Search results for: data mining analytics

Authors: Eric Kumar, Erica Cochran, Zhiang Zhang, Wei Liang, Ronak Mody

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The objective of this article was to create a modelling framework that exposes the marginal costs of shifting workloads across geographically distributed data-centers. Geographical distribution of internet services helps to optimize their performance for localized end users with lowered communications times and increased availability. However, due to the geographical and temporal effects, the physical embodiments of a service's data center infrastructure can vary greatly. In this work, we first identify that the sources of variances in the physical infrastructure primarily stem from local weather conditions, specific user traffic profiles, energy sources, and the types of IT hardware available at the time of deployment. Second, we create a traffic simulator that indicates the IT load at each data-center in the set as an approximator for user traffic profiles. Third, we implement a framework that quantifies the global level energy demands using building energy models and the traffic profiles. The results of the model provide a time series of energy demands that can be used for further life cycle analysis of internet services.

Keywords: data-centers, energy, life cycle, network simulation

Procedia PDF Downloads 147

Authors: Marcho Setiawan Handok, Samuel S. Lemma, Abdoulaye Fofana, Naseef Mansoor

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This paper is proposing a method to predict the outcome of the National Football League match with data from 2019 to 2022 and compare it with other popular models. The model uses open-source statistical data of each team, such as passing yards, rushing yards, fumbles lost, and scoring. Each statistical data has offensive and defensive. For instance, a data set of anticipated values for a specific matchup is created by comparing the offensive passing yards obtained by one team to the defensive passing yards given by the opposition. We evaluated the model’s performance by contrasting its result with those of established prediction algorithms. This research is using a neural network to predict the score of a National Football League match and then predict the winner of the game.

Keywords: game prediction, NFL, football, artificial neural network

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Authors: Mehdi Khaki, Ehsan Forootan, Joseph Awange, Michael Kuhn

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Terrestrial water storage, as a source of freshwater, plays an important role in human lives. Hydrological models offer important tools for simulating and predicting water storages at global and regional scales. However, their comparisons with 'reality' are imperfect mainly due to a high level of uncertainty in input data and limitations in accounting for all complex water cycle processes, uncertainties of (unknown) empirical model parameters, as well as the absence of high resolution (both spatially and temporally) data. Data assimilation can mitigate this drawback by incorporating new sets of observations into models. In this effort, we use multi-mission satellite-derived remotely sensed observations to improve the performance of World-Wide Water Resources Assessment system (W3RA) hydrological model for estimating terrestrial water storages. For this purpose, we assimilate total water storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) and surface soil moisture data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) into W3RA. This is done to (i) improve model estimations of water stored in ground and soil moisture, and (ii) assess the impacts of each satellite of data (from GRACE and AMSR-E) and their combination on the final terrestrial water storage estimations. These data are assimilated into W3RA using the Ensemble Square-Root Filter (EnSRF) filtering technique over Mississippi Basin (the United States) and Murray-Darling Basin (Australia) between 2002 and 2013. In order to evaluate the results, independent ground-based groundwater and soil moisture measurements within each basin are used.

Keywords: data assimilation, GRACE, AMSR-E, hydrological model, EnSRF

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Authors: Aziz Larbi, Widad Sadok

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In Morocco, the mountainous regions extend over about 26% of the national territory where 30% of the total population live. They contain opportunities for agriculture, forestry, pastureland and mining. The production systems in these zones are characterised by crop diversification. However, these areas have become vulnerable to the effects of climate change. To understand these effects in relation to the population living in these areas, a study was carried out in the zone of Taliouine, in the Anti-Atlas. The vulnerability of crop productions to climate change was analysed and the different ways of adaptation adopted by farmers were identified. The work was done on saffron, the most profitable crop in the target area even though it requires much water. Our results show that the majority of the farmers surveyed had noticed variations in the climate of the region: irregularity of precipitation leading to a decrease in quantity and an uneven distribution throughout the year; rise in temperature; reduction in the cold period and less snow. These variations had impacts on the cropping system of saffron and its productivity. To cope with these effects, the farmers adopted various strategies: better management and use of water; diversification of agricultural activities; increase in the contribution of non-agricultural activities to their gross income; and seasonal migration.

Keywords: climate change, Taliouine, saffron, perceptions, adaptation strategies

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Authors: K. Mokatse, O. M. Barei, K. Gabanakgosi, P. Matlhabaphiri

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The mining of pit slopes is often associated with some level of deviation from design recommendations, and this may translate to associated changes in the stability of the excavated pit slopes. Therefore slope angle design reconciliations are essential for assessing and monitoring compliance of excavated pit slopes to accepted slope designs. These associated changes in slope stability may be reflected by changes in the calculated factors of safety and/or probabilities of failure. Reconciliations of as-mined and slope design profiles are conducted periodically to assess the implications of these deviations on pit slope stability. Currently, the slope design reconciliation process being implemented in Jwaneng Mine involves the measurement of as-mined and design slope angles along vertical sections cut along the established geotechnical design section lines on the GEOVIA GEMS™ software. Bench retentions are calculated as a percentage of the available catchment area, less over-mined and under-mined areas, to that of the designed catchment area. This process has proven to be both tedious and requires a lot of manual effort and time to execute. Consequently, a new semi-automated mine-to-design reconciliation approach that utilizes laser scanning and GIS-based tools is being proposed at Jwaneng Mine. This method involves high-resolution scanning of targeted bench walls, subsequent creation of 3D surfaces from point cloud data and the derivation of slope toe lines and crest lines on the Maptek I-Site Studio software. The toe lines and crest lines are then exported to the ArcGIS software where distance offsets between the design and actual bench toe lines and crest lines are calculated. Retained bench catchment capacity is measured as distances between the toe lines and crest lines on the same bench elevations. The assessment of the performance of the inter-ramp and overall slopes entails the measurement of excavated and design slope angles along vertical sections on the ArcGIS software. Excavated and design toe-to-toe or crest-to-crest slope angles are measured for inter-ramp stack slope reconciliations. Crest-to-toe slope angles are also measured for overall slope angle design reconciliations. The proposed approach allows for a more automated, accurate, quick and easier workflow for carrying out slope angle design reconciliations. This process has proved highly effective and timeous in the assessment of slope performance in Jwaneng Mine. This paper presents a newly proposed process for assessing compliance to slope angle designs for Jwaneng Mine.

Keywords: slope angle designs, slope design recommendations, slope performance, slope stability

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Authors: Jacob Wahl, Jane Zhang

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This paper describes the development and characterization of a prototype IoT network for use with agricultural imaging and monitoring applications. The sensor and gateway nodes are designed using the ESP32 SoC with integrated Bluetooth Low Energy 4.2 and Wi-Fi. A development board, the Arducam IoTai ESP32, is used for prototyping, testing, and power measurements. Google’s Firebase is used as the cloud storage site for image data collected by the sensor. The sensor node captures images using the OV2640 2MP camera module and transmits the image data to the gateway via Bluetooth Low Energy. The gateway then uploads the collected images to Firebase via a known nearby Wi-Fi network connection. This image data can then be processed and analyzed by computer vision and machine learning pipelines to assess crop growth or other needs. The sensor node achieves a wireless transmission data throughput of 220kbps while consuming 150mA of current; the sensor sleeps at 162µA. The sensor node device lifetime is estimated to be 682 days on a 6600mAh LiPo battery while acquiring five images per day based on the development board power measurements. This network can be utilized by any application that requires high data rates, low power consumption, short-range communication, and large amounts of data to be transmitted at low-frequency intervals.

Keywords: Bluetooth low energy, ESP32, firebase cloud, IoT, smart farming

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Authors: Lauren C. Porter, Andrew Curtis, Eric Jefferis, Susanne Mitchell

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A wealth of research has been generated examining the variation in crime across neighborhoods. However, there is also a striking degree of crime concentration within neighborhoods. A number of studies show that a small percentage of street segments, intersections, or addresses account for a large portion of crime. Not surprisingly, a focus on these crime hot spots can be an effective strategy for reducing community level crime and related ills, such as health problems. However, research is also limited in an important respect. Studies tend to use official data to identify hot spots, such as 911 calls or calls for service. While the use of call data may be more representative of the actual level and distribution of crime than some other official measures (e.g. arrest data), call data still suffer from the 'dark figure of crime.' That is, there is most certainly a degree of error between crimes that occur versus crimes that are reported to the police. In this study, we present an alternative method of identifying crime hot spots, that does not rely on official data. In doing so, we highlight the potential utility of neighborhood-insiders to identify and understand crime dynamics within geographic spaces. Specifically, we use spatial video and geo-narratives to record the crime insights of 36 police, ex-offenders, and residents of a high crime neighborhood in northeast Ohio. Spatial mentions of crime are mapped to identify participant-identified hot spots, and these are juxtaposed with calls for service (CFS) data. While there are bound to be differences between these two sources of data, we find that one location, in particular, a corner store, emerges as a hot spot for all three groups of participants. Yet it does not emerge when we examine CFS data. A closer examination of the space around this corner store and a qualitative analysis of narrative data reveal important clues as to why this store may indeed be a hot spot, but not generate disproportionate calls to the police. In short, our results suggest that researchers who rely solely on official data to study crime hot spots may risk missing some of the most dangerous places.

Keywords: crime, narrative, video, neighborhood

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Authors: Nasim Eslamirad, Mahdi Rasoulinezhad, Francesco De Luca, Sadok Ben Yahia, Kimmo Sakari Lylykangas, Francesco Pilla

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With the ongoing urbanization, cities face increasing environmental challenges impacting human well-being. To tackle these issues, data-driven approaches in urban analysis have gained prominence, leveraging urban data to promote sustainability. Integrating Machine Learning techniques enables researchers to analyze and predict complex environmental phenomena like Urban Heat Island occurrences in urban areas. This paper demonstrates the implementation of data-driven approach and interpretable Machine Learning algorithms with interpretability techniques to conduct comprehensive data analyses for sustainable urban design. The developed framework and algorithms are demonstrated for Tallinn, Estonia to develop sustainable urban strategies to mitigate urban heat waves. Geospatial data, preprocessed and labeled with UHI levels, are used to train various ML models, with Logistic Regression emerging as the best-performing model based on evaluation metrics to derive a mathematical equation representing the area with UHI or without UHI effects, providing insights into UHI occurrences based on buildings and urban features. The derived formula highlights the importance of building volume, height, area, and shape length to create an urban environment with UHI impact. The data-driven approach and derived equation inform mitigation strategies and sustainable urban development in Tallinn and offer valuable guidance for other locations with varying climates.

Keywords: data-driven approach, machine learning transparent models, interpretable machine learning models, urban heat island effect

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Authors: Ibrahim A. Adeniran, Rui F. Zhu, Man S. Wong

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Owing to the insufficiency in the spatial representativeness and continuity of in situ temperature measurements from weather stations (WS), the use of temperature measurement from WS for large-range diurnal analysis in heterogenous landscapes has been limited. This has made the accurate estimation of land surface temperature (LST) from remotely sensed data more crucial. Moreover, the study of dynamic interaction between the atmosphere and the physical surface of the Earth could be enhanced at both annual and diurnal scales by using optimal LST data derived from satellite sensors. The tradeoff between the spatial and temporal resolution of LSTs from satellite’s thermal infrared sensors (TIRS) has, however, been a major challenge, especially when high spatiotemporal LST data are recommended. It is well-known from existing literature that polar satellites have the advantage of high spatial resolution, while geostationary satellites have a high temporal resolution. Hence, this study is aimed at designing a framework for the cross-comparison of LST data from polar and geostationary satellites in a heterogeneous landscape. This could help to understand the relationship between the LST estimates from the two satellites and, consequently, their integration in diurnal LST analysis. Landsat-8 satellite data will be used as the representative of the polar satellite due to the availability of its long-term series, while the Himawari-8 satellite will be used as the data source for the geostationary satellite because of its improved TIRS. For the study area, Hong Kong Special Administrative Region (HK SAR) will be selected; this is due to the heterogeneity in the landscape of the region. LST data will be retrieved from both satellites using the Split window algorithm (SWA), and the resulting data will be validated by comparing satellite-derived LST data with temperature data from automatic WS in HK SAR. The LST data from the satellite data will then be separated based on the land use classification in HK SAR using the Global Land Cover by National Mapping Organization version3 (GLCNMO 2013) data. The relationship between LST data from Landsat-8 and Himawari-8 will then be investigated based on the land-use class and over different seasons of the year in order to account for seasonal variation in their relationship. The resulting relationship will be spatially and statistically analyzed and graphically visualized for detailed interpretation. Findings from this study will reveal the relationship between the two satellite data based on the land use classification within the study area and the seasons of the year. While the information provided by this study will help in the optimal combination of LST data from Polar (Landsat-8) and geostationary (Himawari-8) satellites, it will also serve as a roadmap in the annual and diurnal urban heat (UHI) analysis in Hong Kong SAR.

Keywords: automatic weather station, Himawari-8, Landsat-8, land surface temperature, land use classification, split window algorithm, urban heat island

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Authors: Ruchi Yadav, Shivani Pandey, Prachi Srivastava

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Microarrays provide a rich source of data on the molecular working of cells. Each microarray reports on the abundance of tens of thousands of mRNAs. Virtually every human disease is being studied using microarrays with the hope of finding the molecular mechanisms of disease. Bioinformatics analysis plays an important part of processing the information embedded in large-scale expression profiling studies and for laying the foundation for biological interpretation. A basic, yet challenging task in the analysis of microarray gene expression data is the identification of changes in gene expression that are associated with particular biological conditions. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. One of the most popular platforms for microarray analysis is Bioconductor, an open source and open development software project based on the R programming language. This paper describes specific procedures for conducting quality assessment, visualization and preprocessing of Affymetrix Gene Chip and also details the different bioconductor packages used to analyze affymetrix microarray data and describe the analysis and outcome of each plots.

Keywords: microarray analysis, R language, affymetrix visualization, bioconductor

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Authors: Najrullah Khan, Athar Ali Khan

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The Topp-Leone distribution was introduced by Topp- Leone in 1955. In this paper, an attempt has been made to fit Topp-Leone Generalized exponential (TPGE) distribution. A real survival data set is used for illustrations. Implementation is done using R and JAGS and appropriate illustrations are made. R and JAGS codes have been provided to implement censoring mechanism using both optimization and simulation tools. The main aim of this paper is to describe and illustrate the Bayesian modelling approach to the analysis of survival data. Emphasis is placed on the modeling of data and the interpretation of the results. Crucial to this is an understanding of the nature of the incomplete or 'censored' data encountered. Analytic approximation and simulation tools are covered here, but most of the emphasis is on Markov chain based Monte Carlo method including independent Metropolis algorithm, which is currently the most popular technique. For analytic approximation, among various optimization algorithms and trust region method is found to be the best. In this paper, TPGE model is also used to analyze the lifetime data in Bayesian paradigm. Results are evaluated from the above mentioned real survival data set. The analytic approximation and simulation methods are implemented using some software packages. It is clear from our findings that simulation tools provide better results as compared to those obtained by asymptotic approximation.

Keywords: Bayesian Inference, JAGS, Laplace Approximation, LaplacesDemon, posterior, R Software, simulation

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Authors: Vladimir Simovic, Matija Varga, Predrag Oreski

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XHTML and XBRL are the standard languages for creating a database for the purpose of displaying financial statements on web applications. Today, XBRL is one of the most popular languages for business reporting. A large number of countries in the world recognize the role of XBRL language for financial reporting and the benefits that the reporting format provides in the collection, analysis, preparation, publication and the exchange of data (information) which is the positive side of this language. Here we present all advantages and opportunities that a company may have by using the XBRL format for business reporting. Also, this paper presents XBRL and other languages that are used for creating the database, such XML, XHTML, etc. The role of the AJAX complex model and technology will be explained in detail, and during the exchange of financial data between the web client and web server. Here will be mentioned basic layers of the network for data exchange via the web.

Keywords: XHTML, XBRL, XML, JavaScript, AJAX technology, data exchange

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Authors: Aymen Sekhri, Emmanuel Kwabena Frimpong, Bolaji Mubarak Ayeyemi, Aleksi Hirvonen, Matias Hirvonen, Tedros Tesfay Andemichael

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Fixation identification, which involves isolating and identifying fixations and saccades in eye-tracking protocols, is an important aspect of eye-movement data processing that can have a big impact on higher-level analyses. However, fixation identification techniques are frequently discussed informally and rarely compared in any meaningful way. With two state-of-the-art algorithms, we will implement fixation detection and analysis in this work. The velocity threshold fixation algorithm is the first algorithm, and it identifies fixation based on a threshold value. For eye movement detection, the second approach is U'n' Eye, a deep neural network algorithm. The goal of this project is to analyze and visualize eye-tracking data from an eye gaze dataset that has been provided. The data was collected in a scenario in which individuals were shown photos and asked whether or not they recognized them. The results of the two-fixation detection approach are contrasted and visualized in this paper.

Keywords: human-computer interaction, eye-tracking, CNN, fixations, saccades

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Authors: Anna Citterbergova

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This study explores the safeguards and guarantees to children’s personal data protection under the current EU and US legislative framework, namely the GDPR (2018) and COPPA (2000). Considering that children are online for the majority of their free time, one cannot overlook the negative side effects that may be associated with online participation, which may put children’s wellbeing and their fundamental rights at risk. The question of whether the current relevant legislative framework in relation to the responsibilities of the internet service providers (ISPs) are adequate safeguards and guarantees to children’s personal data protection has been an evolving debate both in the US and in the EU. From a children’s rights perspective, processors of personal data have certain obligations that must meet the international human rights principles (e. g. the CRC, ECHR), which require taking into account the best interest of the child. Accordingly, the need to protect children’s privacy online remains strong and relevant with the expansion of the number and importance of social media platforms to human life. At the same time, the landscape of the internet is rapidly evolving, and commercial interests are taking a more targeted approach in seeking children’s data. Therefore, it is essential to constantly evaluate the ongoing and evolving newly adopted market policies of ISPs that may misuse the gap in the current letter of the law. Previous studies in the field have already pointed out that both GDPR and COPPA may theoretically not be sufficient in protecting children’s personal data. With the focus on social media platforms, this study uses the doctrinal-descriptive method to identifiy the mechanisms enshrined in the GDPR and COPPA designed to protect children’s personal data. In its second part, the study includes a data gathering phase by the national data protection authorities responsible for monitoring and supervision of the GDPR in relation to children’s personal data protection who monitor the enforcement of the data protection rules throughout the European Union an contribute to their consistent application. These gathered primary source of data will later be used to outline the series of benefits and challenges to children’s persona lata protection faced by these institutes and the analysis that aims to suggest if and/or how to hold ISPs accountable while striking a fair balance between the commercial rights and the right to protection of the personal data of children. The preliminary results can be divided into two categories. First, conclusions in the doctrinal-descriptive part of the study. Second, specific cases and situations from the practice of national data protection authorities. While for the first part, concrete conclusions can already be presented, the second part is currently still in the data gathering phase. The result of this research is a comprehensive analysis on the safeguards and guarantees to children’s personal data protection under the current EU and US legislative framework, based on doctrinal-descriptive approach and original empirical data.

Keywords: personal data of children, personal data protection, GDPR, COPPA, ISPs, social media

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Authors: Sourour Ramzi, Claudia Sarrico

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Little has been done on network DEA in education, and nobody has attempted to model the whole education supply chain using network DEA. As such the contribution of the present paper is to propose a model for measuring the efficiency of education supply chains using network DEA. First, we use a general survey of data envelopment analysis (DEA) to establish the emergent themes for research in DEA, and focus on the theme of Network DEA. Second, we use a survey on two-stage DEA models, and Network DEA to write a state of the art on Network DEA, particularly applied to supply chain management. Third, we use a survey on DEA applications to establish the most influential papers on DEA education applications, in order to establish the state of the art on applications of DEA in education, in general, and applications of DEA to education using network DEA, in particular. Finally, we propose a model for measuring the performance of education supply chains of different education systems (countries or states within a country, for instance). We then use this model on some empirical data.

Keywords: supply chain, education, data envelopment analysis, network DEA

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Authors: Bangwon Seo

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In this paper, we consider multicast device-to-device (D2D) direct communication systems in cellular networks. In multicast D2D communications, nearby mobile devices exchanges, their data directly without going through a base station and a D2D transmitter send its data to multiple D2D receivers that compose of D2D multicast group. We consider wiretap channel where there is an eavesdropper that attempts to overhear the transmitted data of the D2D transmitter. In this paper, we propose a secure transmission scheme in D2D multicast communications in cellular networks. In order to prevent the eavesdropper from overhearing the transmitted data of the D2D transmitter, a precoding vector is employed at the D2D transmitter in the proposed scheme. We perform computer simulations to evaluate the performance of the proposed scheme. Through the simulation, we show that the secrecy rate performance can be improved by selecting an appropriate precoding vector.

Keywords: device-to-device communications, wiretap channel, secure transmission, precoding

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Authors: Andrzej Poszewiecki

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The presented paper contributes to the experimental current of research on privacy. The question of privacy is being discussed at length at present, primarily among lawyers and politicians. However, the matter of privacy has been of interest for economists for some time as well. The valuation of privacy by people is of great importance now. This article is about how people valuate their privacy. An experimental method has been utilised in the conducted research – the survey was carried out among customers of an online store, and the studied issue was whether their readiness to sell their data (WTA) was different from the willingness to buy data back (WTP). The basic aim of this article is to analyse whether people shopping on the Internet differentiate their privacy depending on whether they protect or sell it. The achieved results indicate the presence of major differences in this respect, which do not always come up with the original expectations. The obtained results have supported the hypothesis that people are more willing to sell their data than to repurchase them. However, the hypothesis that the value of proposed remuneration affects the willingness to sell/buy back personal data (one’s privacy) has not been supported.

Keywords: privacy, experimental economics, behavioural economics, internet

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Authors: Ruchika Goyal, Ashwani Kumar, Sandeep Jain

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The experimental data for anticancer potential of curcumin nanoparticles was calculated by means of eclectic data. The optimal descriptors were examined using Monte Carlo method based CORAL SEA software. The statistical quality of the model is following: n = 14, R² = 0.6809, Q² = 0.5943, s = 0.175, MAE = 0.114, F = 26 (sub-training set), n =5, R²= 0.9529, Q² = 0.7982, s = 0.086, MAE = 0.068, F = 61, Av Rm² = 0.7601, ∆R²m = 0.0840, k = 0.9856 and kk = 1.0146 (test set) and n = 5, R² = 0.6075 (validation set). This data can be used to build predictive QSAR models for anticancer activity.

Keywords: anticancer potential, curcumin, model, nanoparticles, optimal descriptors, QSAR

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

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Authors: C. Zimmermann, E. Lackner, M. Ebner

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Massive Open Online Courses (MOOCs) are increasingly popular learning hubs that are boasting considerable participant numbers, innovative technical features, and a multitude of instructional resources. Still, there is a high level of evidence showing that almost all MOOCs suffer from a declining frequency of participant activity and fairly low completion rates. In this paper, we would like to share the lessons learned in implementing several design patterns that have been suggested in order to foster participant activity. Our conclusions are based on experiences with the ‘Dr. Internet’ MOOC, which was created as an xMOOC to raise awareness for a more critical approach to online health information: participants had to diagnose medical case studies. There is a growing body of recommendations (based on Learning Analytics results from earlier xMOOCs) as to how the decline in participant activity can be alleviated. One promising focus in this regard is instructional design patterns, since they have a tremendous influence on the learner’s motivation, which in turn is a crucial trigger of learning processes. Since Medieval Age storytelling, micro-learning units and specific comprehensible, narrative structures were chosen to animate the audience to follow narration. Hence, MOOC participants are not likely to abandon a course or information channel when their curiosity is kept at a continuously high level. Critical aspects that warrant consideration in this regard include shorter course duration, a narrative structure with suspense peaks (according to the ‘storytelling’ approach), and a course schedule that is diversified and stimulating, yet easy to follow. All of these criteria have been observed within the design of the Dr. Internet MOOC: 1) the standard eight week course duration was shortened down to six weeks, 2) all six case studies had a special quiz format and a corresponding resolution video which was made available in the subsequent week, 3) two out of six case studies were split up in serial video sequences to be presented over the span of two weeks, and 4) the videos were generally scheduled in a less predictable sequence. However, the statistical results from the first run of the MOOC do not indicate any strong influences on the retention rate, so we conclude with some suggestions as to why this might be and what aspects need further consideration.

Keywords: case study, Dr. internet, experience, MOOCs, design patterns

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Authors: Sara Bonetti

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The use of quantitative data to support policies and justify investments has become imperative in many fields including the field of education. However, the topic of data literacy has only marginally touched the early care and education (ECE) field. In California, within the ECE workforce, there is a group of professionals working in policy and advocacy that use quantitative data regularly and whose educational and professional experiences have been neglected by existing research. This study aimed at analyzing these experiences in accessing, using, and producing quantitative data. This study utilized semi-structured interviews to capture the differences in educational and professional backgrounds, policy contexts, and power relations. The participants were three key professionals from county-level organizations and one working at a State Department to allow for a broader perspective at systems level. The study followed Núñez’s multilevel model of intersectionality. The key in Núñez’s model is the intersection of multiple levels of analysis and influence, from the individual to the system level, and the identification of institutional power dynamics that perpetuate the marginalization of certain groups within society. In a similar manner, this study looked at the dynamic interaction of different influences at individual, organizational, and system levels that might intersect and affect ECE professionals’ experiences with quantitative data. At the individual level, an important element identified was the participants’ educational background, as it was possible to observe a relationship between that and their positionality, both with respect to working with data and also with respect to their power within an organization and at the policy table. For example, those with a background in child development were aware of how their formal education failed to train them in the skills that are necessary to work in policy and advocacy, and especially to work with quantitative data, compared to those with a background in administration and/or business. At the organizational level, the interviews showed a connection between the participants’ position within the organization and their organization’s position with respect to others and their degree of access to quantitative data. This in turn affected their sense of empowerment and agency in dealing with data, such as shaping what data is collected and available. These differences reflected on the interviewees’ perceptions and expectations for the ECE workforce. For example, one of the interviewees pointed out that many ECE professionals happen to use data out of the necessity of the moment. This lack of intentionality is a cause for, and at the same time translates into missed training opportunities. Another interviewee pointed out issues related to the professionalism of the ECE workforce by remarking the inadequacy of ECE students’ training in working with data. In conclusion, Núñez’s model helped understand the different elements that affect ECE professionals’ experiences with quantitative data. In particular, what was clear is that these professionals are not being provided with the necessary support and that we are not being intentional in creating data literacy skills for them, despite what is asked of them and their work.

Keywords: data literacy, early childhood professionals, intersectionality, quantitative data

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Authors: Alexiou Dimitra, Fragkaki Maria

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The objective of the paper is the study of geographic, economic and educational variables and their contribution to determine the position of each member-state among the EU-28 countries based on the values of seven variables as given by Eurostat. The Data Analysis methods of Multiple Factorial Correspondence Analysis (MFCA) Principal Component Analysis and Factor Analysis have been used. The cross tabulation tables of data consist of the values of seven variables for the 28 countries for 2014. The data are manipulated using the CHIC Analysis V 1.1 software package. The results of this program using MFCA and Ascending Hierarchical Classification are given in arithmetic and graphical form. For comparison reasons with the same data the Factor procedure of Statistical package IBM SPSS 20 has been used. The numerical and graphical results presented with tables and graphs, demonstrate the agreement between the two methods. The most important result is the study of the relation between the 28 countries and the position of each country in groups or clouds, which are formed according to the values of the corresponding variables.

Keywords: Multiple Factorial Correspondence Analysis, Principal Component Analysis, Factor Analysis, E.U.-28 countries, Statistical package IBM SPSS 20, CHIC Analysis V 1.1 Software, Eurostat.eu Statistics

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Authors: Adekunle Kolawole Ojo, Idowu Peter Adebayo, Egwuche Sylvester O.

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Chronic Kidney Disease (CKD) is a significant cause of morbidity and mortality across developed and developing nations and is associated with increased risk. There are no existing electronic means of capturing and monitoring CKD in Nigeria. The work is aimed at developing a spatial data model that can be used to implement renal registries required for tracking and monitoring the spatial distribution of renal diseases by public health officers and patients. In this study, we have developed a spatial data model for a functional renal registry.

Keywords: renal registry, health informatics, chronic kidney disease, interface

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Authors: H. Aksas, S. Boughrara, K. Louhab

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The goal of this study was the use of life cycle assessment (LCA) methodology to assess the environmental impact of pharmaceutical product (four kinds of syrup form and tree kinds of pomade form), which are produced in one leader manufactory in Algeria town that is SAIDAL Company. The impacts generated have evaluated using SimpaPro7.1 with CML92 Method for syrup form and EPD 2007 for pomade form. All impacts evaluated have compared between them, with determination of the compound contributing to each impacts in each case. Data needed to conduct Life Cycle Inventory (LCI) came from this factory, by the collection of theoretical data near the responsible technicians and engineers of the company, the practical data are resulting from the assay of pharmaceutical liquid, obtained at the laboratories of the university. This data represent different raw material imported from European and Asian country necessarily to formulate the drug. Energy used is coming from Algerian resource for the input. Outputs are the result of effluent analysis of this factory with different form (liquid, solid and gas form). All this data (input and output) represent the ecobalance.

Keywords: pharmaceutical product, drug residues, LCA methodology, environmental impacts

Procedia PDF Downloads 246

Authors: Homeira Asadi Kavan, Seyed Nasrollah Sajjadi, Mehrzade Hamidi, Hossein Rajabi, Mahdi Bigdely

Abstract:

Positioning strategies of a sports brand can create a unique impression in the minds of the fans, sponsors, and other stakeholders. In order to influence potential customer's perception in an effective and positive way, a brands positioning strategy must be unique, credible, and relevant. Many sports clubs in Iran have been struggling to implement and achieve brand positioning accomplishments, due to different reasons such as lack of experience, scarcity of experts in the sports branding, and lack of related researches in this field. This study will provide a comprehensive theoretical framework and action plan for sport managers and marketers to design and implement effective brand positioning and to enable them to be distinguishable from competing brands and sports clubs. The study instrument is interviews with sports marketing and brand experts who have been working in this industry for a minimum of 20 years. Qualitative data analysis was performed using Atlast.ti text mining software version 7 and Open, axial and selective coding were employed to uncover and systematically analyze important and complex phenomena and elements. The findings show 199 effective elements in positioning strategies in Iran Professional Soccer League. These elements are categorized into 23 concepts and sub-categories as follows: Structural prerequisites, Strategic management prerequisites, Commercial prerequisites, Major external prerequisites, Brand personality, Club symbols, Emotional aspects, Event aspects, Fans’ strategies, Marketing information strategies, Marketing management strategies, Empowerment strategies, Executive management strategies, League context, Fans’ background, Market context, Club’s organizational context, Support context, Major contexts, Political-Legal elements, Economic factors, Social factors, and Technological factors. Eventually, the study model was developed by 6 main dimensions of Causal prerequisites, Axial Phenomenon (brand position), Strategies, Context Factors, Interfering Factors, and Consequences. Based on the findings, practical recommendations and strategies are suggested that can help club managers and marketers in developing and improving their respective sport clubs, brand positioning, and activities.

Keywords: brand positioning, soccer club, sport marketing, Iran professional soccer league, brand strategy

Procedia PDF Downloads 136

Authors: Rajeev Kumar Bedi, Jaswinder Singh, Sunil Kumar Gupta

Abstract:

Cloud storage is a model of online data storage where data is stored in virtualized pool of servers hosted by third parties (CSPs) and located in different geographical locations. Cloud storage revolutionized the way how users access their data online anywhere, anytime and using any device as a tablet, mobile, laptop, etc. A lot of issues as vendor lock-in, frequent service outage, data loss and performance related issues exist in single cloud storage systems. So to evade these issues, the concept of multi cloud storage introduced. There are a lot of multi cloud storage systems exists in the market for mobile devices. In this article, we are providing comparison of four multi cloud storage systems for mobile devices Otixo, Unclouded, Cloud Fuze, and Clouds and evaluate their performance on the basis of CPU usage, battery consumption, time consumption and data usage parameters on three mobile phones Nexus 5, Moto G and Nexus 7 tablet and using Wi-Fi network. Finally, open research challenges and future scope are discussed.

Keywords: cloud storage, multi cloud storage, vendor lock-in, mobile devices, mobile cloud computing

Procedia PDF Downloads 407

Authors: M. Zayoud, S. Oueida, S. Ionescu, P. AbiChar

Abstract:

Background: Wireless sensor network is encompassed of diversified tools of information technology, which is widely applied in a range of domains, including military surveillance, weather forecasting, and earthquake forecasting. Strengthened grounds are always developed for wireless sensor networks, which usually emerges security issues during professional application. Thus, essential technological tools are necessary to be assessed for secure aggregation of data. Moreover, such practices have to be incorporated in the healthcare practices that shall be serving in the best of the mutual interest Objective: Aggregation of encrypted data has been assessed through homomorphic stream cipher to assure its effectiveness along with providing the optimum solutions to the field of healthcare. Methods: An experimental design has been incorporated, which utilized newly developed cipher along with CPU-constrained devices. Modular additions have also been employed to evaluate the nature of aggregated data. The processes of homomorphic stream cipher have been highlighted through different sensors and modular additions. Results: Homomorphic stream cipher has been recognized as simple and secure process, which has allowed efficient aggregation of encrypted data. In addition, the application has led its way to the improvisation of the healthcare practices. Statistical values can be easily computed through the aggregation on the basis of selected cipher. Sensed data in accordance with variance, mean, and standard deviation has also been computed through the selected tool. Conclusion: It can be concluded that homomorphic stream cipher can be an ideal tool for appropriate aggregation of data. Alongside, it shall also provide the best solutions to the healthcare sector.

Keywords: aggregation, cipher, homomorphic stream, encryption

Procedia PDF Downloads 260

Authors: Omar Al Ali

Abstract:

The current study was aimed to explore the relationships between emotional intelligence, cognitive ability, and leader's performance. Data were collected from 260 senior managers from UAE. The results showed that there are significant relationships between emotional intelligence and leadership performance as measured by the annual internal evaluations of each participant (r = .42, p < .01). Data from regression analysis revealed that both variables namely emotional intelligence (beta = .31, p < .01), and cognitive ability (beta = .29, p < .01), predicted leadership competencies, and together explained 26% of its variance. Data suggests that EI and cognitive ability are significantly correlated with leadership performance. In depth implications of the present findings for human resource development theory and practice are discussed.

Keywords: emotional intelligence, cognitive ability, leadership, performance

Procedia PDF Downloads 477

Authors: Tisciane Perpetuo e Oliveira, Dante Inga Narvaez, Marcelo Gradella Villalva

Abstract:

Installations of solar photovoltaic systems have increased considerably in the last decade. Therefore, it has been noticed that monitoring of meteorological data (solar irradiance, air temperature, wind velocity, etc.) is important to predict the potential of a given geographical area in solar energy production. In this sense, the present work compares two computational tools that are capable of estimating the energy generation of a photovoltaic system through correlation analyzes of solar radiation data: PVsyst software and an algorithm based on the PVlib package implemented in MATLAB. In order to achieve the objective, it was necessary to obtain solar radiation data (measured and from a solarimetric database), analyze the decomposition of global solar irradiance in direct normal and horizontal diffuse components, as well as analyze the modeling of the devices of a photovoltaic system (solar modules and inverters) for energy production calculations. Simulated results were compared with experimental data in order to evaluate the performance of the studied methods. Errors in estimation of energy production were less than 30% for the MATLAB algorithm and less than 20% for the PVsyst software.

Keywords: energy production, meteorological data, irradiance decomposition, solar photovoltaic system

Procedia PDF Downloads 142

Authors: Shaan Khosla, Jon Krohn

Abstract:

In a recruiting context, machine learning models are valuable for recommendations: to predict the best candidates for a vacancy, to match the best vacancies for a candidate, and compile a set of similar candidates for any given candidate. While useful to create these models, validating their accuracy in a recommendation context is difficult due to a sparsity of data. In this report, we use network graph data to generate useful representations for candidates and vacancies. We use candidates and vacancies as network nodes and designate a bi-directional link between them based on the candidate interviewing for the vacancy. After using node2vec, the embeddings are used to construct a validation dataset with a ranked order, which will help validate new recommender systems.

Keywords: AI, machine learning, NLP, recruiting

Procedia PDF Downloads 84