Search results for: Cluster grouping

Authors: Ana Marie T. Acevedo

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This study was conducted to describe variations in the shapes of the elytra, head and pronotum of populations of adult Brontispa longissima (Gestro) infesting coconut farms from selected areas in the Philippines using Cluster Analysis, Relative Warp Analysis coupled with box plot and histograms and Procustean analysis. The data used in this study included shape residuals captured using the method of landmark based geometric morphometrics. Results: The results of the cluster analyses based on the average shapes of the elytra, head and pronotum shows no consistent pattern of similarity between and among five populations of B. longissima. When localized variations using Relative Warp Analysis coupled with box plot and histograms was done, the findings revealed that RWA was only successful in summarizing variations using two relative warps in the shape of the elytra where the first two warps contained 86.29% of the variations of the female and 85.48% for the males. For the head and pronotum, the first two relative warps captured less than 50% of the overall variation. Looking at the shapes of the frequency histograms, all were found to follow a unimodal distribution. The box plots reveal no consistent results. Among the three characters studied only the elytra were more robust and reliable compared to head and pronotum and then Tandag differ from the rest of the other over-lapping populations. On the other hand, Procustean Analyses revealed that elytra were more spread in the posterior region both in male and female. The coordinates in head and pronotum were evenly distributed. In the overlapping consensus configurations show that variability was exaggerated in the right side of the elytra and the posterior parts of the head and pronotum. Results also showed expansion among females while compression among males in elytra. For males, expansion are localized in the posterior part of the elytra, For the head, results showed asymmetry in the distribution of expansion areas where expansion are observed in the right postero-lateral aspect of the female head. Conclusion: The overall results may imply that they might belong to one operational taxonomic unit or ecotype or biotype. Geography might not be the factor responsible for the differentiation of the populations of B. longissima.

Keywords: cluster analysis, relative warp analysis, procrustean analysis, environmental parameters

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Authors: A. Dashti, M. Eskandari, L. Farahmand, P. Parvin, A. Jafargholi

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Breast Cancer is one of the most considerable diseases in the United States and other countries and is the second leading cause of death in women. Common breast cancer treatments would lead to adverse side effects such as loss of hair, nausea, and weakness. These complications arise because these cancer treatments damage some healthy cells while eliminating the cancer cells. In an effort to address these complications, laser radiation was utilized and tested as a targeted cancer treatment for breast cancer. In this regard, tissue engineering approaches are being employed by using an electrospun scaffold in order to facilitate the growth of breast cancer cells. Polycaprolacton (PCL) was used as a material for scaffold fabricating because of its biocompatibility, biodegradability, and supporting cell growth. The specific breast cancer cells have the ability to create a three-dimensional cell cluster due to the spontaneous accumulation of cells in the porosity of the scaffold under some specific conditions. Therefore, we are looking for a higher density of porosity and larger pore size. Fibers showed uniform diameter distribution and final scaffold had optimum characteristics with approximately 40% porosity. The images were taken by SEM and the density and the size of the porosity were determined with the Image. After scaffold preparation, it has cross-linked by glutaraldehyde. Then, it has been washed with glycine and phosphate buffer saline (PBS), in order to neutralize the residual glutaraldehyde. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromidefor (MTT) results have represented approximately 91.13% viability of the scaffolds for cancer cells. In order to create a cluster, Michigan Cancer Foundation-7 (MCF-7, breast cancer cell line) and Michigan Cancer Foundation-10A (MCF-10A, human mammary epithelial cell line) cells were cultured on the scaffold in 24 well plate for five days. Then, we have exposed the cluster to the laser diode 808 nm radiation to investigate the effect of laser on the tumor with different power and time. Under the same conditions, cancer cells lost their viability more than the healthy ones. In conclusion, laser therapy is a viable method to destroy the target cells and has a minimum effect on the healthy tissues and cells and it can improve the other method of cancer treatments limitations.

Keywords: breast cancer, electrospun scaffold, polycaprolacton, laser diode, cancer treatment

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Authors: Moulana Mohammed

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Topic models are widely used in building clusters of documents for more than a decade, yet problems occurring in choosing optimal number of topics. The main problem is the lack of a stable metric of the quality of topics obtained during the construction of topic models. The authors analyzed from previous works, most of the models used in determining the number of topics are non-parametric and quality of topics determined by using perplexity and coherence measures and concluded that they are not applicable in solving this problem. In this paper, we used the parametric method, which is an extension of the traditional topic model with visual access tendency for visualization of the number of topics (clusters) to complement clustering and to choose optimal number of topics based on results of cluster validity indices. Developed hybrid topic models are demonstrated with different Twitter datasets on various topics in obtaining the optimal number of topics and in measuring the quality of clusters. The experimental results showed that the Visual Non-negative Matrix Factorization (VNMF) topic model performs well in determining the optimal number of topics with interactive visualization and in performance measure of the quality of clusters with validity indices.

Keywords: interactive visualization, visual mon-negative matrix factorization model, optimal number of topics, cluster validity indices, Twitter data clustering

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Authors: M. Roberts Masillamani

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There is a phenomenal difference, as regard to the teaching methodology adopted at the urban and the rural area colleges. However, bright and talented student may be from rural back ground even. But there is huge dearth of the digitization in the rural areas and lesser developed countries. Today’s students need new skills to compete and successful in the future. Education should be combination of practical, intellectual, and social skills. What does this mean for rural classrooms and how can it be achieved. Rural colleges are not able to hire the best resources, since the best teacher’s aim is to move towards the city. If city is provided everywhere, then there will be no rural area. This is possible by forming university clusters (UC). The University cluster is a group of renowned and accredited universities coming together to bridge this dearth. The UC will deliver the live lectures and allow the students’ from remote areas to actively participate in the classroom. This paper tries to present a plan of action of providing a better live classroom teaching and learning system from the city to the rural and the lesser developed countries. This paper titled “University Clusters using ICT for teaching and learning” provides a true concept of opening live digital classroom windows for rural colleges, where resources are not available, thus reducing the digital divide. This is different from pod casting a lecture or distance learning and eLearning. The live lecture can be streamed through digital equipment to another classroom. The rural students can collaborate with their peers and critiques, be assessed, collect information, acquire different techniques in assessment and learning process. This system will benefit rural students and teachers and develop socio economic status. This will also will increase the degree of confidence of the Rural students and teachers. Thus bringing about the concept of ‘Train the Trainee’ in reality. An educational university cloud for each cluster will be built remote infrastructure facilities (RIF) for the above program. The users may be informed, about the available lecture schedules, through the RIF service. RIF with an educational cloud can be set by the universities under one cluster. This paper talks a little more about University clusters and the methodology to be adopted as well as some extended features like, tutorial classes, library grids, remote laboratory login, research and development.

Keywords: lesser developed countries, digital divide, digital learning, education, e-learning, ICT, library grids, live classroom windows, RIF, rural, university clusters and urban

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Authors: Laura A. Rodriguez-Villamizar, Alvaro Osornio-Vargas, Brian H. Rowe, Rhonda J. Rosychuk

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Introduction/Objectives: The Census Metropolitan Area of Edmonton (CMAE) has important industrial emissions to the air from the Industrial Heartland Alberta (IHA) at the Northeast and the coal-fired power plants (CFPP) at the West. The objective of the study was to explore the presence of clusters of children asthma ED visits in the areas around the IHA and the CFPP. Methods: Retrospective data on children asthma ED visits was collected at the dissemination area (DA) level for children between 2 and 14 years of age, living in the CMAE between April 1, 2004, and March 31, 2010. We conducted a spatial analysis of disease clusters around putative sources with count (ecological) data using descriptive, hypothesis testing, and multivariable modeling analysis. Results: The mean crude rate of asthma ED visits was 9.3/1,000 children population per year during the study period. Circular spatial scan test for cases and events identified a cluster of children asthma ED visits in the DA where the CFPP are located in the Wabamum area. No clusters were identified around the IHA area. The multivariable models suggest that there is a significant decline in risk for children asthma ED visits as distance increases around the CFPP area this effect is modified at the SE direction with mean angle 125.58 degrees, where the risk increases with distance. In contrast, the regression models for IHA suggest that there is a significant increase in risk for children asthma ED visits as distance increases around the IHA area and this effect is modified at SW direction with mean angle 216.52 degrees, where the risk increases at shorter distances. Conclusions: Different methods for detecting clusters of disease consistently suggested the existence of a cluster of children asthma ED visits around the CFPP but not around the IHA within the CMAE. These results are probably explained by the direction of the air pollutants dispersion caused by the predominant and subdominant wind direction at each point. The use of different approaches to detect clusters of disease is valuable to have a better understanding of the presence, shape, direction and size of clusters of disease around pollution sources.

Keywords: air pollution, asthma, disease cluster, industry

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Authors: Kerem Rızvanoglu, Serhat Güney, Betül Aydoğan, Emre Kızılkaya, Ayşegül Boyalı, Onurcan Güden

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This study investigates the urban experience of international students coming to Istanbul with exchange programs and reveals how locative media applications accompany their urban experiences. The sample of the research consists of international students who lived, perceived, and conceived the city on a daily basis during the academic year of 2022. Focusing on this particular sample would demonstrate the opportunities and authentic experiences offered by the city as well as the prevalent urban problems for the foreigners. In this regard, international students' urban experience in Istanbul, the blockages they encounter as resident tourists, the hotspots that the city offers, and the role of locative media in enriching the urban experience are the main axes to be evaluated. In the first step of the multi-staged research, we conduct an online qualitative survey with a sample; then, we evaluate the data obtained from the survey using cluster analysis to identify the urban experience, consumption habits, and tastes. In the final stage, digital ethnographic fieldwork will be carried out with representative personas identified by the cluster analysis. With this field research on the urban experience accompanied by locative media applications, suggestions will be developed by evaluating the opportunities these applications offer to enrich the urban practice of foreigners.

Keywords: digital ethnography, international students, locative media applications, urban experience

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Authors: Mohsen Bahrami, Banafsheh Nikmehr, Yueqiang Song, Anuradha Koduru, Ayse K. Vuruskan, Hongkun Lu, Tamer M. Yalcinkaya

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In recent years, we have witnessed an explosion of studies aimed at using a combination of artificial intelligence (AI) and time-lapse imaging data on embryos to improve IVF outcomes. However, despite promising results, no study has used a matched cohort of transferred embryos which only differ in pregnancy outcome, i.e., embryos from a single clinic which are similar in parameters, such as: morphokinetic condition, patient age, and overall clinic and lab performance. Here, we used time-lapse data on embryos with known pregnancy outcomes to see if the rich spatiotemporal information embedded in this data would allow the prediction of the pregnancy outcome regardless of such critical parameters. Methodology—We did a retrospective analysis of time-lapse data from our IVF clinic utilizing Embryoscope 100% of the time for embryo culture to blastocyst stage with known clinical outcomes, including live birth vs nonpregnant (embryos with spontaneous abortion outcomes were excluded). We used time-lapse data from 200 elective single transfer embryos randomly selected from January 2019 to June 2021. Our sample included 100 embryos in each group with no significant difference in patient age (P=0.9550) and morphokinetic scores (P=0.4032). Data from all patients were combined to make a 4th order tensor, and feature extraction were subsequently carried out by a tensor decomposition methodology. The features were then used in a machine learning classifier to classify the two groups. Major Findings—The performance of the model was evaluated using 100 random subsampling cross validation (train (80%) - test (20%)). The prediction accuracy, averaged across 100 permutations, exceeded 80%. We also did a random grouping analysis, in which labels (live birth, nonpregnant) were randomly assigned to embryos, which yielded 50% accuracy. Conclusion—The high accuracy in the main analysis and the low accuracy in random grouping analysis suggest a consistent spatiotemporal pattern which is associated with pregnancy outcomes, regardless of patient age and embryo morphokinetic condition, and beyond already known parameters, such as: early cleavage or early blastulation. Despite small samples size, this ongoing analysis is the first to show the potential of AI methods in capturing the complex morphokinetic changes embedded in embryo time-lapse data, which contribute to successful pregnancy outcomes, regardless of already known parameters. The results on a larger sample size with complementary analysis on prediction of other key outcomes, such as: euploidy and aneuploidy of embryos will be presented at the meeting.

Keywords: IVF, embryo, machine learning, time-lapse imaging data

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Authors: Vladimir Kulyukin, Tanwir Zaman, Sarat Kiran Andhavarapu

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Research on nutrition label use identifies four factors that impede comprehension and retention of nutrition information by consumers: label’s location on the package, presentation of information within the label, label’s surface size, and surrounding visual clutter. In this paper, a system is presented that makes nutrition label use more effective for nutrition information comprehension and retention. The system’s front end is a smartphone application. The system’s back end is a four node Linux cluster for image recognition and data storage. Image frames captured on the smartphone are sent to the back end for skewed or aligned barcode recognition. When barcodes are recognized, corresponding nutrition labels are retrieved from a cloud database and presented to the user on the smartphone’s touchscreen. Each displayed nutrition label is positioned centrally on the touchscreen with no surrounding visual clutter. Wikipedia links to important nutrition terms are embedded to improve comprehension and retention of nutrition information. Standard touch gestures (e.g., zoom in/out) available on mainstream smartphones are used to manipulate the label’s surface size. The nutrition label database currently includes 200,000 nutrition labels compiled from public web sites by a custom crawler. Stress test experiments with the node cluster are presented. Implications for proactive nutrition management and food policy are discussed.

Keywords: mobile computing, cloud computing, nutrition label use, nutrition management, barcode scanning

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Authors: Nelson Bii, Christopher Ouma, John Odhiambo

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Non-response is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random non-response using auxiliary data. In this study, it is assumed that random non-response occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random non-response. In particular, the auxiliary information is used via an improved Nadaraya-Watson kernel regression technique to compensate for random non-response. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at a 95% coverage rate. The results obtained in this study are useful, for instance, in choosing efficient estimators of the finite population mean in demographic sample surveys.

Keywords: mean squared error, random non-response, two-stage cluster sampling, confidence interval lengths

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Authors: Z. Ulukan, E. Demircioğlu,

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Facility location is a complex real-world problem which needs a strategic management decision. This paper provides a general review on studies, efforts and developments in Facility Location Problems which are classical optimization problems having a wide-spread applications in various areas such as transportation, distribution, production, supply chain decisions and telecommunication. Our goal is not to review all variants of different studies in FLPs or to describe very detailed computational techniques and solution approaches, but rather to provide a broad overview of major location problems that have been studied, indicating how they are formulated and what are proposed by researchers to tackle the problem. A brief, elucidative table based on a grouping according to “General Problem Type” and “Methods Proposed” used in the studies is also presented at the end of the work.

Keywords: discrete location problems, exact methods, heuristic algorithms, single source capacitated facility location problems

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Authors: Saleh Hayat

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Efficiency, productivity and sustainability are important factors for structure and the application of processes in green building. Various previous studies have addressed efficiency, productivity and sustainability separately. This research study aims to investigate the implications of these three factors taking together. Frequency analysis and the ranking techniques are carried out to explore the connection between these factors. The interconnection matrix has been developed and functional grouping is made based upon data from expert opinion and field professionals. The existence of a relationship, the type of relationship and the scaled impact have been drawn. Additionally, a system diagram has been developed to show the variable correlation. The results of expert opinion show that efficiency, productivity and sustainability have a stronger impact on green buildings.

Keywords: efficiency, green building, productivity, sustainability

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Authors: Maximilian A. Richter

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The public authority needs to understand the role and impacts of autonomous vehicle (AV) on the mobility system. At present, however, research shows that the impact of AV on cities varies. As a consequence, it is difficult to make recommendations to policymakers on how they should prepare for the future when so much remains unknown about this technology. The study aims to provide an overview of the literature on how autonomous vehicles will affect the cities and traffic of the future. To this purpose, the most important studies are first selected, and their results summarized. Further on, it will be clarified which advantages AV have for cities and how it can lead to an improvement in the current problems/challenges of cities. To achieve the research aim and objectives, this paper approaches a literature review. For this purpose, in a first step, the most important studies are extracted. This is limited to studies that are peer-reviewed and have been published in high-ranked journals such as the Journal of Transportation: Part A. In step 2, the most important key performance indicator (KPIs) (such as traffic volume or energy consumption) are selected from the literature research. Due to the fact that different terms are used in the literature for similar statements/KPIs, these must first be clustered. Furthermore, for each cluster, the changes from the respective studies are compiled, as well as their survey methodology. In step 3, a sensitivity analysis per cluster is made. Here, it will be analyzed how the different studies come to their findings and on which assumptions, scenarios, and methods these calculations are based. From the results of the sensitivity analysis, the success factors for the implementation of autonomous vehicles are drawn, and statements are made under which conditions AVs can be successful.

Keywords: autonomous vehicles, city of the future, literature review, traffic simulations

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Authors: Nassima Noufail, Sara Bouhali

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In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.

Keywords: video segmentation, action detection, classification, Kmeans, C3D

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Authors: Akbar Darouie, Mamak Joulaie

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In regard of phonological skills development importance and its influence on other aspects of language, this study has been performed. Determination of some phonological indexes in children with hearing impairment and comparison with hearing children was the objective. A sample of convenience was selected from a rehabilitation center and a kindergarten in Karaj, Iran. Participants consisted of 12 hearing impaired and 12 hearing children (age range: 5 years and 6 months to 6 years and 6 months old). Hearing impaired children suffered from severe to profound hearing loss while three of them were cochlear implanted and the others were wearing hearing aids. Conversational speech of these children was recorded and 50 first utterances were selected to analyze. Percentage of consonant correct (PCC) and vowel correct (PVC), initial and final consonant omission error, cluster consonant omission error and syllabic structure variety were compared in two groups. Data were analyzed with t test (version 16th SPSS). Comparison between PCC and PVC averages in two groups showed a significant difference (P< 0/01). There was a significant difference about final consonant emission error (P<0/001) and initial consonant emission error (P<0/01) too. Also, the differences between two groups on cluster consonant omission were significant (P<0/001). Therefore, some changes were seen in syllabic structures in children with hearing impairment compared to typical group. This study demonstrates some phonological differences in Farsi language between two groups of children. Therefore, it seems, in clinical practices we must notice this issue.

Keywords: hearing impairment, phonology, vowel, consonant

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Authors: Sahila Chopra, Hemdeep, Arshdeep Kaur, Raj K. Gupta

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In recent times, we noticed an interesting and important role of non-coplanar degree-of-freedom (Φ = 00) in heavy ion reactions. Using the dynamical cluster-decay model (DCM) with Φ degree-of-freedom included, we have studied three compound systems 246Bk∗, 164Yb∗ and 105Ag∗. Here, within the DCM with pocket formula for nuclear proximity potential, we look for the effects of including compact, non-coplanar configurations (Φc = 00) on the non-compound nucleus (nCN) contribution in total fusion cross section σfus. For 246Bk∗, formed in 11B+235U and 14N+232Th reaction channels, the DCM with coplanar nuclei (Φc = 00) shows an nCN contribution for 11B+235U channel, but none for 14N+232Th channel, which on including Φ gives both reaction channels as pure compound nucleus decays. In the case of 164Yb∗, formed in 64Ni+100Mo, the small nCN effects for Φ=00 are reduced to almost zero for Φ = 00. Interestingly, however, 105Ag∗ for Φ = 00 shows a small nCN contribution, which gets strongly enhanced for Φ = 00, such that the characteristic property of PCN presents a change of behaviour, like that of a strongly fissioning superheavy element to a weakly fissioning nucleus; note that 105Ag∗ is a weakly fissioning nucleus and Psurv behaves like one for a weakly fissioning nucleus for both Φ = 00 and Φ = 00. Apparently, Φ is presenting itself like a good degree-of-freedom in the DCM.

Keywords: dynamical cluster-decay model, fusion cross sections, non-compound nucleus effects, non-coplanarity

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Authors: Michele Sinico

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The psychological present has an actual extension. When a sequence of instantaneous stimuli falls in this short interval of time, observers perceive a compresence of events in succession and the temporal order depends on the qualitative relationships between the perceptual properties of the events. Two experiments were carried out to study the influence of perceptual grouping, with and without temporal displacement, on the duration of auditory sequences. The psychophysical method of adjustment was adopted. The first experiment investigated the effect of temporal displacement of a white noise on sequence duration. The second experiment investigated the effect of temporal displacement, along the pitch dimension, on temporal shortening of sequence. The results suggest that the temporal order of sounds, in the case of temporal displacement, is organized along the pitch dimension.

Keywords: time perception, perceptual present, temporal displacement, Gestalt laws of perceptual organization

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Authors: G. Tamilpavai, C. Vishnuppriya

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Bioinformatics is an active research area which combines biological matter as well as computer science research. The longest common subsequence (LCSS) is one of the major challenges in various bioinformatics applications. The computation of the LCSS plays a vital role in biomedicine and also it is an essential task in DNA sequence analysis in genetics. It includes wide range of disease diagnosing steps. The objective of this proposed system is to find the longest common subsequence which presents in a normal and various disease affected human DNA sequence using Self Organizing Map (SOM) and LCSS. The human DNA sequence is collected from National Center for Biotechnology Information (NCBI) database. Initially, the human DNA sequence is separated as k-mer using k-mer separation rule. Mean and median values are calculated from each separated k-mer. These calculated values are fed as input to the Self Organizing Map for the purpose of clustering. Then obtained clusters are given to the Longest Common Sub Sequence (LCSS) algorithm for finding common subsequence which presents in every clusters. It returns nx(n-1)/2 subsequence for each cluster where n is number of k-mer in a specific cluster. Experimental outcomes of this proposed system produce the possible number of longest common subsequence of normal and disease affected DNA data. Thus the proposed system will be a good initiative aid for finding disease causing sequence. Finally, performance analysis is carried out for different DNA sequences. The obtained values show that the retrieval of LCSS is done in a shorter time than the existing system.

Keywords: clustering, k-mers, longest common subsequence, SOM

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Authors: Mst. Tuhina-Khatun, Mohamed Hanafi Musa, Mohd Rafii Yosup, Wong Mui Yun, Md. Aktar-Uz-Zaman, Mahbod Sahebi

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Rice is a staple crop of economic importance of most Asian people, and blast is the major constraints for its higher yield. Heritability of plants traits helps plant breeders to make an appropriate selection and to assess the magnitude of genetic improvement through hybridization. Diversity of crop plants is necessary to manage the continuing genetic erosion and address the issues of genetic conservation for successfully meet the future food requirements. Therefore, an experiment was conducted to estimate heritability and to determine the diversity of 27 blast resistant upland rice genotypes based on 18 quantitative traits using randomized complete block design. Heritability value was found to vary from 38 to 93%. The lowest heritability belonged to the character total number of tillers/plant (38%). In contrast, number of filled grains/panicle, and yield/plant (g) was recorded for their highest heritability value viz. 93 and 91% correspondingly. Cluster analysis based on 18 traits grouped 27 rice genotypes into six clusters. Cluster I was the biggest, which comprised 17 genotypes, accounted for about 62.96% of total population. The multivariate analysis suggested that the genotype ‘Chokoto 14’ could be hybridized with ‘IR 5533-55-1-11’ and ‘IR 5533-PP 854-1’ for broadening the gene pool of blast resistant upland rice germplasms for yield and other favorable characters.

Keywords: blast resistant, diversity analysis, heritability, upland rice

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Authors: V. Nagammai

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Very-large-scale integration (VLSI) is the process of creating an integrated circuit (IC) by combining thousands of transistors into a single chip. Development of technology increases the complexity in IC manufacturing which may vary the power consumption, increase the size and latency period. Topology defines a number of connections between network. In this project, NoC topology is generated using atlas tool which will increase performance in turn determination of constraints are effective. The routing is performed by XY routing algorithm and wormhole flow control. In NoC topology generation, the value of power, area and latency are predetermined. In previous work, placement, routing and shortest path evaluation is performed using an algorithm called floor planning with cluster reconstruction and path allocation algorithm (FCRPA) with the account of 4 3x3 switch, 6 4x4 switch, and 2 5x5 switches. The usage of the 4x4 and 5x5 switch will increase the power consumption and area of the block. In order to avoid the problem, this paper has used one 8x8 switch and 4 3x3 switches. This paper uses IPRCA which of 3 steps they are placement, clustering, and shortest path evaluation. The placement is performed using min – cut placement and clustering are performed using an algorithm called cluster generation. The shortest path is evaluated using an algorithm called Dijkstra's algorithm. The power consumption of each block is determined. The experimental result shows that the area, power, and wire length improved simultaneously.

Keywords: application specific noc, b* tree representation, floor planning, t tree representation

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Authors: Nour Charara, Hussein Charara, Omar Abou Khaled, Hani Abdallah, Elena Mugellini

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In this paper, we present a human behavior modeling approach in videos scenes. This approach is used to model the normal behaviors in the conference halls. We exploited the Probabilistic Latent Semantic Analysis technique (PLSA), using the 'Bag-of-Terms' paradigm, as a tool for exploring video data to learn the model by grouping similar activities. Our term vocabulary consists of 3D spatio-temporal patch groups assigned by the direction of motion. Our video representation ensures the spatial information, the object trajectory, and the motion. The main importance of this approach is that it can be adapted to detect abnormal behaviors in order to ensure and enhance human security.

Keywords: activity modeling, clustering, PLSA, video representation

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Authors: Farzad Pargar

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We introduce the preventive maintenance and renewal scheduling problem for a multi-unit system over a finite and discretized time horizon. Given the latest possible time for carrying out the next maintenance and renewal projects after the previous ones and considering several common set-up costs, the introduced scheduling model tries to minimize the cost of projects by grouping them and simultaneously finding the optimal balance between doing maintenance and renewal. We present a 0-1 pure integer linear programming that determines which projects should be performed together on which location and in which period (e.g., week or month). We consider railway track as a case for our study and test the performance of the proposed model on a set of test problems. The experimental results show that the proposed approach performs well.

Keywords: maintenance, renewal, scheduling, mathematical programming model

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Authors: Marit D. Murry, Amy K. Marks

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The transition to college is a critical period that affords abundant opportunities for growth in conjunction with novel challenges for emerging adults. During this time, emerging adults are garnering experiences and acquiring hosts of new information that they are required to synthesize and use to inform life-shaping decisions. This stage is characterized by instability and exploration, which necessitates a diverse set of coping skills to successfully navigate and positively adapt to their evolving environment. However, important sociocultural factors result in differences that occur developmentally for minority emerging adults (i.e., emerging adults with an identity that has been or is marginalized). While the transition to college holds vast potential, not all are afforded the same chances, and many individuals enter into this stage at varying degrees of readiness. Understanding the nuance and diversity of student preparedness for college and contextualizing these factors will better equip systems to support incoming students. Emerging adulthood for ethnic, racial minority students presents itself as an opportunity for growth and resiliency in the face of systemic adversity. Ethnic, racial identity (ERI) is defined as an identity that develops as a function of one’s ethnic-racial group membership. Research continues to demonstrate ERI as a resilience factor that promotes positive adjustment in young adulthood. Adaptive coping responses (e.g., engaging in help-seeking behavior, drawing on personal and community resources) have been identified as possible mechanisms through which ERI buffers youth against stressful life events, including discrimination. Additionally, trait mindfulness has been identified as a significant predictor of general psychological health, and mindfulness practice has been shown to be a self-regulatory strategy that promotes healthy stress responses and adaptive coping strategy selection. The current study employed a person-centered approach to explore emerging patterns across ethnic identity development and psychological well-being criterion variables among college freshmen. Data from 283 incoming college freshmen at Northeastern University were analyzed. The Brief COPE Acceptance and Emotional Support scales, the Five Factor Mindfulness Questionnaire, and MIEM Exploration and Affirmation measures were used to inform the cluster profiles. The TwoStep auto-clustering algorithm revealed an optimal three-cluster solution (BIC = 848.49), which classified 92.6% (n = 262) of participants in the sample into one of the three clusters. The clusters were characterized as ‘Mixed Adjustment’, ‘Lowest Adjustment’, and ‘Moderate Adjustment.’ Cluster composition varied significantly by ethnicity X² (2, N = 262) = 7.74 (p = .021) and gender X² (2, N = 259) = 10.40 (p = .034). The ‘Lowest Adjustment’ cluster contained the highest proportion of students of color, 41% (n = 32), and male-identifying students, 44.2% (n = 34). Follow-up analyses showed higher ERI exploration in ‘Moderate Adjustment’ cluster members, also reported higher levels of psychological distress, with significantly elevated depression scores (p = .011), psychological diagnoses of depression (p = .013), anxiety (p = .005) and psychiatric disorders (p = .025). Supporting prior research, students engaging with identity exploration processes often endure more psychological distress. These results indicate that students undergoing identity development may require more socialization and different services beyond normal strategies.

Keywords: adjustment, coping, college, emerging adulthood, ethnic-racial identity, psychological well-being, resilience

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Authors: Gurkirat Kaur, Manoj Kumar, Manju Bala

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Ethernet is the most influential and widely used technology in the world. With the growing demand of low latency and high throughput technologies like InfiniBand and RoCE, unique features viz. RDMA (Remote Direct Memory Access) have evolved. RDMA is an effective technology which is used for reducing system load and improving performance. InfiniBand is a well known technology which provides high-bandwidth and low-latency and makes optimal use of in-built features like RDMA. With the rapid evolution of InfiniBand technology and Ethernet lacking the RDMA and zero copy protocol, the Ethernet community has came out with a new enhancements that bridges the gap between InfiniBand and Ethernet. By adding the RDMA and zero copy protocol to the Ethernet a new networking technology is evolved, called RDMA over Converged Ethernet (RoCE). RoCE is a standard released by the IBTA standardization body to define RDMA protocol over Ethernet. With the emergence of lossless Ethernet, RoCE uses InfiniBand’s efficient transport to provide the platform for deploying RDMA technology in mainstream data centres over 10GigE, 40GigE and beyond. RoCE provide all of the InfiniBand benefits transport benefits and well established RDMA ecosystem combined with converged Ethernet. In this paper, we evaluate the heterogeneous Linux cluster, having multi nodes with fast interconnects i.e. gigabit Ethernet and Soft RoCE. This paper presents the heterogeneous Linux cluster configuration and evaluates its performance using Intel’s MPI Benchmarks. Our result shows that Soft RoCE is performing better than Ethernet in various performance metrics like bandwidth, latency and throughput.

Keywords: ethernet, InfiniBand, RoCE, RDMA, MPI, Soft RoCE

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Authors: Forrest Kaatz, Adhemar Bultheel

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We use an informational statistical mechanics approach to study the catalytic thermodynamics of platinum and palladium cuboctahedral nanoclusters. Nanoclusters and their adatoms are viewed as chemical graphs with a nearest neighbor adjacency matrix. We use the Morse potential to determine bond energies between cluster atoms in a coordination type calculation. We use adsorbate energies calculated from density functional theory (DFT) to study the adatom effects on the thermodynamic quantities, which are derived from a Hamiltonian. Oxygen radical and molecular adsorbates are studied on platinum clusters and hydrogen on palladium clusters. We calculate the entropy, free energy, and total energy as the coverage of adsorbates increases from bridge and hollow sites on the surface. Thermodynamic behavior versus adatom coverage is related to the structural distribution of adatoms on the nanocluster surfaces. The thermodynamic functions are characterized using a simple adsorption model, with linear trends as the coverage of adatoms increases. The data exhibits size effects for the measured thermodynamic properties with cluster diameters between 2 and 5 nm. Entropy and enthalpy calculations of Pt-O2 compare well with previous theoretical data for Pt(111)-O2, and our Pd-H results show similar trends as experimental measurements for Pd-H2 nanoclusters. Our methods are general and may be applied to wide variety of nanocluster adsorbate systems.

Keywords: catalytic thermodynamics, palladium nanocluster absorbates, platinum nanocluster absorbates, statistical mechanics

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Authors: Faheema Khan

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In order to investigate the influence of genetic background on salt tolerance in Soybean (Glycine max) ten soybean genotypes released/notified in India were selected. (Pusa-20, Pusa-40, Pusa-37, Pusa-16, Pusa-24, Pusa-22, BRAGG, PK-416, PK-1042, and DS-9712). The 10-day-old seedlings were subjected to 0, 25, 50, 75, 100, 125, and 150 mM NaCl for 15 days. Plant growth, leaf osmotic adjustment, and RAPD analysis were studied. In comparison to control plants, the plant growth in all genotypes was decreased by salt stress, respectively. Salt stress decreased leaf osmotic potential in all genotypes however the maximum reduction was observed in genotype Pusa-24 followed by PK-416 and Pusa-20. The difference in osmotic adjustment between all the genotypes was correlated with the concentrations of ion examined such as Na+ and the leaf proline concentration. These results suggest that the genotypic variation for salt tolerance can be partially accounted for by plant physiological measures. The genetic polymorphisms between soybean genotypes differing in response to salt stress were characterized using 25 RAPD primers. These primers generated a total of 1640 amplification products, among which 1615 were found to be polymorphic. A very high degree of polymorphism (98.30%) was observed. UPGMA cluster analysis of genetic similarity indices grouped all the genotypes into two major clusters. Intra-clustering within the two clusters precisely grouped the 10 genotypes in sub-cluster as expected from their physiological findings. Our results show that RAPD technique is a sensitive, precise and efficient tool for genomic analysis in soybean genotypes.

Keywords: glycine max, NaCl, RAPD, proteomics

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Authors: Manana Lobzhanidze

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Reforming the pension system is urgent in Georgia due to socio-economic problems. Replacing the current pension system with a new one requires, on the one hand, an assessment of the challenges in this field and, on the other hand, a study of the best practices of foreign experience. Objectives: The aim of the research is to identify challenges in the pension reform process in Georgia, to study international experience, and to develop recommendations for the implementation of an effective pension system. Methodologies: A desk study was conducted, and methods of analysis, comparison, grouping, matrix charts, and scenario analysis were used. Findings: The advantages of accumulative pension compared to the current pension system are identified. The main challenge is the non-targeting of the pension contributions and the ineffective investment policy; the public's attitude towards the cumulative pension system is determined.

Keywords: pension reform, challenges, international practice, opportunity for development

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Authors: İsmail Kavdır, M. Burak Büyükcan, Ferhat Kurtulmuş

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Table olive is a valuable product especially in Mediterranean countries. It is usually consumed after some fermentation process. Defects happened naturally or as a result of an impact while olives are still fresh may become more distinct after processing period. Defected olives are not desired both in table olive and olive oil industries as it will affect the final product quality and reduce market prices considerably. Therefore it is critical to sort table olives before processing or even after processing according to their quality and surface defects. However, doing manual sorting has many drawbacks such as high expenses, subjectivity, tediousness and inconsistency. Quality criterions for green olives were accepted as color and free of mechanical defects, wrinkling, surface blemishes and rotting. In this study, it was aimed to classify fresh table olives using different classifiers and NIR spectroscopy readings and also to compare the classifiers. For this purpose, green (Ayvalik variety) olives were classified based on their surface feature properties such as defect-free, with bruised defect and with fly defect using FT-NIR spectroscopy and classification algorithms such as artificial neural networks, ident and cluster. Bruker multi-purpose analyzer (MPA) FT-NIR spectrometer (Bruker Optik, GmbH, Ettlingen Germany) was used for spectral measurements. The spectrometer was equipped with InGaAs detectors (TE-InGaAs internal for reflectance and RT-InGaAs external for transmittance) and a 20-watt high intensity tungsten–halogen NIR light source. Reflectance measurements were performed with a fiber optic probe (type IN 261) which covered the wavelengths between 780–2500 nm, while transmittance measurements were performed between 800 and 1725 nm. Thirty-two scans were acquired for each reflectance spectrum in about 15.32 s while 128 scans were obtained for transmittance in about 62 s. Resolution was 8 cm⁻¹ for both spectral measurement modes. Instrument control was done using OPUS software (Bruker Optik, GmbH, Ettlingen Germany). Classification applications were performed using three classifiers; Backpropagation Neural Networks, ident and cluster classification algorithms. For these classification applications, Neural Network tool box in Matlab, ident and cluster modules in OPUS software were used. Classifications were performed considering different scenarios; two quality conditions at once (good vs bruised, good vs fly defect) and three quality conditions at once (good, bruised and fly defect). Two spectrometer readings were used in classification applications; reflectance and transmittance. Classification results obtained using artificial neural networks algorithm in discriminating good olives from bruised olives, from olives with fly defect and from the olive group including both bruised and fly defected olives with success rates respectively changing between 97 and 99%, 61 and 94% and between 58.67 and 92%. On the other hand, classification results obtained for discriminating good olives from bruised ones and also for discriminating good olives from fly defected olives using the ident method ranged between 75-97.5% and 32.5-57.5%, respectfully; results obtained for the same classification applications using the cluster method ranged between 52.5-97.5% and between 22.5-57.5%.

Keywords: artificial neural networks, statistical classifiers, NIR spectroscopy, reflectance, transmittance

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Authors: Kristian Bautista, Ruben A. Idoy

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A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.

Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization

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Authors: Kostas Philippopoulos, Chris G. Tzanis, Despina Deligiorgi

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Climate change adaptation requires the exploitation of renewable energy sources such as wind. However, climate variability can affect the regional wind energy potential and consequently the available wind power production. The goal of the research project is to examine the impact of atmospheric circulation on wind energy production over Greece. In the context of synoptic climatology, the proposed novel methodology employs Self-Organizing Maps for grouping and classifying the atmospheric circulation and nationally-aggregated capacity factor time series for a 30-year period. The results indicate the critical effect of atmospheric circulation on the national aggregated wind energy production values and therefore address the issue of optimum distribution of wind farms for a specific region.

Keywords: wind energy, atmospheric circulation, capacity factor, self-organizing maps

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Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

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This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

Procedia PDF Downloads 189