Search results for: genotype identification

Authors: D. P. Gangwar

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Identification of human through facial images has got great importance in forensic science. The video recordings, CCTV footage, passports, driver licenses and other related documents are invariably sent to the laboratory for comparison of the questioned photographs as well as video recordings with suspected photographs/recordings to prove the identity of a person. More than 300 questioned and 300 control photographs received in actual crime cases, received from various investigation agencies, have been compared by me so far using various familiar analysis and comparison techniques such as Holistic comparison, Morphological analysis, Photo-anthropometry and superimposition. On the basis of findings obtained during the examination huge photo exhibits, a realistic and comprehensive technique has been proposed which could be very useful for forensic.

Keywords: CCTV Images, facial features, photo-anthropometry, superimposition

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Authors: Ahmed Abdrabou, Medhat Abdalla

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The studied stele dates back to Third Intermediate Period (1075-664) B.C in an ancient Egypt. It is made of wood and covered with painted gesso layers. This study aims to use a combination of multi spectral imaging {visible, infrared (IR), Visible-induced infrared luminescence (VIL), Visible-induced ultraviolet luminescence (UVL) and ultraviolet reflected (UVR)}, along with portable x-ray fluorescence in order to map and identify the pigments as well as to provide a deeper understanding of the painting techniques. Moreover; the authors were significantly interested in the identification of wood species. Multispectral imaging acquired in 3 spectral bands, ultraviolet (360-400 nm), visible (400-780 nm) and infrared (780-1100 nm) using (UV Ultraviolet-induced luminescence (UVL), UV Reflected (UVR), Visible (VIS), Visible-induced infrared luminescence (VIL) and Infrared photography. False color images are made by digitally editing the VIS with IR or UV images using Adobe Photoshop. Optical Microscopy (OM), potable X-ray fluorescence spectroscopy (p-XRF) and Fourier Transform Infrared Spectroscopy (FTIR) were used in this study. Mapping and imaging techniques provided useful information about the spatial distribution of pigments, in particular visible-induced luminescence (VIL) which allowed the spatial distribution of Egyptian blue pigment to be mapped and every region containing Egyptian blue, even down to single crystals in some instances, is clearly visible as a bright white area; however complete characterization of the pigments requires the use of p. XRF spectroscopy. Based on the elemental analysis found by P.XRF, we conclude that the artists used mixtures of the basic mineral pigments to achieve a wider palette of hues. Identification of wood species Microscopic identification indicated that the wood used was Sycamore Fig (Ficus sycomorus L.) which is recorded as being native to Egypt and was used to make wooden artifacts since at least the Fifth Dynasty.

Keywords: polychrome wooden stele, multispectral imaging, IR luminescence, Wood identification, Sycamore Fig, p-XRF

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Authors: Svetlana L. Mishlanova, Evgeniia V. Ermakova, Mariia E. Timirkina

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The paper is concerned with the study of metaphor production in essays written by Russian and English native speakers in the framework of cognitive metaphor theory. It considers metaphorical competence as individual’s ability to recognize, understand and use metaphors in speech. The work analyzes the influence of visual metaphor on production and density of conventional and novel verbal metaphors. The main methods of research include experiment connected with image interpretation, metaphor identification procedure (MIPVU) and visual conventional metaphors identification procedure proposed by VisMet group. The research findings will be used in the project aimed at comparing metaphorical competence of native and non-native English speakers.

Keywords: metaphor, metaphorical competence, conventional, novel

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Authors: Roihatul Mutiah, Sukardiman, Aty Widyawaruyanti

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Calotropis gigantiea (L.) R. Br (Apocynaceae) commonly called as “Biduri” or “giant milk weed” is a well-known weed to many cultures for treating various disorders. Several studies reported that C.gigantea roots has anticancer activity. The main aim of this research was to isolate and identify an active marker compound of C.gigantea roots for quality control purpose of its extract in the development as anticancer natural product. The isolation methods was bioactivity guided column chromatography, TLC, and HPLC. Evaluated anticancer activity of there substances using MTT assay methods. Identification structure active compound by UV, 1HNMR, 13CNMR, HMBC, HMQC spectral and other references. The result showed that the marker active compound was identical as Calotropin.

Keywords: calotropin, Calotropis gigantea, anticancer, marker active

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Authors: Gautier Viaud, Paul-Henry Cournède

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Plant growth models have been used extensively for the prediction of the phenotypic performance of plants. However, they remain most often calibrated for a given genotype and therefore do not take into account genotype by environment interactions. One way of achieving such an objective is to consider Bayesian hierarchical models. Three levels can be identified in such models: The first level describes how a given growth model describes the phenotype of the plant as a function of individual parameters, the second level describes how these individual parameters are distributed within a plant population, the third level corresponds to the attribution of priors on population parameters. Thanks to the Bayesian framework, choosing appropriate priors for the population parameters permits to derive analytical expressions for the full conditional distributions of these population parameters. As plant growth models are of a nonlinear nature, individual parameters cannot be sampled explicitly, and a Metropolis step must be performed. This allows for the use of a hybrid Gibbs--Metropolis sampler. A generic approach was devised for the implementation of both general state space models and estimation algorithms within a programming platform. It was designed using the Julia language, which combines an elegant syntax, metaprogramming capabilities and exhibits high efficiency. Results were obtained for Arabidopsis thaliana on both simulated and real data. An organ-scale Greenlab model for the latter is thus presented, where the surface areas of each individual leaf can be simulated. It is assumed that the error made on the measurement of leaf areas is proportional to the leaf area itself; multiplicative normal noises for the observations are therefore used. Real data were obtained via image analysis of zenithal images of Arabidopsis thaliana over a period of 21 days using a two-step segmentation and tracking algorithm which notably takes advantage of the Arabidopsis thaliana phyllotaxy. Since the model formulation is rather flexible, there is no need that the data for a single individual be available at all times, nor that the times at which data is available be the same for all the different individuals. This allows to discard data from image analysis when it is not considered reliable enough, thereby providing low-biased data in large quantity for leaf areas. The proposed model precisely reproduces the dynamics of Arabidopsis thaliana’s growth while accounting for the variability between genotypes. In addition to the estimation of the population parameters, the level of variability is an interesting indicator of the genotypic stability of model parameters. A promising perspective is to test whether some of the latter should be considered as fixed effects.

Keywords: bayesian, genotypic differentiation, hierarchical models, plant growth models

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Authors: Khaled M. El Naggar

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This paper introduces a digital method for fault section identification in transmission lines. The method uses digital set of the measured short circuit current to locate faults in electrical power systems. The digitized current is used to construct a set of overdetermined system of equations. The problem is then constructed and solved using the proposed digital optimization technique to find the fault distance. The proposed optimization methodology is an application of simulated annealing optimization technique. The method is tested using practical case study to evaluate the proposed method. The accurate results obtained show that the algorithm can be used as a powerful tool in the area of power system protection.

Keywords: optimization, estimation, faults, measurement, high voltage, simulated annealing

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Authors: Vikram Saini, Lillie Dewan

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This paper presents the iterative schemes based on Least square, Hierarchical Least Square and Stochastic Approximation Gradient method for the Identification of Wiener model with parametric structure. A gradient method is presented for the parameter estimation of wiener model with noise conditions based on the stochastic approximation. Simulation results are presented for the Wiener model structure with different static non-linear elements in the presence of colored noise to show the comparative analysis of the iterative methods. The stochastic gradient method shows improvement in the estimation performance and provides fast convergence of the parameters estimates.

Keywords: hard non-linearity, least square, parameter estimation, stochastic approximation gradient, Wiener model

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

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Authors: Fatma Susilawati Mohamad, Azizah Abdul Manaf, Fadhillah Ahmad, Zarina Mohamad, Wan Suryani Wan Awang

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A simple and robust multi-class linear classifier is proposed and implemented. For a pair of classes of the linear boundary, a collection of segments of hyper planes created as perpendicular bisectors of line segments linking centroids of the classes or part of classes. Nearest Neighbor and Linear Discriminant Analysis are compared in the experiments to see the performances of each classifier in discriminating ripeness of oil palm. This paper proposes a multi-class linear classifier using Linear Discriminant Analysis (LDA) for image identification. Result proves that LDA is well capable in separating multi-class features for ripeness identification.

Keywords: multi-class, linear classifier, nearest neighbor, linear discriminant analysis

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Authors: Florian Pfeffel, Valentin Nickolai, Christian Louis Kühner

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Digitalization has disrupted the traditional workplace environment by allowing many employees to work from anywhere at any time. This trend of working from home was further accelerated due to the COVID-19 crisis, which forced companies to rethink their workplace models. While in many companies, this shift happened out of pure necessity; many employees were left more satisfied with their job due to the opportunity to work from home. This study focuses on employees’ job satisfaction in the service sector in dependence on the different work models, which are defined as a “work from home” model, the traditional “work in office” model, and a hybrid model. Using structural equation modeling (SEM), these three work models have been analyzed based on 13 influencing factors on job satisfaction that have been further summarized in the three groups “classic influencing factors”, “influencing factors changed by remote working”, and “new remote working influencing factors”. Based on the influencing factors on job satisfaction, a survey has been conducted with n = 684 employees in the service sector. Cronbach’s alpha of the individual constructs was shown to be suitable. Furthermore, the construct validity of the constructs was confirmed by face validity, content validity, convergent validity (AVE > 0.5: CR > 0.7), and discriminant validity. Additionally, confirmatory factor analysis (CFA) confirmed the model fit for the investigated sample (CMIN/DF: 2.567; CFI: 0.927; RMSEA: 0.048). The SEM-analysis has shown that the most significant influencing factor on job satisfaction is “identification with the work” with β = 0.540, followed by “Appreciation” (β = 0.151), “Compensation” (β = 0.124), “Work-Life-Balance” (β = 0.116), and “Communication and Exchange of Information” (β = 0.105). While the significance of each factor can vary depending on the work model, the SEM-analysis shows that the identification with the work is the most significant factor in all three work models and, in the case of the traditional office work model, it is the only significant influencing factor. The study shows that employees who work entirely remotely or have a hybrid work model are significantly more satisfied with their job, with a job satisfaction score of 5.0 respectively on a scale from 1 (very dissatisfied) to 7 (very satisfied), than employees do not have the option to work from home with a score of 4.6. This comes as a result of the lower identification with the work in the model without any remote working. Furthermore, the responses indicate that it is important to consider the individual preferences of each employee when it comes to the work model to achieve overall higher job satisfaction. Thus, it can be argued that companies can profit off of more motivation and higher productivity by considering the individual work model preferences, therefore, increasing the identification with the respective work.

Keywords: home-office, identification with work, job satisfaction, new work, remote work, structural equation modeling

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Authors: Slimane Ouhmad, Abdellah Halimi

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In this work, we have applied neural networks method MLP type to a database from an array of six sensors for the detection of three toxic gases. As the choice of the number of hidden layers and the weight values has a great influence on the convergence of the learning algorithm, we proposed, in this article, a mathematical formulation to determine the optimal number of hidden layers and good weight values based on the method of back propagation of errors. The results of this modeling have improved discrimination of these gases on the one hand, and optimize the computation time on the other hand, the comparison to other results achieved in this case.

Keywords: MLP Neural Network, back-propagation, number of neurons in the hidden layer, identification, computing time

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Authors: Chien-kuo Chang, Bo-wei Wu, Yi-yun Tang, Min-chiu Wu

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This paper proposes a power equipment diagnosis system based on partial discharge (PD), which is characterized by increasing the readability of experimental data and the convenience of operation. This system integrates a variety of analysis programs of different data formats and different programming languages and then establishes a set of interfaces that can follow and expand the structure, which is also helpful for subsequent maintenance and innovation. This study shows a case of using the developed Convolutional Neural Networks (CNN) to integrate with this system, using the designed model architecture to simplify the complex training process. It is expected that the simplified training process can be used to establish an adaptive deep learning experimental structure. By selecting different test data for repeated training, the accuracy of the identification system can be enhanced. On this platform, the measurement status and partial discharge pattern of each equipment can be checked in real time, and the function of real-time identification can be set, and various training models can be used to carry out real-time partial discharge insulation defect identification and insulation state diagnosis. When the electric power equipment entering the dangerous period, replace equipment early to avoid unexpected electrical accidents.

Keywords: partial discharge, convolutional neural network, partial discharge analysis platform, adaptive deep learning

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Authors: Khaled Mohammed

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This paper presents novel face recognition and identification approaches for the real-time attendance management problem in large companies/factories and government institutions. The proposed uses the Minimum Ratio (MR) approach for employee identification. Capturing the authentic face variability from a sequence of video frames has been considered for the recognition of faces and resulted in system robustness against the variability of facial features. Experimental results indicated an improvement in the performance of the proposed system compared to the Previous approaches at a rate between 2% to 5%. In addition, it decreased the time two times if compared with the Previous techniques, such as Extreme Learning Machine (ELM) & Multi-Scale Structural Similarity index (MS-SSIM). Finally, it achieved an accuracy of 99%.

Keywords: attendance management system, face detection and recognition, live face recognition, minimum ratio

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Authors: Ran Vir Singh, Anuj Chauhan, Subhodh Kumar, Rajesh Rathore, Satish Kumar, B Gopi, Sushil Kumar, Tarun Kumar, Ramji Yadav, Donna Phangchopi, Shoor Vir Singh

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Paratuberculosis caused by Mycobacterium avium subspecies paratuberculosis (MAP) is a chronic granulomatous enteritis affecting ruminants. It is responsible for significant economic losses in livestock industry worldwide. This organism is also of public health concern due to an unconfirmed link to Crohn’s disease. Susceptibility to paratuberculosis has been suggested to have genetic component with low to moderate heritability. Number of SNPs in various candidates genes have been observed to be affecting the susceptibility toward paratuberculosis. The objective of this study was to explore the association of various SNPs in the candidate genes and QTL region with MAP. A total of 117 SNPs from SLC11A1, IFNG, CARD15, TLR2, TLR4, CLEC7A, CD209, SP110, ANKARA2, PGLYRP1 and one QTL were selected for study. A total of 1222 cattle from various organized herds, gauhsalas and farmer herds were screened for MAP infection by Johnin intradermal skin test, AGID, serum ELISA, fecal microscopy, fecal culture and IS900 blood PCR. Based on the results of these tests, a case and control population of 200 and 183 respectively was established for study. A total of 117 SNPs from 10 candidate genes and one QTL were selected and validated/tested in our case and control population by PCR-RFLP technique. Data was analyzed using SAS 9.3 software. Statistical analysis revealed that, 107 out of 117 SNPs were not significantly associated with occurrence of MAP. Only SNP rs55617172 of TLR2, rs8193046 and rs8193060 of TLR4, rs110353594 and rs41654445 of CLEC7A, rs208814257of CD209, rs41933863 of ANKRA2, two loci {SLC11A1(53C/G)} and {IFNG (185 G/r) } and SNP rs41945014 in QTL region was significantly associated with MAP. Six SNP from 10 significant SNPs viz., rs110353594 and rs41654445 from CLEC7A, rs8193046 and rs8193060 from TLR4, rs109453173 from SLC11A1 rs208814257 from CD209 were validated in new case and control population. Out of these only one SNP rs8193046 of TLR4 gene was found significantly associated with occurrence of MAP in cattle. ODD ratio indicates that animals with AG genotype were more susceptible to MAP and this finding is in accordance with the earlier report. Hence it reaffirms that AG genotype can serve as a reliable genetic marker for indentifying more susceptible cattle in future selection against MAP infection in cattle.

Keywords: SNP, candidate genes, paratuberculosis, cattle

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Authors: Thanh Nga Thai

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A major issue in air transportation is in flight safety. Recent developments in control engineering have an attractive potential for resolving new issues related to guidance, navigation, and control of flying vehicles. Many future atmospheric missions will require increased on board autonomy including fault diagnosis and the subsequent control and guidance recovery actions. To improve designing system diagnostic, an efficient FDI- fault detection and identification- methodology is necessary to achieve. Contribute to characteristic of different faults in sensor and actuator in the view of mathematics brings a lot of profit in some condition changes in the system. This research finds some profit to reduce a trade-off to achieve between fault detection and performance of the closed loop system and cost and calculated in simulation.

Keywords: fault detection and identification, sensor faults, actuator faults, flight control system

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Authors: Besma Khalfoun

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In many current Human Activity Recognition (HAR) applications, users' data is frequently shared and centrally stored by third parties, posing a significant privacy risk. This practice makes these entities attractive targets for extracting sensitive information about users, including their identity, health status, and location, thereby directly violating users' privacy. To tackle the issue of centralized data storage, a relatively recent paradigm known as federated learning has emerged. In this approach, users' raw data remains on their smartphones, where they train the HAR model locally. However, users still share updates of their local models originating from raw data. These updates are vulnerable to several attacks designed to extract sensitive information, such as determining whether a data sample is used in the training process, recovering the training data with inversion attacks, or inferring a specific attribute or property from the training data. In this paper, we first introduce PUR-Attack, a parameter-based user re-identification attack developed for HAR applications within a federated learning setting. It involves associating anonymous model updates (i.e., local models' weights or parameters) with the originating user's identity using background knowledge. PUR-Attack relies on a simple yet effective machine learning classifier and produces promising results. Specifically, we have found that by considering the weights of a given layer in a HAR model, we can uniquely re-identify users with an attack success rate of almost 100%. This result holds when considering a small attack training set and various data splitting strategies in the HAR model training. Thus, it is crucial to investigate protection methods to mitigate this privacy threat. Along this path, we propose SAFER, a privacy-preserving mechanism based on adaptive local differential privacy. Before sharing the model updates with the FL server, SAFER adds the optimal noise based on the re-identification risk assessment. Our approach can achieve a promising tradeoff between privacy, in terms of reducing re-identification risk, and utility, in terms of maintaining acceptable accuracy for the HAR model.

Keywords: federated learning, privacy risk assessment, re-identification risk, privacy preserving mechanisms, local differential privacy, human activity recognition

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Authors: H. Gholami, M. Jalali Azizpour

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Stress Corrosion Crack (SCC) in pipeline is a type of environmentally assisted cracking (EAC), since its discovery in 1965 as a possible cause of failure in pipeline, SCC has caused, on average, one of two failures per year in the U.S, According to the NACE SCC DA a pipe line segment is considered susceptible to SCC if all of the following factors are met: The operating stress exceeds 60% of specified minimum yield strength (SMYS), the operating temperature exceeds 38°C, the segment is less than 32 km downstream from a compressor station, the age of the pipeline is greater than 10 years and the coating type is other than Fusion Bonded Epoxy(FBE). In this paper as a practical experience in NISOC, Direct Assessment (DA) Method is used for identification SCC defect in unpiggable pipeline located downstream of compressor station.

Keywords: stress corrosion crack, direct assessment, disbondment, transgranular SCC, compressor station

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Authors: G. Blanco-Lizana, C. García-Fernández, J. A. Sánchez

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Growth is a productive trait regulated by a large and complex gene network with very different effect. Some of they (candidate genes) have a higher effect and are excellent resources to search in them polymorphisms correlated with differences in growth rates. This study was focused on the identification of single nucleotide polymorphism (SNP) in MyoD-1 and MyoD-2 genes, members of the family of myogenic regulatory genes with a key role in the differentiation and development of muscular tissue.(MFRs), and its evaluation as potential markers in genetic selection programs for growth in gilthead sea bream (Sparus aurata). Through a sequencing in 30 seabream (classified as unrelated by microsatellite markers) of 1.968bp in MyoD-1 gene [AF478568 .1] and 1.963bp in MyoD-2 gene [AF478569.1], three SNPs were identified in each gene (SaMyoD-1 D2100A (D indicate a deletion) SaMyoD-1 A2143G and SaMyoD-1 A2404G and SaMyoD-2_A785C, SaMyoD-2_C1982T and SaMyoD-2_A2031T). The relationships between SNPs and body weight were evaluated by SNP genotyping of 53 breeders from two broodstocks (A:18♀-9♂; B:16♀-10♂) and 389 offspring divided into two groups (slow- and fast-growth) with significant differences in growth at 18 months of development (A18Slow: N=107, A18Fast: N=103, B18Slow: N=92 and B18Fast: N=87) (Borrell et al., 2011). Haplotype and diplotype were reconstructed from genotype data by Phase 2.1 software. Differences among means of different diplotypes were calculated by one-way ANOVA followed by post-hoc Tukey test. Association analysis indicated that single SNP did not show significant effect on body weight. However, when the analysis is carried out considering haplotype data it was observed that the DGG haplotipe of MyoD-1 gen and CCA haplotipe of MyoD- 2gen were associated to with lower body weight. This haplotype combination always showed the lowest mean body weight (P<0.05) in three (A18Slow, A18Fast & B18Slow) of the four groups tested. Individuals with DGG haplotipe of MyoD-1 gen have a 25,5% and those with CCA haplotipe of MyoD- 2gen showed 14-18% less on mean body weight. Although further studies are need to validate the role of these 3 SNPs as marker for body weight, the polymorphism-trait association established in this work create promising expectations on the use of these variants as genetic tool for future giltead seabream breeding programs.

Keywords: growth, MyoD-1 and MyoD-2 genes, selective breeding, SNP-haplotype

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Authors: Shanika Y. Koreshi

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Sri Lanka housed the lingerie product development and manufacturing subsidiary to renowned brands such as La Senza, Marks & Spencer, H&M, Etam, Lane Bryant, and George. Over the last few years, they have produced local brands such as Amante to cater to the local and regional customers. Past research has identified factors such as quality, price, and design to be vital when marketing lingerie to consumers. However, there has been minimum research that looks into the ethnically targeted market and skin colour within the Asian population. Therefore, the main aim of the research was to identify whether consumer preference for lingerie is influenced by the skin tone of the model wearing it. Moreover, the secondary aim was to investigate if the consumer preference for lingerie is influenced by the consumer’s ethnic identification with the skin tone of the model. An experimental design was used to explore the above aims. The participants constituted of 66 females residing in the western province of Sri Lanka and were gathered via convenience sampling. Six computerized images of a real model were used in the study, and her skin tone was digitally manipulated to express three different skin tones (light, tan and dark). Consumer preferences were measured through a ranking order scale that was constructed via a focus group discussion and ethnic identity was measured by the Multigroup Ethnic Identity Measure-Revised. Wilcoxon signed-rank test, Friedman test, and chi square test of independence were carried out using SPSS version 20. The results indicated that majority of the consumers ethnically identified and preferred the tan skin over the light and dark skin tones. The findings support the existing literature that states there is a preference among consumers when models have a medium skin tone over a lighter skin tone. The preference for a tan skin tone in a model is consistent with the ethnic identification of the Sri Lankan sample. The study implies that lingerie brands should consider the model's skin tones when marketing the brand to different ethnic backgrounds.

Keywords: consumer preference, ethnic identification, lingerie, skin tone

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Authors: A. Medjnoun, R. Bahar

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The disorders caused by the shrinking-swelling phenomenon are prevalent in arid and semi-arid in the presence of swelling clay. This soil has the characteristic of changing state under the effect of water solicitation (wetting and drying). A set of geotechnical parameters is necessary for the characterization of this soil type, such as state parameters, physical and chemical parameters and mechanical parameters. Some of these tests are very long and some are very expensive, hence the use or methods of predictions. The complexity of this phenomenon and the difficulty of its characterization have prompted researchers to use several identification parameters in the prediction of swelling potential. This document is an analytical and statistical study of geotechnical parameters affecting the potential of swelling clays. This work is performing on a database obtained from investigations swelling Algerian soil. The obtained observations have helped us to understand the soil swelling structure and its behavior.

Keywords: analysis, estimated model, parameter identification, swelling of clay

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Authors: Y. Galerkin, K. Soldatova, A. Drozdov

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The 6th version of Universal modeling method for centrifugal compressor stage calculation is described. Identification of the new mathematical model was made. As a result of identification the uniform set of empirical coefficients is received. The efficiency definition error is 0,86 % at a design point. The efficiency definition error at five flow rate points (except a point of the maximum flow rate) is 1,22 %. Several variants of the stage with 3D impellers designed by 6th version program and quasi three-dimensional calculation programs were compared by their gas dynamic performances CFD (NUMECA FINE TURBO). Performance comparison demonstrated general principles of design validity and leads to some design recommendations.

Keywords: compressor design, loss model, performance prediction, test data, model stages, flow rate coefficient, work coefficient

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Authors: Ratneev Kaur, Tajinder Kaur, Anupam Kaur

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Introduction: Polycystic Ovary Syndrome (PCOS) is a heterogenous disorder of endocrine system among women of reproductive age. PCOS is characterized by hyperandrogenism, anovulation, polycystic ovaries, hirsutism, obesity, and hyperinsulinemia. Several pathways are implicated in its etiology including the metabolic pathway of steroid hormone synthesis regulatory pathways. PCOS is an androgen excess disorder, genes operating in steroidogenesis may alter pathogenesis of PCOS. The cytochrome P450scc is a cholesterol side chain cleavage enzyme coded by CYP11A1 gene and catalyzes conversion of cholesterol to pregnenolone, the initial and rate-limiting step in steroid hormone synthesis. It is postulated that polymorphisms in this gene may play an important role in the regulation of CYP11A1 expression and leading to increased or decreased androgen production. The present study will be the first study from north India to best of our knowledge, to analyse the association of CYP11A1 (rs11632698) polymorphism in women suffering from PCOS. Methodology: The present study was approved by ethical committee of Guru Nanak Dev University in consistent with declaration of Helsinki. A total of 300 samples (150 PCOS cases and 150 controls) were recruited from Hartej hospital, for the present study. Venous blood sample (3ml) was withdrawn from women diagnosed with PCOS by doctor, according to Rotterdam 2003 criteria and from healthy age matched controls only after informed consent and detailed filled proforma. For molecular genetics analysis, blood was stored in EDTA vials. After DNA isolation by organic method, PCR-RFLP approach was used for genotyping and association analysis of rs11632698 polymorphism. Statistical analysis was done to check for significance of selected polymorphism with PCOS. Results: In 150 PCOS cases, the frequency of AA, AG and GG genotype was found to be 48%, 35%, and 13% compared to 62%, 27% and 8% in 150 controls. The major allele (A) and minor allele (G) frequency was 68% and 32% in cases and 78% and 22% in controls. Minor allele frequency was higher in cases as compared to controls, as well as the distribution of genotype was observed to be statistically significant (ᵡ²=6.525, p=0.038). Odds ratio in dominant, co-dominant and recessive models observed was 1.81 (p=0.013), 1.54 (p=0.012) and 1.77 (p=0.132) respectively. Conclusion: The present study showed statistically significant association of rs11632698 with PCOS (p=0.038) in North Indian women.

Keywords: polycystic ovary syndrome, CYP11A1, rs11632698, hyperandrogenism

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Authors: Heiko Weiß, Andreas Meister, Christoph Ament, Nils Dreifke

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Linear actuators are deployed in a wide range of applications. This paper presents the modeling and system identification of a variable excited linear direct drive (LDD). The LDD is designed based on linear hybrid stepper technology exhibiting the characteristic tooth structure of mover and stator. A three-phase topology provides the thrust force caused by alternating strengthening and weakening of the flux of the legs. To achieve best possible synchronous operation, the phases are commutated sinusoidal. Despite the fact that these LDDs provide high dynamics and drive forces, noise emission limits their operation in calm workspaces. To overcome this drawback an additional excitation of the magnetic circuit is introduced to LDD using additional enabling coils instead of permanent magnets. The new degree of freedom can be used to reduce force variations and related noise by varying the excitation flux that is usually generated by permanent magnets. Hence, an identified simulation model is necessary to analyze the effects of this modification. Especially the force variations must be modeled well in order to reduce them sufficiently. The model can be divided into three parts: the current dynamics, the mechanics and the force functions. These subsystems are described with differential equations or nonlinear analytic functions, respectively. Ordinary nonlinear differential equations are derived and transformed into state space representation. Experiments have been carried out on a test rig to identify the system parameters of the complete model. Static and dynamic simulation based optimizations are utilized for identification. The results are verified in time and frequency domain. Finally, the identified model provides a basis for later design of control strategies to reduce existing force variations.

Keywords: force variations, linear direct drive, modeling and system identification, variable excitation flux

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Authors: Victor Toom

Abstract:

Albeit it is never possible to anticipate the full range of difficulties after a catastrophe, efforts to identify victims of mass casualty events have become institutionalized and standardized with the aim of effectively and efficiently addressing the many challenges and contingencies. Such ‘disaster victim identification’ (DVI) practices are dependent on the forensic sciences, are subject of national legislation, and are reliant on technical and organizational protocols to mitigate the many complexities in the wake of catastrophe. Apart from such technological, legal and bureaucratic elements constituting a DVI operation, victims’ families and their emotions are also part and parcel of any effort to identify casualties of mass human fatality incidents. Take for example the fact that forensic experts require (antemortem) information from the group of relatives to make identification possible. An identified body or body part is also repatriated to kin. Relatives are thus main stakeholders in DVI operations. Much has been achieved in years past regarding facilitating victims’ families’ issues and their emotions. Yet, how families are dealt with by experts and authorities is still considered a difficult topic. Due to sensitivities and required emphatic interaction with families on the one hand, and the rationalized DVI efforts, on the other hand, there is still scope for improving communication, providing information and meaningful inclusion of relatives in the DVI effort. This paper aims to bridge the standardized world of DVI efforts and families’ experienced realities and makes suggestions to further improve DVI efforts through inclusion of victims’ families. Based on qualitative interviews, the paper narrates involvement and experiences of inter alia DVI practitioners, victims’ families, advocates and clergy in the wake of the 1995 Srebrenica genocide which killed approximately 8,000 men, and the 9/11 in New York City with 2,750 victims. The paper shows that there are several models of including victims’ families into a DVI operation, and it argues for a model of where victims’ families become a partner in DVI operations.

Keywords: disaster victim identification (DVI), victims’ families, social science (qualitative), 9/11 attacks, Srebrenica genocide

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Authors: Nestor A. Garcia Fragoso, Tetyana Baydyk, Ernst Kussul

Abstract:

This article contains information from our investigation in the field of voice recognition. For this purpose, we created a voice database that contains different phrases in two languages, English and Spanish, for men and women. As a classifier, the LIRA (Limited Receptive Area) grayscale neural classifier was selected. The LIRA grayscale neural classifier was developed for image recognition tasks and demonstrated good results. Therefore, we decided to develop a recognition system using this classifier for voice recognition. From a specific set of speakers, we can recognize the speaker’s voice. For this purpose, the system uses spectrograms of the voice signals as input to the system, extracts the characteristics and identifies the speaker. The results are described and analyzed in this article. The classifier can be used for speaker identification in security system or smart buildings for different types of intelligent devices.

Keywords: extreme learning, LIRA neural classifier, speaker identification, voice recognition

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Authors: Marjan Heidarzadeh

Abstract:

High quality and purity of DNA isolated from canned tuna is essential for species identification. In this study, the efficiency of five different methods for DNA extraction was compared. Method of national standard in Iran, the CTAB precipitation method, Wizard DNA Clean Up system, Nucleospin and GenomicPrep were employed. DNA was extracted from two different canned tuna in brine and oil of the same tuna species. Three samples of each type of product were analyzed with the different methods. The quantity and quality of DNA extracted was evaluated using the 260 nm absorbance and ratio A260/A280 by spectrophotometer picodrop. Results showed that the DNA extraction from canned tuna preserved in different liquid media could be optimized by employing a specific DNA extraction method in each case. Best results were obtained with CTAB method for canned tuna in oil and with Wizard method for canned tuna in brine.

Keywords: canned tuna PCR, DNA, DNA extraction methods, species identification

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Authors: O. S. Omorogiuwa, E. J. Omozusi

Abstract:

The monitoring of spectrum usage and signal identification, using cognitive radio, is done to identify frequencies that are vacant for reuse. It has been established that ‘internet of things’ device uses secondary frequency which is free, thereby facing the challenge of interference from other users, where some primary frequencies are not being utilised. The design was done by analysing a specific frequency spectrum, checking if all the frequency stations that range from 87.5-108 MHz are presently being used in Benin City, Edo State, Nigeria. From the results, it was noticed that by using Software Defined Radio/Simulink, we were able to identify vacant frequencies in the range of frequency under consideration. Also, we were able to use the significance of energy detection threshold to reuse this vacant frequency spectrum, when the cognitive radio displays a zero output (that is decision H0), meaning that the channel is unoccupied. Hence, the analysis was able to find the spectrum hole and identify how it can be reused.

Keywords: spectrum, interference, telecommunication, cognitive radio, frequency

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Authors: Abdelhak-Djamel Seriai, Selim Kebir, Allaoua Chaoui

Abstract:

During the last decades, object oriented program- ming has been massively used to build large-scale systems. However, evolution and maintenance of such systems become a laborious task because of the lack of object oriented programming to offer a precise view of the functional building blocks of the system. This lack is caused by the fine granularity of classes and objects. In this paper, we use a post object-oriented technology namely software components, to propose an approach based on the identification of the functional building blocks of an object oriented system by analyzing its source code. These functional blocks are specified as software components and the result is a multi-layer component based software architecture.

Keywords: software comprehension, software component, object oriented, software architecture, reverse engineering

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Authors: Tsu-Wang Shen, Shan-Chun Chang, Chih-Hsien Wang, Te-Chao Fang

Abstract:

For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.

Keywords: high-intensity heart rate, heart rate resistant, ECG human identification, decision based artificial neural network

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Authors: Ruichong Zhang, Fadi Sawaged, Lotfi Gargab

Abstract:

This paper introduces a wave-based approach for system identification of high-rise building structures with a pair of seismic recordings, which can be used to evaluate structural integrity and detect damage in post-earthquake structural condition assessment. The fundamental of the approach is based on wave features of generalized impulse and frequency response functions (GIRF and GFRF), i.e., wave responses at one structural location to an impulsive motion at another reference location in time and frequency domains respectively. With a pair of seismic recordings at the two locations, GFRF is obtainable as Fourier spectral ratio of the two recordings, and GIRF is then found with the inverse Fourier transformation of GFRF. With an appropriate continuous model for the structure, a closed-form solution of GFRF, and subsequent GIRF, can also be found in terms of wave transmission and reflection coefficients, which are related to structural physical properties above the impulse location. Matching the two sets of GFRF and/or GIRF from recordings and the model helps identify structural parameters such as wave velocity or shear modulus. For illustration, this study examines ten-story Millikan Library in Pasadena, California with recordings of Yorba Linda earthquake of September 3, 2002. The building is modelled as piecewise continuous layers, with which GFRF is derived as function of such building parameters as impedance, cross-sectional area, and damping. GIRF can then be found in closed form for some special cases and numerically in general. Not only does this study reveal the influential factors of building parameters in wave features of GIRF and GRFR, it also shows some system-identification results, which are consistent with other vibration- and wave-based results. Finally, this paper discusses the effectiveness of the proposed model in system identification.

Keywords: wave-based approach, seismic responses of buildings, wave propagation in structures, construction

Procedia PDF Downloads 233