Search results for: genetic algorithm

Authors: Elly Usman, S. Dante, Diah Purnamasari

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Type 2 diabetes mellitus ( T2DM ) is a syndrome characterized by a state of increased blood sugar levels due to chronic disorders of insulin secretion by pancreatic beta cells and insulin action or a combination of both. The organic cation transporter 1, encoded by the SLC22A1 gene, responsible for the uptake of the antihyperglycemic drug, metformin, in the hepatocyte. We assessed whether a genetic variation in the SLC22A1 gene was associated with the glucose - lowering effect of metformin. Method case study research design. Samples are children with type 2 diabetes mellitus who meet the inclusion criteria. The results proportions SLC22A1 gene polymorphisms in children with diabetes mellitus type 2 amounted to 52.04 % at position 400T/C, there is one heterozygous and one at position 595T/C Conclusion The presence of SLC22A1 gene polymorphisms in children with diabetes mellitus type 2.

Keywords: diabetes Mellitus type 2, metformin, organic cation transporter 1, pharmacogenomics

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Authors: Vishnu Pratap Singh Kirar, Madhuri Saxena

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Advancement in sequence data generation technologies is churning out voluminous omics data and posing a massive challenge to annotate the biological functional features. With so much data available, the use of machine learning methods and tools to make novel inferences has become obvious. Machine learning methods have been successfully applied to a lot of disciplines, including computational biology and bioinformatics. Researchers in computational biology are interested to develop novel machine learning frameworks to classify the huge amounts of biological data. In this proposal, it plan to employ novel machine learning approaches to aid the understanding of how apparently innocuous mutations (in intergenic DNA and at synonymous sites) cause diseases. We are also interested in discovering novel functional sites in the genome and mutations in which can affect a phenotype of interest.

Keywords: genome wide association studies (GWAS), next generation sequencing (NGS), deep learning, omics

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Authors: Prasad Kalagura, B. Veeresh kumar

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We constructed an interface system that would allow a similarly paralyzed user to interact with a computer with almost full functional capability. A real-time tracking algorithm is implemented based on adaptive skin detection and motion analysis. The clicking of the mouse is activated by the user's eye blinking through a sensor. The keyboard function is implemented by voice recognition kit.

Keywords: embedded ARM7 processor, mouse pointer control, voice recognition

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Authors: O. B. Iskender, K. V. Ling, V. Dubanchet, L. Simonini

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This paper presents a Guidance and Control (G&C) strategy to address spacecraft maneuvering problem for future Rendezvous and Docking (RVD) missions. The proposed strategy allows safe and propellant efficient trajectories for space servicing missions including tasks such as approaching, inspecting and capturing. This work provides the validation test results of the G&C laws using a Hardware-In-the-Loop (HIL) setup with two robotic mockups representing the chaser and the target spacecraft. Through this paper, the challenges of the relative motion control in space are first summarized, and in particular, the constraints imposed by the mission, spacecraft and, onboard processing capabilities. Second, the proposed algorithm is introduced by presenting the formulation of constrained Model Predictive Control (MPC) to optimize the fuel consumption and explicitly handle the physical and geometric constraints in the system, e.g. thruster or Line-Of-Sight (LOS) constraints. Additionally, the coupling between translational motion and rotational motion is addressed via dual quaternion based kinematic description and accordingly explained. The resulting convex optimization problem allows real-time implementation capability based on a detailed discussion on the computational time requirements and the obtained results with respect to the onboard computer and future trends of space processors capabilities. Finally, the performance of the algorithm is presented in the scope of a potential future mission and of the available equipment. The results also cover a comparison between the proposed algorithms with Linear–quadratic regulator (LQR) based control law to highlight the clear advantages of the MPC formulation.

Keywords: autonomous vehicles, embedded optimization, real-time experiment, rendezvous and docking, space robotics

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Authors: Lydia Tan, Philippe Lemey, Lieselot Houspie, Marco Viveen, Darren Martin, Frank Coenjaerts

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Introduction: Human respiratory syncytial virus (hRSV) is the leading cause of severe respiratory tract infections in infants under the age of two. Genomic substitutions and related evolutionary dynamics of hRSV are of great influence on virus transmission behavior. The evolutionary patterns formed are due to a precarious interplay between the host immune response and RSV, thereby selecting the most viable and less immunogenic strains. Studying genomic profiles can teach us which genes and consequent proteins play an important role in RSV survival and transmission dynamics. Study design: In this study, genetic diversity and evolutionary rate analysis were conducted on 36 RSV subgroup B whole genome sequences and 37 subgroup A genome sequences. Clinical RSV isolates were obtained from nasopharyngeal aspirates and swabs of children between 2 weeks and 5 years old of age. These strains, collected during epidemic seasons from 2001 to 2011 in the Netherlands and Belgium by either conventional or 454-sequencing. Sequences were analyzed for genetic diversity, recombination events, synonymous/non-synonymous substitution ratios, epistasis, and translational consequences of mutations were mapped to known 3D protein structures. We used Bayesian statistical inference to estimate the rate of RSV genome evolution and the rate of variability across the genome. Results: The A and B profiles were described in detail and compared to each other. Overall, the majority of the whole RSV genome is highly conserved among all strains. The attachment protein G was the most variable protein and its gene had, similar to the non-coding regions in RSV, more elevated (two-fold) substitution rates than other genes. In addition, the G gene has been identified as the major target for diversifying selection. Overall, less gene and protein variability was found within RSV-B compared to RSV-A and most protein variation between the subgroups was found in the F, G, SH and M2-2 proteins. For the F protein mutations and correlated amino acid changes are largely located in the F2 ligand-binding domain. The small hydrophobic phosphoprotein and nucleoprotein are the most conserved proteins. The evolutionary rates were similar in both subgroups (A: 6.47E-04, B: 7.76E-04 substitution/site/yr), but estimates of the time to the most recent common ancestor were much lower for RSV-B (B: 19, A: 46.8 yrs), indicating that there is more turnover in this subgroup. Conclusion: This study provides a detailed description of whole RSV genome mutations, the effect on translation products and the first estimate of the RSV genome evolution tempo. The immunogenic G protein seems to require high substitution rates in order to select less immunogenic strains and other conserved proteins are most likely essential to preserve RSV viability. The resulting G gene variability makes its protein a less interesting target for RSV intervention methods. The more conserved RSV F protein with less antigenic epitope shedding is, therefore, more suitable for developing therapeutic strategies or vaccines.

Keywords: drug target selection, epidemiology, respiratory syncytial virus, RSV

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Authors: Yasser M. Saad, Heba El-Sebaie Abd El-Sadek

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Some Metapenaeus monoceros cox1 gene fragments were isolated, purified, sequenced, and comparatively analyzed with some other Crustacean Cox1 gene sequences (obtained from National Center for Biotechnology Information). This work was designed for testing the efficiency of this system in reconstruction of phylogenetic relations among some Crustacean species belonging to four genera (Metapenaeus, Artemia, Daphnia and Calanus). The single nucleotide polymorphism and haplotype diversity were calculated for all estimated mt-DNA fragments. The genetic distance values were 0.292, 0.015, 0.151, and 0.09 within Metapenaeus species, Calanus species, Artemia species, and Daphnia species, respectively. The reconstructed phylogenetic tree is clustered into some unique clades. Cytochrome oxidase subunit 1 gene (cox1) was a powerful system in reconstruction of phylogenetic relations among evaluated crustacean species.

Keywords: crustaceans, genetics, Cox1, phylogeny

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Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

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Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

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Authors: Seyedamir Makinejadsanij

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One of the most important factors in the production of quality steel is to know the exact weight of steel in the steelmaking area. In this study, a calculation method is presented to estimate the exact weight of the melt as well as the objects transported by the overhead crane. Iran Alloy Steel Company's steelmaking area has three 90-ton cranes, which are responsible for transferring the ladles and ladle caps between 34 areas in the melt shop. Each crane is equipped with a Disomat Tersus weighing system that calculates and displays real-time weight. The moving object has a variable weight due to swinging, and the weighing system has an error of about +-5%. This means that when the object is moving by a crane, which weighs about 80 tons, the device (Disomat Tersus system) calculates about 4 tons more or 4 tons less, and this is the biggest problem in calculating a real weight. The k-means algorithm is an unsupervised clustering method that was used here. The best result was obtained by considering 3 centers. Compared to the normal average(one) or two, four, five, and six centers, the best answer is with 3 centers, which is logically due to the elimination of noise above and below the real weight. Every day, the standard weight is moved with working cranes to test and calibrate cranes. The results are shown that the accuracy is about 40 kilos per 60 tons (standard weight). As a result, with this method, the accuracy of moving weight is calculated as 99.95%. K-means is used to calculate the exact mean of objects. The stopping criterion of the algorithm is also the number of 1000 repetitions or not moving the points between the clusters. As a result of the implementation of this system, the crane operator does not stop while moving objects and continues his activity regardless of weight calculations. Also, production speed increased, and human error decreased.

Keywords: k-means, overhead crane, melt weight, weight estimation, swing problem

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Authors: Atefeh Esmailnejad, Gholamreza Nikbakht Brujeni

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Major histocompatibility complex (MHC) is one of the best characterized genetic regions associated with immune responses and controlling disease resistance in chicken. Association of the MHC with a wide range of immune responses makes it a valuable predictive factor for the disease pathogenesis and outcome. In this study, the association of MHC with cell-mediated immune responses was analyzed in commercial broiler chicken. The tandem repeat LEI0258 was applied to investigate the MHC polymorphism. Cell-mediated immune response was evaluated by peripheral blood lymphocyte proliferation assay using MTT method. Association study revealed a significant influence of MHC alleles on cellular immune responses in this population. Alleles 385 and 448 bp were associated with elevated cell-mediated immunity. Haplotypes associated with improved immune responses could be considered as candidate markers for disease resistance and applied to breeding strategies.

Keywords: MHC, cell-mediated immunity, broiler, chicken

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Authors: Fatemeh Babaeian, Nemai Chandra Karmakar

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Chipless RFID system is a wireless system for tracking and identification which use passive tags for encoding data. The advantage of using chipless RFID tag is having a planar tag which is printable on different low-cost materials like paper and plastic. The printed tag can be attached to different items in the labelling level. Since the price of chipless RFID tag can be as low as a fraction of a cent, this technology has the potential to compete with the conventional optical barcode labels. However, due to the passive structure of the tag, data processing of the reflection signal is a crucial challenge. The captured reflected signal from a tag attached to an item consists of different components which are the reflection from the reader antenna, the reflection from the item, the tag structural mode RCS component and the antenna mode RCS of the tag. All these components are summed up in both time and frequency domains. The effect of reflection from the item and the structural mode RCS component can distort/saturate the frequency domain signal and cause difficulties in extracting the desired component which is the antenna mode RCS. Therefore, it is required to study the reflection of the tag in both time and frequency domains to have a better understanding of the nature of the captured chipless RFID signal. The other benefits of this study can be to find an optimised encoding technique in tag design level and to find the best processing algorithm the chipless RFID signal in decoding level. In this paper, the reflection from a typical backscattered chipless RFID tag with six resonances is analysed, and different components of the signal are separated in both time and frequency domains. Moreover, the time domain signal corresponding to each resonator of the tag is studied. The data for this processing was captured from simulation in CST Microwave Studio 2017. The outcome of this study is understanding different components of a measured signal in a chipless RFID system and a discovering a research gap which is a need to find an optimum detection algorithm for tag ID extraction.

Keywords: antenna mode RCS, chipless RFID tag, resonance, structural mode RCS

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Authors: Natascha C. D'Amico, Enzo Grossi, Giovanni Valbusa, Ala Malasevschi, Gianpiero Cardone, Sergio Papa

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Purpose: To characterize, through a radiomic approach, the nature of areas classified PI-RADS (Prostate Imaging Reporting and Data System) 3/5, recognized in multiparametric prostate magnetic resonance with T2-weighted (T2w), diffusion and perfusion sequences with paramagnetic contrast. Methods and Materials: 24 cases undergoing multiparametric prostate MR and biopsy were admitted to this pilot study. Clinical outcome of the PI-RADS 3/5 was found through biopsy, finding 8 malignant tumours. The analysed images were acquired with a Philips achieva 1.5T machine with a CE- T2-weighted sequence in the axial plane. Semi-automatic tumour segmentation was carried out on MR images using 3DSlicer image analysis software. 45 shape-based, intensity-based and texture-based features were extracted and represented the input for preprocessing. An evolutionary algorithm (a TWIST system based on KNN algorithm) was used to subdivide the dataset into training and testing set and select features yielding the maximal amount of information. After this pre-processing 20 input variables were selected and different machine learning systems were used to develop a predictive model based on a training testing crossover procedure. Results: The best machine learning system (three-layers feed-forward neural network) obtained a global accuracy of 90% ( 80 % sensitivity and 100% specificity ) with a ROC of 0.82. Conclusion: Machine learning systems coupled with radiomics show a promising potential in distinguishing benign from malign tumours in PI-RADS 3/5 areas.

Keywords: machine learning, MR prostate, PI-Rads 3, radiomics

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Authors: R. Komsa-Penkova, G. Golemanov, G. Georgieva, K. Popovski, N. Slavov, P. Ivanov, K. Kovacheva, S. Rathee, E. Konova, A. Blajev

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Leptin and PAI-1 are important cytokines and may play a role in the regulation of PCOS development. PCOS is frequently associated with obesity, high BMI index and consequently with increased risk of metabolic disorders. The aim of the present study was to evaluate PAI-1 levels, genetic influence of the carriage of 675 4G/5G polymorphism in PAI-1 gene and leptin as a marker of obesity in the development of PCOS. Methods: Genotyping in 84 patients with PCOS and PCO and 100 healthy control subjects to detect single nucleotide deletion 675 G in the promoter of PAI-1 gene. The present study provides evidence that SNP 4G in the PAI-1 gene is associated with early pregnancy losses in patients with polycystosis. Further to this, there is a correlation between leptin levels, PAI-1 levels and BMI in the patients with PCOS, which confirms the role of obesity as a risk factor for PCOS.

Keywords: carriage of 675 4G/5G polymorphism, PCOS, early pregnancy losses, PAI-1 gene

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Authors: Sergo Tsiramua, Sulkhan Sulkhanishvili, Elisabed Asabashvili, Lazare Kvirtia

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The paper presents logical-probabilistic methods, models, and algorithms for reliability assessment of complex systems, based on which a web application for structural analysis and reliability assessment of systems was created. It is important to design systems based on structural analysis, research, and evaluation of efficiency indicators. One of the important efficiency criteria is the reliability of the system, which depends on the components of the structure. Quantifying the reliability of large-scale systems is a computationally complex process, and it is advisable to perform it with the help of a computer. Logical-probabilistic modeling is one of the effective means of describing the structure of a complex system and quantitatively evaluating its reliability, which was the basis of our application. The reliability assessment process included the following stages, which were reflected in the application: 1) Construction of a graphical scheme of the structural reliability of the system; 2) Transformation of the graphic scheme into a logical representation and modeling of the shortest ways of successful functioning of the system; 3) Description of system operability condition with logical function in the form of disjunctive normal form (DNF); 4) Transformation of DNF into orthogonal disjunction normal form (ODNF) using the orthogonalization algorithm; 5) Replacing logical elements with probabilistic elements in ODNF, obtaining a reliability estimation polynomial and quantifying reliability; 6) Calculation of “weights” of elements of system. Using the logical-probabilistic methods, models and algorithms discussed in the paper, a special software was created, by means of which a quantitative assessment of the reliability of systems of a complex structure is produced. As a result, structural analysis of systems, research, and designing of optimal structure systems are carried out.

Keywords: complex systems, logical-probabilistic methods, orthogonalization algorithm, reliability of systems, “weights” of elements

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Authors: Hamid Mustafa, Huson J. Heather, Kim Eiusoo, Adeela Ajmal, Tad S. Sonstegard

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The objective of this study was to evaluate the informativeness of Bovine high density SNPs genotyping in Sahiwal cattle population. This is a first attempt to assess the Bovine HD SNP genotyping array in any Pakistani indigenous cattle population. To evaluate these SNPs on genome wide scale, we considered 777,962 SNPs spanning the whole autosomal and X chromosomes in Sahiwal cattle population. Fifteen (15) non related gDNA samples were genotyped with the bovine HD infinium. Approximately 500,939 SNPs were found polymorphic (MAF > 0.05) in Sahiwal cattle population. The results of this study indicate potential application of Bovine High Density SNP genotyping in Pakistani indigenous cattle population. The information generated from this array can be applied in genetic prediction, characterization and genome wide association studies of Pakistani Sahiwal cattle population.

Keywords: Sahiwal cattle, polymorphic SNPs, genotyping, Pakistan

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Authors: Takuro Kida, Yuichi Kida

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We show a base of the new trend of algorithm mathematically that treats a historical reason of continuous discrimination in the world as well as its solution by introducing new concepts of parallel world that includes an invisible set of errors as its companion. With respect to a matrix operator-filter bank that the matrix operator-analysis-filter bank H and the matrix operator-sampling-filter bank S are given, firstly, we introduce the detailed algorithm to derive the optimum matrix operator-synthesis-filter bank Z that minimizes all the worst-case measures of the matrix operator-error-signals E(ω) = F(ω) − Y(ω) between the matrix operator-input-signals F(ω) and the matrix operator-output signals Y(ω) of the matrix operator-filter bank at the same time. Further, feedback is introduced to the above approximation theory and it is indicated that introducing conversations with feedback does not superior automatically to the accumulation of existing knowledge of signal prediction. Secondly, the concept of category in the field of mathematics is applied to the above optimum signal approximation and is indicated that the category-based approximation theory is applied to the set-theoretic consideration of the recognition of humans. Based on this discussion, it is shown naturally why the narrow perception that tends to create isolation shows an apparent advantage in the short term and, often, why such narrow thinking becomes intimate with discriminatory action in a human group. Throughout these considerations, it is presented that, in order to abolish easy and intimate discriminatory behavior, it is important to create a parallel world of conception where we share the set of invisible error signals, including the words and the consciousness of both worlds.

Keywords: signal prediction, pseudo inverse matrix, artificial intelligence, conditional optimization

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Authors: Shaista Suhail

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As cancer populations is increasing sharply, the incidence of oral squamous cell carcinoma (OSCC) has also been expected to increase. Oral carcinogenesis is a highly complex, multistep process which involves accumulation of genetic alterations that lead to the induction of proteins promoting cell growth (encoded by oncogenes), increased enzymatic (telomerase) activity promoting cancer cell proliferation. The global increase in frequency and mortality, as well as the poor prognosis of oral squamous cell carcinoma, has intensified current research efforts in the field of prevention and early detection of this disease. The advances in the understanding of the molecular basis of oral cancer should help in the identification of new markers. The study of the carcinogenic process of the oral cancer, including continued analysis of new genetic alterations, along with their temporal sequencing during initiation, promotion and progression, will allow us to identify new diagnostic and prognostic factors, which will provide a promising basis for the application of more rational and efficient treatments. Telomerase activity has been readily found in most cancer biopsies, in premalignant lesions or germ cells. Activity of telomerase is generally absent in normal tissues. It is known to be induced upon immortalization or malignant transformation of human cells such as in oral cancer cells. Maintenance of telomeres plays an essential role during transformation of precancer to malignant stage. Mammalian telomeres, a specialized nucleoprotein structures are composed of large conctamers of the guanine-rich sequence 5_-TTAGGG-3_. The roles of telomeres in regulating both stability of genome and replicative immortality seem to contribute in essential ways in cancer initiation and progression. It is concluded that activity of telomerase can be used as a biomarker for diagnosis of malignant oral cancer and a target for inactivation in chemotherapy or gene therapy. Its expression will also prove to be an important diagnostic tool as well as a novel target for cancer therapy. The activation of telomerase may be an important step in tumorgenesis which can be controlled by inactivating its activity during chemotherapy. The expression and activity of telomerase are indispensable for cancer development. There are no drugs which can effect extremely to treat oral cancers. There is a general call for new emerging drugs or methods that are highly effective towards cancer treatment, possess low toxicity, and have a minor environment impact. Some novel natural products also offer opportunities for innovation in drug discovery. Natural compounds isolated from medicinal plants, as rich sources of novel anticancer drugs, have been of increasing interest with some enzyme (telomerase) blockage property. The alarming reports of cancer cases increase the awareness amongst the clinicians and researchers pertaining to investigate newer drug with low toxicity.

Keywords: oral carcinoma, telomere, telomerase, blockage

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Authors: Narin Salehiyan, Ramin Ghasemi Shayan

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The largest genus of plant viruses, the potyvirus, is responsible for significant crop losses. Potyviruses are aphid sent in a nonpersistent way, and some of them are likewise seed communicated. As significant microorganisms, potyviruses are substantially more examined than other plant infections having a place with different genera, and their review covers numerous parts of plant virology, like utilitarian portrayal of viral proteins, sub-atomic communication with hosts and vectors, structure, scientific classification, development, the study of disease transmission, and determination. Biotechnological utilizations of potyviruses are likewise being investigated. During this last ten years, significant advances have been made in the comprehension of the sub-atomic science of these infections and the elements of their different proteins. Potyvirus multiplication, movement, and transmission, as well as potyvirus/plant compatible interactions, including pathogenicity and symptom determinants, are updated following a general overview of the family Potyviridae and the potyviral proteins. it end the survey giving data on biotechnological uses of potyviruses.

Keywords: virology, poty, virus, genome, genetic

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Authors: Zeyad Abdelmageid, Xianbin Wang

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Choosing the operational channel for a WLAN access point (AP) in WLAN networks has been a static channel assignment process initiated by the user during the deployment process of the AP, which fails to cope with the dynamic conditions of the assigned channel at the station side afterward. However, the dramatically growing number of Wi-Fi APs and stations operating in the unlicensed band has led to dynamic, distributed, and often severe interference. This highlights the urgent need for the AP to dynamically select the best overall channel of operation for the basic service set (BSS) by considering the distributed and changing channel conditions at all stations. Consequently, dynamic channel selection algorithms which consider feedback from the station side have been developed. Despite the significant performance improvement, existing channel selection algorithms suffer from very high feedback overhead. Feedback latency from the STAs, due to the high overhead, can cause the eventually selected channel to no longer be optimal for operation due to the dynamic sharing nature of the unlicensed band. This has inspired us to develop our own dynamic channel selection algorithm with reduced overhead through the proposed low-overhead, cluster-based station reporting mechanism. The main idea behind the cluster-based station reporting is the observation that STAs which are very close to each other tend to have very similar channel conditions. Instead of requesting each STA to report on every candidate channel while causing high overhead, the AP divides STAs into clusters then assigns each STA in each cluster one channel to report feedback on. With the proper design of the cluster based reporting, the AP does not lose any information about the channel conditions at the station side while reducing feedback overhead. The simulation results show equal performance and, at times, better performance with a fraction of the overhead. We believe that this algorithm has great potential in designing future dynamic channel selection algorithms with low overhead.

Keywords: channel assignment, Wi-Fi networks, clustering, DBSCAN, overhead

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

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Authors: Magdy Roman, Mostafa Shoeib, Mostafa Rostom

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Global vision, whether provided by overhead fixed cameras, on-board aerial vehicle cameras, or satellite images can always provide detailed information on the environment around mobile robots. In this paper, an intelligent vision-based method of path planning and obstacle avoidance for mobile robots is presented. The method integrates visual perception with a new proposed field-based path-planning method to overcome common path-planning problems such as local minima, unreachable destination and unnecessary lengthy paths around obstacles. The method proposes an exponential angle deviation field around each obstacle that affects the orientation of a close robot. As the robot directs toward, the goal point obstacles are classified into right and left groups, and a deviation angle is exponentially added or subtracted to the orientation of the robot. Exponential field parameters are chosen based on Lyapunov stability criterion to guarantee robot convergence to the destination. The proposed method uses obstacles' shape and location, extracted from global vision system, through a collision prediction mechanism to decide whether to activate or deactivate obstacles field. In addition, a search mechanism is developed in case of robot or goal point is trapped among obstacles to find suitable exit or entrance. The proposed algorithm is validated both in simulation and through experiments. The algorithm shows effectiveness in obstacles' avoidance and destination convergence, overcoming common path planning problems found in classical methods.

Keywords: path planning, collision avoidance, convergence, computer vision, mobile robots

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Authors: Lim Chen Kim, Tan Kian Lam, Chan Yi Chee

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This research presents a multi-modal simulation in the reconstruction of the past and the construction of present in digital cultural heritage on mobile platform. In bringing the present life, the virtual environment is generated through a presented scheme for rapid and efficient construction of 360° panoramic view. Then, acoustical heritage model and crowd model are presented and improvised into the 360° panoramic view. For the reconstruction of past life, the crowd is simulated and rendered in an old trading port. However, the keystone of this research is in a virtual walkthrough that shows the virtual present life in 2D and virtual past life in 3D, both in an environment of virtual heritage sites in George Town through mobile device. Firstly, the 2D crowd is modelled and simulated using OpenGL ES 1.1 on mobile platform. The 2D crowd is used to portray the present life in 360° panoramic view of a virtual heritage environment based on the extension of Newtonian Laws. Secondly, the 2D crowd is animated and rendered into 3D with improved variety and incorporated into the virtual past life using Unity3D Game Engine. The behaviours of the 3D models are then simulated based on the enhancement of the classical model of Boid algorithm. Finally, a demonstration system is developed and integrated with the models, techniques and algorithms of this research. The virtual walkthrough is demonstrated to a group of respondents and is evaluated through the user-centred evaluation by navigating around the demonstration system. The results of the evaluation based on the questionnaires have shown that the presented virtual walkthrough has been successfully deployed through a multi-modal simulation and such a virtual walkthrough would be particularly useful in a virtual tour and virtual museum applications.

Keywords: Boid Algorithm, Crowd Simulation, Mobile Platform, Newtonian Laws, Virtual Heritage

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Authors: Ł. Morawiński, C. Jasiński, M. Jurkiewicz, S. Bou Habib, M. Bondyra

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With the development of optics, electronics, and computers, vision systems are increasingly used in various areas of life, science, and industry. Vision systems have a huge number of applications. They can be used in quality control, object detection, data reading, e.g., QR-code, etc. A large part of them is used for measurement purposes. Some of them make it possible to obtain a 3D reconstruction of the tested objects or measurement areas. 3D reconstruction algorithms are mostly based on creating depth maps from data that can be acquired from active or passive methods. Due to the specific appliance in airfield technology, only passive methods are applicable because of other existing systems working on the site, which can be blinded on most spectral levels. Furthermore, reconstruction is required to work long distances ranging from hundreds of meters to tens of kilometers with low loss of accuracy even with harsh conditions such as fog, rain, or snow. In response to those requirements, HRESS (Heliport REmote Safeguard System) was developed; which main part is a rotational head with a two-camera stereovision rig gathering images around the head in 360 degrees along with stereovision 3D reconstruction and point cloud combination. The sub-pixel analysis introduced in the HRESS system makes it possible to obtain an increased distance measurement resolution and accuracy of about 3% for distances over one kilometer. Ultimately, this leads to more accurate and reliable measurement data in the form of a point cloud. Moreover, the program algorithm introduces operations enabling the filtering of erroneously collected data in the point cloud. All activities from the programming, mechanical and optical side are aimed at obtaining the most accurate 3D reconstruction of the environment in the measurement area.

Keywords: airfield monitoring, artificial intelligence, stereovision, 3D reconstruction

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Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

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Authors: Manel Langar, Riad Bourguiba, Jaouhar Mouine

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In this paper, we propose the design and implementation of an enhanced wormhole virtual channel on chip router. It is a heart of a mesh NoC using the XY deterministic routing algorithm. It is characterized by its simple virtual channel allocation strategy which allows reducing area and complexity of connections without affecting the performance. We implemented our router on a Tezzaron process to validate its performances. This router is a basic element that will be used later to design a 3D mesh NoC.

Keywords: NoC, mesh, router, 3D NoC

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Authors: Adam Majczak, Michał Bialy, Zbigniew Czyż, Zdzislaw Kaminski

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Increasingly stringent emission standards for passenger cars require us to find alternative drives. The share of electric vehicles in the sale of new cars increases every year. However, their performance and, above all, range cannot be today successfully compared to those of cars with a traditional internal combustion engine. Battery recharging lasts hours, which can be hardly accepted due to the time needed to refill a fuel tank. Therefore, the ways to reduce the adverse features of cars equipped with electric motors only are searched for. One of the methods is a combination of an electric engine as a main source of power and a small internal combustion engine as an electricity generator. This type of drive enables an electric vehicle to achieve a radically increased range and low emissions of toxic substances. For several years, the leading automotive manufacturers like the Mazda and the Audi together with the best companies in the automotive industry, e.g., AVL have developed some electric drive systems capable of recharging themselves while driving, known as a range extender. An electricity generator is powered by a Wankel engine that has seemed to pass into history. This low weight and small engine with a rotating piston and a very low vibration level turned out to be an excellent source in such applications. Its operation as an energy source for a generator almost entirely eliminates its disadvantages like high fuel consumption, high emission of toxic substances, or short lifetime typical of its traditional application. The operation of the engine at a constant rotational speed enables a significant increase in its lifetime, and its small external dimensions enable us to make compact modules to drive even small urban cars like the Audi A1 or the Mazda 2. The algorithm to maintain a constant speed was investigated on the engine dynamometer with an eddy current brake and the necessary measuring apparatus. The research object was the Aixro XR50 rotary engine with the electronic power supply developed at the Lublin University of Technology. The load torque of the engine was altered during the research by means of the eddy current brake capable of giving any number of load cycles. The parameters recorded included speed and torque as well as a position of a throttle in an inlet system. Increasing and decreasing load did not significantly change engine speed, which means that control algorithm parameters are correctly selected. This work has been financed by the Polish Ministry of Science and Higher Education.

Keywords: electric vehicle, power generator, range extender, Wankel engine

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Jaehong Yu, Seoung Bum Kim

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Clustering analysis can facilitate the extraction of intrinsic patterns in a dataset and reveal its natural groupings without requiring class information. For effective clustering analysis in high dimensional datasets, unsupervised dimensionality reduction is an important task. Unsupervised dimensionality reduction can generally be achieved by feature extraction or feature selection. In many situations, feature selection methods are more appropriate than feature extraction methods because of their clear interpretation with respect to the original features. The unsupervised feature selection can be categorized as feature subset selection and feature ranking method, and we focused on unsupervised feature ranking methods which evaluate the features based on their importance scores. Recently, several unsupervised feature ranking methods were developed based on ensemble approaches to achieve their higher accuracy and stability. However, most of the ensemble-based feature ranking methods require the true number of clusters. Furthermore, these algorithms evaluate the feature importance depending on the ensemble clustering solution, and they produce undesirable evaluation results if the clustering solutions are inaccurate. To address these limitations, we proposed an ensemble-based feature ranking method with random subspace and multiple-k ensemble (FRRM). The proposed FRRM algorithm evaluates the importance of each feature with the random subspace ensemble, and all evaluation results are combined with the ensemble importance scores. Moreover, FRRM does not require the determination of the true number of clusters in advance through the use of the multiple-k ensemble idea. Experiments on various benchmark datasets were conducted to examine the properties of the proposed FRRM algorithm and to compare its performance with that of existing feature ranking methods. The experimental results demonstrated that the proposed FRRM outperformed the competitors.

Keywords: clustering analysis, multiple-k ensemble, random subspace-based feature evaluation, unsupervised feature ranking

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Authors: Eszter Repasi, Akos Koller

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Our genetic information determines our look, physiology, sports performance and all our features. Maximizing the performances of athletes have adopted a science-based approach to the nutritional support. Nowadays genetics studies have blended with nutritional sciences, and a dynamically evolving, new research field have appeared. Nutritional genomics is needed to be used by nutritional experts. This is a recent field of nutritional science, which can provide a solution to reach the best sport performance using correlations between the athlete’s genome, nutritions, molecules, included human microbiome (links between food, microbiome and epigenetics), nutrigenomics and nutrigenetics. Nutritional genomics has a tremendous potential to change the future of dietary guidelines and personal recommendations. Experts need to use new technology to get information about the athletes, like nutritional genomics profile (included the determination of the oral and gut microbiome and DNA coded reaction for food components), which can modify the preparation term and sports performance. The influence of nutrients on the genes expression is called Nutrigenomics. The heterogeneous response of gene variants to nutrients, dietary components is called Nutrigenetics. The human microbiome plays a critical role in the state of health and well-being, and there are more links between food or nutrition and the human microbiome composition, which can develop diseases and epigenetic changes as well. A nutritional genomics-based profile of athletes can be the best technic for a dietitian to make a unique sports nutrition diet plan. Using functional food and the right food components can be effected on health state, thus sports performance. Scientists need to determine the best response, due to the effect of nutrients on health, through altering genome promote metabolites and result changes in physiology. Nutritional biochemistry explains why polymorphisms in genes for the absorption, circulation, or metabolism of essential nutrients (such as n-3 polyunsaturated fatty acids or epigallocatechin-3-gallate), would affect the efficacy of that nutrient. Controlled nutritional deficiencies and failures, prevented the change of health state or a newly discovered food intolerance are observed by a proper medical team, can support better sports performance. It is important that the dietetics profession informed on gene-diet interactions, that may be leading to optimal health, reduced risk of injury or disease. A special medical application for documentation and monitoring of data of health state and risk factors can uphold and warn the medical team for an early action and help to be able to do a proper health service in time. This model can set up a personalized nutrition advice from the status control, through the recovery, to the monitoring. But more studies are needed to understand the mechanisms and to be able to change the composition of the microbiome, environmental and genetic risk factors in cases of athletes.

Keywords: gene-diet interaction, multidisciplinary team, microbiome, diet plan

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Authors: Niroshan K. Walgama Wellalage, Tieling Zhang, Richard Dwight

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State-based Markov deterioration models (SMDM) sometimes fail to find accurate transition probability matrix (TPM) values, and hence lead to invalid future condition prediction or incorrect average deterioration rates mainly due to drawbacks of existing nonlinear optimization-based algorithms and/or subjective function types used for regression analysis. Furthermore, a set of separate functions for each condition state with age cannot be directly derived by using Markov model for a given bridge element group, which however is of interest to industrial partners. This paper presents a new approach for generating Homogeneous SMDM model output, namely, the Modified Weibull approach, which consists of a set of appropriate functions to describe the percentage condition prediction of bridge elements in each state. These functions are combined with Bayesian approach and Metropolis Hasting Algorithm (MHA) based Markov Chain Monte Carlo (MCMC) simulation technique for quantifying the uncertainty in model parameter estimates. In this study, factors contributing to rail bridge deterioration were identified. The inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered accordingly based on the real operational experience. Network level deterioration model for a typical bridge element group was developed using the proposed Modified Weibull approach. The condition state predictions obtained from this method were validated using statistical hypothesis tests with a test data set. Results show that the proposed model is able to not only predict the conditions in network-level accurately but also capture the model uncertainties with given confidence interval.

Keywords: bridge deterioration modelling, modified weibull approach, MCMC, metropolis-hasting algorithm, bayesian approach, Markov deterioration models

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Authors: M. Siedlikowski, F. Rauch, A. Tsimicalis

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Osteogenesis Imperfecta (OI) is a genetic disorder of childhood onset that causes frequent fractures after minimal physical stress. To date, OI research has focused on medically- and surgically-oriented outcomes with little attention on the perspective of the affected child. It is a challenge to elicit the child’s voice in health care, in other words, their own perspective on their symptoms, but software development offers a way forward. Sisom (Norwegian acronym derived from ‘Si det som det er’ meaning ‘Tell it as it is’) is an award-winning, rigorously tested, interactive, computerized tool that helps children with chronic illnesses express their symptoms to their clinicians. The successful Sisom software tool, that addresses the child directly, has not yet been adapted to attend to symptoms unique to children with OI. The purpose of this study was to develop a Sisom paper prototype for children with OI by seeking the perspectives of end users, particularly, children with OI and clinicians. Our descriptive qualitative study was conducted at Shriners Hospitals for Children® – Canada, which follows the largest cohort of children with OI in North America. Purposive sampling was used to recruit 12 children with OI over three cycles. Nine clinicians oversaw the development process, which involved determining the relevance of current Sisom symptoms, vignettes, and avatars, as well as generating new Sisom OI components. Data, including field notes, transcribed audio-recordings, and drawings, were deductively analyzed using content analysis techniques. Guided by the following framework, data pertaining to symptoms, vignettes, and avatars were coded into five categories: a) Relevant; b) Irrelevant; c) To modify; d) To add; e) Unsure. Overall, 70.8% of Sisom symptoms were deemed relevant for inclusion, with 49.4% directly incorporated, and 21.3% incorporated with changes to syntax, and/or vignette, and/or location. Three additions were made to the ‘Avatar’ island. This allowed children to celebrate their uniqueness: ‘Makes you feel like you’re not like everybody else.’ One new island, ‘About Me’, was added to capture children’s worldviews. One new sub-island, ‘Getting Around’, was added to reflect accessibility issues. These issues were related to the children’s independence, their social lives, as well as the perceptions of others. In being consulted as experts throughout the co-creation of the Sisom OI paper prototype, children coded the Sisom symptoms and provided sound rationales for their chosen codes. In rationalizing their codes, all children shared personal stories about themselves and their relationships, insights about their OI, and an understanding of the strengths and challenges they experience on a day-to-day basis. The child’s perspective on their health is a basic right, and allowing it to be heard is the next frontier in the care of children with genetic diseases. Sisom OI, a methodological breakthrough within OI research, will offer clinicians an innovative and child-centered approach to capture this neglected perspective. It will provide a tool for the delivery of health care in the center that established the worldwide standard of care for children with OI.

Keywords: child health, interactive computerized communication tool, participatory approach, symptom management

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