Search results for: volatility clustering

Authors: Saeid Haghjoo, Ebrahim Reyhani, Fahimeh Kolahdouz

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The present study aimed to evaluate the understanding of the students in Tehran universities (Iran) about the numerical representation of the average rate of change based on the Structure of Observed Learning Outcomes (SOLO). In the present descriptive-survey research, the statistical population included undergraduate students (basic sciences and engineering) in the universities of Tehran. The samples were 604 students selected by random multi-stage clustering. The measurement tool was a task whose face and content validity was confirmed by math and mathematics education professors. Using Cronbach's Alpha criterion, the reliability coefficient of the task was obtained 0.95, which verified its reliability. The collected data were analyzed by descriptive statistics and inferential statistics (chi-squared and independent t-tests) under SPSS-24 software. According to the SOLO model in the prestructural, unistructural, and multistructural levels, basic science students had a higher percentage of understanding than that of engineering students, although the outcome was inverse at the relational level. However, there was no significant difference in the average understanding of both groups. The results indicated that students failed to have a proper understanding of the numerical representation of the average rate of change, in addition to missconceptions when using physics formulas in solving the problem. In addition, multiple solutions were derived along with their dominant methods during the qualitative analysis. The current research proposed to focus on the context problems with approximate calculations and numerical representation, using software and connection common relations between math and physics in the teaching process of teachers and professors.

Keywords: average rate of change, context problems, derivative, numerical representation, SOLO taxonomy

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Authors: Rene Hellmuth

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Factory planning has the task of designing products, plants, processes, organization, areas, and the construction of a factory. The requirements for factory planning and the building of a factory have changed in recent years. Regular restructuring of the factory building is becoming more important in order to maintain the competitiveness of a factory. Restrictions in new areas, shorter life cycles of product and production technology as well as a VUCA world (Volatility, Uncertainty, Complexity & Ambiguity) lead to more frequent restructuring measures within a factory. A building information model (BIM) is the planning basis for rebuilding measures and becomes an indispensable data repository to be able to react quickly to changes. Use as a planning basis for restructuring measures in factories only succeeds if the BIM model has adequate data quality. Under this aspect and the industrial requirement, three data quality factors are particularly important for this paper regarding the BIM model: up-to-dateness, completeness, and correctness. The research question is: how can a BIM model be kept up to date with required data quality and which visualization techniques can be applied in a short period of time on the construction site during conversion measures? An as-is analysis is made of how BIM models and digital factory models (including laser scans) are currently being kept up to date. Industrial companies are interviewed, and expert interviews are conducted. Subsequently, the results are evaluated, and a procedure conceived how cost-effective and timesaving updating processes can be carried out. The availability of low-cost hardware and the simplicity of the process are of importance to enable service personnel from facility mnagement to keep digital factory models (BIM models and laser scans) up to date. The approach includes the detection of changes to the building, the recording of the changing area, and the insertion into the overall digital twin. Finally, an overview of the possibilities for visualizations suitable for construction sites is compiled. An augmented reality application is created based on an updated BIM model of a factory and installed on a tablet. Conversion scenarios with costs and time expenditure are displayed. A user interface is designed in such a way that all relevant conversion information is available at a glance for the respective conversion scenario. A total of three essential research results are achieved: As-is analysis of current update processes for BIM models and laser scans, development of a time-saving and cost-effective update process and the conception and implementation of an augmented reality solution for BIM models suitable for construction sites.

Keywords: building information modeling, digital factory model, factory planning, restructuring

Procedia PDF Downloads 112

Authors: Meimei Shi

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Objectives: The identification of endangered species, especially simultaneous detection of multiple species in complex samples, plays a critical role in alleged wildlife crime incidents and prevents illegal trade. This study was to develop a multi-locus DNA metabarcoding method for endangered animal species identification. Methods: Several pairs of universal primers were designed according to the mitochondria conserved gene regions. Experimental mixtures were artificially prepared by mixing well-defined species, including endangered species, e.g., forest musk, bear, tiger, pangolin, and sika deer. The artificial samples were prepared with 1-16 well-characterized species at 1% to 100% DNA concentrations. After multiplex-PCR amplification and parameter modification, the amplified products were analyzed by capillary electrophoresis and used for NGS library preparation. The DNA metabarcoding was carried out based on Illumina MiSeq amplicon sequencing. The data was processed with quality trimming, reads filtering, and OTU clustering; representative sequences were blasted using BLASTn. Results: According to the parameter modification and multiplex-PCR amplification results, five primer sets targeting COI, Cytb, 12S, and 16S, respectively, were selected as the NGS library amplification primer panel. High-throughput sequencing data analysis showed that the established multi-locus DNA metabarcoding method was sensitive and could accurately identify all species in artificial mixtures, including endangered animal species Moschus berezovskii, Ursus thibetanus, Panthera tigris, Manis pentadactyla, Cervus nippon at 1% (DNA concentration). In conclusion, the established species identification method provides technical support for customs and forensic scientists to prevent the illegal trade of endangered animals and their products.

Keywords: DNA metabarcoding, endangered animal species, mitochondria nucleic acid, multi-locus

Procedia PDF Downloads 136

Authors: Donatella Giuliani

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In this research, we propose an unsupervised grayscale image segmentation method based on a combination of the Firefly Algorithm and the Gaussian Mixture Model. Firstly, the Firefly Algorithm has been applied in a histogram-based research of cluster means. The Firefly Algorithm is a stochastic global optimization technique, centered on the flashing characteristics of fireflies. In this context it has been performed to determine the number of clusters and the related cluster means in a histogram-based segmentation approach. Successively these means are used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The parametric probability density function of a Gaussian Mixture Model is represented as a weighted sum of Gaussian component densities, whose parameters are evaluated applying the iterative Expectation-Maximization technique. The coefficients of the linear super-position of Gaussians can be thought as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities have been evaluated, therefore their maxima are used to assign each pixel to the clusters, according to their gray-level values. The proposed approach appears fairly solid and reliable when applied even to complex grayscale images. The validation has been performed by using different standard measures, more precisely: the Root Mean Square Error (RMSE), the Structural Content (SC), the Normalized Correlation Coefficient (NK) and the Davies-Bouldin (DB) index. The achieved results have strongly confirmed the robustness of this gray scale segmentation method based on a metaheuristic algorithm. Another noteworthy advantage of this methodology is due to the use of maxima of responsibilities for the pixel assignment that implies a consistent reduction of the computational costs.

Keywords: clustering images, firefly algorithm, Gaussian mixture model, meta heuristic algorithm, image segmentation

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Authors: P. Karthick, K. Mahesh

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Video has become an increasingly significant component of our digital everyday contact. With the advancement of greater contents and shows of the resolution, its significant volume poses serious obstacles to the objective of receiving, distributing, compressing, and revealing video content of high quality. In this paper, we propose the primary beginning to complete a deep video compression model that jointly upgrades all video compression components. The video compression method involves splitting the video into frames, comparing the images using convolutional neural networks (CNN) to remove duplicates, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using generative adversarial network (GAN) and recorded with long short-term memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps in frame level compression. Pixel wise comparison is performed using K-nearest neighbours (KNN) over the frame, clustered with K-means, and singular value decomposition (SVD) is applied for each and every frame in the video for all three color channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format, and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, frames per second (FPS), and quality results demonstrate a significant resampling rate. On average, the result produced had approximately a 10% deviation in quality and more than 50% in size when compared with the original video.

Keywords: video compression, K-means clustering, convolutional neural network, generative adversarial network, singular value decomposition, pixel visualization, stochastic gradient descent, frame per second extraction, RGB channel extraction, self-detection and deciding system

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Authors: Arsalan Ansari, Muhammad Dawood Idrees, Maria Hafeez

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Use of mobile phones has been increasing considerably over the past decade. Currently, it is one of the main sources of communication and information. Initially, mobile phones were limited to calls and messages, but with the advent of new technology smart phones were being used for many other purposes including video games. Despite of positive outcomes, addiction to video games on mobile phone has become a leading cause of psychological and physiological problems among many people. Several researchers examined the different aspects of behavior addiction with the use of different scales. Objective of this study is to examine any distinction between mobile game addicted and non-addicted players with the use of electroencephalography (EEG), based upon psycho-physiological indicators. The mobile players were asked to play a mobile game and EEG signals were recorded by BIOPAC equipment with AcqKnowledge as data acquisition software. Electrodes were places, following the 10-20 system. EEG was recorded at sampling rate of 200 samples/sec (12,000samples/min). EEG recordings were obtained from the frontal (Fp1, Fp2), parietal (P3, P4), and occipital (O1, O2) lobes of the brain. The frontal lobe is associated with behavioral control, personality, and emotions. The parietal lobe is involved in perception, understanding logic, and arithmetic. The occipital lobe plays a role in visual tasks. For this study, a 60 second time window was chosen for analysis. Preliminary analysis of the signals was carried out with Acqknowledge software of BIOPAC Systems. From the survey based on CGS manual study 2010, it was concluded that five participants out of fifteen were in addictive category. This was used as prior information to group the addicted and non-addicted by physiological analysis. Statistical analysis showed that by applying clustering analysis technique authors were able to categorize the addicted and non-addicted players specifically on theta frequency range of occipital area.

Keywords: mobile game, addiction, psycho-physiology, EEG analysis

Procedia PDF Downloads 163

Authors: Enrique Claver Cortés, Bartolomé Marco Lajara, Eduardo Sánchez García, Pedro Seva Larrosa, Encarnación Manresa Marhuenda, Lorena Ruiz Fernández, Esther Poveda Pareja

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In recent decades, the analysis of the effects of clustering as an essential factor for the development of innovations and the competitiveness of enterprises has raised great interest in different areas. Nowadays, companies have access to almost all tangible and intangible resources located and/or developed in any country in the world. However, despite the obvious advantages that this situation entails for companies, their geographical location has shown itself, increasingly clearly, to be a fundamental factor that positively influences their innovative performance and competitiveness. Industrial clusters could represent a unique level of analysis, positioned between the individual company and the industry, which makes them an ideal unit of analysis to determine the effects derived from company membership of a cluster. Also, the absorptive capacity (hereinafter 'AC') can mediate the process of innovation development by companies located in a cluster. The transformation and exploitation of knowledge could have a mediating effect between knowledge acquisition and innovative performance. The main objective of this work is to determine the key factors that affect the degree of generation and use of knowledge from the environment by companies and, consequently, their innovative performance and competitiveness. The elements analyzed are the companies' membership of a cluster and the AC. To this end, 30 most relevant papers published on this subject in the "Web of Science" database have been reviewed. Our findings show that, within a cluster, the knowledge coming from the companies' environment can significantly influence their innovative performance and competitiveness, although in this relationship, the degree of access and exploitation of the companies to this knowledge plays a fundamental role, which depends on a series of elements both internal and external to the company.

Keywords: absorptive capacity, clusters, innovation, knowledge

Procedia PDF Downloads 130

Authors: Chang-Fa Liu, Yi-Ting Wang, Yuan Liu, Hai-Feng Wei, Lei Fang, Jin Li

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Heavy metals in sediments can cause adverse ecological effects while it exceeds a given criteria. The present study investigated sediment environmental quality, pollutant enrichment, ecological risk, and source identification for copper, cadmium, lead, zinc, mercury, and arsenic in the sediments collected from tidal flat marsh of Shuangtai estuary, China. The arithmetic mean integrated pollution index, geometric mean integrated pollution index, fuzzy integrated pollution index, and principal component score were used to characterize sediment environmental quality; fuzzy similarity and geo-accumulation Index were used to evaluate pollutant enrichment; correlation matrix, principal component analysis, and cluster analysis were used to identify source of pollution; environmental risk index and potential ecological risk index were used to assess ecological risk. The environmental qualities of sediment are classified to very low degree of contamination or low contamination. The similar order to element background of soil in the Liaohe plain is region of Sanjiaozhou, Honghaitan, Sandaogou, Xiaohe by pollutant enrichment analysis. The source identification indicates that correlations are significantly among metals except between copper and cadmium. Cadmium, lead, zinc, mercury, and arsenic will be clustered in the same clustering as the first principal component. Copper will be clustered as second principal component. The environmental risk assessment level will be scaled to no risk in the studied area. The order of potential ecological risk is As > Cd > Hg > Cu > Pb > Zn.

Keywords: ecological risk assessment, heavy metals, sediment, marsh, Shuangtai estuary

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Authors: Garima Anand, Rupam Kapoor

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Alternaria leaf spot caused by Alternaria carthami is one of the most devastating diseases of safflower. It has resulted in huge losses in crop production and cultivation leading to a fall out of India’s rank as the leading producer of safflower in the world. Understanding the diversity of any pathogen is essential for its management and for the development of disease control strategies. The diversity of A. carthami was therefore analysed on the basis of biochemical, pathogenicity and genetic lines using ISSR markers. Collections and isolations of 95 isolates of A. carthami were made from major safflower producing states of India. Virulence was analysed to evaluate the pathogenic potential of these isolates. The isolates from Bijapur, Dharwad districts (Karnataka), and Parbhani and Solapur districts (Maharashtra) were found to be highly virulent. The virulence assays showed low virulence levels (42%) for the largest part of the population. Biochemical characterization to assess aggressiveness of these isolates was done by estimating the activity of cell wall degrading enzymes where isolates from districts Dharwad, Bijapur of Karnataka and districts Parbhani and Latur of Maharashtra were found to be most aggressive. Genetic diversity among isolates of A. carthami was determined using eighteen ISSR markers. Distance analysis using neighbour joining method and PCoA analysis of the ISSR profiles divided the isolates into three sub-populations. The most virulent isolates clustered in one group in the dendrogram. The study provided no evidence for geographical clustering indicating that isolates are randomly spread across the states, signifying the high potential of the fungus to adapt to diverse regions. The study can, therefore, aid in the breeding and deployment of A. carthami resistant safflower varieties and in the management of Alternaria leaf spot disease.

Keywords: alternaria leaf spot, genetic diversity, pathogenic potential, virulence

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Authors: Nur Sahver Uslu, Ali̇ Hakan Büyüklü

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Maintenance, Repair, and Overhaul (MRO) costs are significant in the aviation industry. On the other hand, companies that provide MRO services to the aviation industry but are not dominant in the sector, need to determine the right strategies for sustainable profitability in a competitive environment. This study examined the operational real data of a small medium enterprise (SME) MRO company where analytical methods are not widely applied. The company's customers were divided into two categories: airline companies and non-airline companies, and the variables that best explained profitability were analyzed with Logistic Regression for each category and the results were compared. First, data reduction was applied to the transformed variables that went through the data cleaning and preparation stages, and the variables to be included in the model were decided. The misclassification rates for the logistic regression results concerning both customer categories are similar, indicating consistent model performance across different segments. Less profit margin is obtained from airline customers, which can be explained by the variables part description, time to quotation (TTQ), turnaround time (TAT), manager, part cost, and labour cost. The higher profit margin obtained from non-airline customers is explained only by the variables part description, part cost, and labour cost. Based on the two models, it can be stated that it is significantly more challenging for the MRO company, which is the subject of our study, to achieve profitability from Airline customers. While operational processes and organizational structure also affect the profit from airline customers, only the type of parts and costs determine the profit for non-airlines.

Keywords: aircraft, aircraft components, aviation, data analytics, data science, gini index, maintenance, repair, and overhaul, MRO, logistic regression, profit, variable clustering, variable reduction

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Authors: Kamil Jonski

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Last year vividly demonstrated, that populism and political instability can endanger democratic institutions in countries regarded as democratic transition champions (Poland) or cornerstones of liberal order (UK, US). So called ‘illiberal democracy’ is winning hearts and minds of voters, keen to believe that rule of strongman is a viable alternative to perceived decay of western values and institutions. These developments pose a serious threat to the democratic institutions (including rule of law), proven critical for both personal freedom and economic development. Although scholars proposed some structural explanations of the illiberal wave (notably focusing on inequality, stagnant incomes and drawbacks of globalization), they seem to have little predictive value. Indeed, events like Trump’s victory, Brexit or Polish shift towards populist nationalism always came as a surprise. Intriguingly, in the case of US election, simple rules like ‘Bread and Peace model’ gauged prospects of Trump’s victory better than pundits and pollsters. This paper attempts to compile set of indicators, in order to gauge various democracies’ vulnerability to populism, instability and pursuance of ‘illiberal’ projects. Among them, it identifies the gap between consensus assessment of institutional performance (as measured by WGI indicators) and citizens’ subjective assessment (survey based confidence in institutions). Plotting these variables against each other, reveals three clusters of countries – ‘predictable’ (good institutions and high confidence, poor institutions and low confidence), ‘blind’ (poor institutions, high confidence e.g. Uzbekistan or Azerbaijan) and ‘disillusioned’ (good institutions, low confidence e.g. Spain, Chile, Poland and US). It seems that this clustering – carried out separately for various institutions (like legislature, executive and courts) and blended with economic indicators like inequality and living standards (using PCA) – offers reasonably good watchlist of countries, that should ‘expect the unexpected’.

Keywords: illiberal democracy, populism, political instability, political risk measurement

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Authors: Shivam Yadav, Neelam Atri

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Cyanobacteria are photoautotrophic prokaryotes able to grow in diverse ecological habitats, originated 2.5 - 3.5 billion years ago and brought oxygenic photosynthesis. Since then superoxide dismutases (SODs) acquired great significance due to their ability to catalyze detoxification of byproducts of oxygenic photosynthesis, i.e. superoxide radicals. Sequence information from several cyanobacterial genomes offers a unique opportunity to conduct a comprehensive comparative analysis of the superoxide dismutases family. In the present study, we extracted information regarding SODs from species of sequenced cyanobacteria and investigated their diversity, conservation, domain structure, and evolution. 144 putative SOD homologues were identified. SODs are present in all cyanobacterial species reflecting their significant role in survival. However, their distribution varies, fewer in unicellular marine strains whereas abundant in filamentous nitrogen-fixing cyanobacteria. Motifs and invariant amino acids typical in eukaryotic SODs were conserved well in these proteins. These SODs were classified into three major families according to their domain structures. Interestingly, they lack additional domains as found in proteins of other family. Phylogenetic relationships correspond well with phylogenies based on 16S rRNA and clustering occurs on the basis of structural characteristics such as domain organization. Similar conserved motifs and amino acids indicate that cyanobacterial SODs make use of a similar catalytic mechanism as eukaryotic SODs. Gene gain-and-loss is insignificant during SOD evolution as evidenced by absence of additional domain. This study has not only examined an overall background of sequence-structure-function interactions for the SOD gene family but also revealed variation among SOD distribution based on ecophysiological and morphological characters.

Keywords: comparative genomics, cyanobacteria, phylogeny, superoxide dismutases

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Authors: Natnael Debalklie Teshome

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The purpose of the study was to empirically investigate the impacts of export performance and its volatility on economic growth in the Ethiopian economy. To do so, time-series data of the sample period from 1974/75 – 2017/18 were collected from databases and annual reports of IMF, WB, NBE, MoFED, UNCTD, and EEA. The extended Cobb-Douglas production function of the neoclassical growth model framed under the endogenous growth theory was used to consider both the performance and instability aspects of export. First, the unit root test was conducted using ADF and PP tests, and data were found in stationery with a mix of I(0) and I(1). Then, the bound test and Wald test were employed, and results showed that there exists long-run co-integration among study variables. All the diagnostic test results also reveal that the model fulfills the criteria of the best-fitted model. Therefore, the ARDL model and VECM were applied to estimate the long-run and short-run parameters, while the Granger causality test was used to test the causality between study variables. The empirical findings of the study reveal that only export and coefficient of variation had significant positive and negative impacts on RGDP in the long run, respectively, while other variables were found to have an insignificant impact on the economic growth of Ethiopia. In the short run, except for gross capital formation and coefficients of variation, which have a highly significant positive impact, all other variables have a strongly significant negative impact on RGDP. This shows exports had a strong, significant impact in both the short-run and long-run periods. However, its positive and statistically significant impact is observed only in the long run. Similarly, there was a highly significant export fluctuation in both periods, while significant commodity concentration (CCI) was observed only in the short run. Moreover, the Granger causality test reveals that unidirectional causality running from export performance to RGDP exists in the long run and from both export and RGDP to CCI in the short run. Therefore, the export-led growth strategy should be sustained and strengthened. In addition, boosting the industrial sector is vital to bring structural transformation. Hence, the government has to give different incentive schemes and supportive measures to exporters to extract the spillover effects of exports. Greater emphasis on price-oriented diversification and specialization on major primary products that the country has a comparative advantage should also be given to reduce value-based instability in the export earnings of the country. The government should also strive to increase capital formation and human capital development via enhancing investments in technology and quality of education to accelerate the economic growth of the country.

Keywords: export, economic growth, export diversification, instability, co-integration, granger causality, Ethiopian economy

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Authors: Leng Jian-Wei, Wang Lai-Jun, Li Ye

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In order to measure and analyze the transportation situation within the counties of Shaanxi province over a certain period of time and to promote the province's future transportation planning and development, this paper proposes a reasonable layout plan and compares model rationality. The study uses entropy weight method to measure the transportation advantages of 107 counties in Shaanxi province from three dimensions: road network density, trunk line influence and location advantage in 2013 and 2021, and applies spatial autocorrelation analysis method to analyze the spatial layout and development trend of county-level transportation, and conducts ordinary least square (OLS)regression on transportation impact factors and other influencing factors. The paper also compares the regression fitting degree of the Geographically weighted regression(GWR) model and the OLS model. The results show that spatially, the transportation advantages of Shaanxi province generally show a decreasing trend from the Weihe Plain to the surrounding areas and mainly exhibit high-high clustering phenomenon. Temporally, transportation advantages show an overall upward trend, and the phenomenon of spatial imbalance gradually decreases. People's travel demands have changed to some extent, and the demand for rapid transportation has increased overall. The GWR model regression fitting degree of transportation advantages is 0.74, which is higher than the OLS regression model's fitting degree of 0.64. Based on the evolution of transportation advantages, it is predicted that this trend will continue for a period of time in the future. To improve the transportation advantages of Shaanxi province increasing the layout of rapid transportation can effectively enhance the transportation advantages of Shaanxi province. When analyzing spatial heterogeneity, geographic factors should be considered to establish a more reliable model

Keywords: traffic dominance, GWR model, spatial autocorrelation analysis, temporal and spatial evolution

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Authors: Dagim Dessalegn Haile

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This study investigated the interaction between the drivers' socio-demographic background information (age, gender, and driving experience) and the risky events score in the i-DREAMS platform. Further, the relationship between the participants' background driving behavior and the i-DREAMS platform behavioral output scores of risky events was also investigated. The i-DREAMS acronym stands for Smart Driver and Road Environment Assessment and Monitoring System. It is a European Union Horizon 2020 funded project consisting of 13 partners, researchers, and industry partners from 8 countries. A total of 25 Belgian car drivers (16 male and nine female) were considered for analysis. Drivers' ages were categorized into ages 18-25, 26-45, 46-65, and 65 and older. Drivers' driving experience was also categorized into four groups: 1-15, 16-30, 31-45, and 46-60 years. Drivers are classified into two clusters based on the recorded score for risky events during phase 1 (baseline) using risky events; acceleration, deceleration, speeding, tailgating, overtaking, and lane discipline. Agglomerative hierarchical clustering using SPSS shows Cluster 1 drivers are safer drivers, and Cluster 2 drivers are identified as risky drivers. The analysis result indicated no significant relationship between age groups, gender, and experience groups except for risky events like acceleration, tailgating, and overtaking in a few phases. This is mainly because the fewer participants create less variability of socio-demographic background groups. Repeated measure ANOVA shows that cluster 2 drivers improved more than cluster 1 drivers for tailgating, lane discipline, and speeding events. A positive relationship between background drivers' behavior and i-DREAMS platform behavioral output scores is observed. It implies that car drivers who in the questionnaire data indicate committing more risky driving behavior demonstrate more risky driver behavior in the i-DREAMS observed driving data.

Keywords: i-dreams, car drivers, socio-demographic background, risky events

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Authors: Novee Lor C. Leyso, Maylin C. Palatino

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Under-five malnutrition continues to persist in the Philippines, particularly in the island Province of Marinduque, with prevalence of some forms of malnutrition even worsening in recent years. Local spatial cluster detection provides a spatial perspective in understanding this phenomenon as key in analyzing patterns of geographic variation, identification of community-appropriate programs and interventions, and focused targeting on high-risk areas. Using data from a province-wide household-based census conducted in 2014–2016, this study aimed to determine and evaluate spatial clusters of under-five malnutrition, across the province and within each municipality at the individual level using household location. Malnutrition was defined as weight-for-age z-score that fall outside the 2 standard deviations from the median of the WHO reference population. The Kulldorff’s elliptical spatial scan statistic in binomial model was used to locate clusters with high-risk of malnutrition, while adjusting for age and membership to government conditional cash transfer program as proxy for socio-economic status. One large significant cluster of under-five malnutrition was found southwest of the province, in which living in these areas at least doubles the risk of malnutrition. Additionally, at least one significant cluster were identified within each municipality—mostly located along the coastal areas. All these indicate apparent geographical variations across and within municipalities in the province. There were also similarities and disparities in the patterns of risk of malnutrition in each cluster across municipalities, and even within municipality, suggesting underlying causes at work that warrants further investigation. Therefore, community-appropriate programs and interventions should be identified and should be focused on high-risk areas to maximize limited government resources. Further studies are also recommended to determine factors affecting variations in childhood malnutrition considering the evidence of spatial clustering found in this study.

Keywords: Binomial model, Kulldorff’s elliptical spatial scan statistic, Philippines, under-five malnutrition

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Authors: Maad Kamal Al-Anni

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This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the  signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness  for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).

Keywords: Intrusion Detection System (IDS), Mobile Adhoc Networks (MANET), Back Propagation Algorithm (BPA), Neural Networks (NN)

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Authors: Vahid Ebrahimipour

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Word representation and context meaning of text-based documents play an essential role in knowledge modeling. Business procedures written in natural language are meant to store technical and engineering information, management decision and operation experience during the production system life cycle. Context meaning representation is highly dependent upon word sense, lexical relativity, and sematic features of the argument. This paper proposes a method for lexical semantic analysis and context meaning representation of maintenance activity in a mass production system. Our approach constructs a straightforward lexical semantic approach to analyze facilitates semantic and syntactic features of context structure of maintenance report to facilitate translation, interpretation, and conversion of human-readable interpretation into computer-readable representation and understandable with less heterogeneity and ambiguity. The methodology will enable users to obtain a representation format that maximizes shareability and accessibility for multi-purpose usage. It provides a contextualized structure to obtain a generic context model that can be utilized during the system life cycle. At first, it employs a co-occurrence-based clustering framework to recognize a group of highly frequent contextual features that correspond to a maintenance report text. Then the keywords are identified for syntactic and semantic extraction analysis. The analysis exercises causality-driven logic of keywords’ senses to divulge the structural and meaning dependency relationships between the words in a context. The output is a word contextualized representation of maintenance activity accommodating computer-based representation and inference using OWL/RDF.

Keywords: lexical semantic analysis, metadata modeling, contextual meaning extraction, ontology modeling, knowledge representation

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Authors: Venkata Madhusudana Rao Kapavarapu

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The Intermediate Slug Catcher (ISC) facility was built to process nominally 234 MSCFD of export gas from the booster station on a day-to-day basis and to receive liquid slugs up to 1600 m³ (10,000 BBLS) in volume when the incoming 24” gas pipelines are pigged following upsets or production of non-dew-pointed gas from gathering centers. The maximum slug sizes expected are 812 m³ (5100 BBLS) in winter and 542 m³ (3400 BBLS) in summer after operating for a month or more at 100 MMSCFD of wet gas, being 60 MMSCFD of treated gas from the booster station, combined with 40 MMSCFD of untreated gas from gathering center. The water content is approximately 60% but may be higher if the line is not pigged for an extended period, owing to the relative volatility of the condensate compared to water. In addition to its primary function as a slug catcher, the ISC facility will receive pigged liquids from the upstream and downstream segments of the 14” condensate pipeline, returned liquids from the AGRP, pigged through the 8” pipeline, and blown-down fluids from the 14” condensate pipeline prior to maintenance. These fluids will be received in the condensate flash vessel or the condensate separator, depending on the specific operation, for the separation of water and condensate and settlement of solids scraped from the pipelines. Condensate meeting the colour and 200 ppm water specifications will be dispatched to the AGRP through the 14” pipeline, while off-spec material will be returned to BS-171 via the existing 10” condensate pipeline. When they are not in operation, the existing 24” export gas pipeline and the 10” condensate pipeline will be maintained under export gas pressure, ready for operation. The gas manifold area contains the interconnecting piping and valves needed to align the slug catcher with either of the 24” export gas pipelines from the booster station and to direct the gas to the downstream segment of either of these pipelines. The manifold enables the slug catcher to be bypassed if it needs to be maintained or if through-pigging of the gas pipelines is to be performed. All gas, whether bypassing the slug catcher or returning to the gas pipelines from it, passes through black powder filters to reduce the level of particulates in the stream. These items are connected to the closed drain vessel to drain the liquid collected. Condensate from the booster station is transported to AGRP through 14” condensate pipeline. The existing 10” condensate pipeline will be used as a standby and for utility functions such as returning condensate from AGRP to the ISC or booster station or for transporting off-spec fluids from the ISC back to booster station. The manifold contains block valves that allow the two condensate export lines to be segmented at the ISC, thus facilitating bi-directional flow independently in the upstream and downstream segments, which ensures complete pipeline integrity and facility integrity. Pipeline failures will be attended to with the latest technologies by remote techno plug techniques, and repair activities will be carried out as needed. Pipeline integrity will be evaluated with ili pigging to estimate the pipeline conditions.

Keywords: integrity, oil & gas, innovation, new technology

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Authors: Hediyeh Talebi, Shokoofeh Ghiam, Changiz Eslahchi

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Traditionally, Alzheimer’s disease research has focused on genes with significant fold changes, potentially neglecting subtle but biologically important alterations. Our study introduces an integrative approach that highlights genes crucial to underlying biological processes, regardless of their fold change magnitude. Alzheimer's Single-cell RNA-seq data related to the peripheral blood mononuclear cells (PBMC) was extracted from the Gene Expression Omnibus (GEO). After quality control, normalization, scaling, batch effect correction, and clustering, differentially expressed genes (DEGs) were identified with adjusted p-values less than 0.05. These DEGs were categorized based on cell-type, resulting in four datasets, each corresponding to a distinct cell type. To distinguish between cells from healthy individuals and those with Alzheimer's, an adversarial autoencoder with a classifier was employed. This allowed for the separation of healthy and diseased samples. To identify the most influential genes in this classification, the weight matrices in the network, which includes the encoder and classifier components, were multiplied, and focused on the top 20 genes. The analysis revealed that while some of these genes exhibit a high fold change, others do not. These genes, which may be overlooked by previous methods due to their low fold change, were shown to be significant in our study. The findings highlight the critical role of genes with subtle alterations in diagnosing Alzheimer's disease, a facet frequently overlooked by conventional methods. These genes demonstrate remarkable discriminatory power, underscoring the need to integrate biological relevance with statistical measures in gene prioritization. This integrative approach enhances our understanding of the molecular mechanisms in Alzheimer’s disease and provides a promising direction for identifying potential therapeutic targets.

Keywords: alzheimer's disease, single-cell RNA-seq, neural networks, blood biomarkers

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Authors: Yu-Wen Huang, Yi-Cheng Chiang

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With urban development, lots of buildings are built around the city. The buildings always affect the urban wind environment. The accelerative situation of wind caused of buildings often makes pedestrians uncomfortable, even causes the accidents and dangers. Factors influencing pedestrian level wind including atmospheric boundary layer, wind direction, wind velocity, planting, building volume, geometric shape of the buildings and adjacent interference effects, etc. Planting has many functions including scraping and slowing urban heat island effect, creating a good visual landscape, increasing urban green area and improve pedestrian level wind. On the other hand, urban square is an important space element supporting the entrance to buildings, city landmarks, and activity collections, etc. The appropriateness of urban square environment usually dominates its success. This research focuses on the effect of tree-planting on the wind environment of urban square. This research studied the square belt of Taichung City Hall. Taichung City Hall is a cuboid building with a large mass opening. The square belt connects the front square, the central opening and the back square. There is often wind draft on the square belt. This phenomenon decreases the activities on the squares. This research applies tree-planting to improve the wind environment and evaluate the effects of two types of planting configuration. The Computational Fluid Dynamics (CFD) simulation analysis and extensive field measurements are applied to explore the improve efficiency of planting configuration on wind environment. This research compares efficiencies of different kinds of planting configuration, including the clustering array configuration and the dispersion, and evaluates the efficiencies by the SET*.

Keywords: micro-climate, wind environment, planting configuration, comfortableness, computational fluid dynamics (CFD)

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Authors: N. Casado-Sanz, B. Guirao

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The population aged 65 and over is projected to double in the coming decades. Due to this increase, driver population is expected to grow and in the near future, all countries will be faced with population aging of varying intensity and in unique time frames. This is the greatest challenge facing industrialized nations and due to this fact, the study of the relationships of dependency between population aging and road safety is becoming increasingly relevant. Although the deterioration of driving skills in the elderly has been analyzed in depth, to our knowledge few research studies have focused on the road infrastructure and the mobility of this particular group of users. In Spain, crosstown roads have one of the highest fatality rates. These rural routes have a higher percentage of elderly people who are more dependent on driving due to the absence or limitations of urban public transportation. Analysing road safety in these routes is very complex because of the variety of the features, the dispersion of the data and the complete lack of related literature. The objective of this paper is to identify key factors that cause traffic accidents. The individuals under study were the accidents with killed or seriously injured in Spanish crosstown roads during the period 2006-2015. Latent cluster analysis was applied as a preliminary tool for segmentation of accidents, considering population aging as the main input among other socioeconomic indicators. Subsequently, a linear regression analysis was carried out to estimate the degree of dependence between the accident rate and the variables that define each group. The results show that segmenting the data is very interesting and provides further information. Additionally, the results revealed the clear influence of the aging variable in the clusters obtained. Other variables related to infrastructure and mobility levels, such as the crosstown roads layout and the traffic intensity aimed to be one of the key factors in the causality of road accidents.

Keywords: cluster analysis, population ageing, rural roads, road safety

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Authors: Cristian Matti, Valerie Fowles, Eva Enyedi, Piotr Pogorzelski

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The system mapping process can be defined as a knowledge service where a team of facilitators, experts and practitioners facilitate a guided conversation, enable the exchange of information and support an iterative curation process. System mapping processes rely on science-based tools to introduce and simplify a variety of components and concepts of socio-technical systems through metaphors while facilitating an interactive dialogue process to enable the design of co-created maps. System maps work then as “artifacts” to provide information and focus the conversation into specific areas around the defined challenge and related decision-making process. Knowledge management facilitates the curation of that data gathered during the system mapping sessions through practices of documentation and subsequent knowledge co-production for which common practices from data science are applied to identify new patterns, hidden insights, recurrent loops and unexpected elements. This study presents empirical evidence on the application of these techniques to explore mechanisms by which visual tools provide guiding principles to portray system components, key variables and types of data through the lens of climate change. In addition, data science facilitates the structuring of elements that allow the analysis of layers of information through affinity and clustering analysis and, therefore, develop simple indicators for supporting the decision-making process. This paper addresses methodological and empirical elements on the horizontal learning process that integrate system mapping through visual tools, interpretation, cognitive transformation and analysis. The process is designed to introduce practitioners to simple iterative and inclusive processes that create actionable knowledge and enable a shared understanding of the system in which they are embedded.

Keywords: indicators, knowledge management, system mapping, visual tools

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Authors: Jin Sheng

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This paper addresses a promising area of insurance sector to develop in Asia. Captive insurance, which provides risk-mitigation services for its parent company, has great potentials to develop in energy, infrastructure, agriculture, logistics, catastrophe, and alternative risk transfer (ART), and will greatly affect the framework of insurance industry. However, the Asian captive insurance market only takes a small proportion in the global market. The recent supply chain interruption case of Hanjin Shipping indicates the significance of risk management for an Asian company’s sustainability and resilience. China has substantial needs and great potentials to develop captive insurance, on account of the currency volatility, enterprises’ credit risks, and legal and operational risks of the Belt and Road initiative. Up to date, Mainland Chinese enterprises only have four offshore captives incorporated by CNOOC, Sinopec, Lenovo and CGN Power), three onshore captive insurance companies incorporated by CNPC, China Railway, and COSCO, as well as one industrial captive insurance organization - China Ship-owners Mutual Assurance Association. Its captive market grows slowly with one or two captive insurers licensed yearly after September 2011. As an international financial center, Hong Kong has comparative advantages in taxation, professionals, market access and well-established financial infrastructure to develop a functional captive insurance market. For example, Hong Kong’s income tax for an insurance company is 16.5%; while China's income tax for an insurance company is 25% plus business tax of 5%. Furthermore, restrictions on market entry and operations of China’s onshore captives make establishing offshore captives in international or regional captive insurance centers such as Singapore, Hong Kong, and other overseas jurisdictions to become attractive options. Thus, there are abundant business opportunities in this area. Using methodology of comparative studies and case analysis, this paper discusses the incorporation, regulatory issues, taxation and prospect of captive insurance market in Hong Kong, China and Singapore. Hong Kong and Singapore are both international financial centers with prominent advantages in tax concessions, technology, implementation, professional services, and well-functioning legal system. Singapore, as the domicile of 71 active captives, has been the largest captive insurance hub in Asia, as well as an established reinsurance hub. Hong Kong is an emerging captive insurance hub with 5 to 10 newly licensed captives each year, according to the Hong Kong Financial Services Development Council. It is predicted that Hong Kong will become a domicile for 50 captive insurers by 2025. This paper also compares the formation of a captive in Singapore with other jurisdictions such as Bermuda and Vermont.

Keywords: Alternative Risk Transfer (ART), captive insurance company, offshore captives, risk management, reinsurance, self-insurance fund

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Authors: Zimu Li, Zheng Wan

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The Suez Canal plays an important role in global shipping networks and is one of the most frequently used waterways in the world. The 2021 canal obstruction by ship Ever Given in March 2021, however, completed blocked the Suez Canal for a week and caused significant disruption to world trade. Therefore, it is very important to quantitatively analyze the impact of the accident on the robustness of the global shipping network. However, the current research on maritime transportation networks is usually limited to local or small-scale networks in a certain region. Based on the complex network theory, this study establishes a global shipping complex network covering 2713 nodes and 137830 edges by using the real trajectory data of the global marine transport ship automatic identification system in 2018. At the same time, two attack modes, deliberate (Suez Canal Blocking) and random, are defined to calculate the changes in network node degree, eccentricity, clustering coefficient, network density, network isolated nodes, betweenness centrality, and closeness centrality under the two attack modes, and quantitatively analyze the actual impact of Suez Canal Blocking on the robustness of global shipping network. The results of the network robustness analysis show that Suez Canal blocking was more destructive to the shipping network than random attacks of the same scale. The network connectivity and accessibility decreased significantly, and the decline decreased with the distance between the port and the canal, showing the phenomenon of distance attenuation. This study further analyzes the impact of the blocking of the Suez Canal on Chinese ports and finds that the blocking of the Suez Canal significantly interferes withChina's shipping network and seriously affects China's normal trade activities. Finally, the impact of the global supply chain is analyzed, and it is found that blocking the canal will seriously damage the normal operation of the global supply chain.

Keywords: global shipping networks, ship AIS trajectory data, main channel, complex network, eigenvalue change

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Authors: Nsikan Affiah, Kayode Osungbade, Williams Uzoma

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Malaria remains one of the major killers of humans worldwide, threatening the lives of more than one-third of the world’s population. Some people refers it to; a disease of poverty because it contributes towards national poverty through its impact on foreign direct investment, tourism, labour productivity, and trade. At the micro level, it may cause poverty through spending on health care, income losses, and premature deaths. Unfortunately, malaria is a disease that affects both low-income household and its high-income counterpart, but low-income households are still at greater risk because significant part of the available monthly income is dedicated to various preventive and treatment measures. The objective of this study is to estimate direct and indirect cost of malaria treatment in households in a section of South-South Region (Akwa Ibom State) of Nigeria. A cross-sectional study of Six Hundred and Forty (640) heads of households or any adult representative of households in three local government areas of Akwa Ibom State, Nigeria from May 1-31, 2015 were ascertained through interviewer-administered questionnaire adapted from Nigerian Malaria Indicator Survey Report. The clustering technique was used to select 640 households with the help of Primary Health Care (PHC) house numbering system. Using exchange rate of 197 Naira/USD, result shows that direct cost of malaria treatment was 8,894.44 USD while the indirect cost of malaria treatment was 11,012.81 USD. Total cost of treatment made up of 44.7% direct cost and 55.3% indirect cost, with average direct cost of malaria treatment per household estimated at 20.6 USD and the average indirect cost of treatment per household estimated at 25.1 USD. Average total cost for each episode (888) of malaria was estimated at 22.4 USD. While at household level, the average total cost was estimated at 45.5 USD. From the average total cost, low-income households would spend 36% of monthly household income on treating malaria and the impact could be said to be catastrophic, compared to high-income households where only 1.2% of monthly household income is spent on malaria treatment. It could be concluded that the cost of malaria treatment is well beyond the means of households and given the reality of repeated bouts of malaria and its contribution to the impoverishment of households, there is a need for urgent action.

Keywords: direct cost, indirect cost, low income households, malaria

Procedia PDF Downloads 255

Authors: Bela Lebedeva

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Characteristics of the Web as a channel of information dissemination - accessibility and openness, interactivity and multimedia news - become wider and cover the audience quickly, positively affecting the perception of content, but blur out the understanding of the journalistic work. As a result audience and advertisers continue migrating to the Internet. Moreover, online targeting allows monetizing not only the audience (as customarily given to traditional media) but also the content and traffic more accurately. While the users identify themselves with the qualitative characteristics of the new market, its actors are formed. Conflict of interests is laid in the base of the economy of their relations, the problem of traffic tax as an example. Meanwhile, content monetization actualizes fiscal interest of the state too. The balance of supply and demand is often violated due to the political risks, particularly in terms of state capitalism, populism and authoritarian methods of governance such social institutions as the media. A unique example of access to journalistic material, limited by monetization of content is a television channel Dozhd' (Rain) in Russian web space. Its liberal-minded audience has a better possibility for discussion. However, the channel could have been much more successful in terms of unlimited free speech. Avoiding state pressure and censorship its management has decided to save at least online performance and monetizing all of the content for the core audience. The study Methodology was primarily based on the analysis of journalistic content, on the qualitative and quantitative analysis of the audience. Reconstructing main events and relationships of actors on the market for the last six years researcher has reached some conclusions. First, under the condition of content monetization the capitalization of its quality will always strive to quality characteristics of user, thereby identifying him. Vice versa, the user's demand generates high-quality journalism. The second conclusion follows the previous one. The growth of technology, information noise, new political challenges, the economy volatility and the cultural paradigm change – all these factors form the content paying model for an individual user. This model defines him as a beneficiary of specific knowledge and indicates the constant balance of supply and demand other conditions being equal. As a result, a new economic quality of information is created. This feature is an indicator of the market as a self-regulated system. Monetized information quality is less popular than that of the Public Broadcasting Service, but this audience is able to make decisions. These very users keep the niche sectors which have more potential of technology development, including the content monetization ways. The third point of the study allows develop it in the discourse of media space liberalization. This cultural phenomenon may open opportunities for the development of social and economic relations architecture both locally and regionally.

Keywords: content monetization, state capitalism, media liberalization, media economy, information quality

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Authors: Pirta Palola, Richard Bailey, Lisa Wedding

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Societies and economies worldwide fundamentally depend on natural capital. Alarmingly, natural capital assets are quickly depreciating, posing an existential challenge for humanity. The development of robust natural capital accounting systems is essential for transitioning towards sustainable economic systems and ensuring sound management of capital assets. However, the accurate, equitable and comprehensive estimation of natural capital asset stocks and their accounting values still faces multiple challenges. In particular, the representation of socio-cultural values held by groups or communities has arguably been limited, as to date, the valuation of natural capital assets has primarily been based on monetary valuation methods and assumptions of individual rationality. People relate to and value the natural environment in multiple ways, and no single valuation method can provide a sufficiently comprehensive image of the range of values associated with the environment. Indeed, calls have been made to improve the representation of multiple types of value (instrumental, intrinsic, and relational) and diverse ontological and epistemological perspectives in environmental valuation. This study addresses this need by establishing a novel valuation framework, Environmental Value Functions (EVF), that allows for the integration of multiple types of value in natural capital accounting systems. The EVF framework is based on the estimation and application of value functions, each of which describes the relationship between the value and quantity (or quality) of an ecosystem component of interest. In this framework, values are estimated in terms of change relative to the current level instead of calculating absolute values. Furthermore, EVF was developed to also support non-marginalist conceptualizations of value: it is likely that some environmental values cannot be conceptualized in terms of marginal changes. For example, ecological resilience value may, in some cases, be best understood as a binary: it either exists (1) or is lost (0). In such cases, a logistic value function may be used as the discriminator. Uncertainty in the value function parameterization can be considered through, for example, Monte Carlo sampling analysis. The use of EVF is illustrated with two conceptual examples. For the first time, EVF offers a clear framework and concrete methodology for the representation of multiple types of value in natural capital accounting systems, simultaneously enabling 1) the complementary use and integration of multiple valuation methods (monetary and non-monetary); 2) the synthesis of information from diverse knowledge systems; 3) the recognition of value incommensurability; 4) marginalist and non-marginalist value analysis. Furthermore, with this advancement, the coupling of EVF and ecosystem modeling can offer novel insights to the study of spatial-temporal dynamics in natural capital asset values. For example, value time series can be produced, allowing for the prediction and analysis of volatility, long-term trends, and temporal trade-offs. This approach can provide essential information to help guide the transition to a sustainable economy.

Keywords: economics of biodiversity, environmental valuation, natural capital, value function

Procedia PDF Downloads 192

Authors: Rene Hellmuth

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The requirements for the factory planning and the building concerned have changed in the last years. Factory planning has the task of designing products, plants, processes, organization, areas, and the building of a factory. Regular restructuring gains more importance in order to maintain the competitiveness of a factory. Restrictions regarding new areas, shorter life cycles of product and production technology as well as a VUCA (volatility, uncertainty, complexity and ambiguity) world cause more frequently occurring rebuilding measures within a factory. Restructuring of factories is the most common planning case today. Restructuring is more common than new construction, revitalization and dismantling of factories. The increasing importance of restructuring processes shows that the ability to change was and is a promising concept for the reaction of companies to permanently changing conditions. The factory building is the basis for most changes within a factory. If an adaptation of a construction project (factory) is necessary, the inventory documents must be checked and often time-consuming planning of the adaptation must take place to define the relevant components to be adapted, in order to be able to finally evaluate them. The different requirements of the planning participants from the disciplines of factory planning (production planner, logistics planner, automation planner) and industrial construction planning (architect, civil engineer) come together during reconstruction and must be structured. This raises the research question: Which requirements do the disciplines involved in the reconstruction planning place on a digital factory model? A subordinate research question is: How can model-based decision support be provided for a more efficient design of the conversion within a factory? Because of the high adaptation rate of factories and its building described above, a methodology for rescheduling factories based on the requirements engineering method from software development is conceived and designed for practical application in factory restructuring projects. The explorative research procedure according to Kubicek is applied. Explorative research is suitable if the practical usability of the research results has priority. Furthermore, it will be shown how to best use a digital factory model in practice. The focus will be on mobile applications to meet the needs of factory planners on site. An augmented reality (AR) application will be designed and created to provide decision support for planning variants. The aim is to contribute to a shortening of the planning process and model-based decision support for more efficient change management. This requires the application of a methodology that reduces the deficits of the existing approaches. The time and cost expenditure are represented in the AR tablet solution based on a building information model (BIM). Overall, the requirements of those involved in the planning process for a digital factory model in the case of restructuring within a factory are thus first determined in a structured manner. The results are then applied and transferred to a construction site solution based on augmented reality.

Keywords: augmented reality, digital factory model, factory planning, restructuring

Procedia PDF Downloads 132

Authors: Faheem Shahzad Baloch, Muhammad Azhar Nadeem, Muhammad Amjad Nawaz, Ephrem Habyarimana, Gonul Comertpay, Tolga Karakoy, Rustu Hatipoglu, Mehmet Zahit Yeken, Vahdettin Ciftci

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Turkey is considered a bridge between Europe, Asia, and Africa and possibly played an important role in the distribution of many crops including common bean. Hundreds of common bean landraces can be found in Turkey, particularly in farmers’ fields, and they consistently contribute to the overall production. To investigate the existing genetic diversity and hybridization events between the Andean and Mesoamerican gene pools in the Turkish common bean, 188 common bean accessions (182 landraces and 6 modern cultivars as controls) were collected from 19 different Turkish geographic regions. These accessions were characterized using phenotypic data (growth habit and seed weight), geographic provenance, 12557 high-quality whole-genome DArTseq markers, and 3767 novel DArTseq loci were also identified. The clustering algorithms resolved the Turkish common bean landrace germplasm into the two recognized gene pools, the Mesoamerican and Andean gene pools. Hybridization events were observed in both gene pools (14.36% of the accessions) but mostly in the Mesoamerican (7.97% of the accessions), and was low relative to previous European studies. The lower level of hybridization witnessed the existence of Turkish common bean germplasm in its original form as compared to Europe. Mesoamerican gene pool reflected a higher level of diversity, while the Andean gene pool was predominant (56.91% of the accessions), but genetically less diverse and phenotypically more pure, reflecting farmers greater preference for the Andean gene pool. We also found some genetically distinct landraces and overall, a meaningful level of genetic variability which can be used by the scientific community in breeding efforts to develop superior common bean strains.

Keywords: bean germplasm, DArTseq markers, genotyping by sequencing, Turkey, whole genome diversity

Procedia PDF Downloads 241