Search results for: probability weighted moment estimation

Authors: Champika Amarasinghe

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Introduction: Covid 19 pandemic was badly hit to the Sri Lankan economy during the year 2021. More than 65% of the Sri Lankan work force is engaged with small and medium scale businesses which no doubt that they had to struggle for their survival and business continuity during the pandemic. Objective: To assess the association of adherence to the new norms during the Covid 19 pandemic and maintenance of healthy working environmental conditions for business continuity. A cross sectional study was carried out to assess the OSH status and adequacy of Covid 19 preventive strategies among the 200 SME’S in selected two districts in Sri Lanka. These two districts were selected considering the highest availability of SME’s. Sample size was calculated, and probability propionate to size was used to select the SME’s which were registered with the small and medium scale development authority. An interviewer administrated questionnaire was used to collect the data, and OSH risk assessment was carried out by a team of experts to assess the OSH status in these industries. Results: According to the findings, more than 90% of the employees in these industries had a moderate awareness related to COVID 19 disease and preventive strategies such as the importance of Mask use, hand sainting practices, and distance maintenance, but the only forty percent of them were adhered to implementation of these practices. Furthermore, only thirty five percent of the employees and employers in these SME’s new the reasons behind the new norms, which may be the reason for reluctance to implement these strategies and reluctance to adhering to the new norms in this sector. The OSH risk assessment findings revealed that the working environmental organization while maintaining the distance between two employees was poor due to the inadequacy of space in these entities. More than fifty five percent of the SME’s had proper ventilation and lighting facilities. More than eighty five percent of these SME’s had poor electrical safety measures. Furthermore, eighty two percent of them had not maintained fire safety measures. Eighty five percent of them were exposed to heigh noise levels and chemicals where they were not using any personal protectives nor any other engineering controls were not imposed. Floor conditions were poor, and they were not maintaining the occupational accident nor occupational disease diseases. Conclusions: Based on the findings, proper awareness sessions were carried out by NIOSH. Six physical training sessions and continues online trainings were carried out to overcome these issues, which made a drastic change in their working environments and ended up with hundred percent implementation of the Covid 19 preventive strategies, which intern improved the worker participation in the businesses. Reduced absentees and improved business opportunities, and continued their businesses without any interruption during the third episode of Covid 19 in Sri Lanka.

Keywords: working environment, Covid 19, occupational diseases, occupational accidents

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Authors: Vanessa Kummer, Maik Meusel

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Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

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Authors: Danyi Wang, Andrew Schriefer, Dennis O'Rourke, Brajendra Kumar, Yang Liu, Fei Zhong, Juergen Scheuenpflug, Zheng Feng

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Purpose: It has been recognized that the microbiome plays critical roles in disease pathogenesis, including cancer, autoimmune disease, and multiple sclerosis. To develop a clinical-grade assay for exploring microbiome-derived clinical biomarkers across disease areas, a two-phase approach is implemented. 1) Identification of the optimal sample preparation reagents using pre-mixed bacteria and healthy donor stool samples coupled with proprietary Sigma-Aldrich® bioinformatics solution. 2) Exploratory analysis of patient samples for enabling precision medicine. Study Procedure: In phase 1 study, we first compared the 16S sequencing results of two ATCC® microbiome standards (MSA 2002 and MSA 2003) across five different extraction kits (Kit A, B, C, D & E). Both microbiome standards samples were extracted in triplicate across all extraction kits. Following isolation, DNA quantity was determined by Qubit assay. DNA quality was assessed to determine purity and to confirm extracted DNA is of high molecular weight. Bacterial 16S ribosomal ribonucleic acid (rRNA) amplicons were generated via amplification of the V3/V4 hypervariable region of the 16S rRNA. Sequencing was performed using a 2x300 bp paired-end configuration on the Illumina MiSeq. Fastq files were analyzed using the Sigma-Aldrich® Microbiome Platform. The Microbiome Platform is a cloud-based service that offers best-in-class 16S-seq and WGS analysis pipelines and databases. The Platform and its methods have been extensively benchmarked using microbiome standards generated internally by MilliporeSigma and other external providers. Data Summary: The DNA yield using the extraction kit D and E is below the limit of detection (100 pg/µl) of Qubit assay as both extraction kits are intended for samples with low bacterial counts. The pre-mixed bacterial pellets at high concentrations with an input of 2 x106 cells for MSA-2002 and 1 x106 cells from MSA-2003 were not compatible with the kits. Among the remaining 3 extraction kits, kit A produced the greatest yield whereas kit B provided the least yield (Kit-A/MSA-2002: 174.25 ± 34.98; Kit-A/MSA-2003: 179.89 ± 30.18; Kit-B/MSA-2002: 27.86 ± 9.35; Kit-B/MSA-2003: 23.14 ± 6.39; Kit-C/MSA-2002: 55.19 ± 10.18; Kit-C/MSA-2003: 35.80 ± 11.41 (Mean ± SD)). Also, kit A produced the greatest yield, whereas kit B provided the least yield. The PCoA 3D visualization of the Weighted Unifrac beta diversity shows that kits A and C cluster closely together while kit B appears as an outlier. The kit A sequencing samples cluster more closely together than both the other kits. The taxonomic profiles of kit B have lower recall when compared to the known mixture profiles indicating that kit B was inefficient at detecting some of the bacteria. Conclusion: Our data demonstrated that the DNA extraction method impacts DNA concentration, purity, and microbial communities detected by next-generation sequencing analysis. Further microbiome analysis performance comparison of using healthy stool samples is underway; also, colorectal cancer patients' samples will be acquired for further explore the clinical utilities. Collectively, our comprehensive qualification approach, including the evaluation of optimal DNA extraction conditions, the inclusion of positive controls, and the implementation of a robust qualified bioinformatics pipeline, assures accurate characterization of the microbiota in a complex matrix for deciphering the deep biology and enabling precision medicine.

Keywords: 16S rRNA sequencing, analytical validation, bioinformatics pipeline, metagenomics

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Authors: Barbora Svechova, Monika Licbinska

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Geochemical modeling of mineralogical changes of various materials in contact with an aqueous solution is an important tool for predicting the processes and development of given materials at the site. The modeling focused on the mutual interaction of groundwater at the contact with the rock mass and its subsequent influence on concrete structures. The studied locality is located in Slovakia in the area of the Liptov Basin, which is a significant inter-mountain lowland, which is bordered on the north and south by the core mountains belt of the Tatras, where in the center the crystalline rises to the surface accompanied by Mesozoic cover. Groundwater in the area is bound to structures with complicated geological structures. From the hydrogeological point of view, it is an environment with a crack-fracture character. The area is characterized by a shallow surface circulation of groundwater without a significant collector structure, and from a chemical point of view, groundwater in the area has been classified as calcium bicarbonate with a high content of CO2 and SO4 ions. According to the European standard EN 206-1, these are waters with medium aggression towards the concrete. Three rock samples were taken from the area. Based on petrographic and mineralogical research, they were evaluated as calcareous shale, micritic limestone and crystalline shale. These three rock samples were placed in demineralized water for one month and the change in the chemical composition of the water was monitored. During the solution-rock interaction there was an increase in the concentrations of all major ions, except nitrates. There was an increase in concentration after a week, but at the end of the experiment, the concentration was lower than the initial value. Another experiment was the interaction of groundwater from the studied locality with a concrete structure. The concrete sample was also left in the water for 1 month. The results of the experiment confirmed the assumption of a reduction in the concentrations of calcium and bicarbonate ions in water due to the precipitation of amorphous forms of CaCO3 on the surface of the sample.Vice versa, it was surprising to increase the concentration of sulphates, sodium, iron and aluminum due to the leaching of concrete. Chemical analyzes from these experiments were performed in the PHREEQc program, which calculated the probability of the formation of amorphous forms of minerals. From the results of chemical analyses and hydrochemical modeling of water collected in situ and water from experiments, it was found: groundwater at the site is unsaturated and shows moderate aggression towards reinforced concrete structures according to EN 206-1a, which will affect the homogeneity and integrity of concrete structures; from the rocks in the given area, Ca, Na, Fe, HCO3 and SO4. Unsaturated waters will dissolve everything as soon as they come into contact with the solid matrix. The speed of this process then depends on the physicochemical parameters of the environment (T, ORP, p, n, water retention time in the environment, etc.).

Keywords: geochemical modeling, concrete , dissolution , PHREEQc

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Authors: Oyedele Ogundana, Amon Simba, Kostas Galanakis, Lynn Oxborrow

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The debate about factors influencing the business growth of women-owned businesses has been a topical issue in business management. Currently, scholars have identified the issues of access to money, market, and management as canvasing factors influencing the business growth of women-owned businesses. However, the influence of motherhood (household/family context) on business growth is inconclusive in the literature; despite that women are more family-oriented than their male counterparts. Therefore, this research study considers the influence of motherhood factor (household/family context) on the business growth of women-owned sewing businesses (WOSBs) in Lagos, Nigeria. The sewing business sector is chosen as the fashion industry (which includes sewing businesses) currently accounts for the second largest number of jobs in Sub-Saharan Africa, following agriculture. Thus, sewing businesses provide a rich ground for contributing to existing scholarly work. Research questions; (1) In what way does the motherhood factor influence the business growth of WOSBs in Lagos? (2) To what extent does the motherhood factor influence the business growth of WOSBs in Lagos? For the method design, a pragmatic approach, a mixed-methods technique and an abductive form of reasoning are adopted. The method design is chosen because it fits, better than other research perspectives, with the research questions posed in this study. For instance, using a positivist approach will not sufficiently answer research question 1, neither will an interpretive approach sufficiently answer research question 2. Therefore, the research method design is divided into 2 phases, and the results from one phase are used to inform the development of the subsequent phases (only phase 1 has been completed at the moment). The first phase uses qualitative data and analytical method to answer research question 1. While the second phase of the research uses quantitative data and analytical method to answer research question 2. For the qualitative phase, 5 WOSBs were purposefully selected and interviewed. The sampling technique is selected as it was not the intention of the researcher to make any statistical inferences, at this phase, rather the purpose was just exploratory. Therefore, the 5 sampled women comprised of 2 unmarried women, 1 married woman with no child, and 2 married women with children. A 40-60 minutes interview was conducted per participants. The interviews were audio-recorded and transcribed. Thereafter, the data were analysed using thematic analysis in order to unearth patterns and relationships. Findings for the first phase of this research reveals that motherhood (household/family context) directly influences (positively/negatively) the performance of WOSBs in Lagos. Apart from a direct influence on WOSBs, motherhood also moderates (positively/negatively) other factors–e.g., access to money, management/human resources and market/opportunities– influencing WOSBs in Lagos. To further strengthen this conclusion, a word frequency query result shows that ‘family,’ ‘husband’ and ‘children’ are among the 10 words used frequently in all the interview transcripts. This first phase contributes to existing studies by showing the various forms by which motherhood influences WOSBs. The second phase (which data are yet to be collected) would reveal the extent to which motherhood influence the business growth of WOSBs in Lagos.

Keywords: women-owned sewing businesses, business growth, motherhood, Lagos

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Authors: Matej Komanec, Jan Bohata, Stanislav Zvanovec, Tomas Nemecek, Jan Broucek, Josef Beran

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With the exponential growth of social networks, video streaming and increasing demands on data rates, the number of newly built data centers rises proportionately. The data centers, however, have to adjust to the rapidly increased amount of data that has to be processed. For this purpose, multi-mode (MM) fiber based infrastructures are often employed. It stems from the fact, the connections in data centers are typically realized within a short distance, and the application of MM fibers and components considerably reduces costs. On the other hand, the usage of MM components brings specific requirements for installation service conditions. Moreover, it has to be taken into account that MM fiber components have a higher production tolerance for parameters like core and cladding diameters, eccentricity, etc. Due to the high demands for the reliability of data center components, the determination of properly excited optical field inside the MM fiber core belongs to the key parameters while designing such an MM optical system architecture. Appropriately excited mode field of the MM fiber provides optimal power budget in connections, leads to the decrease of insertion losses (IL) and achieves effective modal bandwidth (EMB). The main parameter, in this case, is the encircled flux (EF), which should be properly defined for variable optical sources and consequent different mode-field distribution. In this paper, we present detailed investigation and measurements of the mode field distribution for short MM links purposed in particular for data centers with the emphasis on reliability and safety. These measurements are essential for large MM network design. The various scenarios, containing different fibers and connectors, were tested in terms of IL and mode-field distribution to reveal potential challenges. Furthermore, we focused on estimation of particular defects and errors, which can realistically occur like eccentricity, connector shifting or dust, were simulated and measured, and their dependence to EF statistics and functionality of data center infrastructure was evaluated. The experimental tests were performed at two wavelengths, commonly used in MM networks, of 850 nm and 1310 nm to verify EF statistics. Finally, we provide recommendations for data center systems and networks, using OM3 and OM4 MM fiber connections.

Keywords: optical fiber, multi-mode, data centers, encircled flux

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Authors: Ziv Kassner, Gal Ribak

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Members of the Odonata order (dragonflies and damselflies) stand out for their maneuverability and superb flight control, which allow them to catch flying prey in the air. These outstanding aerial abilities were fine-tuned during millions of years of an evolutionary arms race between Odonata and their prey, providing an attractive research model for studying the relationship between sensory input – and aerodynamic output in a flying insect. The ability to catch a maneuvering target in air is interesting not just for insect behavioral ecology and neuroethology but also for designing small and efficient robotic air vehicles. While the aerial prey interception of dragonflies (suborder: Anisoptera) have been studied before, little is known about how damselflies (suborder: Zygoptera) intercept prey. Here, high-speed cameras (filming at 1000 frames per second) were used to explore how damselflies catch unpredictable targets that move through air. Blue-tailed damselflies - Ischnura elegans (family: Coenagrionidae) were introduced to a flight arena and filmed while landing on moving targets that were oscillated harmonically. The insects succeeded in capturing targets that were moved with an amplitude of 6 cm and frequencies of 0-2.5 Hz (fastest mean target speed of 0.3 m s⁻¹) and targets that were moved in 1 Hz (an average speed of 0.3 m s⁻¹) but with an amplitude of 15 cm. To land on stationary or slow targets, damselflies either flew directly to the target, or flew sideways, up to a point in which the target was fixed in the center of the field of view, followed by direct flight path towards the target. As the target moved in increased frequency, damselflies demonstrated an ability to track the targets while flying sideways and minimizing the changes of their body direction on the yaw axis. This was likely an attempt to keep the targets at the center of the visual field while minimizing rotational optic flow of the surrounding visual panorama. Stabilizing rotational optic flow helps in estimation of the velocity and distance of the target. These results illustrate how dynamic visual information is used by damselflies to guide them towards a maneuvering target, enabling the superb aerial hunting abilities of these insects. They also exemplifies the plasticity of the damselfly flight apparatus which enables flight in any direction, irrespective of the direction of the body.

Keywords: bio-mechanics, insect flight, target fixation, tracking and interception

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Authors: Hayder H. Kareem, Shunqi Pan

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The geological environment where the groundwater is collected represents the most important element that affects the behaviour of groundwater aquifer. As groundwater is a worldwide vital resource, it requires knowing the parameters that affect this source accurately so that the conceptualized mathematical models would be acceptable to the broadest ranges. Therefore, groundwater models have recently become an effective and efficient tool to investigate groundwater aquifer behaviours. Groundwater aquifer may contain aquitards, aquicludes, or interfaces within its geological formations. Aquitards and aquicludes have geological formations that forced the modellers to include those formations within the conceptualized groundwater models, while interfaces are commonly neglected from the conceptualization process because the modellers believe that the interface has no effect on aquifer behaviour. The current research highlights the impact of an interface existing in a real unconfined groundwater aquifer called Dibdibba, located in Al-Najaf City, Iraq where it has a river called the Euphrates River that passes through the eastern part of this city. Dibdibba groundwater aquifer consists of two types of soil layers separated by an interface soil layer. A groundwater model is built for Al-Najaf City to explore the impact of this interface. Calibration process is done using PEST 'Parameter ESTimation' approach and the best Dibdibba groundwater model is obtained. When the soil interface is conceptualized, results show that the groundwater tables are significantly affected by that interface through appearing dry areas of 56.24 km² and 6.16 km² in the upper and lower layers of the aquifer, respectively. The Euphrates River will also leak water into the groundwater aquifer of 7359 m³/day. While these results are changed when the soil interface is neglected where the dry area became 0.16 km², the Euphrates River leakage became 6334 m³/day. In addition, the conceptualized models (with and without interface) reveal different responses for the change in the recharge rates applied on the aquifer through the uncertainty analysis test. The aquifer of Dibdibba in Al-Najaf City shows a slight deficit in the amount of water supplied by the current pumping scheme and also notices that the Euphrates River suffers from stresses applied to the aquifer. Ultimately, this study shows a crucial need to represent the interface soil layer in model conceptualization to be the intended and future predicted behaviours more reliable for consideration purposes.

Keywords: Al-Najaf City, groundwater aquifer behaviour, groundwater modelling, interface soil layer, Visual MODFLOW

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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Authors: Morteza Esfandiari, Shabnam Jabari, Heather MacGrath, David Coleman

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Flood is a severe issue in different places in the world as well as the city of Fredericton, New Brunswick, Canada. The downtown area of Fredericton is close to the Saint John River, which is susceptible to flood around May every year. Recently, the frequency of flooding seems to be increased, especially after the fact that the downtown area and surrounding urban/agricultural lands got flooded in two consecutive years in 2018 and 2019. In order to have an explicit vision of flood span and damage to affected areas, it is necessary to use either flood inundation modelling or satellite data. Due to contingent availability and weather dependency of optical satellites, and limited existing data for the high cost of hydrodynamic models, it is not always feasible to rely on these sources of data to generate quality flood maps after or during the catastrophe. Height Above the Nearest Drainage (HAND), a state-of-the-art topo-hydrological index, normalizes the height of a basin based on the relative elevation along with the stream network and specifies the gravitational or the relative drainage potential of an area. HAND is a relative height difference between the stream network and each cell on a Digital Terrain Model (DTM). The stream layer is provided through a multi-step, time-consuming process which does not always result in an optimal representation of the river centerline depending on the topographic complexity of that region. HAND is used in numerous case studies with quite acceptable and sometimes unexpected results because of natural and human-made features on the surface of the earth. Some of these features might cause a disturbance in the generated model, and consequently, the model might not be able to predict the flow simulation accurately. We propose to include a previously existing stream layer generated by the province of New Brunswick and benefit from culvert maps to improve the water flow simulation and accordingly the accuracy of HAND model. By considering these parameters in our processing, we were able to increase the accuracy of the model from nearly 74% to almost 92%. The improved model can be used for generating highly accurate flood maps, which is necessary for future urban planning and flood damage estimation without any need for satellite imagery or hydrodynamic computations.

Keywords: HAND, DTM, rapid floodplain, simplified conceptual models

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Authors: A. G. Akhundov

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Issues of obtaining unbiased data on the status of pipeline systems of oil- and oil product transportation become especially important when laying and operating pipelines under severe nature and climatic conditions. The essential attention is paid here to researching exogenous processes and their impact on linear facilities of the pipeline system. Reliable operation of pipelines under severe nature and climatic conditions, timely planning and implementation of compensating measures are only possible if operation conditions of pipeline systems are regularly monitored, and changes of permafrost soil and hydrological operation conditions are accounted for. One of the main reasons for emergency situations to appear is the geodynamic factor. Emergency situations are proved by the experience to occur within areas characterized by certain conditions of the environment and to develop according to similar scenarios depending on active processes. The analysis of natural and technical systems of main pipelines at different stages of monitoring gives a possibility of making a forecast of the change dynamics. The integration of GIS technologies, traditional means of geotechnical monitoring (in-line inspection, geodetic methods, field observations), and remote methods (aero-visual inspection, aero photo shooting, air and ground laser scanning) provides the most efficient solution of the problem. The united environment of geo information system (GIS) is a comfortable way to implement the monitoring system on the main pipelines since it provides means to describe a complex natural and technical system and every element thereof with any set of parameters. Such GIS enables a comfortable simulation of main pipelines (both in 2D and 3D), the analysis of situations and selection of recommendations to prevent negative natural or man-made processes and to mitigate their consequences. The specifics of such systems include: a multi-dimensions simulation of facilities in the pipeline system, math modelling of the processes to be observed, and the use of efficient numeric algorithms and software packets for forecasting and analyzing. We see one of the most interesting possibilities of using the monitoring results as generating of up-to-date 3D models of a facility and the surrounding area on the basis of aero laser scanning, data of aerophotoshooting, and data of in-line inspection and instrument measurements. The resulting 3D model shall be the basis of the information system providing means to store and process data of geotechnical observations with references to the facilities of the main pipeline; to plan compensating measures, and to control their implementation. The use of GISs for geotechnical monitoring of pipeline systems is aimed at improving the reliability of their operation, reducing the probability of negative events (accidents and disasters), and at mitigation of consequences thereof if they still are to occur.

Keywords: databases, 3D GIS, geotechnical monitoring, pipelines, laser scaning

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Aurea C. L. Lacerda, Paulo R. H. Moreno, Bruna M. P. Vianna, Cristina H. R. Serra, Airton Martin, André R. Baby, Vladi O. Consiglieri, Telma M. Kaneko

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The modified Franz cell is the most widely used model for in vitro permeation studies, however it still presents some disadvantages. Thus, some alternative methods have been developed such as Skin PAMPA, which is a bio- artificial membrane that has been applied for skin penetration estimation of xenobiotics based on HT permeability model consisting. Skin PAMPA greatest advantage is to carry out more tests, in a fast and inexpensive way. The membrane system mimics the stratum corneum characteristics, which is the primary skin barrier. The barrier properties are given by corneocytes embedded in a multilamellar lipid matrix. This layer is the main penetration route through the paracellular permeation pathway and it consists of a mixture of cholesterol, ceramides, and fatty acids as the dominant components. However, there is no consensus on the membrane composition. The objective of this work was to compare the performance among different bio-artificial membranes for studying the permeation in skin PAMPA system. Material and methods: In order to mimetize the lipid composition`s present in the human stratum corneum six membranes were developed. The membrane composition was equimolar mixture of cholesterol, ceramides 1-O-C18:1, C22, and C20, plus fatty acids C20 and C24. The membrane integrity assay was based on the transport of Brilliant Cresyl Blue, which has a low permeability; and Lucifer Yellow with very poor permeability and should effectively be completely rejected. The membrane characterization was performed using Confocal Laser Raman Spectroscopy, using stabilized laser at 785 nm with 10 second integration time and 2 accumulations. The membrane behaviour results on the PAMPA system were statistically evaluated and all of the compositions have shown integrity and permeability. The confocal Raman spectra were obtained in the region of 800-1200 cm-1 that is associated with the C-C stretches of the carbon scaffold from the stratum corneum lipids showed similar pattern for all the membranes. The ceramides, long chain fatty acids and cholesterol in equimolar ratio permitted to obtain lipid mixtures with self-organization capability, similar to that occurring into the stratum corneum. Conclusion: The artificial biological membranes studied for Skin PAMPA showed to be similar and with comparable properties to the stratum corneum.

Keywords: bio-artificial membranes, comparison, confocal Raman, skin PAMPA

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Authors: Mohamed Hendawy Mousa, Warda Youssef Mohamed Morsy

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Background: Sepsis is a major cause of mortality and morbidity in trauma patients. Body mass index as an indicator of nutritional status was reported as a predictor of injury pattern and complications among critically ill injured patients. Aim: The aim of this study is to investigate the relationship between body mass index and the development of sepsis, systemic inflammatory response syndrome among adult trauma patients at emergency hospital - Cairo University. Research design: Descriptive correlational research design was utilized in the current study. Research questions: Q1. What is the body mass index profile of adult trauma patients admitted to the emergency hospital at Cairo University over a period of 6 months?, Q2. What is the frequency of systemic inflammatory response syndrome and sepsis among adult trauma patients admitted to the emergency hospital at Cairo University over a period of 6 months?, and Q3. What is the relationship between the development of sepsis, systemic inflammatory response syndrome and body mass index among adult trauma patients admitted to the emergency hospital at Cairo University over a period of 6 months?. Sample: A purposive sample of 52 adult male and female trauma patients with revised trauma score 10 to 12. Setting: The Emergency Hospital affiliated to Cairo University. Tools: Four tools were utilized to collect data pertinent to the study: Socio demographic and medical data tool, Systemic inflammatory response syndrome assessment tool, Revised Trauma Score tool, and Sequential organ failure assessment tool. Results: The current study revealed that, (61.5 %) of the studied subjects had normal body mass index, (25 %) were overweight, and (13.5 %) were underweight. 84.6% of the studied subjects had systemic inflammatory response syndrome and 92.3% were suffering from mild sepsis. No significant statistical relationship was found between body mass index and occurrence of Systemic inflammatory response syndrome (2= 2.89 & P = 0.23). However, Sequential organ failure assessment scores were affected significantly by body mass index was found mean of initial and last Sequential organ failure assessment score for underweight, normal and obese where t= 7.24 at p = 0.000, t= 16.49 at p = 0.000 and t= 9.80 at p = 0.000 respectively. Conclusion: Underweight trauma patients showed significantly higher rate of developing sepsis as compared to patients with normal body weight and obese. Recommendations: based on finding of this study the following are recommended: replication of the study on a larger probability sample from different geographical locations in Egypt; Carrying out of further studies in order to assess the other risk factors influencing trauma outcome and incidence of its complications; Establishment of standardized guidelines for managing underweight traumatized patients with sepsis.

Keywords: body mass index, sepsis, systemic inflammatory response syndrome, adult trauma

Procedia PDF Downloads 236

Authors: Ning Chang, Zelong Yuan, Yunpeng Wang, Jianchun Wang

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Large eddy simulation (LES) has been extensively used in the investigation of turbulence. LES calculates the grid-resolved large-scale motions and leaves small scales modeled by sublfilterscale (SFS) models. Among the existing SFS models, the deconvolution model has been used successfully in the LES of the engineering flows and geophysical flows. Despite the wide application of deconvolution models, the effects of subfilter scale dynamics and filter anisotropy on the accuracy of SFS modeling have not been investigated in depth. The results of LES are highly sensitive to the selection of filters and the anisotropy of the grid, which has been overlooked in previous research. In the current study, two critical aspects of LES are investigated. Firstly, we analyze the influence of sub-filter scale (SFS) dynamics on the accuracy of direct deconvolution models (DDM) at varying filter-to-grid ratios (FGR) in isotropic turbulence. An array of invertible filters are employed, encompassing Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. The significance of FGR becomes evident, as it acts as a pivotal factor in error control for precise SFS stress prediction. When FGR is set to 1, the DDM models cannot accurately reconstruct the SFS stress due to the insufficient resolution of SFS dynamics. Notably, prediction capabilities are enhanced at an FGR of 2, resulting in accurate SFS stress reconstruction, except for cases involving Helmholtz I and II filters. A remarkable precision close to 100% is achieved at an FGR of 4 for all DDM models. Additionally, the further exploration extends to the filter anisotropy to address its impact on the SFS dynamics and LES accuracy. By employing dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and direct deconvolution model (DDM) with the anisotropic filter, aspect ratios (AR) ranging from 1 to 16 in LES filters are evaluated. The findings highlight the DDM's proficiency in accurately predicting SFS stresses under highly anisotropic filtering conditions. High correlation coefficients exceeding 90% are observed in the a priori study for the DDM's reconstructed SFS stresses, surpassing those of the DSM and DMM models. However, these correlations tend to decrease as lter anisotropy increases. In the a posteriori studies, the DDM model consistently outperforms the DSM and DMM models across various turbulence statistics, encompassing velocity spectra, probability density functions related to vorticity, SFS energy flux, velocity increments, strain-rate tensors, and SFS stress. It is observed that as filter anisotropy intensify, the results of DSM and DMM become worse, while the DDM continues to deliver satisfactory results across all filter-anisotropy scenarios. The findings emphasize the DDM framework's potential as a valuable tool for advancing the development of sophisticated SFS models for LES of turbulence.

Keywords: deconvolution model, large eddy simulation, subfilter scale modeling, turbulence

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Authors: Parichehr Sharifi, Konrad Reschke, Hans-Liudger Dienel

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Psychological intervention generally targets changes in attitudes and behavior. Working with DUIs is part of traffic psychologists’ work. The primary goal of this field is to reduce the probability of re-conspicuous of the delinquent driver. One of these measurements in Germany is IFT courses for DUI s. The IFT course was designed by the Institute for Therapy Research. Participants are drivers who have fallen several times or once with a blood alcohol concentration of 1.6 per mill and who have completed a medical-psychological assessment (MPU) with the result of the course recommendation. The course covers four sessions of 3.5 hours each (1 hour / 60 m) and in a period of 3 to 4 weeks in the group discussion. This work analyzes interventions for the rehabilitation of DUI (Drunk Drivers offenders) offenders in groups under the aspect of activating psychological resources. From the aspect of sustainability, they should also have long-term consequences for the maintenance of unproblematic driving behavior in terms of the activation of resources. It is also addressing a selected consistency-theory-based intervention effect, activating psychological resources. So far, this has only been considered in the psychotherapeutic field but never in the field of traffic psychology. The methodology of this survey is one qualitative and three quantitative. In four sub-studies, it will be examined which measurements can determine the resources and how traffic psychological interventions can strengthen resources. The results of the studies have the following implications for traffic psychology research and practice: (1) In the field of traffic psychology intervention for the restoration of driving fitness, it can be stated that aspects of resource activation in this work have been investigated for the first time by qualitative and quantitative methods. (2) The resource activation could be confirmed based on the determined results as an effective factor of traffic psychological intervention. (3) Two sub-studies show a range of resources and resource activation options that must be given greater emphasis in traffic psychology interventions: - Social resource activation - improvement of the life skills of participants - Reactivation of existing social support options - Re-experiencing self-esteem, self-assurance, and acceptance of traffic-related behaviors. (4) In revising the IFT-§70 course, as well as other courses on recreating aptitude for DUI, new traffic-specific resource-enabling interventions against alcohol abuse should be developed to further enhance the courses through motivational, cognitive, and behavioral effects of resource activation, Resource-activating interventions can not only be integrated into behavioral group interventions but can also be applied in psychodynamic, psychodynamic (individual psychological) and other contexts of individual traffic psychology. The results are indicative but clearly show that personal resources can be strengthened through traffic psychology interventions. In the research, practice, training, and further education of traffic psychology, the aspect of primary resource activation (Grawe, 1999), therefore, always deserves the greatest attention for the rehabilitation of DUIs and Traffic safety.

Keywords: traffic safety, psychological resources, activating of resources, intervention programs for alcohol offenders, empowerment

Procedia PDF Downloads 67

Authors: Evan Lowhorn, Rocio Alba-Flores

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Traditional classification Convolutional Neural Networks (CNN) attempt to classify an image in its entirety. This becomes problematic when trying to perform classification with a drone’s camera in real-time due to unpredictable backgrounds. Object detectors with bounding boxes can be used to isolate individuals and other items, but the original backgrounds remain within these boxes. These basic detectors have been regularly used to determine what type of object an item is, such as “person” or “dog.” Recent advancement in computer vision, particularly with human imaging, is keypoint detection. Human keypoint detection goes beyond bounding boxes to fully isolate humans and plot points, or Regions of Interest (ROI), on their bodies within an image. ROIs can include shoulders, elbows, knees, heads, etc. These points can then be related to each other and used in deep learning methods such as pose estimation. For drone control based on human motions, poses, or signals using the onboard camera, it is important to have a simple method for pilot identification among multiple individuals while also giving the pilot fine control options for the drone. To achieve this, the OpenPose keypoint detection network was used with body and hand keypoint detection enabled. OpenPose supports the ability to combine multiple keypoint detection methods in real-time with a single network. Body keypoint detection allows simple poses to act as the pilot identifier. The hand keypoint detection with ROIs for each finger can then offer a greater variety of signal options for the pilot once identified. For this work, the individual must raise their non-control arm to be identified as the operator and send commands with the hand on their other arm. The drone ignores all other individuals in the onboard camera feed until the current operator lowers their non-control arm. When another individual wish to operate the drone, they simply raise their arm once the current operator relinquishes control, and then they can begin controlling the drone with their other hand. This is all performed mid-flight with no landing or script editing required. When using a desktop with a discrete NVIDIA GPU, the drone’s 2.4 GHz Wi-Fi connection combined with OpenPose restrictions to only body and hand allows this control method to perform as intended while maintaining the responsiveness required for practical use.

Keywords: computer vision, drone control, keypoint detection, openpose

Procedia PDF Downloads 172

Authors: Pavana Basavakumar, Devadas Bhat

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Diabetes is a disease which often goes undiagnosed until its secondary effects are noticed. Early detection of the disease is necessary to avoid serious consequences which could lead to the death of the patient. Conventional invasive tests for screening of diabetes are mostly painful, time consuming and expensive. There’s also a risk of infection involved, therefore it is very essential to develop non-invasive methods to screen and estimate the level of blood glucose. Extensive research is going on with this perspective, involving various techniques that explore optical, electrical, chemical and thermal properties of the human body that directly or indirectly depend on the blood glucose concentration. Thus, non-invasive blood glucose monitoring has grown into a vast field of research. In this project, an attempt was made to device a prototype for screening of diabetes by measuring electrical impedance of the skin and building a model to predict a patient’s condition based on the measured impedance. The prototype developed, passes a negligible amount of constant current (0.5mA) across a subject’s index finger through tetra polar silver electrodes and measures output voltage across a wide range of frequencies (10 KHz – 4 MHz). The measured voltage is proportional to the impedance of the skin. The impedance was acquired in real-time for further analysis. Study was conducted on over 75 subjects with permission from the institutional ethics committee, along with impedance, subject’s blood glucose values were also noted, using conventional method. Nonlinear regression analysis was performed on the features extracted from the impedance data to obtain a model that predicts blood glucose values for a given set of features. When the predicted data was depicted on Clarke’s Error Grid, only 58% of the values predicted were clinically acceptable. Since the objective of the project was to screen diabetes and not actual estimation of blood glucose, the data was classified into three classes ‘NORMAL FASTING’,’NORMAL POSTPRANDIAL’ and ‘HIGH’ using linear Support Vector Machine (SVM). Classification accuracy obtained was 91.4%. The developed prototype was economical, fast and pain free. Thus, it can be used for mass screening of diabetes.

Keywords: Clarke’s error grid, electrical impedance of skin, linear SVM, nonlinear regression, non-invasive blood glucose monitoring, screening device for diabetes

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Authors: Shpetim Rezniqi

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Financial Policies in the process of global crisis the current crisis has swept the world with special emphasis, most developed countries, those countries which have most gross -product world and you have a high level of living.Even those who are not experts can describe the consequences of the crisis to see the reality that is seen, but how far will it go this crisis is impossible to predict. Even the biggest experts have conjecture and large divergence, but agree on one thing: - The devastating effects of this crisis will be more severe than ever before and can not be predicted.Long time, the world was dominated economic theory of free market laws. With the belief that the market is the regulator of all economic problems. The market, as river water will flow to find the best and will find the necessary solution best. Therefore much less state market barriers, less state intervention and market itself is an economic self-regulation. Free market economy became the model of global economic development and progress, it transcends national barriers and became the law of the development of the entire world economy. Globalization and global market freedom were principles of development and international cooperation. All international organizations like the World Bank, states powerful economic, development and cooperation principles laid free market economy and the elimination of state intervention. The less state intervention much more freedom of action was this market- leading international principle. We live in an era of financial tragic. Financial markets and banking in particular economies are in a state of thy good, US stock markets fell about 40%, in other words, this time, was one of the darkest moments 5 since 1920. Prior to her rank can only "collapse" of the stock of Wall Street in 1929, technological collapse of 2000, the crisis of 1973 after the Yom Kippur war, while the price of oil quadrupled and famous collapse of 1937 / '38, when Europe was beginning World war II In 2000, even though it seems like the end of the world was the corner, the world economy survived almost intact. Of course, that was small recessions in the United States, Europe, or Japan. Much more difficult the situation was at crisis 30s, or 70s, however, succeeded the world. Regarding the recent financial crisis, it has all the signs to be much sharper and with more consequences. The decline in stock prices is more a byproduct of what is really happening. Financial markets began dance of death with the credit crisis, which came as a result of the large increase in real estate prices and household debt. It is these last two phenomena can be matched very well with the gains of the '20s, a period during which people spent fists as if there was no tomorrow. All is not away from the mouth of the word recession, that fact no longer a sudden and abrupt. But as much as the financial markets melt, the greater is the risk of a problematic economy for years to come. Thus, for example, the banking crisis in Japan proved to be much more severe than initially expected, partly because the assets which were based more loans had, especially the land that falling in value. The price of land in Japan is about 15 years that continues to fall. (ADRI Nurellari-Published in the newspaper "Classifieds"). At this moment, it is still difficult to çmosh to what extent the crisis has affected the economy and what would be the consequences of the crisis. What we know is that many banks will need more time to reduce the award of credit, but banks have this primary function, this means huge loss.

Keywords: globalisation, finance, crisis, recomandation, bank, credits

Procedia PDF Downloads 375

Authors: Vivek Kumar Gupta, Kripi Vohra, Monika Gupta

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Pulses have been a vital ingredient of the balanced human diet in India. Lentil (Lens culinaris Medikus or Lens esculenta Moench.) is a common legume known since biblical times. Lentil seeds, with or without hulls, are cooked as dhal and this has been the main dish for millennia in the South Asian region. Oxidative stress can damage lipids, proteins, enzymes, carbohydrates and DNA in cells and tissues, resulting in membrane damage, fragmentation or random cross linking of molecules like DNA, enzymes and structural proteins and even lead to cell death induced by DNA fragmentation and lipid peroxidation. These consequences of oxidative stress construct the molecular basis in the development of cancer, neurodegenerative disorders, cardiovascular diseases, diabetes and autoimmune. The aim of the present work is to assess the antioxidant potential of the peteroleum ether, acetone, methanol and water extract of the Lens esculenta seeds. In vitro antioxidant assessment of the extracts was carried out using 1,1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging activity, hydroxyl radical scavenging activity, reducing power assay. The quantitative estimation of total phenolic content, total flavonoid content in extracts and in plant material, total saponin content, total alkaloid content, crude fibre content, total volatile content, fat content and mucilage content in drug material was also carried out. Though all the extracts exhibited dose dependent reducing power activity the acetone extract was found to possess significant hydrogen donating ability in DPPH (45.83%-93.13%) and hydroxyl radical scavenging system (28.7%-46.41%) than the peteroleum ether, methanol and water extracts. Total phenolic content in the acetone and methanol extract was found to be 608 and 188 mg gallic acid equivalent of phenol/g of sample respectively. Total flavonoid content of acetone and methanol extract was found to be 128 and 30.6 mg quercetin equivalent/g of sample respectively. It is evident that acetone extract of Lentil seeds possess high levels of polyphenolics and flavonoids that could be utilized as antioxidants and neutraceuticals.

Keywords: antioxidant, flavanoids, Lens esculenta, polyphenols

Procedia PDF Downloads 464

Authors: Getalem E. Haylia

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The Ribb dam is one of the irrigation projects in the Upper Blue Nile basin, Ethiopia, to irrigate the Fogera plain. Reservoir sedimentation is a major problem because it reduces the useful reservoir capacity by the accumulation of sediments coming from the watersheds. Estimates of sediment yield are needed for studies of reservoir sedimentation and planning of soil and water conservation measures. The objective of this study was to simulate the Ribb dam catchment sediment yield using SWAT model and to estimate Ribb reservoir effective life according to trap efficiency methods. The Ribb dam catchment is found in North Western part of Ethiopia highlands, and it belongs to the upper Blue Nile and Lake Tana basins. Soil and Water Assessment Tool (SWAT) was selected to simulate flow and sediment yield in the Ribb dam catchment. The model sensitivity, calibration, and validation analysis at Ambo Bahir site were performed with Sequential Uncertainty Fitting (SUFI-2). The flow data at this site was obtained by transforming the Lower Ribb gauge station (2002-2013) flow data using Area Ratio Method. The sediment load was derived based on the sediment concentration yield curve of Ambo site. Stream flow results showed that the Nash-Sutcliffe efficiency coefficient (NSE) was 0.81 and the coefficient of determination (R²) was 0.86 in calibration period (2004-2010) and, 0.74 and 0.77 in validation period (2011-2013), respectively. Using the same periods, the NS and R² for the sediment load calibration were 0.85 and 0.79 and, for the validation, it became 0.83 and 0.78, respectively. The simulated average daily flow rate and sediment yield generated from Ribb dam watershed were 3.38 m³/s and 1772.96 tons/km²/yr, respectively. The effective life of Ribb reservoir was estimated using the developed empirical methods of the Brune (1953), Churchill (1948) and Brown (1958) methods and found to be 30, 38 and 29 years respectively. To conclude, massive sediment comes from the steep slope agricultural areas, and approximately 98-100% of this incoming annual sediment loads have been trapped by the Ribb reservoir. In Ribb catchment, as well as reservoir systematic and thorough consideration of technical, social, environmental, and catchment managements and practices should be made to lengthen the useful life of Ribb reservoir.

Keywords: catchment, reservoir effective life, reservoir sedimentation, Ribb, sediment yield, SWAT model

Procedia PDF Downloads 173

Authors: Kamal Ahmed, Othman Ayman, Refat Mostafa

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The post-construction phase represents a critical milestone in the project lifecycle. This is because design errors and omissions, as well as construction defects, are examined during this phase. The traditional procurement approaches that are commonly adopted in construction projects separate design from construction, which ultimately inhibits contractors, suppliers and other parties from providing the design team with constructive comments and feedback to improve the project design. As a result, a lack of considering maintainability aspects during the design process results in increasing maintenance and operation costs as well as reducing building performance. This research aims to investigate the role of Early Supplier Involvement (ESI) in the design process as an approach to enhancing building maintainability. In order to achieve this aim, a research methodology consisting of a literature review, case studies and a survey questionnaire was designed to accomplish four objectives. Firstly, a literature review was used to examine the concepts of building maintenance, maintainability, the design process and ESI. Secondly, three case studies were presented and analyzed to investigate the role of ESI in enhancing building maintainability during the design process. Thirdly, a survey questionnaire was conducted with a representative sample of Architectural Design Firms (ADFs) in Egypt to investigate their perception and application of ESI towards enhancing building maintainability during the design process. Finally, the research developed a framework to facilitate ESI in the design process in ADFs in Egypt. Data analysis showed that the ‘Difficulty of trusting external parties and sharing information with transparency’ was ranked the highest challenge of ESI in ADFs in Egypt, followed by ‘Legal competitive advantage restrictions’. Moreover, ‘Better estimation for operation and maintenance costs’ was ranked the highest contribution of ESI towards enhancing building maintainability, followed by ‘Reduce the number of operation and maintenance problems or reworks’. Finally, ‘Innovation, technical expertise, and competence’ was ranked the highest supplier’s selection criteria, while ‘paying consultation fees for offering advice and recommendations to the design team’ was ranked the highest form of supplier’s remuneration. The proposed framework represents a synthesis that is creative in thought and adds value to the knowledge in a manner that has not previously occurred.

Keywords: maintenance, building maintainability, building life cycle cost (ICC), material supplier

Procedia PDF Downloads 38

Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores

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This work introduces the use of EMGs (electromyography) from muscle sensors to develop an Artificial Neural Network (ANN) for pattern recognition to control a small unmanned aerial vehicle. The objective of this endeavor exhibits interfacing drone applications beyond manual control directly. MyoWare Muscle sensor contains three EMG electrodes (dual and single type) used to collect signals from the posterior (extensor) and anterior (flexor) forearm and the bicep. Collection of raw voltages from each sensor were connected to an Arduino Uno and a data processing algorithm was developed with the purpose of interpreting the voltage signals given when performing flexing, resting, and motion of the arm. Each sensor collected eight values over a two-second period for the duration of one minute, per assessment. During each two-second interval, the movements were alternating between a resting reference class and an active motion class, resulting in controlling the motion of the drone with left and right movements. This paper further investigated adding up to three sensors to differentiate between hand gestures to control the principal motions of the drone (left, right, up, and land). The hand gestures chosen to execute these movements were: a resting position, a thumbs up, a hand swipe right motion, and a flexing position. The MATLAB software was utilized to collect, process, and analyze the signals from the sensors. The protocol (machine learning tool) was used to classify the hand gestures. To generate the input vector to the ANN, the mean, root means squared, and standard deviation was processed for every two-second interval of the hand gestures. The neuromuscular information was then trained using an artificial neural network with one hidden layer of 10 neurons to categorize the four targets, one for each hand gesture. Once the machine learning training was completed, the resulting network interpreted the processed inputs and returned the probabilities of each class. Based on the resultant probability of the application process, once an output was greater or equal to 80% of matching a specific target class, the drone would perform the motion expected. Afterward, each movement was sent from the computer to the drone through a Wi-Fi network connection. These procedures have been successfully tested and integrated into trial flights, where the drone has responded successfully in real-time to predefined command inputs with the machine learning algorithm through the MyoWare sensor interface. The full paper will describe in detail the database of the hand gestures, the details of the ANN architecture, and confusion matrices results.

Keywords: artificial neural network, biosensors, electromyography, machine learning, MyoWare muscle sensors, Arduino

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Authors: Alia Alghosoun, Nabil El Moçayd, Mohammed Seaid

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Modeling dam-break flows over non-flat beds requires an accurate representation of the topography which is the main source of uncertainty in the model. Therefore, developing robust and accurate techniques for reconstructing topography in this class of problems would reduce the uncertainty in the flow system. In many hydraulic applications, experimental techniques have been widely used to measure the bed topography. In practice, experimental work in hydraulics may be very demanding in both time and cost. Meanwhile, computational hydraulics have served as an alternative for laboratory and field experiments. Unlike the forward problem, the inverse problem is used to identify the bed parameters from the given experimental data. In this case, the shallow water equations used for modeling the hydraulics need to be rearranged in a way that the model parameters can be evaluated from measured data. However, this approach is not always possible and it suffers from stability restrictions. In the present work, we propose an adaptive optimal control technique to numerically identify the underlying bed topography from a given set of free-surface observation data. In this approach, a minimization function is defined to iteratively determine the model parameters. The proposed technique can be interpreted as a fractional-stage scheme. In the first stage, the forward problem is solved to determine the measurable parameters from known data. In the second stage, the adaptive control Ensemble Kalman Filter is implemented to combine the optimality of observation data in order to obtain the accurate estimation of the topography. The main features of this method are on one hand, the ability to solve for different complex geometries with no need for any rearrangements in the original model to rewrite it in an explicit form. On the other hand, its achievement of strong stability for simulations of flows in different regimes containing shocks or discontinuities over any geometry. Numerical results are presented for a dam-break flow problem over non-flat bed using different solvers for the shallow water equations. The robustness of the proposed method is investigated using different numbers of loops, sensitivity parameters, initial samples and location of observations. The obtained results demonstrate high reliability and accuracy of the proposed techniques.

Keywords: erodible beds, finite element method, finite volume method, nonlinear elasticity, shallow water equations, stresses in soil

Procedia PDF Downloads 118

Authors: Robin Devillers, Chantal van Dijk

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Children face ambiguities in everyday language use. Particularly ambiguity in pronoun resolution can be challenging, whereas adults can rapidly identify the antecedent of the mentioned pronoun. Two main factors underlie this process, namely the accessibility of the referent and the syntactic cues of the pronoun. After 200ms, adults have converged the accessibility and the syntactic constraints, while relieving cognitive effort by considering contextual cues. As children are still developing their cognitive capacity, they are not able yet to simultaneously assess and integrate accessibility, contextual cues and syntactic information. As such, they fail to identify the correct referent and possibly fixate more on the competitor in comparison to adults. In this study, Dutch while-clauses were used to investigate the interpretation of pronouns by children. The aim is to a) examine the extent to which 7-10 year old children are able to utilise discourse and syntactic information during online and offline sentence processing and b) analyse the contribution of individual factors, including age, working memory, condition and vocabulary. Adult and child participants are presented with filler-items and while-clauses, and the latter follows a particular structure: ‘Anna and Sophie are sitting in the library. While Anna is reading a book, she is taking a sip of water.’ This sentence illustrates the ambiguous situation, as it is unclear whether ‘she’ refers to Anna or Sophie. In the unambiguous situation, either Anna or Sophie would be substituted by a boy, such as ‘Peter’. The pronoun in the second sentence will unambiguously refer to one of the characters due to the syntactic constraints of the pronoun. Children’s and adults’ responses were measured by means of a visual world paradigm. This paradigm consisted of two characters, of which one was the referent (the target) and the other was the competitor. A sentence was presented and followed by a question, which required the participant to choose which character was the referent. Subsequently, this paradigm yields an online (fixations) and offline (accuracy) score. These findings will be analysed using Generalised Additive Mixed Models, which allow for a thorough estimation of the individual variables. These findings will contribute to the scientific literature in several ways; firstly, the use of while-clauses has not been studied much and it’s processing has not yet been identified. Moreover, online pronoun resolution has not been investigated much in both children and adults, and therefore, this study will contribute to adults and child’s pronoun resolution literature. Lastly, pronoun resolution has not been studied yet in Dutch and as such, this study adds to the languages

Keywords: pronouns, online language processing, Dutch, eye-tracking, first language acquisition, language development

Procedia PDF Downloads 88

Authors: Amr Mohamadien, Ali Imanpour, Sylvester Agbo, Nader Yoosef-Ghodsi, Samer Adeeb

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The fracture toughness resistance curve (e.g., J-R curve and crack tip opening displacement (CTOD) or δ-R curve) is important in facilitating strain-based design and integrity assessment of oil and gas pipelines. This paper aims to present laboratory experimental data to characterize the fracture behavior of pipeline steel. The influential parameters associated with the fracture of API 5L X52 pipeline steel, including different initial crack sizes, were experimentally investigated for a single notch edge bend (SENB). A total of 9 small-scale specimens with different crack length to specimen depth ratios were conducted and tested using single edge notch bending (SENB). ASTM E1820 and BS7448 provide testing procedures to construct the fracture resistance curve (Load-CTOD, CTOD-R, or J-R) from test results. However, these procedures are limited by standard specimens’ dimensions, displacement gauges, and calibration curves. To overcome these limitations, this paper presents the use of small-scale specimens and a 3D-digital image correlation (DIC) system to extract the parameters required for fracture toughness estimation. Fracture resistance curve parameters in terms of crack mouth open displacement (CMOD), crack tip opening displacement (CTOD), and crack growth length (∆a) were carried out from test results by utilizing the DIC system, and an improved regression fitting resistance function (CTOD Vs. crack growth), or (J-integral Vs. crack growth) that is dependent on a variety of initial crack sizes was constructed and presented. The obtained results were compared to the available results of the classical physical measurement techniques, and acceptable matchings were observed. Moreover, a case study was implemented to estimate the maximum strain value that initiates the stable crack growth. This might be of interest to developing more accurate strain-based damage models. The results of laboratory testing in this study offer a valuable database to develop and validate damage models that are able to predict crack propagation of pipeline steel, accounting for the influential parameters associated with fracture toughness.

Keywords: fracture toughness, crack propagation in pipeline steels, CTOD-R, strain-based damage model

Procedia PDF Downloads 50

Authors: Ado Ismail Sabo

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Statement of the Problem: The global trend in tertiary education institutions today is changing and moving towards engagement, promotion and marketing. The reason is to upscale reputation and impact positioning. More prominently, existing rivalry today seeks to draw-in the best and brightest students. A university or college is no longer just an institution of higher learning, but one adapting additional business nomenclature. Therefore, huge financial resources are invested by educational institutions to polish their image and improve their global and national ranking. In Nigeria, which boasts of a vast population of over 180 million people, some of whose patronage can bolster its education sector; standard of education continues to decline. Today, some Nigerian tertiary education institutions are shadows of their pasts, in terms of academic excellence. Quality has been relinquished because of the unquenchable quest by government officials, some civil servants, school heads and educators to amass wealth. It is very difficult to gain student satisfaction and their loyalty. Some of the student’s loyalties factor towards public higher educational institutions might be confusing. It is difficult to understand the extent to which students are satisfy on many needs. Some students might feel satisfy with the academic lecturers only, whereas others may want everything, and others will never satisfy. Due to these problems, this research aims to uncover the crucial factors influencing student loyalty and to examine if students’ satisfaction might impact mediate the relationship between service quality, organisational image and students’ loyalty towards public higher education institutions in Kano State, Nigeria. The significance of the current study is underscored by the paucity of similar research in the subject area and public tertiary education in a developing country like Nigeria as shown in existing literature. Methodology: The current study was undertaken by quantitative research methodology. Sample of 600 valid responses were obtained within the study population comprising six selected public higher education institutions in Kano State, Nigeria. These include: North West University Kano, Bayero University Kano, School of Management Studies Kano, School of Technology Kano, Sa’adatu Rimi College Kano and Federal College of Education (FCE) Kano. Four main hypotheses were formulated and tested using structural equation modeling techniques with Analysis of Moment Structure (AMOS Version 22.0). Results: Analysis of the data provided support for the main issue of this study, and the following findings are established: “Student Satisfaction mediates the relationship between Service Quality and Student Loyalty”, “Student Satisfaction mediates the relationship between Organizational Image and Student Loyalty” respectively. The findings of this study contributed to the theoretical implication which proposed a structural model that examined the relationships among overall Organizational image, service quality, student satisfaction and student loyalty. Conclusion: In addition, the findings offered a better insight to the managerial (higher institution of learning service providers) by focusing on portraying the image of service quality with student satisfaction in improving the quality of student loyalty.

Keywords: student loyalty, service quality, student satisfaction, organizational image

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Authors: Moustafa Osman Mohammed

Abstract:

Construction industry, as one of the main contributor in depletion of natural resources, influences climate change. This paper discusses incremental and evolutionary development of the proposed models for optimization of a life-cycle analysis to explicit strategy for evaluation systems. The main categories are virtually irresistible for introducing uncertainties, uptake composite structure model (CSM) as environmental management systems (EMSs) in a practice science of evaluation small and medium-sized enterprises (SMEs). The model simplified complex systems to reflect nature systems’ input, output and outcomes mode influence “framework measures” and give a maximum likelihood estimation of how elements are simulated over the composite structure. The traditional knowledge of modeling is based on physical dynamic and static patterns regarding parameters influence environment. It unified methods to demonstrate how construction systems ecology interrelated from management prospective in procedure reflects the effect of the effects of engineering systems to ecology as ultimately unified technologies in extensive range beyond constructions impact so as, - energy systems. Sustainability broadens socioeconomic parameters to practice science that meets recovery performance, engineering reflects the generic control of protective systems. When the environmental model employed properly, management decision process in governments or corporations could address policy for accomplishment strategic plans precisely. The management and engineering limitation focuses on autocatalytic control as a close cellular system to naturally balance anthropogenic insertions or aggregation structure systems to pound equilibrium as steady stable conditions. Thereby, construction systems ecology incorporates engineering and management scheme, as a midpoint stage between biotic and abiotic components to predict constructions impact. The later outcomes’ theory of environmental obligation suggests either a procedures of method or technique that is achieved in sustainability impact of construction system ecology (SICSE), as a relative mitigation measure of deviation control, ultimately.

Keywords: sustainability, environmental impact assessment, environemtal management, construction ecology

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Authors: Edwin Calderon-Mendoza, Patrick Schweitzer, Serge Weber

Abstract:

Series arc faults appear frequently and unpredictably in low voltage distribution systems. Many methods have been developed to detect this type of faults and commercial protection systems such AFCI (arc fault circuit interrupter) have been used successfully in electrical networks to prevent damage and catastrophic incidents like fires. However, these devices do not allow series arc faults to be located on the line in operating mode. This paper presents a location algorithm for series arc fault in a low-voltage indoor power line in an AC 230 V-50Hz home network. The method is validated through simulations using the MATLAB software. The fault location method uses electrical parameters (resistance, inductance, capacitance, and conductance) of a 49 m indoor power line. The mathematical model of a series arc fault is based on the analysis of the V-I characteristics of the arc and consists basically of two antiparallel diodes and DC voltage sources. In a first step, the arc fault model is inserted at some different positions across the line which is modeled using lumped parameters. At both ends of the line, currents and voltages are recorded for each arc fault generation at different distances. In the second step, a fault map trace is created by using signature coefficients obtained from Kirchhoff equations which allow a virtual decoupling of the line’s mutual capacitance. Each signature coefficient obtained from the subtraction of estimated currents is calculated taking into account the Discrete Fast Fourier Transform of currents and voltages and also the fault distance value. These parameters are then substituted into Kirchhoff equations. In a third step, the same procedure described previously to calculate signature coefficients is employed but this time by considering hypothetical fault distances where the fault can appear. In this step the fault distance is unknown. The iterative calculus from Kirchhoff equations considering stepped variations of the fault distance entails the obtaining of a curve with a linear trend. Finally, the fault distance location is estimated at the intersection of two curves obtained in steps 2 and 3. The series arc fault model is validated by comparing current registered from simulation with real recorded currents. The model of the complete circuit is obtained for a 49m line with a resistive load. Also, 11 different arc fault positions are considered for the map trace generation. By carrying out the complete simulation, the performance of the method and the perspectives of the work will be presented.

Keywords: indoor power line, fault location, fault map trace, series arc fault

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Authors: Klaus Peter Scherer, Daniel Knöll, Constantin Rieder

Abstract:

Problem and Purpose: Intelligent systems are available and helpful to support the human being decision process, especially when complex surgical eye interventions are necessary and must be performed. Normally, such a decision support system consists of a knowledge-based module, which is responsible for the real assistance power, given by an explanation and logical reasoning processes. The interview based acquisition and generation of the complex knowledge itself is very crucial, because there are different correlations between the complex parameters. So, in this project (semi)automated self-learning methods are researched and developed for an enhancement of the quality of such a decision support system. Methods: For ophthalmic data sets of real patients in a hospital, advanced data mining procedures seem to be very helpful. Especially subgroup analysis methods are developed, extended and used to analyze and find out the correlations and conditional dependencies between the structured patient data. After finding causal dependencies, a ranking must be performed for the generation of rule-based representations. For this, anonymous patient data are transformed into a special machine language format. The imported data are used as input for algorithms of conditioned probability methods to calculate the parameter distributions concerning a special given goal parameter. Results: In the field of knowledge discovery advanced methods and applications could be performed to produce operation and patient related correlations. So, new knowledge was generated by finding causal relations between the operational equipment, the medical instances and patient specific history by a dependency ranking process. After transformation in association rules logically based representations were available for the clinical experts to evaluate the new knowledge. The structured data sets take account of about 80 parameters as special characteristic features per patient. For different extended patient groups (100, 300, 500), as well one target value as well multi-target values were set for the subgroup analysis. So the newly generated hypotheses could be interpreted regarding the dependency or independency of patient number. Conclusions: The aim and the advantage of such a semi-automatically self-learning process are the extensions of the knowledge base by finding new parameter correlations. The discovered knowledge is transformed into association rules and serves as rule-based representation of the knowledge in the knowledge base. Even more, than one goal parameter of interest can be considered by the semi-automated learning process. With ranking procedures, the most strong premises and also conjunctive associated conditions can be found to conclude the interested goal parameter. So the knowledge, hidden in structured tables or lists can be extracted as rule-based representation. This is a real assistance power for the communication with the clinical experts.

Keywords: an expert system, knowledge-based support, ophthalmic decision support, self-learning methods

Procedia PDF Downloads 246