Search results for: ageing experiments

Authors: Amirmozafar Benshams, Khatere Kashmari, Farzad Hatami, Mesbah Saybani

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Shape memory alloys with high capability of energy dissipation and large deformation bearing with return ability to their original shape without too much hysteresis strain have opened their place among the other damping systems as smart materials. Ninitol which is the most well-known and most used alloy material from the shape memory alloys family, has high resistance and fatigue and is coverage for large deformations. Shape memory effect and super-elasticity by shape alloys like Nitinol, are the reasons of the high power of these materials in energy depreciation. Thus, these materials are suitable for use in reciprocating dynamic loading conditions. The experiments results showed that Nitinol wires with small diameter have greater energy dissipation capability and by increase of diameter and thickness the damping capability and energy dissipation increase.

Keywords: shape memory alloys, shape memory effect, super elastic effect, nitinol, energy dissipation

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Authors: Li Ni, Pen ManMan, Li KenLi

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Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.

Keywords: chemical reaction optimization, expection maimization, initia, objective function clustering

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Authors: Mohammad Mirjalili, Hamid Akbarpour

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In this work, a bleached well cotton sample was dyed with reactive yellow105 dye and subsequently, the dyed sample was exposed to the plasma condition containing Nitrogen gas at 1 and 5 minutes of plasma exposure time, respectively. The effect of plasma on surface morphology fabric was studied by Scanning Electronic Microscope (SEM). CIELab, K/S, and %R of samples (treated and untreated samples) were measured by a reflective spectrophotometer, and consequently, the experiments show that the sample dyed with Reactive yellow 105 after being washed, with the increase in the operation time of plasma, its dye fastness decreases. In addition, the increase in plasma operation time at constant pressure would increase the destructing effect on the surface morphology of samples dyed with reactive yellow105.

Keywords: cotton fabric, nitrogen cold plasma, reflective spectrophotometer, scanning electronic microscope (SEM), reactive yellow105 dye

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Authors: N. Tugrul, F. T. Senberber, A. S. Kipcak, E. Moroydor Derun, S. Piskin

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In this research, it is aimed not only microwave synthesis of magnesium borates but also evaluation of magnesium wastes. Synthesis process can be described with the reaction of Mg wastes and boric acid using microwave energy. X-Ray Diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FT-IR) were applied to synthesized minerals. According to XRD results, magnesium borate hydrate mixtures were obtained as mcallisterite (pdf# = 01-070-1902, Mg2(B6O7(OH)6)2.9(H2O)) at higher crystallinity properties was achieved at the mole ratio raw material 1:1. Also, other kinds of magnesium borate hydrates were obtained at lower crystallinity such as admontite (pdf # = 01-076-0540, MgO(B2O3)3.7(H2O)), inderite (pdf # = 01-072-2308, 2MgO.3B2O3.15(H2O)) and magnesium borate hydrates (pdf # = 01-076-0539, MgO(B2O3)3.6(H2O)). FT-IR spectrums indicated that minor changes were seen at the band values of characteristic stretching in each experiment. At the end of experiments it is seen that using microwave energy may contribute positive effects to design of synthesis process such as reducing reaction time and products at higher crystallinity.

Keywords: magnesium wastes, boric acid, magnesium borate, microwave energy

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Authors: Kunal Ahluwalia, Ampara Aramcharoen, Chan Wai Luen, Swee Hock Yeo

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The different process engineering applications of vibratory finishing technology have led to its versatile use in the development of aviation components. The most noteworthy applications of vibratory finishing include deburring and imparting the required surface finish. In this paper, vibratory finishing has been used to study its effectiveness in removal of laser shock peened (LSP) layers from Titanium workpieces. A vibratory trough operating at a frequency of 25 Hz, amplitude 3.5 mm and titanium specimens (Ti-6Al-4V, Grade 5) of dimensions 50 x 50 x 10 mm³ were utilized for the experiments. A vibrating fixture operating at 200 Hz was used to provide vibration to the test piece and was immersed in the vibratory trough. It was evident that there is an increase in efficiency of removal of the complex featured layer and smoother surface finish with the introduction of the vibrating fixture in the vibratory finishing setup as compared to the conventional vibratory finishing setup wherein the fixture is not vibrating.

Keywords: laser shock peening, material removal, surface roughness, vibrating fixture, vibratory finishing

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Authors: Chris Neale, Jenny Roe, Peter Aspinall, Sara Tilley, Steve Cinderby, Panos Mavros, Richard Coyne, Neil Thin, Catharine Ward Thompson

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This research directly assesses older people’s neural activation in response to walking through a changing urban environment, as measured by electroencephalography (EEG). As the global urban population is predicted to grow, there is a need to understand the role that the urban environment may play on the health of its older inhabitants. There is a large body of evidence suggesting green space has a beneficial restorative effect, but this effect remains largely understudied in both older people and by using a neuroimaging assessment. For this study, participants aged 65 years and over were required to walk between a busy urban built environment and a green urban environment, in a counterbalanced design, wearing an Emotiv EEG headset to record real-time neural responses to place. Here we report on the outputs for these responses derived from both the proprietary Affectiv Suite software, which creates emotional parameters with a real time value assigned to them, as well as the raw EEG output focusing on alpha and beta changes, associated with changes in relaxation and attention respectively. Each walk lasted around fifteen minutes and was undertaken at the natural walking pace of the participant. The two walking environments were compared using a form of high dimensional correlated component regression (CCR) on difference data between the urban busy and urban green spaces. For the Emotiv parameters, results showed that levels of ‘engagement’ increased in the urban green space (with a subsequent decrease in the urban busy built space) whereas levels of ‘excitement’ increased in the urban busy environment (with a subsequent decrease in the urban green space). In the raw data, low beta (13 – 19 Hz) increased in the urban busy space with a subsequent decrease shown in the green space, similar to the pattern shown with the ‘excitement’ result. Alpha activity (9 – 13 Hz) shows a correlation with low beta, but not with dependent change in the regression model. This suggests that alpha is acting as a suppressor variable. These results suggest that there are neural signatures associated with the experience of urban spaces which may reflect the age of the cohort or the spatiality of the settings themselves. These are shown both in the outputs of the proprietary software as well as the raw EEG output. Built busy urban spaces appear to induce neural activity associated with vigilance and low level stress, while this effect is ameliorated in the urban green space, potentially suggesting a beneficial effect on attentional capacity in urban green space in this participant group. The interaction between low beta and alpha requires further investigation, in particular the role of alpha in this relationship.

Keywords: ageing, EEG, green space, urban space

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Authors: Cesar Hernández, Diego Giral, Fernando Santa

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The spectral handoff is important in cognitive wireless networks to ensure an adequate quality of service and performance for secondary user communications. This work proposes a benchmarking of performance of the three spectrum handoff models: VIKOR, SAW and MEW. Four evaluation metrics are used. These metrics are, accumulative average of failed handoffs, accumulative average of handoffs performed, accumulative average of transmission bandwidth and, accumulative average of the transmission delay. As a difference with related work, the performance of the three spectrum handoff models was validated with captured data of spectral occupancy in experiments realized at the GSM frequency band (824 MHz-849 MHz). These data represent the actual behavior of the licensed users for this wireless frequency band. The results of the comparative show that VIKOR Algorithm provides 15.8% performance improvement compared to a SAW Algorithm and, 12.1% better than the MEW Algorithm.

Keywords: cognitive radio, decision making, MEW, SAW, spectrum handoff, VIKOR

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Authors: Olga Hernán-Vega, Patricia López-Rodríguez, David Escot-Bocanegra, Raúl Fernández-Recio, Ignacio Bravo

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Non-Cooperative Target Identification has become a key research domain in the Defense industry since it provides the ability to recognize targets at long distance and under any weather condition. High Resolution Range Profiles, one-dimensional radar images where the reflectivity of a target is projected onto the radar line of sight, are widely used for identification of flying targets. According to that, to face this problem, an approach to Non-Cooperative Target Identification based on the exploitation of Singular Value Decomposition to a matrix of range profiles is presented. Target Identification based on one-dimensional radar images compares a collection of profiles of a given target, namely test set, with the profiles included in a pre-loaded database, namely training set. The classification is improved by using Singular Value Decomposition since it allows to model each aircraft as a subspace and to accomplish recognition in a transformed domain where the main features are easier to extract hence, reducing unwanted information such as noise. Singular Value Decomposition permits to define a signal subspace which contain the highest percentage of the energy, and a noise subspace which will be discarded. This way, only the valuable information of each target is used in the recognition process. The identification algorithm is based on finding the target that minimizes the angle between subspaces and takes place in a transformed domain. Two metrics, F1 and F2, based on Singular Value Decomposition are accomplished in the identification process. In the case of F2, the angle is weighted, since the top vectors set the importance in the contribution to the formation of a target signal, on the contrary F1 simply shows the evolution of the unweighted angle. In order to have a wide database or radar signatures and evaluate the performance, range profiles are obtained through numerical simulation of seven civil aircraft at defined trajectories taken from an actual measurement. Taking into account the nature of the datasets, the main drawback of using simulated profiles instead of actual measured profiles is that the former implies an ideal identification scenario, since measured profiles suffer from noise, clutter and other unwanted information and simulated profiles don't. In this case, the test and training samples have similar nature and usually a similar high signal-to-noise ratio, so as to assess the feasibility of the approach, the addition of noise has been considered before the creation of the test set. The identification results applying the unweighted and weighted metrics are analysed for demonstrating which algorithm provides the best robustness against noise in an actual possible scenario. So as to confirm the validity of the methodology, identification experiments of profiles coming from electromagnetic simulations are conducted, revealing promising results. Considering the dissimilarities between the test and training sets when noise is added, the recognition performance has been improved when weighting is applied. Future experiments with larger sets are expected to be conducted with the aim of finally using actual profiles as test sets in a real hostile situation.

Keywords: HRRP, NCTI, simulated/synthetic database, SVD

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Authors: Reneiloe Seodigeng, Malwandla Hanabe, Haleden Chiririwa, Hilary Rutto, Tumisang Seodigeng

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Neutralisation of acid-mine drainage (AMD) using limestone is cost effective, and good results can be obtained. However, this process has its limitations; it cannot be used for highly acidic water which consists of Fe(III). When Fe(III) reacts with CaCO₃, it results in armoring. Armoring slows the reaction, and additional alkalinity can no longer be generated. Limestone is easily accessible, so this problem can be easily dealt with. Experiments were carried out to evaluate the effect of PVC pipe length on ferric and ferrous ions. It was found that the shorter the pipe length the more these dissolved metals precipitate. The effect of the pipe length on the hydrogen ions was also studied, and it was found that these two have an inverse relationship. Experimental data were further compared with the model prediction data to see if they behave in a similar fashion. The model was able to predict the behaviour of 1.5m and 2 m pipes in ferric and ferrous ion precipitation.

Keywords: acid mine drainage, neutralisation, limestone, mathematical modelling

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Authors: A. Elain, M. Le Fellic, A. Le Pemp, N. Hachet

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Iodinated Compounds (IC) are widely detected contaminants in most aquatic environments including sewage treatment plant, surface water, ground water and even drinking water, up to the µg.L-1 range. As IC contribute in the adsorbable organic halides (AOX) level, their removal or dehalogenation is expected. We report here on the biodegradability of a mixture of IC from an industrial effluent using a microbial consortium adapted to grow on IC as well as the native microorganisms. Both aerobic and anaerobic treatments were studied during batch experiments in 500-mL flasks. The degree of mineralization and recovery of iodide were monitored by HPLC-UV, TOC analysis and potentiometric titration. Providing ethanol as an electron acceptor was found to stimulate anaerobic reductive deiodination of IC while sodium chloride even at high concentration (22 g.l-1) had no influence on the degradation rates nor on the microbial viability. Phylogenetic analysis of 16S RNA gene sequence (MicroSeq®) was applied to provide a better understanding of the degradative microbial community.

Keywords: iodinated compounds, biodegradability, deiodination, electron-accepting conditions, microbial consortium

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Authors: Ebrahim Akbarzadeh, Josep A. Picas Barrachina, Maite Baile Puig

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This study focuses on a novel method for dispersion and distribution of reinforcement under high intensive shear stress to produce metal composites. The polyacrylonitrile (PAN)-based short carbon fiber (Csf) and Nextel 610 alumina fiber were dispersed under high intensive shearing at mushy zone in semi-solid of A356 by a novel method. The bundles and clusters were embedded by infiltration of slurry into the clusters, thus leading to a uniform microstructure. The fibers were embedded homogenously into the aluminum around 576-580°C with around 46% of solid fraction. Other experiments at 615°C and 568°C which are contained 0% and 90% solid respectively were not successful for dispersion and infiltration of aluminum into bundles of Csf. The alumina fiber has been cracked by high shearing load. The morphologies and crystalline phase were evaluated by SEM and XRD. The adopted thixo-process effectively improved the adherence and distribution of Csf into Al that can be developed to produce various composites by thixomixing.

Keywords: aluminum, carbon fiber, alumina fiber, thixomixing, adhesion

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Authors: Jesús Antonio Puente Fernández, Luis Javier Garcia Villalba

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New trends on streaming videos such as series or films require a high demand of network resources. This fact results in a huge problem within traditional IP networks due to the rigidity of its architecture. In this way, Software Defined Networks (SDN) is a new concept of network architecture that intends to be more flexible and it simplifies the management in networks with respect to the existing ones. These aspects are possible due to the separation of control plane (controller) and data plane (switches). Taking the advantage of this separated control, it is easy to deploy a monitoring tool independent of device vendors since the existing ones are dependent on the installation of specialized and expensive hardware. In this paper, we propose a framework that optimizes the traffic monitoring in SDN networks that decreases the number of monitoring queries to improve the network traffic and also reduces the overload. The performed experiments (with and without the optimization) using a video streaming delivery between two hosts demonstrate the feasibility of our monitoring proposal.

Keywords: optimization, monitoring, software defined networking, statistics, query

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Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

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Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

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Authors: Nishtha Madaan, Prateek Chaudhury, Nishant Kumar, Srikanta Bedathur

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In modern NLP applications, word embeddings are a crucial backbone that can be readily shared across a number of tasks. However, as the text distributions change and word semantics evolve over time, the downstream applications using the embeddings can suffer if the word representations do not conform to the data drift. Thus, maintaining word embeddings to be consistent with the underlying data distribution is a key problem. In this work, we tackle this problem and propose TransDrift, a transformer-based prediction model for word embeddings. Leveraging the flexibility of the transformer, our model accurately learns the dynamics of the embedding drift and predicts future embedding. In experiments, we compare with existing methods and show that our model makes significantly more accurate predictions of the word embedding than the baselines. Crucially, by applying the predicted embeddings as a backbone for downstream classification tasks, we show that our embeddings lead to superior performance compared to the previous methods.

Keywords: NLP applications, transformers, Word2vec, drift, word embeddings

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Authors: Yulong Wang, Yuan Yan Tang, Cuiming Zou, Lina Yang

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This paper presents a novel sparse representation method for robust pattern classification. Generalized orthogonal matching pursuit (GOMP) is a recently proposed efficient sparse representation technique. However, GOMP adopts the mean square error (MSE) criterion and assign the same weights to all measurements, including both severely and slightly corrupted ones. To reduce the limitation, we propose an information-theoretic GOMP (ITGOMP) method by exploiting the correntropy induced metric. The results show that ITGOMP can adaptively assign small weights on severely contaminated measurements and large weights on clean ones, respectively. An ITGOMP based classifier is further developed for robust pattern classification. The experiments on public real datasets demonstrate the efficacy of the proposed approach.

Keywords: correntropy induced metric, matching pursuit, pattern classification, sparse representation

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Authors: Ghouas Halima, Haddou Boumedienne, Jean Peal Cancelier, Cristophe Gourdon, Ssaka Collines

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This work concerns the coacervate extraction of industrial dye, namely BezanylGreen - F2B, from an aqueous solution by nonionic surfactant “Lutensol AO7 and TX-114” (readily biodegradable). Binary water/surfactant and pseudo-binary (in the presence of solute) phase diagrams were plotted. The extraction results as a function of wt.% of the surfactant and temperature are expressed by the following four quantities: percentage of solute extracted, E%, residual concentrations of solute and surfactant in the dilute phase (Xs,w, and Xt,w, respectively) and volume fraction of coacervate at equilibrium (Фc). For each parameter, whose values are determined by a design of experiments, these results are subjected to empirical smoothing in three dimensions. The aim of this study is to find out the best compromise between E% and Фc. E% increases with surfactant concentration and temperature in optimal conditions, and the extraction extent of TA reaches 98 and 96 % using TX-114 and Lutensol AO7, respectively. The effect of sodium sulfate or cetyltrimethylammonium bromide (CTAB) addition is also studied. Finally, the possibility of recycling the surfactant is proved.

Keywords: extraction, cloud point, non ionic surfactant, bezanyl green

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Authors: Fadilah Fadilah, Sperisa Distantina, Santi T. Wijayanti, Rahmawati Andayani

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Chemical modification process on glucomannan from porang via carboxymethylation have been conducted. The process was done in two stages, the alkalization, and the carboxymethylation. The alkalization was done by adding NaOH solution into the medium which was contained glucomannan and then stirred it in ambient temperature for thirty minutes. The carboxymethylation process was done by adding sodium mono chloroacetate solution into the alkalization product. The carboxymethylation process was conducted for a certain time, and the product was then analyzed for determining the degree of substitution. In this research, the influence of medium to the degree of substitution was studied. Three different medium were used, namely water, 70% ethanol, and 90% ethanol. The results show that 70% ethanol was a better medium than two others because give a higher degree of substitution. Using 70% ethanol as a medium, the experiments for studying the influence of temperature on the carboxymethylation stages were conducted. The results show that the degree of substitution at 65°C is higher than at 45°C.

Keywords: carboxymethylation, degree of substitution, ethanol medium, glucomannan

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Authors: Saba Rahimi, Ozan Oktay, Javier Alvarez-Valle, Sujeeth Bharadwaj

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Successful application of deep learning in medical image analysis necessitates unprecedented amounts of labeled training data. Unlike conventional 2D applications, radiological images can be three-dimensional (e.g., CT, MRI), consisting of many instances within each image. The problem is exacerbated when expert annotations are required for effective pixel-wise labeling, which incurs exorbitant labeling effort and cost. Active learning is an established research domain that aims to reduce labeling workload by prioritizing a subset of informative unlabeled examples to annotate. Our contribution is a cost-effective approach for U-Net 3D models that uses Monte Carlo sampling to analyze pixel-wise uncertainty. Experiments on the AAPM 2017 lung CT segmentation challenge dataset show that our proposed framework can achieve promising segmentation results by using only 42% of the training data.

Keywords: image segmentation, active learning, convolutional neural network, 3D U-Net

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Authors: Qian Sun, Dannie Dai, Vivian Lou

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Background: Changes in population structure and modernization have left traditional channels of achieving intergenerational solidarity in crisis. Policies and projects purposefully structuring intergenerational interaction are regarded as effective ways to enhance positive attitude changes between generations. However, few inter-generational interaction program has put equal emphasis on promoting positive changes on both attitude and behavior across generational groups. Objective: This study evaluated the effectiveness of an intergenerational interaction program which aims to facilitate positive attitude and behavioral interaction between both young and old individuals in Hong Kong. Method: A quasi-experimental design was adopted with the sample of 150 older participants and 161 young participants. Among 73 older and 78 young participants belong to experiment groups while 77 older participants and 84 young participants belong to control groups. The Age Group Evaluation and Description scale (AGED) was adopted to measure attitude toward young people by older participants and the Chinese version of Kogan’s Attitude towards Older People (KAOP) as well as Polizzi’s refined version of the Ageing Semantic Differential Scale (ASD) were used to measure attitude toward older people by the younger generation. The interpersonal behaviour of participants was assessed using Beglgrave’s behavioural observation tool. Six primary verbal or non-verbal interpersonal behaviours including smiles, looks, touches, encourages, initiated conversations and assists were identified and observed. Findings Effectiveness of attitude and behavior changes on both younger and older participants was confirmed in results. Compared with participants from the control group, experimental participants of elderly showed significant positive changes of attitudes toward the younger generation as assessed by AGED (F=138.34, p < .001). Moreover, older participants showed significant positive changes on three out of six behaviours (visual attention: t=2.26, p<0.05; initiate conversation: t=3.42, p<0.01; and touch: t=2.28, p<0.05). For younger participants, participants from experimental group showed significant positive changes in attitude toward older people (with F-score of 47.22 for KAOP and 72.75 for ASD, p<.001). Young participants also showed significant positive changes in two out of six behaviours (visual attention: t=3.70, p<0.01; initiate conversation: t=2.04, p<0.001). There is no significant relationship between attitude change and behaviour change in both older (p=0.86) and younger (p=0.22) groups. Conclusion: This study has brought practical implications for social work. The effective model of this program could assist social workers and allied professionals to design relevant projects for nurture intergenerational solidarity. Furthermore, insignificant results between attitude and behavior changes revealed that attitude change was not a strong predictor for behavior change, hence, intergenerational programs against age-stereotype should put equal emphasis on both attitudinal and behavioral aspects.

Keywords: attitude and behaviour changes, intergenerational interaction, intergenerational solidarity, program design

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Authors: Z. Hormozi Moghaddam, M. Mokhtari-Dizaji, M. Movahedin, M. E. Ravari

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Mechanical index (MI) is used for quantifying acoustic cavitation and the relationship between acoustic pressure and the frequency. In this study, modeling of the MI was applied to provide treatment protocol and to understand the effective physical processes on reproducibility of stem cells. The acoustic pressure and MI equations are modeled and solved to estimate optimal MI for 28, 40, 150 kHz and 1 MHz frequencies. Radial and axial acoustic pressure distribution was extracted. To validate the results of the modeling, the acoustic pressure in the water and near field depth was measured by a piston hydrophone. Results of modeling and experiments show that the model is consistent well to experimental results with 0.91 and 0.90 correlation of coefficient (p<0.05) for 1 MHz and 40 kHz. Low intensity ultrasound with 0.40 MI is more effective on the proliferation rate of the spermatogonial stem cells during the seven days of culture, in contrast, high MI has a harmful effect on the spermatogonial stem cells. This model provides proper treatment planning in vitro and in vivo by estimating the cavitation phenomenon.

Keywords: ultrasound, mechanical index, modeling, stem cell

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on HSI dataset Indian Pines. The results confirm the capability of the proposed method.

Keywords: continual learning, data reconstruction, remote sensing, hyperspectral image segmentation

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Authors: Dejan Tanikić, Miodrag Manić, Jelena Đoković, Saša Kalinović

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This paper deals with the determination of the optimum machining parameters, according to the measured and modelled data of the cutting temperature and surface roughness, during the turning of the AISI 4140 steel. The high cutting temperatures are unwanted occurences in the metal cutting process. They impact negatively on the quality of the machined part. The machining experiments were performed using different cutting regimes (cutting speed, feed rate and depth of cut), with different values of the workpiece hardness, which causes different values of the measured cutting temperature as well as the measured surface roughness. The temperature and surface roughness data were modelled after that using Response Surface Methodology (RSM). The obtained RSM models are used in the process of optimization of the cutting regimes using the Genetic Algorithms (GA) tool, which enables the metal cutting process in the optimum conditions.

Keywords: genetic algorithms, machining parameters, response surface methodology, turning process

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Authors: Guray Arslan, Riza Secer Orkun Keskin

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Numerical investigations were conducted to study the influence of flexural reinforcement ratio on the diagonal cracking strength and ultimate shear strength of reinforced concrete (RC) beams without stirrups. Three-dimensional nonlinear finite element analyses (FEAs) of the beams with flexural reinforcement ratios ranging from 0.58% to 2.20% subjected to a mid-span concentrated load were carried out. It is observed that the load-deflection and load-strain curves obtained from the numerical analyses agree with those obtained from the experiments. It is concluded that flexural reinforcement ratio has a significant effect on the shear strength and deflection capacity of RC beams without stirrups. The predictions of the diagonal cracking strength and ultimate shear strength of beams obtained by using the equations defined by a number of codes and researchers are compared with each other and with the experimental values.

Keywords: finite element, flexural reinforcement, reinforced concrete beam, shear strength

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Authors: Fuad M. Alkoot

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We aim at highlighting the benefits of classifier systems especially in supporting educational management decisions. The paper aims at using classifiers in an educational application where an outcome is predicted based on given input parameters that represent various conditions at the institute. We present a classifier system that is designed using a limited training set with data for only one semester. The achieved system is able to reach at previously known outcomes accurately. It is also tested on new input parameters representing variations of input conditions to see its prediction on the possible outcome value. Given the supervised expectation of the outcome for the new input we find the system is able to predict the correct outcome. Experiments were conducted on one semester data from two departments only, Switching and Mathematics. Future work on other departments with larger training sets and wider input variations will show additional benefits of classifier systems in supporting the management decisions at an educational institute.

Keywords: machine learning, pattern recognition, classifier design, educational management, outcome estimation

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Authors: M. Mortada, A. Seleem, M. El-Morsi, M. Younan

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The present study introduces the folding technology to be utilized for the first time in vertical diffusion stills. This work represents a model of the distillation process by utilizing chevron pattern of folded structure. An experimental setup has been constructed, to investigate the performance of the folded sheets in the vertical effect diffusion still for a specific range of operating conditions. An experimental comparison between the folded type and the flat type sheets has been carried out. The folded pattern showed a higher performance and there is an increase in the condensate to feed ratio that ranges from 20-30 % through the operating hot plate temperature that ranges through 60-90°C. In addition, a parametric analysis of the system using Design of Experiments statistical technique, has been developed using the experimental results to determine the effect of operating conditions on the system's performance and the best operating conditions of the system has been evaluated.

Keywords: chevron pattern, fold structure, solar distillation, vertical diffusion still

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Authors: Yuqing Chen, Ying Xu, Renfa Li

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The development of matrix completion theory provides new approaches for data gathering in Wireless Sensor Networks (WSN). The existing matrix completion algorithms for WSN mainly consider how to reduce the sampling number without considering the real-time performance when recovering the data matrix. In order to guarantee the recovery accuracy and reduce the recovery time consumed simultaneously, we propose a new ALM algorithm to recover the weather monitoring data. A lot of experiments have been carried out to investigate the performance of the proposed ALM algorithm by using different parameter settings, different sampling rates and sampling models. In addition, we compare the proposed ALM algorithm with some existing algorithms in the literature. Experimental results show that the ALM algorithm can obtain better overall recovery accuracy with less computing time, which demonstrate that the ALM algorithm is an effective and efficient approach for recovering the real world weather monitoring data in WSN.

Keywords: wireless sensor network, matrix completion, singular value thresholding, augmented Lagrange multiplier

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Authors: Cristian P. Topa, Esteban A. Valencia, Victor H. Hidalgo, Marco A. Martinez

Abstract:

Wind tunnel experiments for aerodynamic profiles display numerous advantages, such as: clean steady laminar flow, controlled environmental conditions, streamlines visualization, and real data acquisition. However, the experiment instrumentation usually is expensive, and hence, each test implies a incremented in design cost. The aim of this work is to select and implement a low-cost static pressure data acquisition system for a NACA 2412 airfoil in an open cycle wind tunnel. This work compares wind tunnel experiment with Computational Fluid Dynamics (CFD) simulation and parametric analysis. The experiment was evaluated at Reynolds of 1.65 e⁵, with increasing angles from -5° to 15°. The comparison between the approaches show good enough accuracy, between the experiment and CFD, additional parametric analysis results differ widely from the other methods, which complies with the lack of accuracy of the lateral approach due its simplicity.

Keywords: wind tunnel, low cost instrumentation, experimental testing, CFD simulation

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Authors: Geeta Negi, Pankaj, Anjana Srivastava, Anita Sharma

Abstract:

In the present study, the presence of endosulfan, imidacloprid, carbendazim, in the soil /vegetables/cereals and water samples was observed in agriculture fields of Uttarakhand. In view of biodegradation of these pesticides, nine bacterial isolates were recovered from the soil samples of the fields which tolerated endosulfan, imidacloprid, carbendazim from 100 to 200 µg/ml. Three bacterial consortia used for in vitro bioremediation experiments were three bacterial isolates for carbendazim, imidacloprid and endosulfan, respectively. Maximum degradation (87 and 83%) of α and β endosulfan respectively was observed in soil slurry by consortium. Degradation of Imidacloprid and carbendazim under similar conditions was 88.4 and 77.5% respectively. FT-IR analysis of biodegraded samples of pesticides in liquid media showed stretching of various bonds. GC-MS of biodegraded endosulfan sample in soil slurry showed the presence of non-toxic intermediates. A pot trial with Bacterial treatments lowered down the uptake of pesticides in onion plants.

Keywords: biodegradation, carbendazim, consortium, endosulfan

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Authors: Oleksii Bychkov

Abstract:

The fundamental difference between randomness and vagueness is that the former requires statistical research. These issues were studied by Zadeh L, Dubois D., Prad A. The theory of possibility works with expert assessments, hypotheses, etc. gives an idea of the characteristics of the problem situation, the nature of the goals and real limitations. Possibility theory examines experiments that are not repeated. The article discusses issues related to the formalization of a fuzzy, uncertain idea of reality. The author proposes to expand the classical model of the theory of possibilities by introducing a measure of necessity. The proposed model of the theory of possibilities allows us to extend the measures of possibility and necessity onto a Boolean while preserving the properties of the measure. Thus, upper and lower estimates are obtained to describe the fact that the event will occur. Knowledge of the patterns that govern mass random, uncertain, fuzzy events allows us to predict how these events will proceed. The article proposed for publication quite fully reveals the essence of the construction and use of the theory of probability and the theory of possibility.

Keywords: possibility, artificial, modeling, axiomatics, intellectual approach

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Authors: C. L. Popa, S. L. Iconaru, A. Costescu, C. S. Ciobanu, M. Motelica Heino, R. Guegan, D. Predoi

Abstract:

Surface functionalization of ceramic composites with a special focus on tetraethyl orthosilicate (TEOS) and hydroxyapatite (HAp) is discoursed. Mesoporous ceramic HAp-TEOS composites were prepared by the incorporation of hydroxyapatite into tetraethyl orthosilicate by sol-gel method. The resulting samples were analysed by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared (FT-IR) spectroscopy, and Raman spectroscopy and nitrogen physisorption. The removal of Pb2+ ions from aqueous solutions was evaluated using Atomic Absorbtion Spectroscopy (AAS). Removal experiments of Pb2+ ions were carried out in aqueous solutions with controlled Pb2+ at pH ~ 3 and pH ~ 5. After removal experiment of Pb2+ at pH 3 and pH 5, porous hydroxyapatite nanoparticles is transformed into PbHAp_3 and PbHAp_5 via the adsorption of Pb2+ ions followed by the cation exchange reaction. The diffraction patterns show that THAp nanoparticles were successfully coated with teos without any structural changes. On the other, the AAS analysis showed that THAp can be useful in the removal Pb2+ from water contaminated.

Keywords: teos, hydroxyapatite, environment applications, biosystems engineering

Procedia PDF Downloads 384