Search results for: sequential extraction

Authors: Mounir Baccar, Imen Jmal

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Because of the rise in energy costs, thermal storage systems designed for the heating and cooling of buildings are becoming increasingly important. Energy storage can not only reduce the time or rate mismatch between energy supply and demand but also plays an important role in energy conservation. One of the most preferable storage techniques is the Latent Heat Thermal Energy Storage (LHTES) by Phase Change Materials (PCM) due to its important energy storage density and isothermal storage process. This paper presents a numerical study of the solidification of a PCM (paraffin RT27) in a rectangular thermal storage exchanger for air conditioning systems taking into account the presence of natural convection. Resolution of continuity, momentum and thermal energy equations are treated by the finite volume method. The main objective of this numerical approach is to study the effect of natural convection on the PCM solidification time and the impact of fins number on heat transfer enhancement. It also aims at investigating the temporal evolution of PCM solidification, as well as the longitudinal profiles of the HTF circling in the duct. The present research undertakes the study of two cases: the first one treats the solidification of PCM in a PCM-air heat exchanger without fins, while the second focuses on the solidification of PCM in a heat exchanger of the same type with the addition of fins (3 fins, 5 fins, and 9 fins). Without fins, the stratification of the PCM from colder to hotter during the heat transfer process has been noted. This behavior prevents the formation of thermo-convective cells in PCM area and then makes transferring almost conductive. In the presence of fins, energy extraction from PCM to airflow occurs at a faster rate, which contributes to the reduction of the discharging time and the increase of the outlet air temperature (HTF). However, for a great number of fins (9 fins), the enhancement of the solidification process is not significant because of the effect of confinement of PCM liquid spaces for the development of thermo-convective flow. Hence, it can be concluded that the effect of natural convection is not very significant for a high number of fins. In the optimum case, using 3 fins, the increasing temperature of the HTF exceeds approximately 10°C during the first 30 minutes. When solidification progresses from the surfaces of the PCM-container and propagates to the central liquid phase, an insulating layer will be created in the vicinity of the container surfaces and the fins, causing a low heat exchange rate between PCM and air. As the solid PCM layer gets thicker, a progressive regression of the field of movements is induced in the liquid phase, thus leading to the inhibition of heat extraction process. After about 2 hours, 68% of the PCM became solid, and heat transfer was almost dominated by conduction mechanism.

Keywords: heat transfer enhancement, front solidification, PCM, natural convection

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Authors: Y. Xu, L. Xiong, Z. Xu

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In order to protect data privacy, image with sensitive or private information needs to be encrypted before being outsourced to the cloud. However, this causes difficulties in image retrieval and data management. A secure image retrieval method based on orthogonal decomposition is proposed in the paper. The image is divided into two different components, for which encryption and feature extraction are executed separately. As a result, cloud server can extract features from an encrypted image directly and compare them with the features of the queried images, so that the user can thus obtain the image. Different from other methods, the proposed method has no special requirements to encryption algorithms. Experimental results prove that the proposed method can achieve better security and better retrieval precision.

Keywords: secure image retrieval, secure search, orthogonal decomposition, secure cloud computing

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Authors: Natalia Efremova, Sergey Tarasenko

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We propose a novel neural network architecture for visual saliency detections, which utilizes neuro physiologically plausible mechanisms for extraction of salient regions. The model has been significantly inspired by recent findings from neuro physiology and aimed to simulate the bottom-up processes of human selective attention. Two types of features were analyzed: color and direction of maximum variance. The mechanism we employ for processing those features is PCA, implemented by means of normalized Hebbian learning and the waves of spikes. To evaluate performance of our model we have conducted psychological experiment. Comparison of simulation results with those of experiment indicates good performance of our model.

Keywords: neural network models, visual saliency detection, normalized Hebbian learning, Oja's rule, psychological experiment

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Authors: Thiago M. R. Dias, Patrícia M. Dias, Gray F. Moita

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The production and publication of scientific works have increased significantly in the last years, being the Internet the main factor of access and distribution of these works. Faced with this, there is a growing interest in understanding how scientific research has evolved, in order to explore this knowledge to encourage research groups to become more productive. Therefore, the objective of this work is to explore repositories containing data from scientific publications and to characterize keyword networks of these publications, in order to identify the most relevant keywords, and to highlight those that have the greatest impact on the network. To do this, each article in the study repository has its keywords extracted and in this way the network is  characterized, after which several metrics for social network analysis are applied for the identification of the highlighted keywords.

Keywords: bibliometrics, data analysis, extraction and data integration, scientometrics

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Authors: Elizabeth McDermott, Elizabeth Hughes, Victoria Rawlings

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Globally, suicide is the second leading cause of death among 15–29 year-olds. Young people who identify as lesbian, gay, bisexual and transgender (LGBT) have elevated rates of suicide and self-harm. Despite the increased risk, there is a paucity of research on LGBT help-seeking and suicidality. This is the first national study to investigate LGBT youth help-seeking for suicidal feelings and self-harm. We report on a UK sequential exploratory mixed method study that employed face-to-face and online methods in two stages. Stage one involved 29 online (n=15) and face-to-face (n=14) semi-structured interviews with LGBT youth aged under 25 years old. Stage two utilized an online LGBT youth questionnaire employing a community-based sampling strategy (n=789). We found across the sample that LGBT youth who self-harmed or felt suicidal were reluctant to seek help. Results indicated that participants were normalizing their emotional distress and only asked for help when they reached crisis point and were no longer coping. Those who self-harmed (p<0.001, OR=2.82), had attempted or planned suicide (p<0.05, OR=1.48), or had experience of abuse related to their sexuality or gender (p<0.01, OR=1.80), were most likely to seek help. There were a number of interconnecting reasons that contributed to participants’ problems accessing help. The most prominent of these were: negotiating norms in relation to sexuality, gender, mental health and age; being unable to talk about emotions, and coping and self-reliance. It is crucial that policies and practices that aim to prevent LGBT youth suicide recognize that norms and normalizing processes connected to sexual orientation and gender identity are additional difficulties that LGBT youth have accessing mental health support.

Keywords: help-seeking, LGBT, suicide, youth

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Maryam Ghamsari, Mitchell Nye-Wood, Kelvin Wang, Angela Juhasz, Michelle Colgrave, Don Otter, Jun Lu, Nazimah Hamid, Thao T. Le

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Bee venom, a complex mixture of peptides, proteins, enzymes, and other bioactive compounds, has been widely studied for its therapeutic application. This study investigated the proteins present in New Zealand (NZ) honeybee venom (BV) using bottom-up proteomics. Two sample digestion techniques, in-solution digestion and filter-aided sample preparation (FASP), were employed to obtain the optimal method for protein digestion. Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH–MS) analysis was conducted to quantify the protein compositions of NZ BV and investigate variations in collection years. Our results revealed high protein content (158.12 µg/mL), with the FASP method yielding a larger number of identified proteins (125) than in-solution digestion (95). SWATH–MS indicated melittin and phospholipase A2 as the most abundant proteins. Significant variations in protein compositions across samples from different years (2018, 2019, 2021) were observed, with implications for venom's bioactivity. In vitro testing demonstrated immunomodulatory and antioxidant activities, with a viable range for cell growth established at 1.5-5 µg/mL. The study underscores the value of proteomic tools in characterizing bioactive compounds in bee venom, paving the way for deeper exploration into their therapeutic potentials. Further research is needed to fractionate the venom and elucidate the mechanisms of action for the identified bioactive components.

Keywords: honeybee venom, proteomics, bioactivity, fractionation, swath-ms, melittin, phospholipase a2, new zealand, immunomodulatory, antioxidant

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Authors: Farhad Eizakshiri

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The potential for groupthink to explain the issues contributing to deterioration of decision-making ability within the unitary team and so to cause poor outcomes attracted a great deal of attention from a variety of disciplines, including psychology, social and organizational studies, political science, and others. Yet what remains unclear is how and why the team members’ strivings for unanimity and cohesion override their motivation to realistically appraise alternative courses of action. In this paper, the findings of a sequential explanatory mixed-methods research containing an experiment with thirty groups of three persons each and interviews with all experimental groups to investigate this issue is reported. The experiment sought to examine how individuals aggregate their views in order to reach a consensual group decision concerning the completion time of a task. The results indicated that groups made better estimates when they had no interaction between members in comparison with the situation that groups collectively agreed on time estimates. To understand the reasons, the qualitative data and informal observations collected during the task were analyzed through conversation analysis, thus leading to four reasons that caused teams to neglect divergent viewpoints and reduce the number of ideas being considered. Reasons found were the concurrence-seeking tendency, pressure on dissenters, self-censorship, and the illusion of invulnerability. It is suggested that understanding the dynamics behind the aforementioned reasons of groupthink will help project teams to avoid making premature group decisions by enhancing careful evaluation of available information and analysis of available decision alternatives and choices.

Keywords: groupthink, group decision, cohesiveness, project teams, mixed-methods research

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Authors: Muslim Khan, Kenneth B. Jensen, Kevin A. Francesconi

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Trace amounts (ca 1 µg/L, 13 nM) of arsenic are present in sea water mostly as the oxyanion arsenate. In contrast, arsenic is present in marine biota (animals and algae) at very high levels (up to100,000 µg/kg) a significant portion of which is present as lipid-soluble compounds collectively termed arsenolipids. The complex nature of sea water presents an analytical challenge to detect trace compounds and monitor their environmental path. We developed a simple method using liquid-liquid extraction combined with HPLC-High Resolution Mass Spectrometer capable of detecting trace of arsenolipids (99 % of the sample matrix while recovering > 80 % of the six target arsenolipids with limit of detection of 0.003 µg/L.)

Keywords: arsenolipids, sea water, HPLC-high resolution mass spectrometry

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Authors: A. N. Paithane, D. S. Bormane, S. D. Shirbahadurkar

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It has been seen that emotion recognition is an important research topic in the field of Human and computer interface. A novel technique for Feature Extraction (FE) has been presented here, further a new method has been used for human emotion recognition which is based on HHT method. This method is feasible for analyzing the nonlinear and non-stationary signals. Each signal has been decomposed into the IMF using the EMD. These functions are used to extract the features using fission and fusion process. The decomposition technique which we adopt is a new technique for adaptively decomposing signals. In this perspective, we have reported here potential usefulness of EMD based techniques.We evaluated the algorithm on Augsburg University Database; the manually annotated database.

Keywords: intrinsic mode function (IMF), Hilbert-Huang transform (HHT), empirical mode decomposition (EMD), emotion detection, electrocardiogram (ECG)

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Authors: A. Rong, P. B. Luh, R. Lahdelma

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High penetration of intermittent renewable energy sources (RES) such as solar power and wind power into the energy system has caused temporal and spatial imbalance between electric power supply and demand for some countries and regions. This brings about the critical need for coordinating power production and power exchange for different regions. As compared with the power-only systems, the combined heat and power (CHP) systems can provide additional flexibility of utilizing RES by exploiting the interdependence of power and heat production in the CHP plant. In the CHP system, power production can be influenced by adjusting heat production level and electric power can be used to satisfy heat demand by electric boiler or heat pump in conjunction with heat storage, which is much cheaper than electric storage. This paper addresses multi-site CHP systems without considering RES, which lay foundation for handling penetration of RES. The problem under study is the unit commitment (UC) of the transmission-constrained multi-site CHP systems. We solve the problem by combining linear relaxation of ON/OFF states and sequential dynamic programming (DP) techniques, where relaxed states are used to reduce the dimension of the UC problem and DP for improving the solution quality. Numerical results for daily scheduling with realistic models and data show that DP-based algorithm is from a few to a few hundred times faster than CPLEX (standard commercial optimization software) with good solution accuracy (less than 1% relative gap from the optimal solution on the average).

Keywords: dynamic programming, multi-site combined heat and power system, relaxed states, transmission-constrained generation unit commitment

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Authors: Tudor Barbu

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An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Second, an automatic pixel classification approach is proposed. The feature vectors are clustered using some unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.

Keywords: image segmentation, moment-based, texture analysis, automatic classification, validation indexes

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Document Analysis is an important research field that aims to gather the information by analyzing the data in documents. As one of the important targets for many fields is to understand what people actually want, sentimental analysis field has been one of the vital fields that are tightly related to the document analysis. This research focuses on analyzing text documents to classify each document according to its opinion. The aim of this research is to detect the emotions from text documents based on enriching the lexicon with adapting their content based on semantic patterns extraction. The proposed approach has been presented, and different experiments are applied by different perspectives to reveal the positive impact of the proposed approach on the classification results.

Keywords: document analysis, sentimental analysis, emotion detection, WEKA tool, NRC lexicon

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Authors: Y. Wang, S. Zhang, X. Chen

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The longitudinal compressive strength of fibre reinforced plastic (FRP) possess a large stochastic variability, which limits efficient application of composite structures. This study aims to address how the random fibre packing affects the uncertainty of FRP compressive strength. An novel approach is proposed to generate random fibre packing status by a combination of Latin hypercube sampling and random sequential expansion. 3D nonlinear finite element model is built which incorporates both the matrix plasticity and fibre geometrical instability. The matrix is modeled by isotropic ideal elasto-plastic solid elements, and the fibres are modeled by linear-elastic rebar elements. Composite with a series of different nominal fibre volume fractions are studied. Premature fibre waviness at different magnitude and direction is introduced in the finite element model. Compressive tests on uni-directional CFRP (carbon fibre reinforced plastic) are conducted following the ASTM D6641. By a comparison of 3D FE models and compressive tests, it is clearly shown that the stochastic variation of compressive strength is partly caused by the random fibre packing, and normal or lognormal distribution tends to be a good fit the probabilistic compressive strength. Furthermore, it is also observed that different random fibre packing could trigger two different fibre micro-buckling modes while subjected to longitudinal compression: out-of-plane buckling and twisted buckling. The out-of-plane buckling mode results much larger compressive strength, and this is the major reason why the random fibre packing results a large uncertainty in the FRP compressive strength. This study would contribute to new approaches to the quality control of FRP considering higher compressive strength or lower uncertainty.

Keywords: compressive strength, FRP, micro-buckling, random fibre packing

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Authors: Boikanyego Sebina

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The present study investigates the effects of socioeconomic status (SES) and bilingualism on the Setswana speech rhythm of Batswana (citizens) children aged 6-7 years with typical development born and residing in Botswana. Botswana is a country in which there is a diglossic Setswana/English language setting, where English is the dominant high-status language in educational and public contexts. Generally, children from low SES have lower linguistic and cognitive profiles than their age-matched peers from high SES. A greater understanding of these variables would allow educators to distinguish between underdeveloped language skills in children due to impairment and environmental issues for them to successfully enroll children in language development enhancement programs specific to the child’s needs. There are 20 participants: 10 high SES private English-medium educated early sequential Setswana-English bilingual children, taught full-time in English (L2) from the age of 3 years, and for whom English has become dominant; and 10 low SES children who are educated in public schools for whom English is considered a learner language, i.e., L1 Setswana is dominant. The aim is to see whether SES and bilingualism, have had an effect on the Setswana speech rhythm of children in either group. The study primarily uses semi-spontaneous speech based on the telling of the wordless picture storybook. A questionnaire is used to elicit the language use pattern of the children and that of their parents, as well as the education level of the parents and the school the children attend. A comparison of the rhythm shows that children from high SES have a lower durational variability than those from low SES. The findings of the study are that the low durational variability by children from high SES may suggest an underdeveloped rhythm. In conclusion, the results of the present study are against the notion that children from high SES outperform those from low SES in linguistic development.

Keywords: bilingualism, Setswana English, socio-economic status, speech-rhythm

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Authors: S. Vasailor, C. Rattanakawin

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Electro-winning of copper metal from dilute sulfate solution (13.7 g/L) was performed in a lab electrolytic cell with stainless-steel cathode and lead-alloy anode. The effects of various parameters including cell voltage, electro-winning temperature and time were studied in order to acquire an appropriate current efficiency of copper deposition. The highest efficiency is about 95% obtaining from electro-winning condition of 3V, 55°C and 3,600 s correspondingly. The cathode copper with 95.5% Cu analyzed using atomic absorption spectrometry can be obtained from this single-winning condition. In order to increase the copper grade, solvent extraction should be used to increase the sulfate concentration, say 50 g/L, prior to winning the cathode copper effectively.

Keywords: copper metal, current efficiency, dilute sulfate solution, electro-winning

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Authors: Avishkar Gawade, Omkar Bansode, Ketan Bhambure, Bhargav Deore

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In recent year wide range of business intelligence technology have been applied to different area in order to support decision making process BI enables extraction of knowledge from data store. BI tools usually used in public health field for financial and administrative purposes.BI uses a dashboard in presentation stage to deliver information to information to end users.In this paper,we intend to analyze some open source BI tools on the market and their applicability in the clinical sphere taking into consideration the general characteristics of the clinical environment.A pervasive BI platform was developed using a real case in order to prove the tool viability.Analysis of various BI Tools in done with the help of several parameters such as data security,data integration,data quality reporting and anlaytics,performance,scalability and cost effectivesness.

Keywords: CDSS, EHR, business intelliegence, tools

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Authors: F. Fernandes, C. Poleto

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This paper aims at assessing the concentrations of heavy metals and the isotopic composition of lead ²¹⁰Pb in different fractions of sediment produced in the watershed that makes up the Mãe d'água dam and thus characterizing the distribution of metals along the sedimentary column and inferencing in the urbanization of the same process. Sample collection was carried out in June 2014; eight sediment cores were sampled in the lake of the dam. For extraction of the sediments core, a core sampler “Piston Core” was used. The trace metal concentrations were determined by conventional atomic absorption spectrophotometric methods. The samples were subjected to radiochemical analysis of ²¹⁰Po. ²¹⁰Pb activity was obtained by measuring ²¹⁰Po activity. The chronology was calculated using the constant rate of supply (CRS). ²¹⁰Pb is used to estimate the sedimentation rate.

Keywords: ²¹⁰Pb dating method, heavy metal, lakes urban, pollution history

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Authors: Lamyaa Gamal El-Deen Taha, Ashraf Sharawi

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China launched satellite GF-2 in 2014. This study deals with comparing nearest neighbor object-based classification and neural network classification methods for classification of the fused GF-2 image. Firstly, rectification of GF-2 image was performed. Secondly, a comparison between nearest neighbor object-based classification and neural network classification for classification of fused GF-2 was performed. Thirdly, the overall accuracy of classification and kappa index were calculated. Results indicate that nearest neighbor object-based classification is better than neural network classification for urban mapping.

Keywords: GF-2 images, feature extraction-rectification, nearest neighbour object based classification, segmentation algorithms, neural network classification, multilayer perceptron

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Authors: Zineb Nougrara

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In this paper, we propose a novel methodology for extracting a road network and its nodes from satellite images of Algeria country. This developed technique is a progress of our previous research works. It is founded on the information theory and the mathematical morphology; the information theory and the mathematical morphology are combined together to extract and link the road segments to form a road network and its nodes. We, therefore, have to define objects as sets of pixels and to study the shape of these objects and the relations that exist between them. In this approach, geometric and radiometric features of roads are integrated by a cost function and a set of selected points of a crossing road. Its performances were tested on satellite images of Algeria country.

Keywords: satellite image, road network, nodes, image analysis and processing

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Authors: M. A. Ben Messaoud, A. Bouzid, N. Ellouze

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Speech Segmentation is the measure of the change point detection for partitioning an input speech signal into regions each of which accords to only one speaker. In this paper, we apply two features based on multi-scale product (MP) of the clean speech, namely the spectral centroid of MP, and the zero crossings rate of MP. We focus on multi-scale product analysis as an important tool for segmentation extraction. The multi-scale product is based on making the product of the speech wavelet transform coefficients at three successive dyadic scales. We have evaluated our method on the Keele database. Experimental results show the effectiveness of our method presenting a good performance. It shows that the two simple features can find word boundaries, and extracted the segments of the clean speech.

Keywords: multiscale product, spectral centroid, speech segmentation, zero crossings rate

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Authors: Mustapha Mahmoud, Fouzia Toumi Benali, Mohamed Benyahia, Sofiane Bouazza

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This work focuses on the influences of biological and chemical parameters on the phenolic compounds such as flavonoids and tannins in different medicinal plants in western Algeria (Papaver rhoeas, Daphnegnidium, Lavandula multifida, Lavandula dentata, Lavandula stoicha, ...). Thus we look the difference between species of the same genus, difference between the different organs of the same species, the influence of environment all temperature influences, time, percentage of solvent on the extraction. Quantification of the phenolic compounds was performed by spectrophotometric method then treated with statistics tools such as variance analysis, multivariant analyzes, response surface methodology). The results show that the polyphenols are influenced by the parameters mentioned.

Keywords: polyphenols, influences, medicinal plants, west Algeria

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Authors: Hilary Rutto

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The investigation was aimed at determining the extent at which the zinc will be extracted from secondary sources generated from galvanising process using dilute sulphuric acid under controlled laboratory conditions of temperature, solid-liquid ratio, and agitation rate. The leaching experiment was conducted for a period of 2 hours and to total zinc extracted calculated in relation to the amount of zinc dissolved at a unit time in comparison to the initial zinc content of the zinc ash. Sulphuric acid was found to be an effective leaching agent with an overall extraction of 91.1% when concentration is at 2M, and solid/liquid ratio kept at 1g/200mL leaching solution and temperature set at 65ᵒC while slurry agitation is at 450rpm. The leaching mechanism of zinc ash with sulphuric acid was conformed well to the shrinking core model.

Keywords: leaching, kinetics, shrinking core model, zinc slag

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Authors: Lamidi Lawal Aduozava

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Masquerade tradition is an indigenous culture of the Anetuno an extraction of the Ebira referred to as Ebira chao. This paper seeks to make aesthetic analysis of the masquerades in terms of their costumes and socio-cultural significance. To this end, the study examined and documented the functions and roles of Anipo and Idowo masquerades in terms of therapeutic, economic, prophetic and divination, entertainment, and funeral functions to the owner community(Eziobe group of families) in Igarra, Edo State of Nigeria, West Africa. For the purpose of data collection, focus group discussion, participatory, visual and observatory methods of data collection were used. All the data collected were aesthetically, descriptively and historically analyzed.

Keywords: Aesthetics, , Costume, , Masquerades, , Significance.

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Authors: Siti Noor Baiti Binti Mustafa

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Energy management studies regarding energy efficiency investments in Malaysia has yet to address the lack of empirical research that examines pro- sustainability behavior of managers in the industrial sector and how it influences energy efficiency investment decision-making. This study adopts the Theory of Planned Behavior (TPB) to examine the relationship between personal attitude, subjective norms, and perceived behavioral control (PBC), the intention of energy efficiency investments, and how perceptions of Non-Energy Benefits (NEB) influence these intentions among managers in the industrial sector in Malaysia. Managers from various sub-sectors in the industrial sector were selected from a sample of companies that are participants of the Government-led program named the Energy Audit Conditional Grant (EACG) that aimed to promote energy efficiency. Data collection was conducted through an online semi-structured, open-ended questionnaire and then later interviewed. The results of this explorative sequential qualitative study showed that perceived behavioral control was a significant predictor of energy efficiency investment intentions as compared to factors such as attitude and subjective norms. The level of awareness and perceptions towards NEB further played a significant factor in influencing energy efficiency investment decision-making as well. Various measures and policy recommendations are provided together with insights on factors that influence decision-makers intention to invest in energy efficiency, whilst new knowledge on NEB perceptions will be useful to enhance the attractiveness of energy-efficient investments.

Keywords: energy efficiency investments, non-energy benefits, theory of planned behavior, personal attitude, subjective norms, perceived behavioral control, Malaysia industrial sector

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Authors: Maria Reichenber Arcilla

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Objective: To determine the risk factors for maternal and neonatal complications associated with operative vaginal deliveries. Methods: A retrospective chart review of 435 patients who underwent operative vaginal deliveries was done. Patient profiles – age, parity, AOG, duration of labor – and outcomes – birthweight, maternal and neonatal complications - were tabulated and multivariable analysis and logistic regression were performed using SPSS® Statistics Base. Results and Conclusion: There was no significant difference in the incidence of maternal and neonatal complications between those that underwent vacuum and forceps extraction. Among the variables analysed, parity and duration of labor reached statistical significance. The odds of maternal complications were 3 times higher among nulliparous patients. Neonatal complications were seen in those whose labor lasted more than 9 hours.

Keywords: operative vaginal deliveries, maternal, neonatal, morbidity

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Authors: A. A Warra, S. Fatima

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The experimental investigation revealed that the hexane extract of onion seed oil has acid value, iodine value, peroxide value, saponification value, relative density and refractive index of 0.03±0.01 mgKOH/g, 129.80±0.21 gI2/100g, 3.00± 0.00 meq H2O2 203.00±0.71 mgKOH/g, 0.82±0.01and 1.44±0.00 respectively. The percentage yield was 50.28±0.01%. The colour of the oil was light green. We restricted our GC-MS spectra interpretation to compounds identification, particularly fatty acids and they are identified as palmitic acid, linolelaidic acid, oleic acid, stearic acid, behenic acid, linolenic acid and eicosatetraenoic acid. The pH , foam ability (cm³), total fatty matter, total alkali and percentage chloride of the onion oil soap were 11.03± 0.02, 75.13±0.15 (cm³), 36.66 ± 0.02 %, 0.92 ± 0.02% and 0.53 ± 0.15 % respectively. The texture was soft and the colour was lighter green. The results indicated that the hexane extract of the onion seed oil has potential for cosmetic industries.

Keywords: onion seeds, soxhlet extraction, physicochemical, GC-MS, cold saponification

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Authors: Mounir Zekkaoui, Abdelhadi Fennan

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The software evolution control requires a deep understanding of the changes and their impact on different system heterogeneous artifacts. And an understanding of descriptive knowledge of the developed software artifacts is a prerequisite condition for the success of the evolutionary process. The implementation of an evolutionary process is to make changes more or less important to many heterogeneous software artifacts such as source code, analysis and design models, unit testing, XML deployment descriptors, user guides, and others. These changes can be a source of degradation in functional, qualitative or behavioral terms of modified software. Hence the need for a unified approach for extraction and representation of different heterogeneous artifacts in order to ensure a unified and detailed description of heterogeneous software artifacts, exploitable by several software tools and allowing to responsible for the evolution of carry out the reasoning change concerned.

Keywords: heterogeneous software artifacts, software evolution control, unified approach, meta model, software architecture

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

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Authors: O. S. Ebrahim, K. O. Shawky, M. O. S. Ebrahim, P. K. Jain

Abstract:

Induction Motor (IM) driving pump is the main consumer of electricity in a typical fluid transportation system (FTS). It was illustrated that changing the connection of the stator windings from delta to star at no load could achieve noticeable active and reactive energy savings. This paper proposes a supervisory hysteresis liquid-level control with three-state energy saving mode (ESM) for IM in FTS including storage tank. The IM pump drive comprises modified star/delta switch and hydromantic coupler. Three-state ESM is defined, along with the normal running, and named analog to computer ESMs as follows: Sleeping mode in which the motor runs at no load with delta stator connection, hibernate mode in which the motor runs at no load with a star connection, and motor shutdown is the third energy saver mode. A logic flow-chart is synthesized to select the motor state at no-load for best energetic cost reduction, considering the motor thermal capacity used. An artificial neural network (ANN) state estimator, based on the recurrent architecture, is constructed and learned in order to provide fault-tolerant capability for the supervisory controller. Sequential test of Wald is used for sensor fault detection. Theoretical analysis, preliminary experimental testing and, computer simulations are performed to show the effectiveness of the proposed control in terms of reliability, power quality and energy/coenergy cost reduction with the suggestion of power factor correction.

Keywords: ANN, ESM, IM, star/delta switch, supervisory control, FT, reliability, power quality

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