Search results for: time series feature extraction

Authors: Akbar Esmaeili, Zahra Salarieh

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Cerium oxide nanoparticles (nano-series) are used as catalysts in industrial applications due to their free radical scavenging properties. Given that free radicals play an essential role in the pathology of many neurological diseases, we investigated the use of nanocrystals as a potential therapeutic agent for oxidative damage. This project synthesized nano-series from a new and environmentally friendly bio-pathway. Investigation of cerium nitrate in culture medium containing inoculated Lactobacillus acidophilus strain before incubation produces nano-series. Loaded with glatiramer acetate (GA) was formed by coating carboxymethylcellulose (CMC) and CeO2. FE-SEM analysis showed nano-series in the 9-11 nm range, spherical shape, and uniform particle size distribution. Cubic nanoparticles containing anti-multiple sclerosis (anti-Ms) treatment called GA were used. Glycerol monostearate (GMS) was used as a fat base, and evening primrose extract was used as an anti-inflammatory in cubosomes. Design-Expert® software was used to study the effects of different formulation factors on the properties of GAloaded cubic dispersions. Thirty GA-labeled cubic dispersions were prepared with GA-labeled carboxymethylcellulose and evaluated in vitro. The results showed an average nano-series size of 89.02 and a zeta potential of -49.9. Cubosomes containing GA-CMC/CeO2 showed a stable release profile for 180 min. The results showed that cubosomes containing GA-CMC/CeO2 could be a promising drug carrier with normal release behavior.

Keywords: ciochemistry, biotechnology, molecular, biology

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Authors: Mehrosadat Mirmohammadi, Sara Taghdisi, Ali Padash, Mohammad Hossein Pazandeh

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Gossypol, a yellowish anti-nutritional compound found in cotton plants, exists in various plant parts, including seeds, husks, leaves, and stems. Chemically, gossypol is a potent polyphenolic aldehyde with antioxidant and therapeutic properties. However, its free form can be toxic, posing risks to both humans and animals. Initially, we extracted gossypol from cotton seeds using n-hexane as a solvent (yield: 84.0 ± 4.0%). We also obtained cotton seed and cotton boll extracts via Soxhlet extraction (25:75 hydroalcoholic ratio). These extracts, combined with cornstarch, formed four herbal medicinal formulations. Ethical approval allowed us to investigate their effects on Leishmania-caused skin wounds, comparing them to glucantime (local ampoule). Herbal formulas outperformed the control group (ethanol only) in wound treatment (p-value 0.05). The average wound diameter after two months did not significantly differ between plant extract ointments and topical glucantime. Notably, cotton boll extract with 1% extra gossypol crystal showed the best therapeutic effect. We extracted gossypol from cotton seeds using n-hexane via Soxhlet extraction. Saponification, acidification, and recrystallization steps followed. FTIR, UV-Vis, and HPLC analyses confirmed the product’s identity. Herbal medicines from cotton seeds effectively treated chronic wounds compared to the ethanol-only control group. Wound diameter differed significantly between extract ointments and glucantime injections. It seems that due to the presence of large amounts of fat in the oil, the extraction of gossypol from it faces many obstacles. The extraction of this compound with our technique showed that extraction from oil has a higher efficiency, perhaps because of the preparation of oil by cold pressing method, the possibility of losing this compound is much less than when extraction is done with Soxhlet. On the other hand, the gossypol in the oil is mostly bound to the protein, which somehow protects the gossypol until the last stage of the extraction process. Since this compound is very sensitive to light and heat, it was extracted as a derivative with acetic acid. Also, in the treatment section, it was found that the ointment prepared with the extract is more effective and Gossypol is one of the effective ingredients in the treatment. Therefore, gossypol can be extracted from the oil and added to the extract from which gossypol has been extracted to make an effective medicine with a certain dose.

Keywords: cottonseed, glucantime, gossypol, leishmaniasis

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Authors: Hui Wei, Zheng Dong

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Object recognition has long been a challenging task in computer vision. Yet the human brain, with the ability to rapidly and accurately recognize visual stimuli, manages this task effortlessly. In the past decades, advances in neuroscience have revealed some neural mechanisms underlying visual processing. In this paper, we present a novel model inspired by the visual pathway in primate brains. This multi-layer neural network model imitates the hierarchical convergent processing mechanism in the visual pathway. We show that local image features generated by this model exhibit robust discrimination and even better generalization ability compared with some existing image descriptors. We also demonstrate the application of this model in an object recognition task on image data sets. The result provides strong support for the potential of this model.

Keywords: biological model, feature extraction, multi-layer neural network, object recognition

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Authors: Saurabh Farkya, Govinda Surampudi

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Optical Character Recognition is one of the current major research areas. This paper is focussed on recognition of Devanagari script and its sound generation. This Paper consists of two parts. First, Optical Character Recognition of Devnagari handwritten Script. Second, speech synthesis of the recognized text. This paper shows an implementation of support vector machines for the purpose of Devnagari Script recognition. The Support Vector Machines was trained with Multi Domain features; Transform Domain and Spatial Domain or Structural Domain feature. Transform Domain includes the wavelet feature of the character. Structural Domain consists of Distance Profile feature and Gradient feature. The Segmentation of the text document has been done in 3 levels-Line Segmentation, Word Segmentation, and Character Segmentation. The pre-processing of the characters has been done with the help of various Morphological operations-Otsu's Algorithm, Erosion, Dilation, Filtration and Thinning techniques. The Algorithm was tested on the self-prepared database, a collection of various handwriting. Further, Unicode was used to convert recognized Devnagari text into understandable computer document. The document so obtained is an array of codes which was used to generate digitized text and to synthesize Hindi speech. Phonemes from the self-prepared database were used to generate the speech of the scanned document using concatenation technique.

Keywords: Character Recognition (OCR), Text to Speech (TTS), Support Vector Machines (SVM), Library of Support Vector Machines (LIBSVM)

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Authors: Balpreet Singh Madan

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The Middle Eastern countries have preserved their heritage, tradition, and culture in their buildings by incorporating vernacular features of Arab-Islamic Architecture. The harsh sun and arid climate in the Gulf region make their buildings and infrastructure extremely hot and challenging to live in. One such iconic feature of Arab architecture is the Mashrabiya, which has been refined and updated for both functional and aesthetic purposes. This feature helps reduce the impact of solar radiation in buildings and lowers the energy requirements for creating livable conditions. The incorporation of Mashrabiya in modern buildings in the region symbolizes the amalgamation of tradition with innovation and modern technology. These buildings depict Mashrabiya with refinements for its better functional performance and aesthetic appeal to make superior built forms. This paper emphasizes the study of Mashrabiya as a vernacular feature with its adaptability for Energy Conservation and Sustainability, as seen in some of the recent iconic buildings of the Middle East, through a literature review and case studies of renowned buildings.

Keywords: energy efficiency, climate responsive, sustainability, innovation, heritage, vernacular

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Authors: Zhaoan Wang

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Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation

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Authors: Mayar Almasri, Rosa Helmi, Rayana Enany

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This report describes the features of AutoCAD software released by Adobe. It explains how the program makes it easier for engineers and designers and reduces their time and effort spent using AutoCAD. Moreover, it highlights how AutoCAD works, how some of the commands used in it, such as Shortcut, make it easy to use, and features that make it accurate in measurements. The results of the report show that most users of this program are designers and engineers, but few people know about it and find it easy to use. They prefer to use it because it is easy to use, and the shortcut commands shorten a lot of time for them. The feature got a high rate and some suggestions for improving AutoCAD in Aperture, but it was a small percentage, and the highest percentage was that they didn't need to improve the program, and it was good.

Keywords: artificial intelligence, design, planning, commands, autodesk, dimensions

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Authors: Mona Hejazi, Ali Motie Nasrabadi

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Epilepsy is a persistent neurological disorder that affects more than 50 million people worldwide. Hence, there is a necessity to introduce an efficient prediction model for making a correct diagnosis of the epileptic seizure and accurate prediction of its type. In this study we consider how the Effective Connectivity (EC) patterns obtained from intracranial Electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict imminent seizures, as this will enable the patients (and caregivers) to take appropriate precautions. We use this definition because we believe that effective connectivity near seizures begin to change, so we can predict seizures according to this feature. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. Six channels of EEG from each patients are considered and effective connectivity using Directed Transfer Function (DTF) and Granger Causality (GC) methods is estimated. We concentrate on effective connectivity standard deviation over time and feature changes in five brain frequency sub-bands (Alpha, Beta, Theta, Delta, and Gamma) are compared. The performance obtained for the proposed scheme in predicting seizures is: average prediction time is 50 minutes before seizure onset, the maximum sensitivity is approximate ~80% and the false positive rate is 0.33 FP/h. DTF method is more acceptable to predict epileptic seizures and generally we can observe that the greater results are in gamma and beta sub-bands. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.

Keywords: effective connectivity, Granger causality, directed transfer function, epilepsy seizure prediction, EEG

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Authors: Natalie M. Khazaal

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The effects of the civil war on Lebanese women have been challenging to conceptualize. For some, war is a liberating and empowering force for women, while for others it is one that subjugates women and disempowers them in new ways. Scholars have explored the impact on the Lebanese civil war (1975-1990) on women in the fields of labor history, political activism and literary production. In all these arenas, women’s role and visibility were contested and negotiated in diverse ways. But probably the most visible arena where this contestation took place was television. Dramatized entertainment series were crucial sites where fictional women battled out the gender question, and which reflected and participated in the negotiations of gender politics. Even more stunningly, actual television stations became part of this battle through the plots and portrayals of women that they created. The state-backed Tele-Liban (TL) peddled patriarchal articulations of gender that directly competed with the edgy vision of liberated, independent women on the pirate Lebanese Broadcasting Corporation (LBC). This presentation explores how LBC used gender to distinguish its brand against the retrograde TL programing. Television series are an important medium for creating, testing and reenacting gender politics. They are even more consequential in another way. They are the sites where a dramatic shift in the relationship between Arab television and Arab publics—from benign neglect of public concerns towards engagement with audiences—took place for the first time. As this shift is at the heart of why Arab media was seen as a participant in the Arab uprisings, it is important to explore the roots of the shift in the dramas and comedy series of the mid-1980s Lebanese television. This presentation argues that television battles over gender were consequential and need serious consideration as sites of unexpected meaning.

Keywords: gender, Lebanon, television, war, women

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Authors: Burçe Boyraz

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This study examines appearances of brand placement as an alternative communication strategy in television series by focusing on Yalan Dünya which is one of the most popular television series in Turkey. Consequently, this study has a descriptive research design and quantitative content analysis method is used in order to analyze frequency and time data of brand placement appearances in first 3 seasons of Yalan Dünya with 16 episodes. Analysis of brand placement practices in Yalan Dünya is dealt in three categories: episode-based analysis, season-based analysis and comparative analysis. At the end, brand placement practices in Yalan Dünya are evaluated in terms of type, form, duration and legal arrangements. As a result of this study, it is seen that brand placement plays a determinant role in Yalan Dünya content. Also, current legal arrangements make brand placement closer to other traditional communication strategies instead of differing brand placement from them distinctly.

Keywords: advertising, alternative communication strategy, brand placement, Yalan Dünya

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Authors: Yixiu Liu, Yunzhou Zhang, Jianning Chi, Hao Chu, Rui Zheng, Libo Sun, Guanghao Chen, Fangtong Zhou

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In the last few years, large-scale person re-identification has attracted a lot of attention from video surveillance since it has a potential application prospect in public safety management. However, it is still a challenging job considering the variation in human pose, the changing illumination conditions and the lack of paired samples. Although the accuracy has been significantly improved, the data dependence of the sample training is serious. To tackle this problem, a new strategy is proposed based on bag of visual words (BoVW) model of designing the feature representation which has been widely used in the field of image retrieval. The local features are extracted, and more discriminative feature representation is obtained by cross-view dictionary learning (CDL), then the assignment map is obtained through k-means clustering. Finally, the BoVW histograms are formed which encodes the images with the statistics of the feature classes in the assignment map. Experiments conducted on the CUHK03, Market1501 and MARS datasets show that the proposed method performs favorably against existing approaches.

Keywords: bag of visual words, cross-view dictionary learning, person re-identification, reranking

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Authors: Wei-Jong Yang, Wei-Hau Du, Pau-Choo Chang, Jar-Ferr Yang, Pi-Hsia Hung

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The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.

Keywords: color moments, visual thing recognition system, SIFT, color SIFT

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Authors: Janya Sainui

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As a rapid growth of digital videos and data communications, video summarization that provides a shorter version of the video for fast video browsing and retrieval is necessary. Key frame extraction is one of the mechanisms to generate video summary. In general, the extracted key frames should both represent the entire video content and contain minimum redundancy. However, most of the existing approaches heuristically select key frames; hence, the selected key frames may not be the most different frames and/or not cover the entire content of a video. In this paper, we propose a method of video summarization which provides the reasonable objective functions for selecting key frames. In particular, we apply a statistical dependency measure called quadratic mutual informaion as our objective functions for maximizing the coverage of the entire video content as well as minimizing the redundancy among selected key frames. The proposed key frame extraction algorithm finds key frames as an optimization problem. Through experiments, we demonstrate the success of the proposed video summarization approach that produces video summary with better coverage of the entire video content while less redundancy among key frames comparing to the state-of-the-art approaches.

Keywords: video summarization, key frame extraction, dependency measure, quadratic mutual information

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Authors: F. J. Adeyeye, D. Eni, K. M. Okedoye

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In this paper we present a Hybrid of Adomian decomposition method (ADM). This is the substitution of a One-step method of Taylor’s series approximation of orders I and II, into the nonlinear part of Adomian decomposition method resulting in a convergent series scheme. This scheme is applied to solve some Logistic problems represented as Abelian differential equation and the results are compared with the actual solution and Runge-kutta of order IV in order to ascertain the accuracy and efficiency of the scheme. The findings shows that the scheme is efficient enough to solve logistic problems considered in this paper.

Keywords: Adomian decomposition method, nonlinear part, one-step method, Taylor series approximation, hybrid of Adomian polynomial, logistic problem, Malthusian parameter, Verhulst Model

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Authors: Lae-Jeong Park, Kyusoo Chung, Jungho Moon

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This paper introduces an effective method of segmenting Korean text (place names in Korean) from a Korean road sign image. A Korean advanced directional road sign is composed of several types of visual information such as arrows, place names in Korean and English, and route numbers. Automatic classification of the visual information and extraction of Korean place names from the road sign images make it possible to avoid a lot of manual inputs to a database system for management of road signs nationwide. We propose a series of problem-specific heuristics that correctly segments Korean place names, which is the most crucial information, from the other information by leaving out non-text information effectively. The experimental results with a dataset of 368 road sign images show 96% of the detection rate per Korean place name and 84% per road sign image.

Keywords: segmentation, road signs, characters, classification

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Authors: Oscar Corredor, Alexander Guzman, Adan Leon

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Density is a bulk physical property that indicates the quality of a petroleum fraction. It is also a useful property to estimate various physicochemical properties of fraction and petroleum fluids; however, the determination of density of extra heavy residual (EHR) fractions by standard methodologies, (ASTM D70) shows limitations for samples with higher densities than 1.0879 g/cm3. For this reason, a dilution methodology was developed in order to determinate density for those particular fractions, 87 (EHR) fractions were obtained as products of the fractionation of Colombian typical Vacuum Distillation Residual Fractions using molecular distillation (MD) and extraction with Solvent N-hexane in Supercritical Conditions (SFEF) pilot plants. The proposed methodology showed reliable results that can be demonstrated with the standard deviation of repeatability and reproducibility values of 0.0031 and 0.0061 g/ml respectively. In the same way, it was possible to determine densities in fractions EHR up to 1.1647g/cm3 and °API values obtained were ten times less than the water reference value.

Keywords: API, density, vacuum residual, molecular distillation, supercritical fluid extraction

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Authors: Masoud Fathi Kazerouni, Jens Schlemper, Klaus-Dieter Kuhnert

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Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained.

Keywords: SIFT combination, feature extraction, feature detection, natural images, natural plant recognition, HARRIS-SIFT, FAST-SIFT

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: K. M. Jananni, R. Nandini

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The proposed wireless, inverter type design of a LPG leakage monitoring system aims to provide a smart and safe kitchen. The system detects the LPG gas leak using Nano-sensors and alerts the concerned individual through GSM system. The system uses two sensors, one attached to the chimney and other to the regulator of the LPG cylinder. Upon a leakage being detected, the sensor at the regulator actuates the system to cut off the gas supply immediately using a solenoid control valve. The sensor at the chimney checks for the permissible level of LPG mix in the air and when the level exceeds the threshold, the system sends an automatic SMS to the numbers saved. Further the sensor actuates the mini suction system fixed at the chimney within 20 seconds of a leakage to suck out the gas until the level falls well below the threshold. As a safety measure, an automatic window opening and alarm feature is also incorporated into the system. The key feature of this design is that the system is provided with a special inverter designed to make the device function effectively even during power failures. In this paper, utilization of sensors in the kitchen area is discussed and this gives the proposed architecture for real time field monitoring with a PIC Micro-controller.

Keywords: nano sensors, global system for mobile communication, GSM, micro controller, inverter

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Authors: Mohammed A. Hajeeh

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Pumping systems are an integral part of water desalination plants, their effective functioning is vital for the operation of a plant. In this research work, the reliability and availability of pressurized pumps in a reverse osmosis desalination plant are studied with the objective of finding configurations that provides optimal performance. Six configurations of a series system with different number of warm and cold standby components were examined. Closed form expressions for the mean time to failure (MTTF) and the long run availability are derived and compared under the assumption that the time between failures and repair times of the primary and standby components are exponentially distributed. Moreover, a cost/ benefit analysis is conducted in order to identify a configuration with the best performance and least cost. It is concluded that configurations with cold standby components are preferable especially when the pumps are of the size.

Keywords: availability, cost/benefit, mean time to failure, pumps

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Authors: Karen Hong

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Smart tactile textiles are a series of textile-based products that incorporates smart embedded technology to be utilized as tactile therapeutic applications for 2 main groups of target users. The first group of users will be children with sensory processing disorder who are suffering from tactile sensory dysfunction. Children with tactile sensory issues may have difficulty tolerating the sensations generated from the touch of certain textures on the fabrics. A series of smart tactile textiles, collectively known as ‘Tactile Toys’ are developed as tactile therapy play objects, exposing children to different types of touch sensations within textiles, enabling them to enjoy tactile experiences together with interactive play which will help them to overcome fear of certain touch sensations. The second group of users will be the elderly or geriatric patients who are suffering from deteriorating sense of touch. One of the common consequences of aging is suffering from deteriorating sense of touch and a decline in motoric function. With the focus in stimulating the sense of touch for this particular group of end users, another series of smart tactile textiles, collectively known as ‘Tactile Aids’ are developed also as tactile therapy. This range of products can help to maintain touch sensitivity and at the same time allowing the elderly to enjoy interactive play to practice their hand-eye coordination and enhancing their motor skills. These smart tactile textile products are being designed and tested out by the end users and have proofed their efficacy as tactile therapy enabling the users to lead a better quality of life.

Keywords: smart textiles, embedded technology, tactile therapy, tactile aids, tactile toys

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Authors: Mounir Belattar

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In this paper the Analysis of voltage waves that propagate along a lossless exponential nonuniform line is presented. For this analysis the parameters of this line are assumed to be varying function of the distance x along the line from the source end. The approach is based on the tow-port networks cascading presentation to derive the ABDC parameters of transmission using Picard-Carson Method which is a powerful method in getting a power series solution for distributed network because it is easy to calculate poles and zeros and solves differential equations such as telegrapher equations by an iterative sequence. So the impedance, admittance voltage and current along the line are expanded as a Taylor series in x/l where l is the total length of the line to obtain at the end, the main transmission line parameters such as voltage response and transmission and reflexion coefficients represented by scattering parameters in frequency domain.

Keywords: ABCD parameters, characteristic impedance exponential nonuniform transmission line, Picard-Carson's method, S parameters, Taylor's series

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Authors: Bazaraliyeva Aigerim Bakytzhanovna, Turgumbayeva Aknur Amanbekovna

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The phytosubstance from garlic was obtained by extraction with liquid carbon dioxide under critical conditions. Methods of processing raw materials are proposed, and the chemical composition of garlic is studied by gas chromatography and mass spectrometry. The garlic extract's composition was determined using gas chromatography (GC) and gas chromatography-mass spectrophotometry (GC-MS). The phytosubstance had 54 constituents. The extract included the following main compounds: Manool (39.56%), Viridifrolol (7%), Podocarpa-1,8,11,13-tetraen-3-one, 14-isopropyl-1,13-dimethoxy- 5,15 percent, (+)-2-Bornanone (4.29%), Thujone (3.49%), Linolic acid ethyl ester (3.41%), and 12-O-Methylcarn.

Keywords: allium sativum, bioactive compounds of garlic, carbon dioxide extraction of garlic, GS-MS method

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Authors: Abu Harera Nadeem, Kingsley Donkor

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Tea is a popular beverage due to its flavour, aroma and antioxidant properties—with the most consumed varieties being green and black tea. Antioxidants in tea can lower the risk of Alzheimer’s and heart disease and obesity. However, these teas contain heavy metals such as Hg, Cd, or Pb, which can cause autoimmune diseases like Graves disease. In this study, 11 heavy metals in various commercial green, black, and oolong tea samples were determined using inductively coupled plasma-mass spectrometry (ICP-MS). Two methods of sample preparation were compared for accuracy and precision, which were microwave-assisted digestion and ultrasonic-assisted extraction. The developed method was further validated by detection limit, precision, and accuracy. Results showed that the proposed method was highly sensitive with detection limits within parts-per-billion levels. Reasonable method accuracy was obtained by spiked experiments. The findings of this study can be used to delve into the link between tea consumption and disease and to provide information for future studies on metal determination in tea.

Keywords: ICP-MS, green tea, black tea, microwave-assisted acid digestion, ultrasound-assisted extraction

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Authors: S. Jurado, E. Pazmino

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Determination of the medial axis of a porous media sample is a non-trivial problem of interest for several disciplines, e.g., hydrology, fluid dynamics, contaminant transport, filtration, oil extraction, etc. However, the computational tools available for researchers are limited and restricted. The primary aim of this work was to develop a series of algorithms to extract porosity, medial axis structure, and pore-throat size distributions from porous media domains. A complementary objective was to provide the algorithms as free computational software available to the academic community comprising researchers and students interested in 3D data processing. The burn algorithm was tested on porous media data obtained from High-Resolution X-Ray Microtomography (HRXMT) and idealized computer-generated domains. The real data and idealized domains were discretized in voxels domains of 550³ elements and binarized to denote solid and void regions to determine porosity. Subsequently, the algorithm identifies the layer of void voxels next to the solid boundaries. An iterative process removes or 'burns' void voxels in sequence of layer by layer until all the void space is characterized. Multiples strategies were tested to optimize the execution time and use of computer memory, i.e., segmentation of the overall domain in subdomains, vectorization of operations, and extraction of single burn layer data during the iterative process. The medial axis determination was conducted identifying regions where burnt layers collide. The final medial axis structure was refined to avoid concave-grain effects and utilized to determine the pore throat size distribution. A graphic user interface software was developed to encompass all these algorithms, including the generation of idealized porous media domains. The software allows input of HRXMT data to calculate porosity, medial axis, and pore-throat size distribution and provide output in tabular and graphical formats. Preliminary tests of the software developed during this study achieved medial axis, pore-throat size distribution and porosity determination of 100³, 320³ and 550³ voxel porous media domains in 2, 22, and 45 minutes, respectively in a personal computer (Intel i7 processor, 16Gb RAM). These results indicate that the software is a practical and accessible tool in postprocessing HRXMT data for the academic community.

Keywords: medial axis, pore-throat distribution, porosity, porous media

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Authors: Yuyan Yi, Jingyi Zheng, Nedret Billor

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Independent component analysis (ICA) is one of the most commonly used blind source separation (BSS) techniques for signal pre-processing, such as noise reduction and feature extraction. The main parameter in the ICA method is the number of independent components (IC). Although there have been several methods for the determination of the number of ICs, it has not been given sufficient attentionto this important parameter. In this study, wereview the mostused methods fordetermining the number of ICs and providetheir advantages and disadvantages. Further, wepropose an improved version of column-wise ICAByBlock method for the determination of the number of ICs.To assess the performance of the proposed method, we compare the column-wise ICAbyBlock with several existing methods through different ICA methods by using simulated and real signal data. Results show that the proposed column-wise ICAbyBlock is an effective and stable method for determining the optimal number of components in ICA. This method is simple, and results can be demonstrated intuitively with good visualizations.

Keywords: independent component analysis, optimal number, column-wise, correlation coefficient, cross-validation, ICAByblock

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Authors: Tomas Menard

Abstract:

The synthesis of output-feedback control law has been investigated by many researchers since the last century. While many results exist for the case of Linear Time Invariant systems whose measurements are continuously available, nowadays, control laws are usually implemented on micro-controller, then the measurements are discrete-time by nature. This fact has to be taken into account explicitly in order to obtain a satisfactory behavior of the closed-loop system. One considers here a general class of systems corresponding to an observability normal form and which is subject to uncertainties in the dynamics and sampling of the output. Indeed, in practice, the modeling of the system is never perfect, this results in unknown uncertainties in the dynamics of the model. We propose here an output feedback algorithm which is based on a linear state feedback and a continuous-discrete time observer. The main feature of the proposed control law is that only discrete-time measurements of the output are needed. Furthermore, it is formally proven that the state of the closed loop system exponentially converges toward the origin despite the unknown uncertainties. Finally, the performances of this control scheme are illustrated with simulations.

Keywords: dynamical systems, output feedback control law, sampling, uncertain systems

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Authors: Jesmin Akter, Chang Hyuk Ahn, Ilho Kim, Fumitake Nishimura, Jaiyeop Lee

Abstract:

This study aimed to compare two pre-treatment and two RNA extraction methods, namely PEG, and Nano bubble, Viral RNA Soil, and Mini Kit, in terms of their efficiency in detecting SARS-CoV-2 and PMMoV in influent and 1st sedimentation samples from a wastewater treatment plant. The extracted RNA samples were quantified and evaluated for purity, yield, and integrity. The results indicated that the nanobubble PEG method provided the highest yield of RNA, while the QIAamp Viral RNA Mini Kit produced the purest RNA samples. In terms of sensitivity and specificity, all these methods were able to detect SARS-CoV-2 and PMMoV in both influent and 1st sedimentation samples. However, the nanobubble PEG method showed slightly higher sensitivity compared to the other methods. These findings suggest that the choice of RNA extraction method should depend on the downstream application and the quality of the RNA required. The study also highlights the potential of wastewater-based epidemiology as an effective and non-invasive method for monitoring the spread of infectious diseases in a community.

Keywords: influent, PMMoV, SARS-CoV-2, wastewater based epidemiology

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Authors: Ergün Şakalar, Şeyma Özçirak Ergün, Emrah Yalazi̇, Emine Altinkaya, Cengiz Ataşoğlu

Abstract:

In recent years, food allergies which cause serious health problems affect to public health around the world. Foodstuffs which contain allergens are either intentionally used as ingredients or are encased as contaminant in food products. The prevalence of clinical allergy to peanuts and nuts is estimated at about 0.4%-1.1% of the adult population, representing the allergy to pistachio the 7% of the cases of tree nut causing allergic reactions. In order to protect public health and enforce the legislation, methods for sensitive analysis of pistachio and peanut contents in food are required. Pea, pistachio and peanut are used together, to reduce the cost in food production such as baklava, snack foods.DNA technology-based methods in food analysis are well-established and well-roundedtools for species differentiation, allergen detection. Especially, the probe-based TaqMan real-time PCR assay can amplify target DNA with efficiency, specificity, and sensitivity.In this study, pistachio, peanut and pea were finely ground and three separate series of triplet mixtures containing 0.1, 1, 10, 100, 1000, 10,000 and 100,000 mg kg-1 of each sample were prepared for each series, to a final weight of 100 g. DNA from reference samples and industrial products was successfully extracted with the GIDAGEN® Multi-Fast DNA Isolation Kit. TaqMan probes were designed for triplex determination of ITS, Ara h 3 and pea lectin genes which are specific regions for identification pistachio, peanut and pea, respectively.The real-time PCR as quantitative detected pistachio, peanut and pea in these mixtures down to the lowest investigated level of 0.1, 0.1 and 1 mg kg-1, respectively. Also, the methods reported here are capable of detecting of as little as 0.001% level of peanut DNA, 0,000001% level of pistachio DNA and 0.000001% level of pea DNA. We accomplish that the quantitative triplex real-time PCR method developed in this study canbe applied to detect pistachio, peanut and peatraces for three allergens at once in commercial food products.

Keywords: allergens, DNA, real-time PCR, TaqMan probe

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Authors: Simon Peter Wafula, Ziporah Nakabazzi Kitooke

Abstract:

Graphene is a carbon allotrope made of a two-dimensional shape. This material has got a number of materials researchers’ interest due to its properties that are special compared to ordinary material. Graphene is thought to enhance a number of material properties in the manufacturing, energy, and construction industries. Many studies consider graphene to be a wonder material, just like plastic in the 21st century. This shows how much should be invested in graphene research. This review highlights the status of graphene extracted from various biomass sources together with their appropriate extraction techniques, including the pretreatment methods for a better product. The functional groups and structure of graphene extracted using several common methods of synthesis are in this paper as well. The review explores methods like chemical vapor deposition (CVD), hydrothermal, chemical exfoliation method, liquid exfoliation, and Hummers. Comparative analysis of the various extraction techniques gives an insight into each of their advantages, challenges, and potential scalability. The review also highlights the pretreatment process for biomass before carbonation for better quality of bio-graphene. The various graphene modes, as well as their applications, are in this study. Recommendations for future research for improving the efficiency and sustainability of bio-graphene are highlighted.

Keywords: exfoliation, nanomaterials, biochar, large-scale, two-dimension

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