is mixed in the dielectric fluid. The responses such as material removal rate, tool wear rate and surface roughness are measured by considering current, pulse on time and aluminium powder concentration as process parameters. Response surface methodology along with Fuzzy AHP TOPSIS and Grey relational analysis are used for optimization.