Search results for: time-frequency feature extraction

Authors: Payal Abichandani, Rishi Prakash, Paras Nath Barwal, B. K. Murthy

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Information generated from various computerization processes is a potential rich source of knowledge for its designated community. To pass this information from generation to generation without modifying the meaning is a challenging activity. To preserve and archive the data for future generations it’s very essential to prove the authenticity of the data. It can be achieved by extracting the metadata from the data which can prove the authenticity and create trust on the archived data. Subsequent challenge is the technology obsolescence. Metadata extraction and standardization can be effectively used to resolve and tackle this problem. Metadata can be categorized at two levels i.e. Technical and Domain level broadly. Technical metadata will provide the information that can be used to understand and interpret the data record, but only this level of metadata isn’t sufficient to create trustworthiness. We have developed a tool which will extract and standardize the technical as well as domain level metadata. This paper is about the different features of the tool and how we have developed this.

Keywords: digital preservation, metadata, OAIS, PDI, XML

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Authors: Lila Boulekbache-Makhlouf, Fatiha Brahmi

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This study was carried out with the aim of enriching fresh cheese with the food by-products, which are the leaves of shallots and the peels of potatoes. Firstly, the conditions for extracting the total polyphenols using ultrasound are optimized. Then, the contents of total polyphenols PPT , flavonoids and antioxidant activity were evaluated for the extracts obtained by adopting the optimal parameter. On the other hand, we have carried out some physicochemical, microbiological and sensory analyzes of the cheese produced. The maximum total polyphenols value of 70.44 mg GAE gallic acid equivalent / g of dry matter DM of shallot leaves was reached with 40% (v/v) ethanol, an extraction time of 90 min and a temperature of 10 °C. While, the maximum TPP total polyphenols content of potato peels of 45.03 ± 4.16 mg gallic acid equivalent / g of dry matter DM was obtained using an ethanol /water mixture (40%, v/v), a time of 30 min and a temperature of 60 °C and the flavonoid contents were 13.99 and 7.52 QE quercetin equivalent/g dry matter DM, respectively. From the antioxidant tests, we deduced that the potato peels present a higher antioxidant power with the concentration of extracts causing a 50% inhibition IC50s of 125.42 ± 2.78 μg/mL for 2,2-diphényl 1-picrylhydrazyle DPPH, of 87.21 ± 7.72 μg/mL for phosphomolybdate and 200.77 ± 13.38 μg/mL for iron chelation, compared with the results obtained for shallot leaves which were 204.29 ± 0.09, 45.85 ± 3,46 and 1004.10 ± 145.73 μg/mL, respectively. The results of the physicochemical analyzes have shown that the formulated cheese was compliant with standards. Microbiological analyzes show that the hygienic quality of the cheese produced was satisfactory. According to the sensory analysis, the experts liked the cheese enriched with the powder and pieces of the leaves of the shallots.

Keywords: shallots leaves, potato peels, ultrasound extraction, phenolics, cheese

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Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

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Dissolved Gas Analysis (DGA) has been widely used for fault diagnosis in a transformer. Artificial neural networks (ANN) have high accuracy but are regarded as black boxes that are difficult to interpret. For many problems it is desired to extract knowledge from trained neural networks (NN) so that the user can gain a better understanding of the solution arrived by the NN. This paper applies a pedagogical approach for rule extraction from function approximating neural networks (REFANN) with application to incipient fault diagnosis using the concentrations of the dissolved gases within the transformer oil, as the input to the NN. The input space is split into subregions and for each subregion there is a linear equation that is used to predict the type of fault developing within a transformer. The experiments on real data indicate that the approach used can extract simple and useful rules and give fault predictions that match the actual fault and are at times also better than those predicted by the IEC method.

Keywords: artificial neural networks, dissolved gas analysis, rules extraction, transformer

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Authors: Salima Behdenna, Fatiha Barigou, Ghalem Belalem

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Due to the quick increase in the volume of Arabic opinions posted on various social media, Arabic sentiment analysis has become one of the most important areas of research. Compared to English, there is very little works on Arabic sentiment analysis, in particular aspect-based sentiment analysis (ABSA). In ABSA, aspect extraction is the most important task. In this paper, we propose a semantic aspect-based sentiment analysis approach for standard Arabic reviews to extract explicit aspect terms and identify the polarity of the extracted aspects. The proposed approach was evaluated using HAAD datasets. Experiments showed that the proposed approach achieved a good level of performance compared with baseline results. The F-measure was improved by 19% for the aspect term extraction tasks and 55% aspect term polarity task.

Keywords: sentiment analysis, opinion mining, Arabic, aspect level, opinion, polarity

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Authors: Shaolin Allen Liao, Hual-Te Chien

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Various THz phase extraction algorithms have been developed for a novel THz Modulating Interferometric Doppler Radar (THz-MIDR) developed recently by the author. The THz-MIDR differs from the well-known FTIR technique in that it introduces a continuously modulating reference branch, compared to the time-consuming discrete FTIR stepping reference branch. Such change allows real-time tracking of a moving object and capturing of its Doppler signature. The working principle of the THz-MIDR is similar to the FTIR technique: the incoming THz emission from the scene is split by a beam splitter/combiner; one of the beams is continuously modulated by a vibrating mirror or phase modulator and the other split beam is reflected by a reflection mirror; finally both the modulated reference beam and reflected beam are combined by the same beam splitter/combiner and detected by a THz intensity detector (for example, a pyroelectric detector). In order to extract THz phase from the single intensity measurement signal, we have derived rigorous mathematical formulas for 3 Frequency Banded (FB) signals: 1) DC Low-Frequency Banded (LFB) signal; 2) Fundamental Frequency Banded (FFB) signal; and 3) Harmonic Frequency Banded (HFB) signal. The THz phase extraction algorithms are then developed based combinations of 2 or all of these 3 FB signals with efficient algorithms such as Levenberg-Marquardt nonlinear fitting algorithm. Numerical simulation has also been performed in Matlab with simulated THz-MIDR interferometric signal of various Signal to Noise Ratio (SNR) to verify the algorithms.

Keywords: algorithm, modulation, THz phase, THz interferometry doppler radar

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Authors: Bairam Khan, Premlata Vaishnava

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The ability to conceptualize one entity in terms of another allows us to communicate through metaphors. This central feature of human cognition has evolved with the development of language, and the processing of metaphors is without any conscious appraisal and is quite effortless. South Asians, like other speech communities, have been using the kitchenary [culinary] metaphor in a very simple yet interesting way and are known for bringing into new and unique constellations wherever they are. This composite feature of our language is used to communicate in a precise and compact manner and maneuvers the expression. The present study explores the role of kitchenary metaphors in the making and shaping of idioms by applying Cognitive Metaphor Theories. Drawing on examples from a corpus of adverts, print, and electronic media, the study looks at the metaphorical language used by real people in real situations. The overarching theme throughout the course is that kitchenary metaphors are powerful tools of expression in Hindi-Urdu.

Keywords: cognitive metaphor theory, source domain, target domain, signifier- signified, kitchenary, ethnocultural elements of south asia and hindi- urdu language

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Authors: Chika C. Ogueke, Ijeoma M. Agunwah

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The antimicrobial activities and preservative potentials of crude extracts of Alstonia boonei stem bark and Euphorbia hirta leaves were studied. Soxhlet extraction and cold ethanol extraction methods were used for the extraction of the dried and ground plant samples. Well in agar diffusion method was used for the antimicrobial screening at different concentrations of 25mg/ml, 50mg/ml, 100mg/ml and 200mg/ml on E.coli and B.subtilis. The preservative effects of the extracts at 0.1%, 0.2% and 0.3% singly and in combination were determined in minced meat using E. coli and B. subtilis as test isolates. Phytochemical analysis was also conducted on the extracts using standard analytical methods. E.hirta cold and A.boonei cold extracts gave the highest zone of growth inhibition on E. coli and B.substilis with 20mm zone diameter at 200mg/ml concentration. Phytochemical analysis revealed the presence of alkaloids, flavonoids, tannins, saponins and cardiac glycosides. A.boonei at 0.1, 0.2 and 0.3% produced a log cycle reduction on the growth of E.coli. Mixture of A. boonei and E. hirta extracts (1:1) at 0.1% and 0.2% also produced a log cycle reduction on the growth of E.coli and B. subtilis, however the A. boonei extracts had more significant effect on the isolates. The observed antimicrobial activities are attributed to the phytochemicals identified in the extracts. The results reveal the potentials of plant extracts as natural antimicrobial preservatives in minced meat. Thus the crude extracts can act as inhibitors of bacteria in a food system. Upon further purification better results may be obtained.

Keywords: antimicrobial preservative, crude extracts, minced meat, test isolates

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Authors: Swarnadip Ghosh

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Sentiment Analysis is the process of detecting the contextual polarity of text. In other words, it determines whether a piece of writing is positive, negative or neutral.Sentiment analysis of documents holds great importance in today's world, when numerous information is stored in databases and in the world wide web. An efficient algorithm to illicit such information, would be beneficial for social, economic as well as medical purposes. In this project, we have developed an algorithm to classify a document into positive or negative. Using our algorithm, we obtained a feature set from the data, and classified the documents based on this feature set. It is important to note that, in the classification, we have not used the independence assumption, which is considered by many procedures like the Naive Bayes. This makes the algorithm more general in scope. Moreover, because of the sparsity and high dimensionality of such data, we did not use empirical distribution for estimation, but developed a method by finding degree of close clustering of the data points. We have applied our algorithm on a movie review data set obtained from IMDb and obtained satisfactory results.

Keywords: sentiment, Run's Test, cross validation, higher dimensional pmf estimation

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Authors: Yao-Hong Tsai

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This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition.

Keywords: image fusion, iris recognition, local binary pattern, wavelet

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Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

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Authors: Atef Youssef, Marwa Mostafa Moharam

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The effects of poly pyrrole (PP) addition on LiFePO4 have been studied by electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and galvanostatic measurements. PP was prepared with LiFePO₄ in different ways, such as chemically dispersion, insinuation polymerization, and electrochemically polymerization. The EIS results showed that the charge transfer resistance (Rct) of LiFePO₄ was decreased by adding 10% PP polymerized in a situation to 153 vs. 1660  for bare LiFePO₄. The CV curves show that 10% PP added LiFePO₄ had higher electrochemical reactivity for lithium insertion and extraction than the un-doped material. The mean redox potential is E1/2 = 3.45 V vs. Li+/Li. The first discharge curve of the 10% poly pyrrole doped LiFePO₄ showed a mainly flat voltage plateau over the 3.45–3.5 V range, indicating the lithium extraction and insertion reactions between LiFePO₄ and FePO₄. A specific discharge capacity of cells prepared from in-situ 10% PP added LiFePO4to was about 210 vs. 65 mAhg-1 for bare LiFePO₄.

Keywords: liFePO₄, poly pyrrole addition, positive electrode, lithium battery

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Authors: Bahaa Eltahawy, Mikko Ylihärsilä, Reino Virrankoski, Esko Petäjä

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Sheet metal processing is automated, but the step from product models to the production machine control still requires human intervention. This may cause time consuming bottlenecks in the production process and increase the risk of human errors. In this paper we present a system, which automatically recognizes features from the CAD-model of the sheet metal product. By using these features, the system produces a complete model of the particular sheet metal product. Then the model is used as an input for the sheet metal processing machine. Currently the system is implemented, capable to recognize more than 11 of the most common sheet metal structural features, and the procedure is fully automated. This provides remarkable savings in the production time, and protects against the human errors. This paper presents the developed system architecture, applied algorithms and system software implementation and testing.

Keywords: feature recognition, automation, sheet metal manufacturing, CAD, CAM

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Authors: Achmad Buhori, Reza Imam Pratama, Tissa Wiraatmaja, Wanti Megawati

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Data from many countries indicates that there is a marked increase of dental caries. The increases in caries appear to occur in lower socioeconomic groups. It is possible that the benefits of prevention of dental caries are not reaching these groups. However, there is a way to decrease dental caries by adding 5% of bromelain and calcium as an active agent in toothpaste. Bromelain can break glutamine-alanine bond and arginine-alanine bond which is a constituent of amino acid that causes dental plague which is one of the factors of dental caries. Calcium help rebuilds the teeth by strengthening and repairing enamel. Bromelain can be found from the extraction of pineapple (Ananas comosus) cobs (88.86-94.22 % of bromelain recovery during extraction based on the enzyme unit) and calcium can be taken from eggshell (95% of dry eggshell consist of calcium). The aim of this experiment is to make a toothpaste which contains bromelain and calcium as an effective, cheap, and healthy way to decrease dental caries around the world.

Keywords: bromelain, calcium, dental caries, dental plague, toothpaste

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Evgeny E. Tereshatov, C. M. Folden

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Ionic liquids and deep eutectic mixtures represent so-called non-conventional solvents. The former, composed of discrete ions, is a salt with a melting temperature below 100°С. The latter, consisting of hydrogen bond donors and acceptors, is a mixture of at least two compounds, resulting in a melting temperature depression in comparison with that of the individual moiety. These systems also can be water-immiscible, which makes them applicable for metal extraction. This work will cover interactions of In, Tl, Ir, and Rh in hydrochloric acid media with eutectic mixtures and Er, Ir, and At in a gas phase with chemically modified α-detectors. The purpose is to study chemical systems based on non-conventional solvents in terms of their interaction with metals. Once promising systems are found, the next step is to modify the surface of α-detectors used in the online element production at cyclotrons to get the detector chemical selectivity. Initially, the metal interactions are studied by means of the liquid-liquid extraction technique. Then appropriate molecules are chemisorbed on the surrogate surface first to understand the coating quality. Finally, a detector is covered with the same molecule, and the metal sorption on such detectors is studied in the online regime. It was found that chemical treatment of the surface can result in 99% coverage with a monolayer formation. This surface is chemically active and can adsorb metals from hydrochloric acid solutions. Similarly, a detector surface was modified and tested during cyclotron-based experiments. Thus, a procedure of detectors functionalization has been developed, and this opens an interesting opportunity of studying chemisorption of elements which do not have stable isotopes.

Keywords: mechanism, radioisotopes, solvent extraction, gas phase sorption

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Authors: Derya Ören, Şeyma Akalın

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Cold pres technique is a traditional method to obtain oil. The cold-pressing procedure, involves neither heat nor chemical treatments, so cold press technique has low oil yield and cold pressed herbal material residue still contains some oil. In this study, the oil that is remained in the cold pressed aniseed extracted with hegzan and analysed to determine physicochemical properties and quality parameters. It is found that the aniseed after cold press process contains % 10 oil. Other analysis parametres free fatty acid (FFA) is 2,1 mgKOH/g, peroxide value is 7,6 meq02/kg. Cold pressed aniseed oil values are determined for fatty acid (FFA) value as 2,1 mgKOH/g, peroxide value 4,5 meq02/kg respectively. Also fatty acid composition is analysed, it is found that both of these oil have same fatty acid composition. The main fatty acids are; oleic, linoleic, and palmitic acids.

Keywords: aniseed oil, cold press, extraction, residue

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Authors: Sandabad Sara, Sayd Tahri Yassine, Hammouch Ahmed

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The development of science has allowed computer scientists to touch the medicine and bring aid to radiologists as we are presenting it in our article. Our work focuses on the detection and localization of tumors areas in the human brain; this will be a completely automatic without any human intervention. In front of the huge volume of MRI to be treated per day, the radiologist can spend hours and hours providing a tremendous effort. This burden has become less heavy with the automation of this step. In this article we present an automatic and effective tumor detection, this work consists of two steps: the first is the image filtering using the filter Nl-means, then applying the expectation maximization algorithm (EM) for retrieving the tumor mask from the brain MRI and extracting the tumor area using the mask obtained from the second step. To prove the effectiveness of this method multiple evaluation criteria will be used, so that we can compare our method to frequently extraction methods used in the literature.

Keywords: MRI, Em algorithm, brain, tumor, Nl-means

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Authors: Sahar Khedr, Dina Sayed, Ayman Hanafy

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Arabic is one of the most ancient and critical languages in the world. It has over than 250 million Arabic native speakers and more than twenty countries having Arabic as one of its official languages. In the past decade, we have witnessed a rapid evolution in smart devices, social network and technology sector which led to the need to provide tools and libraries that properly tackle the Arabic language in different domains. Stemming is one of the most crucial linguistic fundamentals. It is used in many applications especially in information extraction and text mining fields. The motivation behind this work is to enhance the Arabic light stemmer to serve the data mining industry and leverage it in an open source community. The presented implementation works on enhancing the Arabic light stemmer by utilizing and enhancing an algorithm that provides an extension for a new set of rules and patterns accompanied by adjusted procedure. This study has proven a significant enhancement for better search accuracy with an average 10% improvement in comparison with previous works.

Keywords: Arabic data mining, Arabic Information extraction, Arabic Light stemmer, Arabic stemmer

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Ping Tan, Xiaomeng Su, Yi Shen

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The motion intention in the motor imagery braincomputer interface is identified by classifying the event-related desynchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the effective sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidt signals by using multiple band-pass filters firstly; Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. these feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the effective sensorimotor frequency bands. During the test phase, the test signals are filtered by the bandpass filter of the effective sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experiments on the BCI competition IV 2a dataset show that the proposed method is superior to other classification methods.

Keywords: non-uniform filter banks, motor imagery, brain-computer interface, minimum distance to Riemannian mean

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Authors: Ovanes Chakoyan

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MOH (mineral oil hydrocarbons), which consist of mineral oil saturated hydrocarbons(MOSH) and mineral oil aromatic hydrocarbons(MOAH), are present in various products such as vegetable oils, animal feed, foods, and infant formula. Contamination of foods with mineral oil hydrocarbons, particularly mineral oil aromatic hydrocarbons(MOAH), exhibiting carcinogenic, mutagenic, and hormone-disruptive effects. Identifying toxic substances among the many thousands comprising mineral oils in food samples is a difficult analytical challenge. A method based on an offline-solid phase extraction approach coupled with gas chromatography-triple quadrupole(GC-MS/MS) was developed for the determination of MOSH/MOAH in various products such as vegetable oils, animal feed, foods, and infant formula. A glass solid phase extraction cartridge loaded with 7 g of activated silica gel impregnated with 10 % silver nitrate for removal of olefins and lipids. The MOSH/MOAH fractions were eluated with hexane and hexane: dichloromethane : toluene, respectively. Each eluate was concentrated to 50 µl in toluene and injected on splitless mode into GC-MS/MS. Accuracy of the method was estimated as measurement of recovery of spiked oil samples at 2.0, 15.0, and 30.0 mg kg -1, and recoveries varied from 85 to 105 %. The method was applied to the different types of samples (sunflower meal, chocolate ships, santa milk chocolate, biscuits, infant milk, cornflakes, refined sunflower oil, crude sunflower oil), detecting MOSH up to 56 mg/kg and MOAH up to 5 mg/kg. The limit of quantification(LOQ) of the proposed method was estimated at 0.5 mg/kg and 0.3 mg/kg for MOSH and MOAH, respectively.

Keywords: MOSH, MOAH, GC-MS/MS, foods, solid phase extraction

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Authors: Saeed Bagherifam, Trevor Brown, Chris Fellows, Ravi Naidu

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Over the last century, rapid urbanization, industrial emissions, and mining activities have resulted in widespread contamination of the environment by heavy metal(loid)s. Arsenic (As) is a toxic metalloid belonging to group 15 of the periodic table, which occurs naturally at low concentrations in soils and the earth’s crust, although concentrations can be significantly elevated in natural systems as a result of dispersion from anthropogenic sources, e.g., mining activities. Bioavailability is the fraction of a contaminant in soils that is available for uptake by plants, food chains, and humans and therefore presents the greatest risk to terrestrial ecosystems. Numerous attempts have been made to establish in situ and ex-situ technologies of remedial action for remediation of arsenic-contaminated soils. In situ stabilization techniques are based on deactivation or chemical immobilization of metalloid(s) in soil by means of soil amendments, which consequently reduce the bioavailability (for biota) and bioaccessibility (for humans) of metalloids due to the formation of low-solubility products or precipitates. This study investigated the effectiveness of two different types of synthetic manganese and iron oxides (birnessite and goethite) for stabilization of As in a soil spiked with 1000 mg kg⁻¹ of As and treated with 10% dosages of soil amendments. Birnessite was made using HCl and KMnO₄, and goethite was synthesized by the dropwise addition of KOH into Fe(NO₃) solution. The resulting contaminated soils were subjected to a series of chemical extraction studies including sequential extraction (BCR method), single-step extraction with distilled (DI) water, 2M HNO₃ and simplified bioaccessibility extraction tests (SBET) for estimation of bioaccessible fractions of As in two different soil fractions ( < 250 µm and < 2 mm). Concentrations of As in samples were measured using inductively coupled plasma mass spectrometry (ICP-MS). The results showed that soil with birnessite reduced bioaccessibility of As by up to 92% in both soil fractions. Furthermore, the results of single-step extractions revealed that the application of both birnessite and Goethite reduced DI water and HNO₃ extractable amounts of arsenic by 75, 75, 91, and 57%, respectively. Moreover, the results of the sequential extraction studies showed that both birnessite and goethite dramatically reduced the exchangeable fraction of As in soils. However, the amounts of recalcitrant fractions were higher in birnessite, and Goethite amended soils. The results revealed that the application of both birnessite and goethite significantly reduced bioavailability and the exchangeable fraction of As in contaminated soils, and therefore birnessite and Goethite amendments might be considered as promising adsorbents for stabilization and remediation of As contaminated soils.

Keywords: arsenic, bioavailability, in situ stabilisation, metalloid(s) contaminated soils

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Authors: Vijayalakshmi Marella, NageswaraRaoPilli

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This paper describes a simple, rapid and sensitive liquid chromatography / tandem mass spectrometry assay for the determination of montelukast in human plasma using montelukast d6 as an internal standard. Analyte and the internal standard were extracted from 50 µL of human plasma via solid phase extraction technique without evaporation, drying and reconstitution steps. The chromatographic separation was achieved on a C18 column by using a mixture of methanol and 5mM ammonium acetate (80:20, v/v) as the mobile phase at a flow rate of 0.8 mL/min. Good linearity results were obtained during the entire course of validation. Method validation was performed as per FDA guidelines and the results met the acceptance criteria. A run time of 2.5 min for each sample made it possible to analyze more number of samples in short time, thus increasing the productivity. The proposed method was found to be applicable to clinical studies.

Keywords: Montelukast, tandem mass spectrometry, montelukast d6, FDA guidelines

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Authors: Jia-Hong Lee, Mei-Yi Wu, Hsien-Tsung Kuo

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Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers.

Keywords: texture classification, texture descriptor, SIFT, SURF, ORB

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Authors: Mary Konadu

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The rectal route for drug administration is becoming attractive to drug formulators because it can avoid hepatic first-pass effects, decrease gastrointestinal side effects and avoid undesirable effects of meals on drug absorption. Suppositories have been recognized as an alternative to the oral route in situations such as when the patient is comatose, unable to swallow, or when the drug produces nausea or vomiting. Effective drug delivery with appropriate pharmaceutical excipient is key in the production of clinically useful preparations. The high cost of available excipients coupled with other disadvantages have led to the exploration of potential excipients from natural sources. Allanblackia floribunda butter, a naturally occurring lipid, is used for medicinal, culinary, and cosmetic purposes. Different extraction methods (solvent (hexane) extraction, traditional/hot water extraction, and cold/screw press extraction) were employed to extract the oil. The different extracts of A. floribunda oil were analyzed for their physicochemical properties and mineral content. The oil was used as a base to formulate Paracetamol and Diclofenac suppositories. Quality control test were carried out on the formulated suppositories. The %age oil yield for hexane extract, hot water extract, and cold press extract were 50.40 ±0.00, 37.36±0.00, and 20.48±0.00, respectively. The acid value, saponification value, iodine value and free fatty acid were 1.159 ± 0.065, 208.51 ± 8.450, 49.877 ± 0.690 and 0.583 ± 0.032 respectively for hexane extract; 3.480 ± 0.055, 204.672±2.863, 49.04 ± 0.76 and 1.747 ± 0.028 respectively for hot water/traditional extract; 4.43 ± 0.055, 192.05±1.56, 49.96 ± 0.29 and 2.23 ± 0.03 respectively for cold press extract. Calcium, sodium, magnesium, potassium, and iron were minerals found to be present in the A. floribunda butter extracts. The uniformity of weight, hardness, disintegration time, and uniformity of content were found to be within the acceptable range. The melting point ranges for all the suppositories were found to be satisfactory. The cumulative drug release (%) of the suppositories at 45 minutes was 90.19±0.00 (Hot water extract), 93.75±0.00 (Cold Pres Extract), and 98.16±0.00 (Hexane Extract) for Paracetamol suppositories. Diclofenac sodium suppositories had a cumulative %age release of 81.60±0.00 (Hot water Extract), 95.33±0.00 (Cold Press Extract), and 99.20±0.00 (Hexane Extract). The physicochemical parameters obtained from this study shows that Allanblackia floribunda seed oil is edible and can be used as a suppository base. The suppository formulation was successful, and the quality control tests conformed to Pharmacopoeia standard.

Keywords: allanblackia foribunda, paracetamol, diclofenac, suppositories

Procedia PDF Downloads 123

Authors: Lucia Billeci, Gennaro Tartarisco, Maurizio Varanini

Abstract:

Fetal cardiac monitoring by fetal electrocardiogram (fECG) can provide significant clinical information about the healthy condition of the fetus. Despite this potentiality till now the use of fECG in clinical practice has been quite limited due to the difficulties in its measuring. The recovery of fECG from the signals acquired non-invasively by using electrodes placed on the maternal abdomen is a challenging task because abdominal signals are a mixture of several components and the fetal one is very weak. This paper presents an approach for fECG extraction from abdominal maternal recordings, which exploits the characteristics of pseudo-periodicity of fetal ECG. It consists of devising a quality index (fQI) for fECG and of finding the linear combinations of preprocessed abdominal signals, which maximize these fQI (quality index optimization - QIO). It aims at improving the performances of the most commonly adopted methods for fECG extraction, usually based on maternal ECG (mECG) estimating and canceling. The procedure for the fECG extraction and fetal QRS (fQRS) detection is completely unsupervised and based on the following steps: signal pre-processing; maternal ECG (mECG) extraction and maternal QRS detection; mECG component approximation and canceling by weighted principal component analysis; fECG extraction by fQI maximization and fetal QRS detection. The proposed method was compared with our previously developed procedure, which obtained the highest at the Physionet/Computing in Cardiology Challenge 2013. That procedure was based on removing the mECG from abdominal signals estimated by a principal component analysis (PCA) and applying the Independent component Analysis (ICA) on the residual signals. Both methods were developed and tuned using 69, 1 min long, abdominal measurements with fetal QRS annotation of the dataset A provided by PhysioNet/Computing in Cardiology Challenge 2013. The QIO-based and the ICA-based methods were compared in analyzing two databases of abdominal maternal ECG available on the Physionet site. The first is the Abdominal and Direct Fetal Electrocardiogram Database (ADdb) which contains the fetal QRS annotations thus allowing a quantitative performance comparison, the second is the Non-Invasive Fetal Electrocardiogram Database (NIdb), which does not contain the fetal QRS annotations so that the comparison between the two methods can be only qualitative. In particular, the comparison on NIdb was performed defining an index of quality for the fetal RR series. On the annotated database ADdb the QIO method, provided the performance indexes Sens=0.9988, PPA=0.9991, F1=0.9989 overcoming the ICA-based one, which provided Sens=0.9966, PPA=0.9972, F1=0.9969. The comparison on NIdb was performed defining an index of quality for the fetal RR series. The index of quality resulted higher for the QIO-based method compared to the ICA-based one in 35 records out 55 cases of the NIdb. The QIO-based method gave very high performances with both the databases. The results of this study foresees the application of the algorithm in a fully unsupervised way for the implementation in wearable devices for self-monitoring of fetal health.

Keywords: fetal electrocardiography, fetal QRS detection, independent component analysis (ICA), optimization, wearable

Procedia PDF Downloads 281

Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

Abstract:

Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition

Procedia PDF Downloads 123

Authors: Sanaz Mosadeghsedghi, Mathew Hudder, Mohammad Ali Baghbanzadeh, Charbel Atallah, Seyedeh Laleh Dashtban Kenari, Konstantin Volchek

Abstract:

Lithium has recently been extensively used in lithium-ion batteries (LIBs) for electric vehicles and portable electronic devices. The conventional evaporative approach to recover and concentrate lithium is extremely slow and may take 10-24 months to concentrate lithium from dilute sources, such as geothermal brines. To response to the increasing industrial lithium demand, alternative extraction and concentration technologies should be developed to recover lithium from brines with low concentrations. In this study, a combination of electrocoagulation (EC) and electrodialysis (ED) was evaluated for the recovery of lithium from geothermal brines. The brine samples in this study, collected in Western Canada, had lithium concentrations of 50-75 mg/L on a background of much higher (over 10,000 times) concentrations of sodium. This very high sodium-to-lithium ratio poses challenges to the conventional direct-lithium extraction processes which employ lithium-selective adsorbents. EC was used to co-precipitate lithium using a sacrificial aluminium electrode. The precipitate was then dissolved, and the leachate was treated using ED to separate and concentrate lithium from other ions. The focus of this paper is on the study of ED, including a two-step ED process that included a mono-valent selective stage to separate lithium from multi-valent cations followed by a bipolar ED stage to convert lithium chloride (LiCl) to LiOH product. Eventually, the ED cell was reconfigured using mono-valent cation exchange with the bipolar membranes to combine the two ED steps in one. Using this process at optimum conditions, over 95% of the co-existing cations were removed and the purity of lithium increased to over 90% in the final product.

Keywords: electrochemical separation, electrocoagulation, electrodialysis, lithium extraction

Procedia PDF Downloads 94

Authors: Jinlu Ruan, Liang Chen, Bo Liu, Xiaoping Ouyang, Zhichao Zhu, Zhongbing Zhang, Shiyi He, Mengxuan Xu

Abstract:

Plastic scintillators play an important role in the measurement of a mixed neutron/gamma pulsed radiation, neutron radiography and pulse shape discrimination technology. In some research, these luminescent properties are necessary that photons produced by the interactions between a plastic scintillator and radiations can be detected as much as possible by the photoelectric detectors and more photons can be emitted from the scintillators along a specific direction where detectors are located. Unfortunately, a majority of these photons produced are trapped in the plastic scintillators due to the total internal reflection (TIR), because there is a significant light-trapping effect when the incident angle of internal scintillation light is larger than the critical angle. Some of these photons trapped in the scintillator may be absorbed by the scintillator itself and the others are emitted from the edges of the scintillator. This makes the light extraction of plastic scintillators very low. Moreover, only a small portion of the photons emitted from the scintillator easily can be detected by detectors effectively, because the distribution of the emission directions of this portion of photons exhibits approximate Lambertian angular profile following a cosine emission law. Therefore, enhancing the light extraction efficiency and adjusting the emission angular profile become the keys for improving the number of photons detected by the detectors. In recent years, photonic crystal structures have been covered on inorganic scintillators to enhance the light extraction efficiency and adjust the angular profile of scintillation light successfully. However, that, preparation methods of photonic crystals will deteriorate performance of plastic scintillators and even destroy the plastic scintillators, makes the investigation on preparation methods of photonic crystals for plastic scintillators and luminescent properties of plastic scintillators with photonic crystal structures inadequate. Although we have successfully made photonic crystal structures covered on the surface of plastic scintillators by a modified self-assembly technique and achieved a great enhance of light extraction efficiency without evident angular-dependence for the angular profile of scintillation light, the preparation of photonic crystal structures with large area (the diameter is larger than 6cm) and perfect periodic structure is still difficult. In this paper, large area photonic crystals on the surface of scintillators were prepared by nanoimprint lithography firstly, and then a conformal layer with material of high refractive index on the surface of photonic crystal by atomic layer deposition technique in order to enhance the stability of photonic crystal structures and increase the number of leaky modes for improving the light extraction efficiency. The luminescent properties of the plastic scintillator with photonic crystals prepared by the mentioned method are compared with those of the plastic scintillator without photonic crystal. The results indicate that the number of photons detected by detectors is increased by the enhanced light extraction efficiency and the angular profile of scintillation light exhibits evident angular-dependence for the scintillator with photonic crystals. The mentioned preparation of photonic crystals is beneficial to scintillation detection applications and lays an important technique foundation for the plastic scintillators to meet special requirements under different application backgrounds.

Keywords: angular profile, atomic layer deposition, light extraction efficiency, plastic scintillator, photonic crystal

Procedia PDF Downloads 200

Authors: Chiara Della Rocca, Gavino Piras, Andrea Berti, Alessandro Mameli

Abstract:

DNA identification of human skeletal remains plays a valuable role in the forensic field, especially in missing persons and mass disaster investigations. Hard tissues, such as bones and teeth, represent a very common kind of samples analyzed in forensic laboratories because they are often the only biological materials remaining. However, the major limitation of using these compact samples relies on the extremely time–consuming and labor–intensive treatment of grinding them into powder before proceeding with the conventional DNA purification and extraction step. In this context, a DNA extraction assay called the TBone Ex kit (DNA Chip Research Inc.) was developed to digest bone chips without powdering. Here, we simultaneously analyzed bone and tooth samples that arrived at our police laboratory and belonged to 15 different forensic casework that occurred in Sardinia (Italy). A total of 27 samples were recovered from different scenarios and were exposed to extreme environmental factors, including sunlight, seawater, soil, fauna, vegetation, and high temperature and humidity. The TBone Ex kit was used prior to the EZ2 DNA extraction kit on the EZ2 Connect Fx instrument (Qiagen), and high-quality autosomal and Y-chromosome STRs profiles were obtained for the 80% of the caseworks in an extremely short time frame. This study provides additional support for the use of the TBone Ex kit for digesting bone fragments/whole teeth as an effective alternative to pulverization protocols. We empirically demonstrated the effectiveness of the kit in processing multiple bone samples simultaneously, largely simplifying the DNA extraction procedure and the good yield of recovered DNA for downstream genetic typing in highly compromised forensic real specimens. In conclusion, this study turns out to be extremely useful for forensic laboratories, to which the various actors of the criminal justice system – such as potential jury members, judges, defense attorneys, and prosecutors – required immediate feedback.

Keywords: DNA, skeletal remains, bones, tbone ex kit, extreme conditions

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