Search results for: matrix analytic methods

Authors: Marcel Rojahn, Edzard Weber, Norbert Gronau

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Terminology is a critical instrument for each researcher. Different terminologies for the same research object may arise in different research communities. By this inconsistency, many synergistic effects get lost. Theories and models will be more understandable and reusable if a common terminology is applied. This paper examines the terminological (in) consistency for the research field of job-shop scheduling through a literature review. There is an enormous variety in the choice of terms and mathematical notation for the same concept. The comparability, reusability, and combinability of scheduling methods are unnecessarily hampered by the arbitrary use of homonyms and synonyms. The acceptance in the community of used variables and notation forms is shown by means of a compliance quotient. This is proven by the evaluation of 240 scientific publications on planning methods.

Keywords: job-shop scheduling, terminology, notation, standardization

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Authors: Navneet Singh Deora, Aastha Deswal, H. N. Mishra

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Pasta is one of the most widely consumed food products around the world. Rapid determination of the moisture content in pasta will assist food processors to provide online quality control of pasta during large scale production. Rapid Fourier transform near-infrared method (FT-NIR) was developed for determining moisture content in pasta. A calibration set of 150 samples, a validation set of 30 samples and a prediction set of 25 samples of pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 4,000-12,000 cm-1 spectral range. Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 10 to 15 percent (w.b) of the pasta. The prediction models based on partial least squares (PLS) regression, were developed in the near-infrared. Conventional criteria such as the R2, the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre-processing (vector normalization, minimum-maximum normalization and multiplicative scatter correction) methods. Spectra of pasta sample were treated with different mathematic pre-treatments before being used to build models between the spectral information and moisture content. The moisture content in pasta predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (R2 = 0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of moisture content in pasta. The best calibration model was developed with min-max normalization (MMN) spectral pre-processing (R2 = 0.9775). The MMN pre-processing method was found most suitable and the maximum coefficient of determination (R2) value of 0.9875 was obtained for the calibration model developed.

Keywords: FT-NIR, pasta, moisture determination, food engineering

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Authors: Ali Berkan Ural

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This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems.

Keywords: computer aided diagnosis, breast cancer, region growing, segmentation, deep learning

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Authors: Khaled M. Alhawiti

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This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition.

Keywords: speech recognition, acoustic phonetic, artificial intelligence, hidden markov models (HMM), statistical models of speech recognition, human machine performance

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Authors: Khenenou Tarek, Benzaoui Hassina, Melizi Mohamed

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The diagnosis requires a background of current knowledge in the field and also complementary means in which the laboratory occupies the central place for a better investigation. A correct diagnosis allows to establish the most appropriate treatment as soon as possible and avoids both the economic losses associated with mortality and growth retardation often observed in poultry furthermore it may reduce the high cost of treatment. Epedemiologic survey, hematologic and histopathologic study’s are three aspects of diagnosis heavily used in both human and veterinary pathology and the advanced researches in human medicine would be exploited to be applied in veterinary medicine with given modification .Whereas, the diagnostic methods in the east of Algeria are limited to the clinical signs and necropsy finding. Therefore, the diagnosis is based simply on the success or the failure of the therapeutic methods (therapeutic diagnosis).

Keywords: chicken, diagnosis, hematology, histopathology

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Authors: Ana Staneva, Vessela Stoimenova

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A parametric estimation of the MBP with Power Series offspring distribution family is considered in this paper. The MLE for the parameters is obtained in the case when the observable data are incomplete and consist only with the generation sizes of the family tree of MBP. The parameter estimation is calculated by using the Monte Carlo EM algorithm. The estimation for the posterior distribution and for the offspring distribution parameters are calculated by using the Bayesian approach and the Gibbs sampler. The article proposes various examples with bivariate branching processes together with computational results, simulation and an implementation using R.

Keywords: Bayesian, branching processes, EM algorithm, Gibbs sampler, Monte Carlo methods, statistical estimation

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Authors: Maha Sliti, Walid Abdallah, Noureddine Boudriga

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In the frame of this work, we present an optical multicasting approach based on optical code-words. Our approach associates, in the edge node, an optical code-word to a group multicast address. In the core node, a set of tunable decoders are used to send a traffic data to multiple destinations based on the received code-word. The use of code-words, which correspond to the combination of an input port and a set of output ports, allows the implementation of an optical switching matrix. At the reception of a burst, it will be delayed in an optical memory. And, the received optical code-word is split to a set of tunable optical decoders. When it matches a configured code-word, the delayed burst is switched to a set of output ports.

Keywords: optical multicast, optical burst switching networks, optical code-words, tunable decoder, virtual optical memory

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Authors: Tai-Ping Chang

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In the present study, the effects on chaotic motion of single-walled carbon nanotube (SWCNT) due to the linear and nonlinear damping are investigated. By using the Hamilton’s principle, the nonlinear governing equation of the single-walled carbon nanotube embedded in a matrix is derived. The Galerkin’s method is adopted to simplify the integro-partial differential equation into a nonlinear dimensionless governing equation for the SWCNT, which turns out to be a forced Duffing equation. The variations of the Lyapunov exponents of the SWCNT with damping and harmonic forcing amplitudes are investigated. Based on the computations of the top Lyapunov exponent, it is concluded that the chaotic motion of the SWCNT occurs when the amplitude of the periodic excitation exceeds certain value, besides, the chaotic motion of the SWCNT occurs with small linear damping and tiny nonlinear damping.

Keywords: chaotic motion, damping, Lyapunov exponents, single-walled carbon nanotube

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Authors: Meva Apaydin, Fatih Apaydin

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Role of education in the development of society is great. Teaching and training started with the beginning of the history and different methods and techniques which have been applied as the time passed and changed everything with the aim of raising the level of learning. In addition to the traditional teaching methods, technology has been used in recent years. With the beginning of the use of internet in education, some problems which could not be soluted till that time has been dealt and it is inferred that it is possible to educate the learners by using contemporary methods as well as traditional methods. As an advantage of technological developments, distance education is a system which paves the way for the students to be educated individually wherever and whenever they like without the needs of physical school environment. Distance education has become prevalent because of the physical inadequacies in education institutions, as a result; disadvantageous circumstances such as social complexities, individual differences and especially geographical distance disappear. What’s more, the high-speed of the feedbacks between teachers and learners, improvement in student motivation because there is no limitation of time, low-cost, the objective measuring and evaluation are on foreground. In spite of the fact that there is teaching beneficences in distance education, there are also limitations. Some of the most important problems are that : Some problems which are highly possible to come across may not be solved in time, lack of eye-contact between the teacher and the learner, so trust-worthy feedback cannot be got or the problems stemming from the inadequate technological background are merely some of them. Courses are conducted via distance education in many departments of the universities in our country. In recent years, giving lectures such as Turkish Language, English, and History in the first grades of the academic departments in the universities is an application which is constantly becoming prevalent. In this study, the application of Turkish Language course via distance education system by analyzing advantages and disadvantages of the distance education system which is based on internet.

Keywords: distance education, Turkish language, motivation, benefits

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Authors: Shael Brown, Reza Farivar

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Persistence diagrams capture valuable topological features of datasets that other methods cannot uncover. Still, their adoption in data pipelines has been limited due to the lack of publicly available tools in R (and python) for analyzing groups of them with machine learning and statistical inference. In an easy-to-use and scalable R package called TDApplied, we implement several applied analysis methods tailored to groups of persistence diagrams. The two main contributions of our package are comprehensiveness (most functions do not have implementations elsewhere) and speed (shown through benchmarking against other R packages). We demonstrate applications of the tools on simulated data to illustrate how easily practical analyses of any dataset can be enhanced with topological information.

Keywords: machine learning, persistence diagrams, R, statistical inference

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Authors: Lok Bahadur Shrestha, Narayan Raj Bhattarai, Basudha Khanal

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Introduction: Coagulase-negative staphylococci (CoNS) are the normal commensal of human skin and mucous membranes. The study was carried out to study the prevalence of CoNS among clinical isolates, to characterize them up to species level and to compare the three conventional methods for detection of biofilm formation. Objectives: to characterize the clinically significant coagulase-negative staphylococci up to species level, to compare the three phenotypic methods for the detection of biofilm formation and to study the antimicrobial susceptibility pattern of the isolates. Methods: CoNS isolates were obtained from various clinical samples during the period of 1 year. Characterization up to species level was done using biochemical test and study of biofilm formation was done by tube adherence, congo red agar, and tissue culture plate method. Results: Among 71 CoNS isolates, seven species were identified. S. epidermidis was the most common species followed by S. saprophyticus, S. haemolyticus. Antimicrobial susceptibility pattern of CoNS documented resistance of 90% to ampicillin. Resistance to cefoxitin and ceftriaxone was observed in 55% of the isolates. We detected biofilm formation in 71.8% of isolates. The sensitivity of tube adherence method was 82% while that of congo red agar method was 78%. Conclusion: Among 71 CoNS isolated, S. epidermidis was the most common isolates followed by S. saprophyticus and S. haemolyticus. Biofilm formation was detected in 71.8% of the isolates. All of the methods were effective at detecting biofilm-producing CoNS strains. Biofilm former strains are more resistant to antibiotics as compared to biofilm non-formers.

Keywords: CoNS, congo red agar, bloodstream infections, foreign body-related infections, tissue culture plate

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Authors: Ehsan Khademi, Mahdi Jalili

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Despite the special features of bipartite networks, they are common in many systems. Real-world bipartite networks may show community structure, similar to what one can find in one-mode networks. However, the interpretation of the community structure in bipartite networks is different as compared to one-mode networks. In this manuscript, we compare a number of available methods that are frequently used to discover community structure of bipartite networks. These networks are categorized into two broad classes. One class is the methods that, first, transfer the network into a one-mode network, and then apply community detection algorithms. The other class is the algorithms that have been developed specifically for bipartite networks. These algorithms are applied on a model network with prescribed community structure.

Keywords: community detection, bipartite networks, co-clustering, modularity, network projection, complex networks

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Authors: Christina Adly, Meena Abdelmeseeh, Tamer Basha

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This paper proposes a system for hand movement recognition using multichannel surface EMG(sEMG) signals obtained from 40 subjects using 40 different exercises, which are available on the Ninapro(Non-Invasive Adaptive Prosthetics) database. First, we applied processing methods to the raw sEMG signals to convert them to their amplitudes. Second, we used deep learning methods to solve our problem by passing the preprocessed signals to Fully connected neural networks(FCNN) and recurrent neural networks(RNN) with Long Short Term Memory(LSTM). Using intrasubject evaluation, The accuracy using the FCNN is 72%, with a processing time for training around 76 minutes, and for RNN's accuracy is 79.9%, with 8 minutes and 22 seconds processing time. Third, we applied some postprocessing methods to improve the accuracy, like majority voting(MV) and Movement Error Rate(MER). The accuracy after applying MV is 75% and 86% for FCNN and RNN, respectively. The MER value has an inverse relationship with the prediction delay while varying the window length for measuring the MV. The different part uses the RNN with the intersubject evaluation. The experimental results showed that to get a good accuracy for testing with reasonable processing time, we should use around 20 subjects.

Keywords: hand movement recognition, recurrent neural network, movement error rate, intrasubject evaluation, intersubject evaluation

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Authors: Parastoo Kazemiyan

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Over the past 80 years, in compliance with the advent of modernity in Iran and Turkey, the presence of women in economic and social arenas has creates serious challenges in the capacity of urban spaces to respond to their presence and transport because urban spaces up until then were based on masculine criteria and therefore, women could use such spaces in the company of their fathers or husbands. However, as modernity expanded by Reza Shah and Ataturk, women found the opportunity to work and be present in urban spaces alongside men and their presence in economic and social domains resulted in their presence in these spaces in the early and late hours of the day. Therefore, the city had to be transformed in structural, social, and environmental terms to accommodate women's activities and presence in various urban arenas, which was a huge step in transition from a masculine man-based culture to an all-inclusive human-based culture in these two countries. However, the optimization of urban space was subject to political changes in the two countries, leading to significant differences in designing urban spaces in Tehran and Istanbul. What shows the importance and novelty of the present study lie in the differences in urban planning and optimization in the two capital cities, which gave rise to different outcomes in desirability and quality of living in these two capital cities. Due to the importance of the topic, one of the most significant factors in desirability and acceptability of urban space for women was examined using a descriptive-analytic method based on qualitative methodology in Tehran and Istanbul. The results showed that the infrastructural factors in Istanbul, including safety of access, variety, and number of public transport modes, transparency, and supervision over public spaces have provided women with a safer and more constant presence compared to Tehran. It seems that challenges involved in providing access to urban spaces in Tehran in terms of infrastructure and function have made Tehran unable to respond to the most basic needs of its female citizens.

Keywords: gender differences, urban space security, access to transportation systems, women's challenges

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Authors: Woledegebrieal Aregay

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Introduction: Family planning is defined as the ability of individuals and couples to anticipate and attain their desired number of children and the spacing and timing of their births. It is part of a strategy to reduce poverty, maternal, infant and child mortality; empowers women by lightening the burden of excessive childbearing. Family planning is achieved through the use of different contraceptive methods among which the most effective method is modern family planning methods like Long-Acting Reversible Contraceptive (LARCs) which are IUCD and Implant and these methods have multiple advantages over other reversible methods. Most importantly, once in place, they do not require maintenance and their duration of action is long, ranging from 3 to10 years. Methods: An institutional-based cross-sectional study was conducted in Gondar town among female college students from April-May. A simple random sampling technique was employed to recruit a total of 1166 study subjects. Descriptive variables were computed for all predictors & dependent variables. The presence of an association between covariates & LARC use was observed by two tables’ findings using the chi-square test. Bivariate logistic regression was conducted to identify all possible factors affecting LARC utilization & its crude Odds Ratio, 95% Confidence Interval (CI) & P-value was observed. A multivariable logistic regression model was developed to control possible confounding variables. Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) &P-values will be computed to identify significantly associated factors (P < 0.05) with LARC utilization. Result: Utilization of LARCs was 20.4%, the most common is Implant 86(96.5%), and followed by Intra-Uterine Contraceptive Device (IUCD) 3(3.5%). The result of the multivariate analysis revealed that the significant association of marital status of the respondent on utilization of LARC [AOR 3.965(2.051-7.665)], discussion of the respondent about LARC utilization with the husband/boyfriend [AOR 2.198(1.191-4.058)], and attitude of the respondent on implant was found to be associated [AOR 0.365(0.143-0.933)].Conclusion: The level of knowledge and attitude in this study was not satisfactory, the utilization of long-acting reversible contraceptives among college students was relatively satisfactory but if the knowledge and attitude of the participant has improved the prevalence of LARC were increased.

Keywords: utilization, long-acting reversible contraceptive, Ethiopia, Gondar

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Authors: Zainab Shaikh, Yusuf Miyanji

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Pilomatrixoma is a benign neoplasm of the hair follicle matrix that is not commonly diagnosed in general practice. This is a case report of a 9-year-old boy who presented with a one-year history of a 19mm x 11 mm swelling in the left infra-orbital region. This was previously undiagnosed in Spain, where the patient resided at the time of initial presentation, due to the language barrier the patient’s family encountered. An ultrasound and magnetic resonance imaging gave useful information regarding surrounding structures for complete tumor excision and indicated that the risk of facial nerve palsy is low. The lesion was surgically excised and a definitive diagnosis was made after histopathology. Pilomatrixoma, although not rare in its occurrence, is rarely this large at the time of excision due to early presentation. This case highlights the importance of including pilomatrixoma in the differential diagnosis of dermal and subcutaneous lesions in the head and neck region, as it is often misdiagnosed due to the lack of awareness of its clinical presentation.

Keywords: pilomatrixoma, swelling, infra-orbital, facial swelling

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Authors: A. Maghari, V. M. Maleki

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In this paper, we present a quantum statistical mechanical formulation from our recently analytical expressions for partial-wave transition matrix of a three-particle system. We report the quantum reactive cross sections for three-body scattering processes 1 + (2,3)-> 1 + (2,3) as well as recombination 1 + (2,3) -> 2 + (3,1) between one atom and a weakly-bound dimer. The analytical expressions of three-particle transition matrices and their corresponding cross-sections were obtained from the three-dimensional Faddeev equations subjected to the rank-two non-local separable potentials of the generalized Yamaguchi form. The equilibrium quantum statistical mechanical properties such partition function and equation of state as well as non-equilibrium quantum statistical properties such as transport cross-sections and their corresponding transport collision integrals were formulated analytically. This leads to obtain the transport properties, such as viscosity and diffusion coefficient of a moderate dense gas.

Keywords: statistical mechanics, nonlocal separable potential, three-body interaction, faddeev equations

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Authors: Emine Yılmaz, Serap Fındık

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Baker’s yeast industry uses molasses as a raw material. Molasses is end product of sugar industry. Wastewater from molasses processing presents large amount of coloured substances that give dark brown color and high organic load to the effluents. The main coloured compounds are known as melanoidins. Melanoidins are product of Maillard reaction between amino acid and carbonyl groups in molasses. Dark colour prevents sunlight penetration and reduces photosynthetic activity and dissolved oxygen level of surface waters. Various methods like biological processes (aerobic and anaerobic), ozonation, wet air oxidation, coagulation/flocculation are used to treatment of baker’s yeast effluent. Before effluent is discharged adequate treatment is imperative. In addition to this, increasingly stringent environmental regulations are forcing distilleries to improve existing treatment and also to find alternative methods of effluent management or combination of treatment methods. Sonochemical oxidation is one of the alternative methods. Sonochemical oxidation employs ultrasound resulting in cavitation phenomena. In this study, decolorization of baker’s yeast effluent was investigated by using ultrasound. Baker’s yeast effluent was supplied from a factory which is located in the north of Turkey. An ultrasonic homogenizator used for this study. Its operating frequency is 20 kHz. TiO2-ZnO catalyst has been used as sonocatalyst. The effects of molar proportion of TiO2-ZnO, calcination temperature and time, catalyst amount were investigated on the decolorization of baker’s yeast effluent. The results showed that prepared composite TiO2-ZnO with 4:1 molar proportion treated at 700°C for 90 min provides better result. Initial decolorization rate at 15 min is 3% without catalyst, 14,5% with catalyst treated at 700°C for 90 min respectively.

Keywords: baker’s yeast effluent, decolorization, sonocatalyst, ultrasound

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Authors: Miao Li, Mengyuan Xu

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As a low-carbon travel mode, bicycling has drawn increasing political interest in the contemporary Chinese urban context, and the public sharing bikes have become the most popular ways of bike usage in China now. This research aims to explore the spatial-temporal relationship between sharing bike usage and different characteristics of the urban street built environment. In the research, street segments were used as the analytic unit of the street built environment defined by street intersections. The sharing bike usage data in the research include a total of 2.64 million samples that are the entire sharing bike distribution data recorded in two days in 2018 within a neighborhood of 185.4 hectares in the city of Wuhan, China. And these data are assigned to the 97 urban street segments in this area based on their geographic location. The built environment variables used in this research are categorized into three sections: 1) street design characteristics, such as street width, street greenery, types of bicycle lanes; 2) condition of other public transportation, such as the availability of metro station; 3) Street function characteristics that are described by the categories and density of the point of interest (POI) along the segments. Spatial Lag Models (SLM) were used in order to reveal the relationships of specific urban streets built environment characteristics and the likelihood of sharing bicycling usage in whole and different periods a day. The results show: 1) there is spatial autocorrelation among sharing bicycling usage of urban streets in case area in general, non-working day, working day and each period of a day, which presents a clustering pattern in the street space; 2) a statistically strong association between bike sharing usage and several different built environment characteristics such as POI density, types of bicycle lanes and street width; 3) the pattern that bike sharing usage is influenced by built environment characteristics depends on the period within a day. These findings could be useful for policymakers and urban designers to better understand the factors affecting bike sharing system and thus propose guidance and strategy for urban street planning and design in order to promote the use of sharing bikes.

Keywords: big data, sharing bike usage, spatial statistics, urban street built environment

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Authors: Hy Dang, Bo Mei

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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: classification, machine learning, time representation, stock prediction

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Authors: Rogelio Portillo Vélez, Eduardo Vázquez-Santacruz, Mariano Gamboa-Zúñiga

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In this paper a new mechanical architecture design of a multi functional robot bed, is presented. The importance of this design relies on the fact that in next years the need of assistive devices development will increase in such way that elderly patients will use this kind of devices. This mechanical design implies following specific mechanisms which attend Mexican hospital requirements. This design is the base of next step of this kind of development given that it shows all technical details of the mechanical systems which are needed in order to construct the bed. This is first hospital bed design which could responds to the Latin America hospital requirements. We have obtained these hospital requirements using our diagnosis methodology [14]. From these results we have designed the mechanical system. This is the mechanical base of the hospital robotic bed which is being developed in our robotics laboratory. It will be useful in different hospital environments for elderly and disabled patients.

Keywords: assistive robotics, methodology, feasibility analysis, robotics, operational feasibility, assistive technology, viability analysis matrix, social impact

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Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza

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Authors: Ghada Badr, Arwa Alturki

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The biological function of an RNA molecule depends on its structure. The objective of the alignment is finding the homology between two or more RNA secondary structures. Knowing the common functionalities between two RNA structures allows a better understanding and a discovery of other relationships between them. Besides, identifying non-coding RNAs -that is not translated into a protein- is a popular application in which RNA structural alignment is the first step A few methods for RNA structure-to-structure alignment have been developed. Most of these methods are partial structure-to-structure, sequence-to-structure, or structure-to-sequence alignment. Less attention is given in the literature to the use of efficient RNA structure representation and the structure-to-structure alignment methods are lacking. In this paper, we introduce an O(N2) Component-based Pairwise RNA Structure Alignment (CompPSA) algorithm, where structures are given as a component-based representation and where N is the maximum number of components in the two structures. The proposed algorithm compares the two RNA secondary structures based on their weighted component features rather than on their base-pair details. Extensive experiments are conducted illustrating the efficiency of the CompPSA algorithm when compared to other approaches and on different real and simulated datasets. The CompPSA algorithm shows an accurate similarity measure between components. The algorithm gives the flexibility for the user to align the two RNA structures based on their weighted features (position, full length, and/or stem length). Moreover, the algorithm proves scalability and efficiency in time and memory performance.

Keywords: alignment, RNA secondary structure, pairwise, component-based, data mining

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Authors: Anil Saini, Jatinder Kumar Ratan

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Now a day, the key issue for the scientific community is to devise the innovative technologies for sustainable control of urban pollution. In urban cities, a large surface area of the masonry structures, buildings, and pavements is exposed to the open environment, which may be utilized for the control of air pollution, if it is built from the photocatalytically active cement-based constructional materials such as concrete, mortars, paints, and blocks, etc. The photocatalytically active cement is formulated by incorporating a photocatalyst in the cement matrix, and such cement is generally known as self-cleaning cement In the literature, self-cleaning cement has been synthesized by incorporating nanosized-TiO₂ (n-TiO₂) as a photocatalyst in the formulation of the cement. However, the utilization of n-TiO₂ for the formulation of self-cleaning cement has the drawbacks of nano-toxicity, higher cost, and agglomeration as far as the commercial production and applications are concerned. The use of microsized-TiO₂ (m-TiO₂) in place of n-TiO₂ for the commercial manufacture of self-cleaning cement could avoid the above-mentioned problems. However, m-TiO₂ is less photocatalytically active as compared to n- TiO₂ due to smaller surface area, higher band gap, and increased recombination rate. As such, the use of m-TiO₂ in the formulation of self-cleaning cement may lead to a reduction in photocatalytic activity, thus, reducing the self-cleaning, depolluting, and antimicrobial abilities of the resultant cement material. So improvement in the photoactivity of m-TiO₂ based self-cleaning cement is the key issue for its practical applications in the present scenario. The current work proposes the use of surface-fluorinated m-TiO₂ for the formulation of self-cleaning cement to enhance its photocatalytic activity. The calcined dolomite, a constructional material, has also been utilized as co-adsorbent along with the surface-fluorinated m-TiO₂ in the formulation of self-cleaning cement to enhance the photocatalytic performance. The surface-fluorinated m-TiO₂, calcined dolomite, and the formulated self-cleaning cement were characterized using diffuse reflectance spectroscopy (DRS), X-ray diffraction analysis (XRD), field emission-scanning electron microscopy (FE-SEM), energy dispersive x-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), BET (Brunauer–Emmett–Teller) surface area, and energy dispersive X-ray fluorescence spectrometry (EDXRF). The self-cleaning property of the as-prepared self-cleaning cement was evaluated using the methylene blue (MB) test. The depolluting ability of the formulated self-cleaning cement was assessed through a continuous NOX removal test. The antimicrobial activity of the self-cleaning cement was appraised using the method of the zone of inhibition. The as-prepared self-cleaning cement obtained by uniform mixing of 87% clinker, 10% calcined dolomite, and 3% surface-fluorinated m-TiO₂ showed a remarkable self-cleaning property by providing 53.9% degradation of the coated MB dye. The self-cleaning cement also depicted a noteworthy depolluting ability by removing 5.5% of NOx from the air. The inactivation of B. subtiltis bacteria in the presence of light confirmed the significant antimicrobial property of the formulated self-cleaning cement. The self-cleaning, depolluting, and antimicrobial results are attributed to the synergetic effect of surface-fluorinated m-TiO₂ and calcined dolomite in the cement matrix. The present study opens an idea and route for further research for acile and economical formulation of self-cleaning cement.

Keywords: microsized-titanium dioxide (m-TiO₂), self-cleaning cement, photocatalysis, surface-fluorination

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Authors: Regina Rekuviene, Vykintas Samaitis, Liudas Mažeika, Audrius Jankauskas, Virginija Jankauskaitė, Laura Gegeckienė, Abdolali Sadaghiani, Shaghayegh Saeidiharzand

Abstract:

Icing brings significant damage to aviation and renewable energy installations. Air-conditioning, refrigeration, wind turbine blades, airplane and helicopter blades often suffer from icing phenomena, which cause severe energy losses and impair aerodynamic performance. The icing process is a complex phenomenon with many different causes and types. Icing mechanisms, distributions, and patterns are still relevant to research topics. The adhesion strength between ice and surfaces differs in different icing environments. This makes the task of anti-icing very challenging. The techniques for various icing environments must satisfy different demands and requirements (e.g., efficient, lightweight, low power consumption, low maintenance and manufacturing costs, reliable operation). It is noticeable that most methods are oriented toward a particular sector and adapting them to or suggesting them for other areas is quite problematic. These methods often use various technologies and have different specifications, sometimes with no clear indication of their efficiency. There are two major groups of anti-icing methods: passive and active. Active techniques have high efficiency but, at the same time, quite high energy consumption and require intervention in the structure’s design. It’s noticeable that vast majority of these methods require specific knowledge and personnel skills. The main effect of passive methods (ice-phobic, superhydrophobic surfaces) is to delay ice formation and growth or reduce the adhesion strength between the ice and the surface. These methods are time-consuming and depend on forecasting. They can be applied on small surfaces only for specific targets, and most are non-biodegradable (except for anti-freezing proteins). There is some quite promising information on ultrasonic ice mitigation methods that employ UGW (Ultrasonic Guided Wave). These methods are have the characteristics of low energy consumption, low cost, lightweight, and easy replacement and maintenance. However, fundamental knowledge of ultrasonic de-icing methodology is still limited. The objective of this work was to identify the ice formation processes and its progress by employing ultrasonic guided wave technique. Throughout this research, the universal set-up for acoustic measurement of ice formation in a real condition (temperature range from +240 C to -230 C) was developed. Ultrasonic measurements were performed by using high frequency 5 MHz transducers in a pitch-catch configuration. The selection of wave modes suitable for detection of ice formation phenomenon on copper metal surface was performed. Interaction between the selected wave modes and ice formation processes was investigated. It was found that selected wave modes are sensitive to temperature changes. It was demonstrated that proposed ultrasonic technique could be successfully used for the detection of ice layer formation on a metal surface.

Keywords: ice formation processes, ultrasonic GW, detection of ice formation, ultrasonic testing

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Authors: Elena Barbaro, Elena Gregoris, Rossano Piazza, Boris Mifka, Tatjana Ivošević, Ivo Orlić, Ana Alebić-Juretić, Andrea Gambaro, Daniele Contini

Abstract:

The aim of the POSEIDON project is to quantify the relative contribution of maritime traffic and harbor activities to atmospheric pollutants concentration in four port-cities of the Adriatic Sea. This study focuses on the harbors of Venice and Rijeka. In order to investigate the main pollution sources, emission inventories were used as input for receptor models: PMF (positive matrix factorization) and PCA (principal components analysis); moreover source identification was also conducted using PAHs diagnostic ratios. The ship traffic impact was quantified: i) on gaseous and particulate PAHs, collected using a new method which consisted in a double simultaneous sampling, in different wind sectors; ii) applying PMF to data of metals, PAHs and ions in PM10; iii) using the vanadium concentration according to the Agrawal methodology.

Keywords: ship traffic, PMF, harbor, POSEIDON

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Authors: Reshma Jolly, Mohammad Shakir, Mohammad Shoeb Khan, Noor E. Iram

Abstract:

A nanocomposite system integrating hydroxyapatite, chitin and starch (n-HA/CT/ST) has been synthesized via co-precipitation approach at room temperature, addressing the issues of biocompatibility, mechanical strength and cytotoxicity required for Bone tissue engineering. The interactions, crystallite size and surface morphology against n-HA/CT (nano-hydroxyapatite/chitin) nanocomposite have been obtained by correlating and comparing the results of FTIR, SEM, TEM and XRD. The comparative study of the bioactivity of n-HA/CT and n-HA/CT/ST nanocomposites revealed that the incorporation of starch as templating agent improved these properties in n-HA/CT/ST nanocomposite. The rise in thermal stability in n-HA/CT/ST nanocomposite as compared to n-HA/CT has been observed by comparing the TGA results. The comparison of SEM images of both the scaffolds indicated that the addition of ST influenced the surface morphology of n-HA/CT scaffold which appeared to be rougher and porous. The MTT assay on murine fibroblast L929 cells and in-vitro bioactivity of n-HA/CT/ST matrix referred superior non-toxic property of n-HA/CT/ST nanocomposite and higher possibility of osteo-integration in-vivo, respectively.

Keywords: bioactive, chitin, hyroxyapatite, nanocomposite

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Authors: F. Brunke, L. Waalkes, C. Siemers

Abstract:

Due to reduced stiffness, research on second generation titanium alloys for implant applications, like the metastable β-titanium alloy Ti-15Mo, become more and more important in the recent years. The machinability of these alloys is generally poor leading to problems during implant production and comparably large production costs. Therefore, in the present study, Ti 15Mo was alloyed with 0.8 wt.-% of the rare earth metals lanthanum (Ti-15Mo+0.8La) and neodymium (Ti-15Mo+0.8Nd) to improve its machinability. Their microstructure consisted of a titanium matrix and micrometer-size particles of the rare earth metals and two of their oxides. The particles stabilized the micro structure as grain growth was minimized. As especially the ductility might be affected by the precipitates, the behavior of Ti-15Mo+0.8La and Ti-15Mo+0.8Nd was investigated during static and dynamic deformation at elevated temperature to develop a processing route. The resulting mechanical properties (static strength and ductility) were similar in all investigated alloys.

Keywords: Ti 15Mo, titanium alloys, rare earth metals, free machining alloy

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Authors: Nicole M. Martino

Abstract:

Reinforced concrete bridge deck condition assessments primarily use visual inspection methods, where an inspector looks for and records locations of cracks, potholes, efflorescence and other signs of probable deterioration. Sounding is another technique used to diagnose the condition of a bridge deck, however this method listens for damage within the subsurface as the surface is struck with a hammer or chain. Even though extensive procedures are in place for using these inspection techniques, neither one provides the inspector with a comprehensive understanding of the internal condition of a bridge deck – the location where damage originates from.  In order to make accurate estimates of repair locations and quantities, in addition to allocating the necessary funding, a total understanding of the deck’s deteriorated state is key. The research presented in this paper collected infrared thermography and ground penetrating radar data from reinforced concrete bridge decks without an asphalt overlay. These decks were of various ages and their condition varied from brand new, to in need of replacement. The goals of this work were to first verify that these nondestructive evaluation methods could identify similar areas of healthy and damaged concrete, and then to see if combining the results of both methods would provide a higher confidence than if the condition assessment was completed using only one method. The results from each method were presented as plan view color contour plots. The results from one of the decks assessed as a part of this research, including these plan view plots, are presented in this paper. Furthermore, in order to answer the interest of transportation agencies throughout the United States, this research developed a step-by-step guide which demonstrates how to collect and assess a bridge deck using these nondestructive evaluation methods. This guide addresses setup procedures on the deck during the day of data collection, system setups and settings for different bridge decks, data post-processing for each method, and data visualization and quantification.

Keywords: bridge deck deterioration, ground penetrating radar, infrared thermography, NDT of bridge decks

Procedia PDF Downloads 154

Authors: Danny Barash

Abstract:

Riboswitches are RNA genetic control elements that were originally discovered in bacteria and provide a unique mechanism of gene regulation. They work without the participation of proteins and are believed to represent ancient regulatory systems in the evolutionary timescale. One of the biggest challenges in riboswitch research is that many are found in prokaryotes but only a small percentage of known riboswitches have been found in certain eukaryotic organisms. The few examples of eukaryotic riboswitches were identified using sequence-based bioinformatics search methods that include some slight structural considerations. These pattern-matching methods were the first ones to be applied for the purpose of riboswitch detection and they can also be programmed very efficiently using a data structure called affix arrays, making them suitable for genome-wide searches of riboswitch patterns. However, they are limited by their ability to detect harder to find riboswitches that deviate from the known patterns. Several methods have been developed since then to tackle this problem. The most commonly used by practitioners is Infernal that relies on Hidden Markov Models (HMMs) and Covariance Models (CMs). Profile Hidden Markov Models were also carried out in the pHMM Riboswitch Scanner web application, independently from Infernal. Other computational approaches that have been developed include RMDetect by the use of 3D structural modules and RNAbor that utilizes Boltzmann probability of structural neighbors. We have tried to incorporate more sophisticated secondary structure considerations based on RNA folding prediction using several strategies. The first idea was to utilize window-based methods in conjunction with folding predictions by energy minimization. The moving window approach is heavily geared towards secondary structure consideration relative to sequence that is treated as a constraint. However, the method cannot be used genome-wide due to its high cost because each folding prediction by energy minimization in the moving window is computationally expensive, enabling to scan only at the vicinity of genes of interest. The second idea was to remedy the inefficiency of the previous approach by constructing a pipeline that consists of inverse RNA folding considering RNA secondary structure, followed by a BLAST search that is sequence-based and highly efficient. This approach, which relies on inverse RNA folding in general and our own in-house fragment-based inverse RNA folding program called RNAfbinv in particular, shows capability to find attractive candidates that are missed by Infernal and other standard methods being used for riboswitch detection. We demonstrate attractive candidates found by both the moving-window approach and the inverse RNA folding approach performed together with BLAST. We conclude that structure-based methods like the two strategies outlined above hold considerable promise in detecting riboswitches and other conserved RNAs of functional importance in a variety of organisms.

Keywords: riboswitches, RNA folding prediction, RNA structure, structure-based methods

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