Search results for: large amounts of data

Authors: Younies Saeed Hassan Mahmoud, Mai Mabrouk, Elsayed Sallam

Abstract:

Analysing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection.

Keywords: DNA microarray, feature selection, missing data, bioinformatics

Procedia PDF Downloads 554

Authors: Lutful Karim, Mohammed S. Al-kahtani

Abstract:

Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay.

Keywords: big data, clustering, tree topology, data aggregation, sensor networks

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Authors: Faraj M. Elkhatri, Hana Ali Alafi

Abstract:

The spatial and temporal distribution of diagenetic alterations related impact on the reservoir quality of the Sarir Formation. (present-day burial depth of about 9000 feet) Depositional facies and diagenetic alterations are the main controls on reservoir quality of Sarir Formation Sirt Basin Libya; these based on lithology and grain size as well as authigenic clay mineral types and their distributions. However, petrology investigation obtained on study area with five sandstone wells concentrated on main rock components and the parameters that may have impacts on reservoirs. the main authigenic clay minerals are kaolinite and dickite, these investigations have confirmed by X.R.D analysis and clay fraction. mainly Kaolinite and Dickite were extensively presented on all of wells with high amounts. As well as trace of detrital smectite and less amounts of illitized mud-matrix are possibly found by SEM image. Thin layers of clay presented as clay-grain coatings in local depth interpreted as remains of dissolved clay matrix is partly transformed into kaolinite adjacent and towards pore throat. This also may have impacts on most of the pore throats of this sandstone which are open and relatively clean with some of fine martial have been formed on occluded pores. This material is identified by EDS analysis to be collections of not only kaolinite booklets but also small disaggregated kaolinite platelets derived from the disaggregation of larger kaolinite booklets. These patches of kaolinite not only fill this pore, but also coat some of the surrounding framework grains. Quartz grains often enlarged by authigenic quartz overgrowths partially occlude and reduce porosity. Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM) was conducted on the post-test samples to examine any mud filtrate particles that may be in the pore throats. Semi-qualitative elemental data on selected minerals observed during the SEM study were obtained through the use of an Energy Dispersive Spectroscopy (EDS) unit. The samples showed mostly clean open pore throats, with limited occlusion by kaolinite. very fine-grained elemental combinations (Si/Al/Na/Cl, Si/Al Ca/Cl/Ti, and Qtz/Ti) have been identified and conformed by EDS analysis. However, the identification of the fine grained disaggregated material as mainly kaolinite though study area.

Keywords: fine migration, formation damage, kaolinite, soled bulging.

Procedia PDF Downloads 56

Authors: Jianjun Wu, Fanhui Guo, Yixin Zhang

Abstract:

In this study, gasification-coke were produced by blending the middlings (MC), and coking coal (CC) and a large proportion of long flame coal (Shenfu coal, SC), the effects of blending ratio were investigated. Mineral evolution and crystalline order obtained by XRD methods were reproduced within reasonable accuracy. Structure characteristics of partially gasification-coke such as surface area and porosity were determined using the N₂ adsorption and mercury porosimetry. Experimental data of gasification-coke was dominated by the TGA results provided trend, reactivity differences between gasification-cokes are discussed in terms of structure characteristic, crystallinity, and alkali index (AI). The first-order reaction equation was suitable for the gasification reaction kinetics of CO₂ atmosphere which was represented by the volumetric reaction model with linear correlation coefficient above 0.985. The differences in the microporous structure of gasification-coke and catalysis caused by the minerals in parent coals were supposed to be the main factors which affect its reactivity. The addition of MC made the samples enriched with a large amount of ash causing a higher surface area and a lower crystalline order to gasification-coke which was beneficial to gasification reaction. The higher SiO₂ and Al₂O₃ contents, causing a decreasing AI value and increasing activation energy, which reduced the gasification reaction activity. It was found that the increasing amount of MC got a better performance on the coke gasification reactivity by blending > 30% SC with this coking process.

Keywords: low-rank coal, middlings, structure characteristic, mineral evolution, alkali index, gasification-coke, gasification kinetics

Procedia PDF Downloads 158

Authors: Asma Ahmed, Farah Khalid, Sahlah Sohail, Saira Banusokwalla, Sabiha Banu, Inaara Akbar, Safia Awan, Syed Iqbal Azam

Abstract:

Background and Objectives: The level of knowledge regarding obesity as a disease condition and health-seeking behavior regarding its management is grossly lacking. We present data from our multidisciplinary obesity clinic at the large tertiary care facility in Karachi, Pakistan, to provide baseline profiles and outcomes of patients attending these clinics. Methods: 260 who attended the obesity clinic between June 2018 to March 2020 were enrolled in this study. The analysis included descriptive and ROC analysis to identify the best cut-offs of theanthropometric measurements to diagnose obesity-related comorbid conditions. Results: The majority of the studied population were women (72.3%) and employed(43.7%) with a mean age of 35.5 years. Mean BMIwas 37.4, waist circumference was 112.4 cm, visceral fat was 11.7%, and HbA1C was 6.9%. The most common comorbidities were HTN & D.M (33 &31%, respectively). The prevalence of MetS was 16.3% in patients and was slightly higher in males. Visceral fat was the main factor in predicting D.M (0.750; 95% CI: 0.665, 0.836) and MetS (0.709; 95% CI: 0.590, 0.838) compared to total body fat, waist circumference, and BMI. The risk of predicting DM &MetS for the visceral fat above 9.5% in women had the highest sensitivity (80% for D.M & 79% for MetS) and an NPV (92.75% for D.M & 95% for MetS). Conclusions: This study describes and establishes characteristics of these obese individuals, which can help inform clinical practices. These practices may involve using visceral fat for earlier identification and counseling-based interventions to prevent more severe surgical interventions down the line.

Keywords: obesity, metabolic syndrome, tertiary care facility, BMI, waist circumference, visceral fat

Procedia PDF Downloads 140

Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

Abstract:

In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

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Authors: S. Zhuiykov, Zh. Hai, H. Xu, C. Xue

Abstract:

Unique and distinctive properties could be obtained on such two-dimensional (2D) semiconductor as tungsten trioxide (WO₃) when the reduction from multi-layer to one fundamental layer thickness takes place. This transition without damaging single-layer on a large spatial resolution remained elusive until the atomic layer deposition (ALD) technique was utilized. Here we report the ALD-enabled atomic-layer-precision development of a single layer WO₃ with thickness of 0.77±0.07 nm on a large spatial resolution by using (^tBuN)₂W(NMe₂)₂ as tungsten precursor and H₂O as oxygen precursor, without affecting the underlying SiO₂/Si substrate. Versatility of ALD is in tuning recipe in order to achieve the complete WO₃ with desired number of WO₃ layers including monolayer. Governed by self-limiting surface reactions, the ALD-enabled approach is versatile, scalable and applicable for a broader range of 2D semiconductors and various device applications.

Keywords: Atomic Layer Deposition (ALD), tungsten oxide, WO₃, two-dimensional semiconductors, single fundamental layer

Procedia PDF Downloads 227

Authors: Avi Shrivastava

Abstract:

Title 42, an emergency health decree, has forced the federal authorities to turn away asylum seekers and all other border crossers since last year. When Title 42 was first deployed in immigration detention centers, where many migrants are held when they arrive at the U.S.-Mexico border, the Trump administration embraced it as a strategy. Expulsions Policy and New Border Challenges will be examined in regard to Title 42 concerns. Humanitarian measures for refugees arriving at the US-Mexico border are the focus of this article. To a large extent, this article addresses the implications of the United States' use of Title 42 in expelling refugees and the possible ramifications of doing away with it. A secondary data collecting strategy was used to gather the information for this study, allowing researchers to examine a large number of previously collected data sets. Information about Title 42 may be found in a variety of places, such as scholarly publications, newspapers, books, and the internet. The inquiry employed qualitative and explanatory research approaches. The claim that 1.7 million individuals were forced to leave the country as a result of it was withdrawn. Since CBP and ICE were limited in their ability to process deportees, it employed a very random patchwork technique in selecting the expelled individuals. As a consequence, repeat offenders, particularly those who were single, got a reduced punishment. The government will be compelled to focus on long-overdue but vital border enhancements if expulsions are halted. Title 42 provisions may help expedite the processing of asylum and other types of humanitarian relief. The government is prepared for an increase in arrivals, but ending the program would lead to a return to arrival levels seen during the Title 42 period.

Keywords: migrants, refugees, title 42, medical, trump administration

Procedia PDF Downloads 75

Authors: Alireza Karimi

Abstract:

Artificial Intelligence (AI) is reshaping various industries, and accounting is no exception. With the ability to process vast amounts of data quickly and accurately, AI is revolutionizing how financial professionals manage, analyze, and report financial information. In this article, we will explore the diverse applications of AI in accounting and its profound impact on the field. Automation of Repetitive Tasks: One of the most significant contributions of AI in accounting is automating repetitive tasks. AI-powered software can handle data entry, invoice processing, and reconciliation with minimal human intervention. This not only saves time but also reduces the risk of errors, leading to more accurate financial records. Pattern Recognition and Anomaly Detection: AI algorithms excel at pattern recognition. In accounting, this capability is leveraged to identify unusual patterns in financial data that might indicate fraud or errors. AI can swiftly detect discrepancies, enabling auditors and accountants to focus on resolving issues rather than hunting for them. Real-Time Financial Insights: AI-driven tools, using natural language processing and computer vision, can process documents faster than ever. This enables organizations to have real-time insights into their financial status, empowering decision-makers with up-to-date information for strategic planning. Fraud Detection and Prevention: AI is a powerful tool in the fight against financial fraud. It can analyze vast transaction datasets, flagging suspicious activities and reducing the likelihood of financial misconduct going unnoticed. This proactive approach safeguards a company's financial integrity. Enhanced Data Analysis and Forecasting: Machine learning, a subset of AI, is used for data analysis and forecasting. By examining historical financial data, AI models can provide forecasts and insights, aiding businesses in making informed financial decisions and optimizing their financial strategies. Artificial Intelligence is fundamentally transforming the accounting profession. From automating mundane tasks to enhancing data analysis and fraud detection, AI is making financial processes more efficient, accurate, and insightful. As AI continues to evolve, its role in accounting will only become more significant, offering accountants and finance professionals powerful tools to navigate the complexities of modern finance. Embracing AI in accounting is not just a trend; it's a necessity for staying competitive in the evolving financial landscape.

Keywords: artificial intelligence, accounting automation, financial analysis, fraud detection, machine learning in finance

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Authors: Edward Ridyard, Vinary Varadarajan

Abstract:

Background and Aims: The first year as a doctor in any country represents a major career transition in any physician's life. During this period, many physicians concentrate on obtaining clinical skills but may not obtain the important skills necessary to cope with stress. In this study we elucidate stress levels amongst FY1 doctors regarding the transitioning into specialty career choices, working in the NHS and anxiety about future career success. Methods: A prospective single blinded analysis of Foundation Year one (FY1) trainees using a non-mandatory online questionnaire was distributed. No exclusion criteria were applied. The only inclusion criteria was the doctor was in a full-time FY1 post and this was their first job in the UK. A total of n= 22 doctors were included in the study. After data collection, statistical analysis using chi-squared testing was applied. Results: The large majority of FY1 doctors (72.7%) already knew what specialty they wished to pursue (p=0.0001). With regards to their future careers 45.5% of FY1 doctors stated "above average" stress levels. The majority of FY1 doctors (64.3%) stated their stress levels working in the NHS were either "above average" or "high". Finally, 81.8% of respondents know colleagues who have been put off from pursuing specialties due to the stress of competition. Conclusions: A large majority of FY1 doctors already know at this early stage what area they would like to specialise in. With this in mind, a large proportion have above "average" levels of stress with regards to securing this future career path. The most worrying finding is that 64.3% of FY1s stated they had "above average" or "high" stress levels working in the NHS. We therefore recommend formal stress management education to be incorporated into the foundation programme curriculum.

Keywords: stress, anxiety, junior doctor, education

Procedia PDF Downloads 357

Authors: Akeel A. Shah, Tong Zhang

Abstract:

Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

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Authors: Oluseun A. Sanuade, Sanlinn I. Kaka, Adesoji O. Akanji, Olukole A. Akinbiyi

Abstract:

Prospect evaluation of ‘the ‘ADE’ field was done using 3D seismic data and well log data. The field is located in the offshore Niger Delta where water depth ranges from 450 to 800 m. The objectives of this study are to explore deeper prospects and to ascertain the kind of traps that are favorable for the accumulation of hydrocarbon in the field. Six horizons with major and minor faults were identified and mapped in the field. Time structure maps of these horizons were generated and using the available check-shot data the maps were converted to top structure maps which were used to calculate the hydrocarbon volume. The results show that regional structural highs that are trending in northeast-southwest (NE-SW) characterized a large portion of the field. These highs were observed across all horizons revealing a regional post-depositional deformation. Three prospects were identified and evaluated to understand the different opportunities in the field. These include stratigraphic pinch out and bi-directional downlap. The results of this study show that the field has potentials for new opportunities that could be explored for further studies.

Keywords: hydrocarbon, play, prospect, stratigraphy

Procedia PDF Downloads 245

Authors: K. Noah, F.-S. Lien

Abstract:

In Computational Fluid Dynamics (CFD), there are a variety of numerical methods, of which some depend on macroscopic model representatives. These models can be solved by finite-volume, finite-element or finite-difference methods on a microscopic description. However, the lattice Boltzmann method (LBM) is considered to be a mesoscopic particle method, with its scale lying between the macroscopic and microscopic scales. The LBM works well for solving incompressible flow problems, but certain limitations arise from solving compressible flows, particularly at high Mach numbers. An improved lattice Boltzmann model for compressible flow problems is presented in this research study. A higher-order Taylor series expansion of the Maxwell equilibrium distribution function is used to overcome limitations in LBM when solving high-Mach-number flows. Large eddy simulation (LES) is implemented in LBM to simulate turbulent jet flows. The results have been validated with available experimental data for turbulent compressible free jet flow at subsonic speeds.

Keywords: compressible lattice Boltzmann method, multiple relaxation times, large eddy simulation, turbulent jet flows

Procedia PDF Downloads 258

Authors: Dhruv Mehta, Alexander van Zuijlen, Hester Bijl

Abstract:

Large Eddy Simulation (LES) numerically resolves the large energy-containing eddies of a turbulent flow, while modelling the small dissipative eddies. On a wind farm, these large scales carry the energy wind turbines extracts and are also responsible for transporting the turbines’ wakes, which may interact with downstream turbines and certainly with the atmospheric boundary layer (ABL). In this situation, it is important to conserve the energy that these wake’s carry and which could be altered artificially through numerical dissipation brought about by the schemes used for the spatial discretisation and temporal integration. Numerical dissipation has been reported to cause the premature recovery of turbine wakes, leading to an over prediction in the power produced by wind farms.An energy-conserving scheme is free from numerical dissipation and ensures that the energy of the wakes is increased or decreased only by the action of molecular viscosity or the action of wind turbines (body forces). The aim is to create an LES package with energy-conserving schemes to simulate wind turbine wakes correctly to gain insight into power-production, wake meandering etc. Such knowledge will be useful in designing more efficient wind farms with minimal wake interaction, which if unchecked could lead to major losses in energy production per unit area of the wind farm. For their research, the authors intend to use the Energy-Conserving Navier-Stokes code developed by the Energy Research Centre of the Netherlands.

Keywords: energy-conserving schemes, modelling turbulence, Large Eddy Simulation, atmospheric boundary layer

Procedia PDF Downloads 452

Authors: Victor Vasques Ribeiro

Abstract:

The Santa Cruz dos Navegantes beach is located in the city of Guarujá, in the central portion of the coast of the state of São Paulo. Next to this beach is located the Channel of the Port of Santos, configured as a source of plastic pellets for marine environments. On sandy beaches near the sources, especially during the winter and after cold front entrance events, the amounts of pellets can be very high. This study aimed to determine the influence of a cold front entry event of the winter of 2019 on the amount of pellets found on Santa Cruz dos Navegantes beach, besides assuming the proximity of the sources. During six consecutive collection campaigns, three of which were previous and three after the cold front entry peak, 30.0 square meters of surface sediments were sampled in each campaign. The color and shape of the pellets were determined to assume the length of the permanence of these granules in the marine environment and, consequently, the proximity of the sources. This beach was considered ideal for this type of research. The pellet pollution index (PPI) was from moderate to very high right after the peak of the cold front entry. The cold front peak event significantly influenced the amount of pellets found on the beach of Santa Cruz dos Navegantes. The factors that can bury the pellets in the sediments were classified as low when compared to other beaches in the region. Most of the pellets found were recently produced and lost to aquatic environments. Like the other beaches near Santos Bay, Santa Cruz dos Navegantes beach receives significant amounts of pellets that have nearby origins. Therefore, it was supposed that the activities of the Santos port complex are sources of pellets for the marine environment. This pollution can be further worsened in certain meteoceanographic events. The beaches of this region need to be constantly monitored and evaluated for pollution by pellets.

Keywords: beach, cold front, pellets, sources

Procedia PDF Downloads 173

Authors: Faraj M. Elkhatri

Abstract:

The spatial and temporal distribution of diagenetic alterations related impact on the reservoir quality of the Sarir Formation. ( present day burial depth of about 9000 feet) Depositional facies and diagenetic alterations are the main controls on reservoir quality of Sarir Formation Sirt Basin Libya; these based on lithology and grain size as well as authigenic clay mineral types and their distributions. However, petrology investigation obtained on study area with five sandstone wells concentrated on main rock components and the parameters that may have impacts on reservoirs. the main authigenic clay minerals are kaolinite and dickite, these investigations have confirmed by X.R.D analysis and clay fraction. mainly Kaolinite and Dickite were extensively presented on all of wells with high amounts. As well as trace of detrital smectite and less amounts of illitized mud-matrix are possibly find by SEM image. Thin layers of clay presented as clay-grain coatings in local depth interpreted as remains of dissolved clay matrix is partly transformed into kaolinite adjacent and towards pore throat. This also may have impacts on most of the pore throats of this sandstone which are open and relatively clean with some fine martial have been formed on occluded pores. This material is identified by EDS analysis to be collections of not only kaolinite booklets but also small disaggregated kaolinite platelets derived from the disaggregation of larger kaolinite booklets. These patches of kaolinite not only fill this pore but also coat some of the surrounding framework grains. Quartz grains often enlarged by authigenic quartz overgrowths partially occlude and reduce porosity. Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM) was conducted on the post-test samples to examine any mud filtrate particles that may be in the pore throats. Semi-qualitative elemental data on selected minerals observed during the SEM study were obtained through the use of an Energy Dispersive Spectroscopy (EDS) unit. The samples showed mostly clean open pore throats with limited occlusion by kaolinite. very fine-grained elemental combinations (Si/Al/Na/Cl, Si/Al Ca/Cl/Ti, and Qtz/Ti) have been identified and conformed by EDS analysis. However, the identification of the fine grained disaggregated material as mainly kaolinite though study area.

Keywords: pore throat, fine migration, formation damage, solids plugging, porosity loss

Procedia PDF Downloads 136

Authors: Dachuan Shi, M. Hecht, Y. Ye

Abstract:

With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection.

Keywords: fault detection, wheel flat, convolutional neural network, machine learning

Procedia PDF Downloads 113

Authors: Nermine A. Harraz, Aliaa Abouali

Abstract:

The retail stores put up for sale multiple items while the spaces in the backroom and display areas constitute a scarce resource. Availability, volume, and location of the product displayed in the showroom influence the customer’s demand. Managing these operations individually will result in sub-optimal overall retail store’s profit; therefore, a non-linear integer programming model (NLIP) is developed to determine the inventory replenishment and shelf space allocation decisions that together maximize the retailer’s profit under shelf space and backroom storage constraints taking into consideration that the demand rate is positively dependent on the amount and location of items displayed in the showroom. The developed model is solved using LINGO® software. The NLIP model is implemented in a real world case study in a large retail outlet providing a large variety of products. The proposed model is validated and shows logical results when using the experimental data collected from the market.

Keywords: retailing management, inventory replenishment, shelf space allocation, showroom, backroom

Procedia PDF Downloads 335

Authors: Sirwe Ghaderpour, Nasrollah Ahmadifard, Naser Agh, Zakaria Vahabzadeh

Abstract:

Rotifers are used in many hatcheries for feeding the earliest stages of fish larvae and crustaceans due to their small size, slow movements, fast reproduction, and easy cultivation. One of the disadvantages of using rotifers as live prey is their lower content of some nutrients compared to copepods, so it is necessary to increase the amounts of these nutrients by means of enrichment. Minerals are a group of micro-elements, essential to fish, that is lacking in the rotifers, for example, selenium (30 fold) and zinc (5 fold) are present in lower quantities than the minimum amounts found in copepods. In this study, the condensed Isochrysis aff. galbana (T-ISO) and Nannochloropsis oculata were suspended at concentration of 18 × 109 cell mL⁻¹ of water with 20 ppt of salinity. Four different levels (0, 1000, 2000, and 4000 mg L⁻¹) of each Na₂SeO₃ and ZnSO₄.7H₂O separately were prepared, and 1 mL of each stock was poured to the algae enrichment vessels for 1 h simultaneously. After that, the material was centrifuged (at 4000 rpm for 5 min), and the precipitated enriched algae was used for rotifer feeding. The contents of Se, Zn, Cu, and Mn were determined in enriched microalgae and rotifer by Atomic absorption. The highest content of both minerals was observed in 0.4 Zn + 0.4 Se treatment and also rotifer enriched with these enriched microalgae. The enrichment of microalgae with Zn and Se does not affect the content of Cu in the microalgae. Also, the content of Cu in rotifer fed with the enriched microalgae showed the highest Cu content in the treatments than the control. But, the enrichment with both minerals had a negative effect on the content Mn in enriched mixed microalgae except 0.4 Zn + 0.4 Se. The Mn content in enriched rotifer decreased in the treatments than the control except for 0.1 Zn + 0.1 Se. There was no significant effect on rotifer growth in combined enrichment with both minerals (p < 0.05). Overall, rotifers enrichment with Se and Zn mixed microalgae resulted in increasing Se, Zn, and Cu. This will allow Se and Zn microalgae enriched rotifers to be used as the minerals delivery method for fish larvae nutritional requirements.

Keywords: enrichment, larvae, microalgae, mineral, rotifer

Procedia PDF Downloads 116

Authors: Mohammad Bagher Hassanpouraghdam, Mohammad Ali Aazami Mavaloo

Abstract:

Due to the huge side-effects of chemicals; essential oils have been considered as suitable alternatives for keeping the vase-life of cut flowers mainly owing to the availability and environment-friend nature of these bio-chemicals. In the present experiment, 50% substitution of STS was achieved and tested on cut carnation flowers cv. Liberty by using the essential oils from four plants; Satureja sahendica Bornm., Echinophora platyloba DC., Tanacetum balsamita L. and Cupressus arizonica Greene., as CRD with five treatments and 3 replications. Vase-life and flower diameter were affected with 50% substitution of STS by essential oils from C. arizonica and T. balsamita. Membrane stability index, Malondialdehyde (MDA) content and Hydrogen peroxide (H2O2) amounts were affected by the substitution treatments as well. The main preservative effect belonged to the substitution with C. arizonica. So that, 50% STS substitution with Cupressus oil holds the highest membrane integrity and the least data for MDA and H2O2 content.

Keywords: Carnation, essential oil, Membrane stability index (MSI), vase life

Procedia PDF Downloads 479

Authors: Md Al Masum Bhuiyan, Maria C. Mariani, Osei K. Tweneboah

Abstract:

In the present work, we develop a technique for estimating the volatility of financial time series by using stochastic differential equation. Taking the daily closing prices from developed and emergent stock markets as the basis, we argue that the incorporation of stochastic volatility into the time-varying parameter estimation significantly improves the forecasting performance via Maximum Likelihood Estimation. While using the technique, we see the long-memory behavior of data sets and one-step-ahead-predicted log-volatility with ±2 standard errors despite the variation of the observed noise from a Normal mixture distribution, because the financial data studied is not fully Gaussian. Also, the Ornstein-Uhlenbeck process followed in this work simulates well the financial time series, which aligns our estimation algorithm with large data sets due to the fact that this algorithm has good convergence properties.

Keywords: financial time series, maximum likelihood estimation, Ornstein-Uhlenbeck type models, stochastic volatility model

Procedia PDF Downloads 224

Authors: Amrashaa Singh

Abstract:

In the digital age and the large cyberspace, Data Protection and Privacy have become major issues in this technological era. There was a time when social media and online shopping websites were treated as a blessing for the people. But now the tables have turned, and the people have started to look at them with suspicion. They are getting aware of the privacy implications, and they do not feel as safe as they used to initially. When Edward Snowden informed the world about the snooping United States Security Agencies had been doing, that is when the picture became clear for the people. After the Cambridge Analytica case where the data of Facebook users were stored without their consent, the doubts arose in the minds of people about how safe they actually are. In India, the case of spyware Pegasus also raised a lot of concerns. It was used to snoop on a lot of human right activists and lawyers and the company which invented the spyware claims that it only sells it to the government. The paper will be dealing with the privacy concerns in the Indian perspective with an analytical methodology. The Supreme Court here had recently declared a right to privacy a Fundamental Right under Article 21 of the Constitution of India. Further, the Government is also working on the Data Protection Bill. The point to note is that India is still a developing country, and with the bill, the government aims at data localization. But there are doubts in the minds of many people that the Government would actually be snooping on the data of the individuals. It looks more like an attempt to curb dissenters ‘lawfully’. The focus of the paper would be on these issues in India in light of the European Union (EU) General Data Protection Regulation (GDPR). The Indian Data Protection Bill is also said to be loosely based on EU GDPR. But how helpful would these laws actually be is another concern since the economic and social conditions in both countries are very different? The paper aims at discussing these concerns, how good or bad is the intention of the government behind the bill, and how the nations can act together and draft common regulations so that there is some uniformity in the laws and their application.

Keywords: Article 21, data protection, dissent, fundamental right, India, privacy

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Authors: Vidya Yadav

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India’s youth bulge is now sharpest at the critical 15-24 age group, even as its youngest, and oldest age groups begin to narrow. As the ‘single year, age data’ for the 2011 Census releases the data on the number of people at each year of age in the population. The data shows that India’s working age population (15-64 years) is now 63.4 percent of the total, as against just short of 60 percent in 2001. The numbers also show that the ‘dependency ratio’ the ratio of children (0-14) and the elderly (65 above) to those in the working age has shrunk further to 0.55. “Even as the western world is in ageing situation, these new numbers show that India’s population is still very young”. As the fertility falls faster in urban areas, rural India is younger than urban India; while 51.73 percent of rural Indians are under the age of 24 and 45.9 percent of urban Indians are under 24. The percentage of the population under the age of 24 has dropped, but many demographers say that it should not be interpreted as a sign of the youth bulge is shrinking. Rather it is because of “declining fertility, the number of infants and children reduces first, and this is what we see with the number of under age 24. Indeed the figure shows that the proportion of children in the 0-4 and 5-9 age groups has fallen in 2011 compared to 2001. For the first time, the percentage of children in the 10-14 age group has also fallen, as the effect of families reducing the number of children they have begins to be felt. The present paper key issue is to examine that “whether this growing youth bulge has the right skills for the workforce or not”. The study seeks to examine the youth population structure and employment distribution among them in India during 2001-2011 in different industrial category. It also tries to analyze the workforce participation rate as main and marginal workers both for male and female workers in rural and urban India by utilizing an abundant source of census data from 2001-2011. Result shows that an unconscionable number of adolescents are working when they should study. In rural areas, large numbers of youths are working as an agricultural labourer. Study shows that most of the youths working are in the 15-19 age groups. In fact, this is the age of entry into higher education, but due to economic compulsion forces them to take up jobs, killing their dreams of higher skills or education. Youths are primarily engaged in low paying irregular jobs which are clearly revealed by census data on marginal workers. That is those who get work for less than six months in a year. Large proportions of youths are involved in the cultivation and household industries works.

Keywords: main, marginal, youth, work

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Authors: G. Magalhães-Mota, C. Magro, S. Sério, E. Mateus, P. A. Ribeiro, A. B. Ribeiro, M. Raposo

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Triclosan [5-chloro-2-(2,4-dichlorophenoxy) phenol], belonging to the class of Pharmaceuticals and Personal Care Products (PPCPs), is a broad-spectrum antimicrobial agent and bactericide. Because of its antimicrobial efficacy, it is widely used in personal health and skin care products, such as soaps, detergents, hand cleansers, cosmetics, toothpastes, etc. However, it has been considered to disrupt the endocrine system, for instance, thyroid hormone homeostasis and possibly the reproductive system. Considering the widespread use of triclosan, it is expected that environmental and food safety problems regarding triclosan will increase dramatically. Triclosan has been found in river water samples in both North America and Europe and is likely widely distributed wherever triclosan-containing products are used. Although significant amounts are removed in sewage plants, considerable quantities remain in the sewage effluent, initiating widespread environmental contamination. Triclosan undergoes bioconversion to methyl-triclosan, which has been demonstrated to bio accumulate in fish. In addition, triclosan has been found in human urine samples from persons with no known industrial exposure and in significant amounts in samples of mother's milk, demonstrating its presence in humans. The action of sunlight in river water is known to turn triclosan into dioxin derivatives and raises the possibility of pharmacological dangers not envisioned when the compound was originally utilized. The aim of this work is to detect low concentrations of triclosan in an aqueous complex matrix through the use of a sensor array system, following the electronic tongue concept based on impedance spectroscopy. To achieve this goal, we selected the appropriate molecules to the sensor so that there is a high affinity for triclosan and whose sensitivity ensures the detection of concentrations of at least nano-molar. Thin films of organic molecules and oxides have been produced by the layer-by-layer (LbL) technique and sputtered onto glass solid supports already covered by gold interdigitated electrodes. By submerging the films in complex aqueous solutions with different concentrations of triclosan, resistance and capacitance values were obtained at different frequencies. The preliminary results showed that an array of interdigitated electrodes sensor coated or uncoated with different LbL and films, can be used to detect TCS traces in aqueous solutions in a wide range concentration, from 10⁻¹² to 10⁻⁶ M. The PCA method was applied to the measured data, in order to differentiate the solutions with different concentrations of TCS. Moreover, was also possible to trace a curve, the plot of the logarithm of resistance versus the logarithm of concentration, which allowed us to fit the plotted data points with a decreasing straight line with a slope of 0.022 ± 0.006 which corresponds to the best sensitivity of our sensor. To find the sensor resolution near of the smallest concentration (Cs) used, 1pM, the minimum measured value which can be measured with resolution is 0.006, so the ∆logC =0.006/0.022=0.273, and, therefore, C-Cs~0.9 pM. This leads to a sensor resolution of 0.9 pM for the smallest concentration used, 1pM. This attained detection limit is lower than the values obtained in the literature.

Keywords: triclosan, layer-by-layer, impedance spectroscopy, electronic tongue

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Authors: Bhogendra Mishra, Andy Nelson, Mirco Boschetti, Lorenzo Busetto, Alice Laborte

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Rice is grown on over 100 million hectares in almost every country of Asia. It is the most important staple crop for food security and has high economic and cultural importance in Asian societies. The combination of genetic diversity and management options, coupled with the large geographic extent means that there is a large variation in seasonality (when it is grown) and cropping intensity (how often it is grown per year on the same plot of land), even over relatively small distances. Seasonality and intensity can and do change over time depending on climatic, environmental and economic factors. Detecting where and when these changes happen can provide information to better understand trends in regional and even global rice production. Remote sensing offers a unique opportunity to estimate these trends. We apply the recently published PhenoRice algorithm to 14 years of moderate resolution remote sensing (MODIS) data (utilizing 250m resolution 16 day composites from Terra and Aqua) to estimate seasonality and cropping intensity per year and changes over time. We compare the results to the surveyed data collected by International Rice Research Institute (IRRI). The study results in a unique and validated dataset on the extent and change of extent, the seasonality and change in seasonality and the cropping intensity and change in cropping intensity between 2003 and 2016 for the Philippines. Observed trends and their implications for food security and trade policies are also discussed.

Keywords: rice, cropping intensity, moderate resolution remote sensing (MODIS), phenology, seasonality

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Authors: Tanveer Hussain, Khan Muhammad, Amin Ullah, Mi Young Lee, Sung Wook Baik

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The Big Data generated from distributed vision sensors installed on large scale in smart cities create hurdles in its efficient and beneficial exploration for browsing, retrieval, and indexing. This paper presents a three-folded framework for effective video summarization of such data and provide a compact and representative format of Big Video Data. In the first fold, the paper acquires input video data from the installed cameras and collect clues such as type and count of objects and clarity of the view from a chunk of pre-defined number of frames of each view. The decision of representative view selection for a particular interval is based on fuzzy inference system, acquiring a precise and human resembling decision, reinforced by the known clues as a part of the second fold. In the third fold, the paper forwards the selected view frames to the summary generation mechanism that is supported by a saliency-aware convolution neural network (CNN) model. The new trend of fuzzy rules for view selection followed by CNN architecture for saliency computation makes the multi-view video summarization (MVS) framework a suitable candidate for real-world practice in smart cities.

Keywords: big video data analysis, fuzzy logic, multi-view video summarization, saliency detection

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Authors: Natalia Kalinowska

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The adoption of the General Data Protection Regulation, (GDPR) finished the four-year work of the European Commission in this area in the European Union. Considering far-reaching changes, which will be applied by GDPR, the European legislator envisaged two-year transitional period. Member states and companies have to prepare for a new regulation until 25 of May 2018. The idea, which becomes a new look at an attitude to data protection in the European Union is risk-based approach. So far, as a result of implementation of Directive 95/46/WE, in many European countries (including Poland) there have been adopted very particular regulations, specifying technical and organisational security measures e.g. Polish implementing rules indicate even how long password should be. According to the new approach from May 2018, controllers and processors will be obliged to apply security measures adequate to level of risk associated with specific data processing. The risk in GDPR should be interpreted as the likelihood of a breach of the rights and freedoms of the data subject. According to Recital 76, the likelihood and severity of the risk to the rights and freedoms of the data subject should be determined by reference to the nature, scope, context and purposes of the processing. GDPR does not indicate security measures which should be applied – in recitals there are only examples such as anonymization or encryption. It depends on a controller’s decision what type of security measures controller considered as sufficient and he will be responsible if these measures are not sufficient or if his identification of risk level is incorrect. Data protection regulation indicates few levels of risk. Recital 76 indicates risk and high risk, but some lawyers think, that there is one more category – low risk/now risk. Low risk/now risk data processing is a situation when it is unlikely to result in a risk to the rights and freedoms of natural persons. GDPR mentions types of data processing when a controller does not have to evaluate level of risk because it has been classified as „high risk” processing e.g. processing on a large scale of special categories of data, processing with using new technologies. The methodology will include analysis of legal regulations e.g. GDPR, the Polish Act on the Protection of personal data. Moreover: ICO Guidelines and articles concerning risk based approach in GDPR. The main conclusion is that an appropriate risk assessment is a key to keeping data safe and avoiding financial penalties. On the one hand, this approach seems to be more equitable, not only for controllers or processors but also for data subjects, but on the other hand, it increases controllers’ uncertainties in the assessment which could have a direct impact on incorrect data protection and potential responsibility for infringement of regulation.

Keywords: general data protection regulation, personal data protection, privacy protection, risk based approach

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Authors: Mike Dee

Abstract:

Urban public spaces are sutured with a range of surveillance and sensor technologies that claim to enable new forms of ‘data based citizen participation’, but also increase the tendency for ‘function-creep’, whereby vast amounts of data are gathered, stored and analysed in a broad application of urban surveillance. This kind of monitoring and capacity for surveillance connects with attempts by civic authorities to regulate, restrict, rebrand and reframe urban public spaces. A direct consequence of the increasingly security driven, policed, privatised and surveilled nature of public space is the exclusion or ‘unfavourable inclusion’ of those considered flawed and unwelcome in the ‘spectacular’ consumption spaces of many major urban centres. In the name of urban regeneration, programs of securitisation, ‘gentrification’ and ‘creative’ and ‘smart’ city initiatives refashion public space as sites of selective inclusion and exclusion. In this context of monitoring and control procedures, in particular, children and young people’s use of space in parks, neighbourhoods, shopping malls and streets is often viewed as a threat to the social order, requiring various forms of remedial action. This paper suggests that cities, places and spaces and those who seek to use them, can be resilient in working to maintain and extend democratic freedoms and processes enshrined in Marshall’s concept of citizenship, calling sensor and surveillance systems to account. Such accountability could better inform the implementation of public policy around the design, build and governance of public space and also understandings of urban citizenship in the sensor saturated urban environment.

Keywords: citizenship, public space, surveillance, young people

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Authors: I. C. Tolias, A. G. Venetsanos, N. Markatos

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In the present work, CFD simulations of a large scale open deflagration experiment are performed. Stoichiometric hydrogen-air mixture occupies a 20 m hemisphere. Two combustion models are compared and are evaluated against the experiment. The Eddy Dissipation Model and a Multi-physics combustion model which is based on Yakhot’s equation for the turbulent flame speed. The values of models’ critical parameters are investigated. The effect of the turbulence model is also examined. k-ε model and LES approach were tested.

Keywords: CFD, deflagration, hydrogen, combustion model

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Authors: Luigi Delle Rose, Oliver Fischer, Ahmed Hammad

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In this article, we study the prospects of the proposed Large Hadron electron Collider (LHeC) in the search for heavy neutral scalar particles. We consider a minimal model with one additional complex scalar singlet that interacts with the Standard Model (SM) via mixing with the Higgs doublet, giving rise to an SM-like Higgs boson and a heavy scalar particle. Both scalar particles are produced via vector boson fusion and can be tested via their decays into pairs of SM particles, analogously to the SM Higgs boson. Using multivariate techniques, we show that the LHeC is sensitive to heavy scalars with masses between 200 and 800 GeV down to scalar mixing of order 0.01.

Keywords: beyond the standard model, large hadron electron collider, multivariate analysis, scalar singlet

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