Search results for: data source

Authors: R. N. Pateriya, T. K. Bhattacharya

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In India, rice-wheat cropping system occupies the major area and contributes about 40% of the country’s total food grain production. It is necessary that production of rice and wheat must keep pace with growing population. However, various factors such as degradation in natural resources, shift in cropping pattern, energy constraints etc. are causing reduction in the productivity of these crops. Seedbed for rice after wheat is difficult to prepare due to presence of straw and stubbles, and require excessive tillage operations to bring optimum tilth. In addition, delayed sowing and transplanting of rice is mainly due to poor crop residue management, multiplicity of tillage operations and non-availability of the power source. With increasing concern for fuel conservation and energy management, farmers might wish to estimate the best cultivation system for more productivity. The widest spread method of tilling land is ploughing with mould board plough. However, with the mould board plough upper layer of soil is neither always loosened at the desired extent nor proper mixing of different layers are achieved. Therefore, additional operations carried out to improve tilth. The farmers are becoming increasingly aware of the need for minimum tillage by minimizing the use of machines. Soil management can be achieved by using the combined active-passive tillage machines. A study was therefore, undertaken in wheat-harvested field to study the impact of conventional and modified tillage practices on paddy crop cultivation. Tillage treatments with tractor as a power source were selected during the experiment. The selected level of tillage treatments of tractor machinery management were (T1:- Direct Sowing of Rice), (T2:- 2 to 3 harrowing and no Puddling with manual transplanting), (T3:- 2 to 3 harrowing and Puddling with paddy harrow with manual transplanting), (T4:- 2 to 3 harrowing and Puddling with Rotavator with manual transplanting). The maximum output was obtained with treatment T1 (7.85 t/ha)) followed by T4 (6.4 t/ha), T3 (6.25 t/ha) and T2 (6.0 t/ha)) respectively.

Keywords: crop residues, cropping system, minimum tillage, yield

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Authors: Aminah Muchdar, Nuraeni, Eddy

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The aims of the research are: (1) to verify the cropsyst plant model of experimental data in the field of soybean plants and (2) to predict planting time and potential yield soybean plant with the use of cropsyst model. This research is divided into several stages: (1) first calibration stage which conducted in the field from June until September 2015.(2) application models stage, where the data obtained from calibration in the field will be included in cropsyst models. The required data models are climate data, ground data/soil data,also crop genetic data. The relationship between the obtained result in field with simulation cropsyst model indicated by Efficiency Index (EF) which the value is 0,939.That is showing that cropsyst model is well used. From the calculation result RRMSE which the value is 1,922%.That is showing that comparative fault prediction results from simulation with result obtained in the field is 1,92%. The conclusion has obtained that the prediction of soybean planting time cropsyst based models that have been made valid for use. and the appropriate planting time for planting soybeans mainly on rain-fed land is at the end of the rainy season, in which the above study first planting time (June 2, 2015) which gives the highest production, because at that time there was still some rain. Tanggamus varieties more resistant to slow planting time cause the percentage decrease in the yield of each decade is lower than the average of all varieties.

Keywords: soybean, Cropsyst, calibration, efficiency Index, RRMSE

Procedia PDF Downloads 180

Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

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Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

Procedia PDF Downloads 429

Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

Procedia PDF Downloads 386

Authors: Khyamling Parane, B. M. Prabhu Prasad, Basavaraj Talawar

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Fast simulations are critical in reducing time to market in CMPs and SoCs. Several simulators have been used to evaluate the performance and power consumed by Network-on-Chips. Researchers and designers rely upon these simulators for design space exploration of NoC architectures. Our experiments show that simulating large NoC topologies take hours to several days for completion. To speed up the simulations, it is necessary to investigate and optimize the hotspots in simulator source code. Among several simulators available, we choose Booksim2.0, as it is being extensively used in the NoC community. In this paper, we analyze the cache and memory system behaviour of Booksim2.0 to accurately monitor input dependent performance bottlenecks. Our measurements show that cache and memory usage patterns vary widely based on the input parameters given to Booksim2.0. Based on these measurements, the cache configuration having least misses has been identified. To further reduce the cache misses, we use software optimization techniques such as removal of unused functions, loop interchanging and replacing post-increment operator with pre-increment operator for non-primitive data types. The cache misses were reduced by 18.52%, 5.34% and 3.91% by employing above technology respectively. We also employ thread parallelization and vectorization to improve the overall performance of Booksim2.0. The OpenMP programming model and SIMD are used for parallelizing and vectorizing the more time-consuming portions of Booksim2.0. Speedups of 2.93x and 3.97x were observed for the Mesh topology with 30 × 30 network size by employing thread parallelization and vectorization respectively.

Keywords: cache behaviour, network-on-chip, performance profiling, vectorization

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Authors: Qiuxiao Chen, Yan Hou, Ning Wu

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As there are deficiencies of the classic Douglas-Peucker Algorithm (DPA), such as high risks of deleting key nodes by mistake, high complexity, time consumption and relatively slow execution speed, a new Deletion-Cost Based Compression Algorithm (DCA) for linear vector data was proposed. For each curve — the basic element of linear vector data, all the deletion costs of its middle nodes were calculated, and the minimum deletion cost was compared with the pre-defined threshold. If the former was greater than or equal to the latter, all remaining nodes were reserved and the curve’s compression process was finished. Otherwise, the node with the minimal deletion cost was deleted, its two neighbors' deletion costs were updated, and the same loop on the compressed curve was repeated till the termination. By several comparative experiments using different types of linear vector data, the comparison between DPA and DCA was performed from the aspects of compression quality and computing efficiency. Experiment results showed that DCA outperformed DPA in compression accuracy and execution efficiency as well.

Keywords: Douglas-Peucker algorithm, linear vector data, compression, deletion cost

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Authors: Sonya Barzgar, J. Patrick, A. Hettiaratchi

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Methane (CH4) is reputed as the second largest contributor to greenhouse effect with a global warming potential (GWP) of 34 related to carbon dioxide (CO2) over the 100-year horizon, so there is a growing interest in reducing the emissions of this gas. Methane biofiltration (MBF) is a cost effective technology for reducing low volume point source emissions of methane. In this technique, microbial oxidation of methane is carried out by methane-oxidizing bacteria (methanotrophs) which use methane as carbon and energy source. MBF uses a granular medium, such as soil or compost, to support the growth of methanotrophic bacteria responsible for converting methane to carbon dioxide (CO₂) and water (H₂O). Even though the biofiltration technique has been shown to be an efficient, practical and viable technology, the design and operational parameters, as well as the relevant microbial processes have not been investigated in depth. In particular, limited research has been done on the effects of sulphur on methane bio-oxidation. Since bacteria require a variety of nutrients for growth, to improve the performance of methane biofiltration, it is important to establish the input quantities of nutrients to be provided to the biofilter to ensure that nutrients are available to sustain the process. The study described in this paper was conducted with the aim of determining the influence of sulphur on methane elimination in a biofilter. In this study, a set of experimental measurements has been carried out to explore how the conversion of elemental sulphur could affect methane oxidation in terms of methanotrophs growth and system pH. Batch experiments with different concentrations of sulphur were performed while keeping the other parameters i.e. moisture content, methane concentration, oxygen level and also compost at their optimum level. The study revealed the tolerable limit of sulphur without any interference to the methane oxidation as well as the particular sulphur concentration leading to the greatest methane elimination capacity. Due to the sulphur oxidation, pH varies in a transient way which affects the microbial growth behavior. All methanotrophs are incapable of growth at pH values below 5.0 and thus apparently are unable to oxidize methane. Herein, the certain pH for the optimal growth of methanotrophic bacteria is obtained. Finally, monitoring methane concentration over time in the presence of sulphur is also presented for laboratory scale biofilters.

Keywords: global warming, methane biofiltration (MBF), methane oxidation, methanotrophs, pH, sulphur

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Authors: Janusz Bobulski, Mariusz Kubanek

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The main goal of the research is to develop a multimedia container structure containing three types of images: RGB, lidar and infrared, properly calibrated to each other. An additional goal is to develop program libraries for creating and saving this type of file and for restoring it. It will also be necessary to develop a method of data synchronization from lidar and RGB cameras as well as infrared. This type of file could be used in autonomous vehicles, which would certainly facilitate data processing by the intelligent autonomous vehicle management system. Autonomous cars are increasingly breaking into our consciousness. No one seems to have any doubts that self-driving cars are the future of motoring. Manufacturers promise that moving the first of them to showrooms is the prospect of the next few years. Many experts believe that creating a network of communicating autonomous cars will be able to completely eliminate accidents. However, to make this possible, it is necessary to develop effective methods of detection of objects around the moving vehicle. In bad weather conditions, this task is difficult on the basis of the RGB(red, green, blue) image. Therefore, in such situations, you should be supported by information from other sources, such as lidar or infrared cameras. The problem is the different data formats that individual types of devices return. In addition to these differences, there is a problem with the synchronization of these data and the formatting of this data. The goal of the project is to develop a file structure that could be containing a different type of data. This type of file is calling a multimedia container. A multimedia container is a container that contains many data streams, which allows you to store complete multimedia material in one file. Among the data streams located in such a container should be indicated streams of images, films, sounds, subtitles, as well as additional information, i.e., metadata. This type of file could be used in autonomous vehicles, which would certainly facilitate data processing by the intelligent autonomous vehicle management system. As shown by preliminary studies, the use of combining RGB and InfraRed images with Lidar data allows for easier data analysis. Thanks to this application, it will be possible to display the distance to the object in a color photo. Such information can be very useful for drivers and for systems in autonomous cars.

Keywords: an autonomous car, image processing, lidar, obstacle detection

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Authors: S. Varnagiris, M. Urbonavicius, S. Tuckute, M. Lelis, K. Bockute

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Amongst many water purification techniques, TiO₂ photocatalysis is recognized as one of the most promising sustainable methods. It is known that for photocatalytical applications anatase is the most suitable TiO₂ phase, however heterojunction of anatase/rutile phases could improve the photocatalytical activity of TiO₂ even further. Despite the relative simplicity of TiO₂ different synthesis methods lead to the highly dispersed crystal phases and photocatalytic activity of the corresponding samples. Accordingly, suggestions and investigations of various innovative methods of TiO₂ synthesis are still needed. In this work structural and photocatalytical properties of TiO₂ films deposited by the unconventional method of simultaneous co-sputtering from two magnetrons powered by pulsed-Direct Current (pDC) and Radio Frequency (RF) power sources with negative bias voltage have been studied. More specifically, TiO₂ film thickness, microstructure, surface roughness, crystal structure, optical transmittance and photocatalytical properties were investigated by profilometer, scanning electron microscope, atomic force microscope, X-ray diffractometer and UV-Vis spectrophotometer respectively. The proposed unconventional two magnetron co-sputtering based TiO₂ film formation method showed very promising results for crystalline TiO₂ film formation while keeping process temperatures below 100 °C. XRD analysis revealed that by using proper combination of power source type and bias voltage various TiO₂ phases (amorphous, anatase, rutile or their mixture) can be synthesized selectively. Moreover, strong dependency between power source type and surface roughness, as well as between the bias voltage and band gap value of TiO₂ films was observed. Interestingly, TiO₂ films deposited by two magnetron co-sputtering without bias voltage had one of the highest band gap values between the investigated films but its photocatalytic activity was superior compared to all other samples. It is suggested that this is due to the dominating nanocrystalline anatase phase with various exposed surfaces including photocatalytically the most active {001}.

Keywords: films, magnetron co-sputtering, photocatalysis, TiO₂

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Authors: Monojit Manna, Arpan Adhikary

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In Mobile cloud sensing across the globe, an emerging paradigm is selected by the user to compute sensing tasks. In urban cities current days, Mobile vehicles are adapted to perform the task of data sensing and data collection for universality and mobility. In this work, we focused on the optimality and mobile nodes that can be selected in order to collect the maximum amount of data from urban areas and fulfill the required data in the future period within a couple of minutes. We map out the requirement of the vehicle to configure the maximum data optimization problem and budget. The Application implementation is basically set up to generalize a realistic online platform in which real-time vehicles are moving apparently in a continuous manner. The data center has the authority to select a set of vehicles immediately. A deep learning-based scheme with the help of mobile vehicles (DLMV) will be proposed to collect sensing data from the urban environment. From the future time perspective, this work proposed a deep learning-based offline algorithm to predict mobility. Therefore, we proposed a greedy approach applying an online algorithm step into a subset of vehicles for an NP-complete problem with a limited budget. Real dataset experimental extensive evaluations are conducted for the real mobility dataset in Rome. The result of the experiment not only fulfills the efficiency of our proposed solution but also proves the validity of DLMV and improves the quantity of collecting the sensing data compared with other algorithms.

Keywords: mobile crowdsensing, deep learning, vehicle recruitment, sensing coverage, data collection

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: Ramon Santana

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The use of biometric identifiers in the field of information security, access control to resources, authentication in ATMs and banking among others, are of great concern because of the safety of biometric data. In the general architecture of a biometric system have been detected eight vulnerabilities, six of them allow obtaining minutiae template in plain text. The main consequence of obtaining minutia templates is the loss of biometric identifier for life. To mitigate these vulnerabilities several models to protect minutiae templates have been proposed. Several vulnerabilities in the cryptographic security of these models allow to obtain biometric data in plain text. In order to increase the cryptographic security and ease of reversibility, a minutiae templates protection model is proposed. The model aims to make the cryptographic protection and facilitate the reversibility of data using two levels of security. The first level of security is the data transformation level. In this level generates invariant data to rotation and translation, further transformation is irreversible. The second level of security is the evaluation level, where the encryption key is generated and data is evaluated using a defined evaluation function. The model is aimed at mitigating known vulnerabilities of the proposed models, basing its security on the impossibility of the polynomial reconstruction.

Keywords: fingerprint, template protection, bio-cryptography, minutiae protection

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Authors: G. Raviraja Shetty, K. G. Poojitha, Pranay Kumar

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Biodiversity refers to the numbers, variety and variability of living organisms and ecosystem. The climatic and altitudinal variations, coupled with varied ecological habitats of this country, have contributed to the development of immensely rich vegetation with a unique diversity in medicinal plants which provides an important source of medicinal raw materials for traditional medicine systems as well as for pharmaceutical industries in the country and abroad. World Health Organization has listed over 21000 plant species used around the world for medicinal purpose. In India, about 2500 plant species are being used in indigenous system of medicine. The red data book lists 427 Indian Medicinal plant entries on endangered species, of which 28 are considered extinct, 124 endangered, 81 rare, and 34 insufficiently known. It is abundantly clear from the experience of all govt agencies that on their own they cannot efficiently conserve the biodiversity. Participatory Approach with the involvement of local people in conservation is found to be more effective these days. Involvement of local people reduces the cost involved in conservation. Local communities have long tradition of resource use in particular area, hold in depth knowledge and experience of plant which can be invaluable for conservation efforts.Medicinal plants occupy a vital sector of health care system in India and represent a major national resource.There is an immense need for conservation of diversity of medicinal plant wealth for the present and fore coming generations, by adapting the suitable strategy with most appropriate method of conservation.

Keywords: conservation, biodiversity, participatory, medicinal plants

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Authors: Veysel Çelik, Aynur Aker, Ebru Güç

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Developments in information and communication technologies have increased both the speed of producing information and the speed of accessing new information. Accordingly, the daily lives of individuals have started to change. New concepts such as e-mail, e-government, e-school, e-signature have emerged. For this reason, prospective teachers who will be future teachers or school administrators are expected to have a high awareness of digital data security. The aim of this study is to reveal the effect of the digital storytelling technique on the data security awareness of pre-service teachers of computer and instructional technology education departments. For this purpose, participants were selected based on the principle of volunteering among third-grade students studying at the Computer and Instructional Technologies Department of the Faculty of Education at Siirt University. In the research, the pretest/posttest half experimental research model, one of the experimental research models, was used. In this framework, a 6-week lesson plan on digital data security awareness was prepared in accordance with the digital narration technique. Students in the experimental group formed groups of 3-6 people among themselves. The groups were asked to prepare short videos or animations for digital data security awareness. The completed videos were watched and evaluated together with prospective teachers during the evaluation process, which lasted approximately 2 hours. In the research, both quantitative and qualitative data collection tools were used by using the digital data security awareness scale and the semi-structured interview form consisting of open-ended questions developed by the researchers. According to the data obtained, it was seen that the digital storytelling technique was effective in creating data security awareness and creating permanent behavior changes for computer and instructional technology students.

Keywords: digital storytelling, self-regulation, digital data security, teacher candidates, self-efficacy

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Authors: Mehmet Karaata

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Given two distinct vertices (nodes) source s and target t of a graph G = (V, E), the two node-disjoint paths problem is to identify two node-disjoint paths between s ∈ V and t ∈ V . Two paths are node-disjoint if they have no common intermediate vertices. In this paper, we present an algorithm with O(m)-time complexity for finding two node-disjoint paths between s and t in arbitrary graphs where m is the number of edges. The proposed algorithm has a wide range of applications in ensuring reliability and security of sensor, mobile and fixed communication networks.

Keywords: disjoint paths, distributed systems, fault-tolerance, network routing, security

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Authors: Kamel Allaw, Jocelyne Adjizian Gerard, Makram Chehayeb, Nada Badaro Saliba

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In developing countries, such as Lebanon, the demographic data are hardly available due to the absence of the mechanization of population system. The aim of this study is to evaluate, using only remote sensing data, the correlations between the number of population and the characteristics of roads network (length of primary roads, length of secondary roads, total length of roads, density and percentage of roads and the number of intersections). In order to find the influence of the different factors on the demographic data, we studied the degree of correlation between each factor and the number of population. The results of this study have shown a strong correlation between the number of population and the density of roads and the number of intersections.

Keywords: population, road network, statistical correlations, remote sensing

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Authors: Tuğçe Yücel

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A Wireless Sensor Network(WSN) is a collection of sensor nodes which are deployed randomly in an area for surveillance. Efficient utilization of limited battery energy of sensors for increased network lifetime as well as data security are major design objectives for WSN. Moreover secure transmission of data sensed to a base station for further processing. Producing multiple copies of data packets and sending them on different paths is one of the strategies for this purpose, which leads to redundant energy consumption and hence reduced network lifetime. In this work we develop a restricted multi-copy multipath strategy where data move through ‘frequently’ or ‘heavily’ used sensors is copied by the sensor incident to such central nodes and sent on node-disjoint paths. We develop a mixed integer programing(MIP) model and heuristic approach present some preleminary test results.

Keywords: MIP, sensor, telecommunications, WSN

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Authors: V. V. Fomin, A. P. Mikhailovich, E. M. Agapitov, V. E. Rogachev, E. A. Kostousova, E. S. Perekhodova

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Ground-level landscape photos can be used as a source of objective data on woody vegetation and vegetation dynamics. We proposed a method for processing, analyzing, and presenting ground photographs, which has the following advantages: 1) researcher has to form holistic representation of the study area in form of a set of interlapping ground-level landscape photographs; 2) it is necessary to define or obtain characteristics of the landscape, objects, and phenomena present on the photographs; 3) it is necessary to create new or supplement existing textual descriptions and annotations for the ground-level landscape photographs; 4) single or multiple ground-level landscape photographs can be used to develop specialized geoinformation layers, schematic maps or thematic maps; 5) it is necessary to determine quantitative data that describes both images as a whole, and displayed objects and phenomena, using algorithms for automated image analysis. It is suggested to match each photo with a polygonal geoinformation layer, which is a sector consisting of areas corresponding with parts of the landscape visible in the photos. Calculation of visibility areas is performed in a geoinformation system within a sector using a digital model of a study area relief and visibility analysis functions. Superposition of the visibility sectors corresponding with various camera viewpoints allows matching landscape photos with each other to create a complete and wholesome representation of the space in question. It is suggested to user-defined data or phenomenons on the images with the following superposition over the visibility sector in the form of map symbols. The technology of geoinformation layers’ spatial superposition over the visibility sector creates opportunities for image geotagging using quantitative data obtained from raster or vector layers within the sector with the ability to generate annotations in natural language. The proposed method has proven itself well for relatively open and clearly visible areas with well-defined relief, for example, in mountainous areas in the treeline ecotone. When the polygonal layers of visibility sectors for a large number of different points of photography are topologically superimposed, a layer of visibility of sections of the entire study area is formed, which is displayed in the photographs. Also, as a result of this overlapping of sectors, areas that did not appear in the photo will be assessed as gaps. According to the results of this procedure, it becomes possible to obtain information about the photos that display a specific area and from which points of photography it is visible. This information may be obtained either as a query on the map or as a query for the attribute table of the layer. The method was tested using repeated photos taken from forty camera viewpoints located on Ray-Iz mountain massif (Polar Urals, Russia) from 1960 until 2023. It has been successfully used in combination with other ground-based and remote sensing methods of studying the climate-driven dynamics of woody vegetation in the Polar Urals. Acknowledgment: This research was collaboratively funded by the Russian Ministry for Science and Education project No. FEUG-2023-0002 (image representation) and Russian Science Foundation project No. 24-24-00235 (automated textual description).

Keywords: woody, vegetation, repeated, photographs

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Authors: L. Rajaonarivo, M. N. Bessagnet, C. Sallaberry, A. Le Parc Lacayrelle, L. Leveque

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TCVPYR is a European FEDER (European Regional Development Fund) project which aims to promote tourism in the French Pyrenees region by leveraging its cultural heritage. It involves scientists from various domains (geographers, historians, anthropologists, computer scientists...). This paper presents a fully automated process to publish any dataset as Wikipedia articles as well as the corresponding linked information on Wikidata and Wikimedia Commons. We validate this process on a sample of geo-referenced cultural heritage data collected by TCVPYR researchers in different regions of the Pyrenees. The main result concerns the technological prerequisites, which are now in place. Moreover, we demonstrated that we can automatically publish cultural heritage data on Wikimedia.

Keywords: cultural heritage dissemination, digital humanities, open data, Wikimedia automated publishing

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Authors: M. Sagarika, M. Chandra Sekhar

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Agricultural residues are producing in large quantities in India and account for abundant but underutilized source of renewable biomass in agriculture. In India, the amount of crop residues available is estimated to be approximately 686 million tons. Anaerobic digestion is a promising option to utilize the surplus agricultural residues and can produce biogas and digestate. Biogas is mainly methane (CH4), which can be utilized as an energy source in replacement for fossil fuels such as natural gas, oil, in other hand, digestate contains high amounts of nutrients, can be employed as fertilizer. Solid state anaerobic digestion (total solids ≥ 15%) is suitable for agricultural residues, as it reduces the problems like stratification and floating issues that occur in liquid anaerobic digestion (total solids < 15%). The major concern in solid-state anaerobic digestion is the low mass transfer of feedstock and inoculum that resulting in low performance. To resolve this low mass transfer issue, effective mixing of feedstock and inoculum is required. Mechanical mixing using stirrer at the time of digestion process can be done, but it is difficult to operate the stirring of feedstock with high solids percentage and high viscosity. Complete premixing of feedstock and inoculum is an alternative method, which is usual in lab scale studies but may not be affordable due to high energy demand in large-scale digesters. Developing partial premixing methods may reduce this problem. Current study is to improve the performance of solid-state anaerobic digestion of corn stover at feedstock to inoculum ratios 3 and 5, by applying partial premixing methods and to compare the complete premixing method with two partial premixing methods which are two alternative layers of feedstock and inoculum and three alternative layers of feedstock and inoculum where higher inoculum ratios in the top layers. From experimental studies it is observed that, partial premixing method with three alternative layers of feedstock and inoculum yielded good methane.

Keywords: anaerobic digestion, premixing methods, methane yield, corn stover, volatile solids

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Authors: Masooma Al-Balushi, Tamer Mohamed Atef

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The Sultanate of Oman is located on the South-eastern tip of the Arabian Peninsula. It is bordered by the Gulf of Oman and the Arabian Sea and has borders with the United Arab Emirates, Saudi Arabia and Yemen. Arabic is the official language. Islam is the official religion. Islam has a great impact on most Omanis, Shari’a law is the law of Oman. The tribal structure plays an essential role in the lives of Omanis. Most people in the Gulf States bear a tribal name rather than a family name. Religion, tribe, and family are highly influential in shaping individuals’ values and behaviors, and have a very noticeable influence on a person’s career choices. Tourism development has been given special attention by the Sultanate of Oman’s government aspiring that the industry would assist in creating direct job opportunities as well as boost the economy through provision of hard currency to improve the balance of payments. This study aims to assess the impact of socio-cultural and religious factors on Omanis employment in the hotel sector. The socio-cultural and religious factors have serious impacts on Omani employment in the hotel sector. Some employees are concerned about the source of income because of the idea that since the hotel business is based on activities such as serving alcohol and pork, gambling, and accommodating unmarried couples, their source of income would be questionable religion wise. For females, the designated job uniform and the interaction with males are major concerns. Ability to fulfil family obligations for married Omanis, and marriage opportunity for singles were other raised concerns. Whilst the future prosperity of the hotel industry depends on the quality of its people, in Oman, the hospitality industry has failed, for a number of reasons, to project an image that could generate interest amongst Omanis. Furthermore, the characteristics and the very nature of the hotel sector are in direct conflict with Islamic doctrines which are embedded in Omani life and society.

Keywords: culture, society, hotel, hospitality, Islam, Oman

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Authors: Alaa Abdullah Altaee

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This paper presents an adaptive decision feedback equalizer (ADFE) for multi-Gbps serial links utilizing a fix-step error signal extracted from cross-points of received data symbols. The extracted signal is generated based on violation of received data symbols with minimum detection requirements at the clock and data recovery (CDR) stage. The iterations of the adaptation process search for the optimum feedback tap coefficients to maximize the data eye-opening and minimize the adaptation convergence time. The effectiveness of the proposed architecture is validated using the simulation results of a serial link designed in an IBM 130 nm 1.2V CMOS technology. The data link with variable channel lengths is analyzed using Spectre from Cadence Design Systems with BSIM4 device models.

Keywords: adaptive DFE, CMOS equalizer, error detection, serial links, timing jitter, wire-line communication

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Authors: Rik van Leeuwen, Ger Koole

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Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

Procedia PDF Downloads 108

Authors: Muhammad Suhail Ibrahim, Muhammad Nadeem, Muhammad Sultan, Uzair Sajjad, Khalid Hamid, Tahir Mahmood Qureshi, Sadaf Javaria

Abstract:

Metabolic disorders, including hypertension, diabetes, cardiovascular disease etc., are major threats to public health and economy. Management and prevention of alarmingly increasing disorders have attracted researchers to explore natural barriers against these disorders. The objective of this study was to explore oats as a potential source of dietary fiber. Extraction of dietary was optimized by response surface methodology, and five indigenous oat cultivars, including SGD2011, Avon, SGD81, PD2LV65, and S2000, were also characterized for techno-functional characteristics, mineral composition and phytochemical quantification. These cultivars varied significantly (p < 0.05) for oil holding capacity, water saturation, and water holding capacity, respectively. SGD81 showed the highest oil-holding capacity, water-holding capacity, and water saturation due to the highest fraction of dietary fiber. The highest values of total phenolic contents, total flavonoid contents, total flavonol contents, 2, 2-Diphenyl-1-picrylhydrazyl radical scavenging activity, and anthocyanin were shown by SGD81, and SGD2011, respectively. All cultivars varied significantly (P<0.05) with respect to phytochemical quantification. Oat cultivars SGD81 and SGD2011 showed the best phenolic acid profile and can be effectively used as a source of nutraceuticals. Beyond the nutritional properties of oats, these also contribute and emerged as potential sources of dietary fiber and have gained attention as nutraceutical cereal crops. This approach offers oats as a natural means of dietary fiber to protect humans from alarmingly increasing metabolic disorders, and its extraction by sonication has made it a sustainable and eco-friendly strategy.

Keywords: oat cultivars, dietary fibers, mineral profile, antioxidant activity, color properties

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Authors: N. Boontian

Abstract:

Biological conversion of biomass to methane has received increasing attention in recent years. Grasses have been explored for their potential anaerobic digestion to methane. In this review, extensive literature data have been tabulated and classified. The influences of several parameters on the potential of these feedstocks to produce methane are presented. Lignocellulosic biomass represents a mostly unused source for biogas and ethanol production. Many factors, including lignin content, crystallinity of cellulose, and particle size, limit the digestibility of the hemicellulose and cellulose present in the lignocellulosic biomass. Pretreatments have used to improve the digestibility of the lignocellulosic biomass. Each pretreatment has its own effects on cellulose, hemicellulose and lignin, the three main components of lignocellulosic biomass. Solid-state anaerobic digestion (SS-AD) generally occurs at solid concentrations higher than 15%. In contrast, liquid anaerobic digestion (AD) handles feedstocks with solid concentrations between 0.5% and 15%. Animal manure, sewage sludge, and food waste are generally treated by liquid AD, while organic fractions of municipal solid waste (OFMSW) and lignocellulosic biomass such as crop residues and energy crops can be processed through SS-AD. An increase in operating temperature can improve both the biogas yield and the production efficiency, other practices such as using AD digestate or leachate as an inoculant or decreasing the solid content may increase biogas yield but have negative impact on production efficiency. Focus is placed on substrate pretreatment in anaerobic digestion (AD) as a means of increasing biogas yields using today’s diversified substrate sources.

Keywords: anaerobic digestion, lignocellulosic biomass, methane production, optimization, pretreatment

Procedia PDF Downloads 379

Authors: Meng-Tzu Cheng, Louisa Rosenheck, Chen-Yen Lin, Eric Klopfer

Abstract:

The purpose of the research is to explore some of the ways in which gameplay data can be analyzed to yield results that feedback into the learning ecosystem. Back-end data for all users as they played an MMOG, The Radix Endeavor, was collected, and this study reports the analyses on a specific genetics quest by using the data mining techniques, including the decision tree method. In the study, different reasons for quest failure between participants who eventually succeeded and who never succeeded were revealed. Regarding the in-game tools use, trait examiner was a key tool in the quest completion process. Subsequently, the results of decision tree showed that a lack of trait examiner usage can be made up with additional Punnett square uses, displaying multiple pathways to success in this quest. The methods of analysis used in this study and the resulting usage patterns indicate some useful ways that gameplay data can provide insights in two main areas. The first is for game designers to know how players are interacting with and learning from their game. The second is for players themselves as well as their teachers to get information on how they are progressing through the game, and to provide help they may need based on strategies and misconceptions identified in the data.

Keywords: MMOG, decision tree, genetics, gaming-learning interaction

Procedia PDF Downloads 357

Authors: Marwa Hussien Mohamed, Mohamed Helmy Khafagy

Abstract:

Map-Reduce is a programming model which is widely used to extract valuable information from enormous volumes of data. Map-reduce designed to support heterogeneous datasets. Apache Hadoop map-reduce used extensively to uncover hidden pattern like data mining, SQL, etc. The most important operation for data analysis is joining operation. But, map-reduce framework does not directly support join algorithm. This paper explains and compares two-way and multi-way map-reduce join algorithms for map reduce also we implement MR join Algorithms and show the performance of each phase in MR join algorithms. Our experimental results show that map side join and map merge join in two-way join algorithms has the longest time according to preprocessing step sorting data and reduce side cascade join has the longest time at Multi-Way join algorithms.

Keywords: Hadoop, MapReduce, multi-way join, two-way join, Ubuntu

Procedia PDF Downloads 487

Authors: Aurelija Burinskienė, Dalė Dzemydienė, Arūnas Miliauskas

Abstract:

This research aims at developing the approach for more effective freight delivery and transportation process management. The road congestions and the identification of causes are important, as well as the context information recognition and management. The measure of many parameters during the transportation period and proper control of driver work became the problem. The number of vehicles per time unit passing at a given time and point for drivers can be evaluated in some situations. The collection of data is mainly used to establish new trips. The flow of the data is more complex in urban areas. Herein, the movement of freight is reported in detail, including the information on street level. When traffic density is extremely high in congestion cases, and the traffic speed is incredibly low, data transmission reaches the peak. Different data sets are generated, which depend on the type of freight delivery network. There are three types of networks: long-distance delivery networks, last-mile delivery networks and mode-based delivery networks; the last one includes different modes, in particular, railways and other networks. When freight delivery is switched from one type of the above-stated network to another, more data could be included for reporting purposes and vice versa. In this case, a significant amount of these data is used for control operations, and the problem requires an integrated methodological approach. The paper presents an approach for providing e-services for drivers by including the assessment of the multi-component infrastructure needed for delivery of freights following the network type. The construction of such a methodology is required to evaluate data flow conditions and overloads, and to minimize the time gaps in data reporting. The results obtained show the possibilities of the proposing methodological approach to support the management and decision-making processes with functionality of incorporating networking specifics, by helping to minimize the overloads in data reporting.

Keywords: transportation networks, freight delivery, data flow, monitoring, e-services

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Authors: Jumana Majdi Qutub

Abstract:

Rationale: Cloud computing has rapidly developed the past few years. Because of the importance of providing protection for personal data used in cloud computing, the role of data protection in promoting trust and confidence in users’ data has become an important policy priority. This research examines key regulatory challenges rose by the growing use and importance of cloud computing with focusing on protection of individuals personal data. Methodology: Describing and analyzing governance challenges facing policymakers and industry in Saudi Arabia, with an account of anticipated governance responses. The aim of the research is to describe and define the regulatory challenges on cloud computing for policy making in Saudi Arabia and comparing it with potential complied issues rose in respect of transported data to EU member state. In addition, it discusses information privacy issues. Finally, the research proposes policy recommendation that would resolve concerns surrounds the privacy and effectiveness of clouds computing frameworks for data protection. Results: There are still no clear regulation in Saudi Arabia specialized in legalizing cloud computing and specialty regulations in transferring data internationally and locally. Decision makers need to review the applicable law in Saudi Arabia that protect information in cloud computing. This should be from an international and a local view in order to identify all requirements surrounding this area. It is important to educate cloud computing users about their information value and rights before putting it in the cloud to avoid further legal complications, such as making an educational program to prevent giving personal information to a bank employee. Therefore, with many kinds of cloud computing services, it is important to have it covered by the law in all aspects.

Keywords: cloud computing, cyber crime, data protection, privacy

Procedia PDF Downloads 262

Authors: Mesbaholdin Salami, Farzad Movahedi Sobhani, Mohammad Sadegh Ghazizadeh

Abstract:

Evaluation of organizational performance is among the most important measures that help organizations and entities continuously improve their efficiency. Organizations can use the existing data and results from the comparison of units under investigation to obtain an estimation of their performance. The Malmquist Productivity Index (MPI) is an important index in the evaluation of overall productivity, which considers technological developments and technical efficiency at the same time. This article proposed a model based on the multistage MPI, considering limited data (Grey’s theory). This model can evaluate the performance of units using limited and uncertain data in a multistage process. It was applied by the electricity market manager to Iran’s electric power supply chain (EPSC), which contains uncertain data, to evaluate the performance of its actors. Results from solving the model showed an improvement in the accuracy of future performance of the units under investigation, using the Grey’s system theory. This model can be used in all case studies, in which MPI is used and there are limited or uncertain data.

Keywords: Malmquist Index, Grey's Theory, CCR Model, network data envelopment analysis, Iran electricity power chain

Procedia PDF Downloads 165