Search results for: survival data analysis
41206 Operating Speed Models on Tangent Sections of Two-Lane Rural Roads
Authors: Dražen Cvitanić, Biljana Maljković
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This paper presents models for predicting operating speeds on tangent sections of two-lane rural roads developed on continuous speed data. The data corresponds to 20 drivers of different ages and driving experiences, driving their own cars along an 18 km long section of a state road. The data were first used for determination of maximum operating speeds on tangents and their comparison with speeds in the middle of tangents i.e. speed data used in most of operating speed studies. Analysis of continuous speed data indicated that the spot speed data are not reliable indicators of relevant speeds. After that, operating speed models for tangent sections were developed. There was no significant difference between models developed using speed data in the middle of tangent sections and models developed using maximum operating speeds on tangent sections. All developed models have higher coefficient of determination then models developed on spot speed data. Thus, it can be concluded that the method of measuring has more significant impact on the quality of operating speed model than the location of measurement.Keywords: operating speed, continuous speed data, tangent sections, spot speed, consistency
Procedia PDF Downloads 45041205 AI Software Algorithms for Drivers Monitoring within Vehicles Traffic - SiaMOTO
Authors: Ioan Corneliu Salisteanu, Valentin Dogaru Ulieru, Mihaita Nicolae Ardeleanu, Alin Pohoata, Bogdan Salisteanu, Stefan Broscareanu
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Creating a personalized statistic for an individual within the population using IT systems, based on the searches and intercepted spheres of interest they manifest, is just one 'atom' of the artificial intelligence analysis network. However, having the ability to generate statistics based on individual data intercepted from large demographic areas leads to reasoning like that issued by a human mind with global strategic ambitions. The DiaMOTO device is a technical sensory system that allows the interception of car events caused by a driver, positioning them in time and space. The device's connection to the vehicle allows the creation of a source of data whose analysis can create psychological, behavioural profiles of the drivers involved. The SiaMOTO system collects data from many vehicles equipped with DiaMOTO, driven by many different drivers with a unique fingerprint in their approach to driving. In this paper, we aimed to explain the software infrastructure of the SiaMOTO system, a system designed to monitor and improve driver driving behaviour, as well as the criteria and algorithms underlying the intelligent analysis process.Keywords: artificial intelligence, data processing, driver behaviour, driver monitoring, SiaMOTO
Procedia PDF Downloads 7341204 Multistage Data Envelopment Analysis Model for Malmquist Productivity Index Using Grey's System Theory to Evaluate Performance of Electric Power Supply Chain in Iran
Authors: Mesbaholdin Salami, Farzad Movahedi Sobhani, Mohammad Sadegh Ghazizadeh
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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 15641203 A Neural Network Based Clustering Approach for Imputing Multivariate Values in Big Data
Authors: S. Nickolas, Shobha K.
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The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing
Procedia PDF Downloads 27041202 Apollo Clinical Excellence Scorecard (ACE@25): An Initiative to Drive Quality Improvement in Hospitals
Authors: Anupam Sibal
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Whatever is measured tends to improve. With a view to objectively measuring and improving clinical quality across the Apollo Group Hospitals, the initiative of ACE @ 25 (Apollo Clinical Excellence@25) was launched on Jan 09. ACE @ 25 is a clinically balanced scorecard incorporating 25 clinical quality parameters involving complication rates, mortality rates, one-year survival rates and average length of stay after major procedures like liver and renal transplant, CABG, TKR, THR, TURP, PTCA, endoscopy, large bowel resection and MRM covering all major specialties. Also included are hospital acquired infection rates, pain satisfaction and medication errors. Benchmarks have been chosen from the world’s best hospitals. There are weighted scores for outcomes color coded green, orange and red. The cumulative score is 100. Data is reported monthly by 43 Group Hospitals online on the Lighthouse platform. Action taken reports for parameters falling in red are submitted quarterly and reviewed by the board. An audit team audits the data at all locations every six months. Scores are linked to appraisal of the medical head and there is an “ACE @ 25” Champion Award for the highest scorer. Scores for different parameters were variable from green to red at the start of the initiative. Most hospitals showed an improvement in scores over the last four years for parameters where they had showed scores in red or orange at the start of the initiative. The overall scores for the group have shown an increase from 72 in 2010 to 81 in 2015.Keywords: benchmarks, clinical quality, lighthouse, platform, scores
Procedia PDF Downloads 29241201 Factors Contributing to Delayed Diagnosis and Treatment of Breast Cancer and Its Outcome in Jamhoriat Hospital Kabul, Afghanistan
Authors: Ahmad Jawad Fardin
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Over 60% of patients with breast cancer in Afghanistan present late with advanced stage III and IV, a major cause for the poor survival rate. The objectives of this study were to identify the contributing factors for the diagnosis and treatment delay and its outcome. This cross-sectional study was conducted on 318 patients with histologically confirmed breast cancer in the oncology department of Jamhoriat hospital, which is the first and only national cancer center in Afghanistan; data were collected from medical records and interviews conducted with women diagnosed with breast cancer, linear regression and logistic regression were used for analysis. Patient delay was defined as the time from first recognition of symptoms until first medical consultation and doctor form first consultation with a health care provider until histological confirmation of breast cancer. The mean age of patients was 49.2+_ 11.5years. The average time for the final diagnosis of breast cancer was 8.5 months; most patients had ductal carcinoma 260.7 (82%). Factors associated with delay were low education level 76% poor socioeconomic and cultural conditions 81% lack of cancer center 73% lack of screening 19%. The stage distribution was as follows stage IV 4 22% stage III 44.4% stage II 29.3% stage I 4.3%. Complex associated factors were identified to delayed the diagnosis of breast cancer and increased adverse outcomes consequently. Raising awareness and education in women, the establishment of cancer centers and providing accessible diagnosis service and screening, training of general practitioners; required to promote early detection, diagnosis and treatment.Keywords: delayed diagnosis and poor outcome, breast cancer in Afghanistan, poor outcome of delayed breast cancer treatment, breast cancer delayed diagnosis and treatment in Afghanistan
Procedia PDF Downloads 17641200 Real-Time Big-Data Warehouse a Next-Generation Enterprise Data Warehouse and Analysis Framework
Authors: Abbas Raza Ali
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Big Data technology is gradually becoming a dire need of large enterprises. These enterprises are generating massively large amount of off-line and streaming data in both structured and unstructured formats on daily basis. It is a challenging task to effectively extract useful insights from the large scale datasets, even though sometimes it becomes a technology constraint to manage transactional data history of more than a few months. This paper presents a framework to efficiently manage massively large and complex datasets. The framework has been tested on a communication service provider producing massively large complex streaming data in binary format. The communication industry is bound by the regulators to manage history of their subscribers’ call records where every action of a subscriber generates a record. Also, managing and analyzing transactional data allows service providers to better understand their customers’ behavior, for example, deep packet inspection requires transactional internet usage data to explain internet usage behaviour of the subscribers. However, current relational database systems limit service providers to only maintain history at semantic level which is aggregated at subscriber level. The framework addresses these challenges by leveraging Big Data technology which optimally manages and allows deep analysis of complex datasets. The framework has been applied to offload existing Intelligent Network Mediation and relational Data Warehouse of the service provider on Big Data. The service provider has 50+ million subscriber-base with yearly growth of 7-10%. The end-to-end process takes not more than 10 minutes which involves binary to ASCII decoding of call detail records, stitching of all the interrogations against a call (transformations) and aggregations of all the call records of a subscriber.Keywords: big data, communication service providers, enterprise data warehouse, stream computing, Telco IN Mediation
Procedia PDF Downloads 16941199 Syndromic Surveillance Framework Using Tweets Data Analytics
Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden
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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza
Procedia PDF Downloads 10641198 Is Swaziland on Track with the 2015 Millennium Development Goals?
Authors: A. Sathiya Susuman
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Background: The importance of maternal and child healthcare services cannot be stressed enough. These services are very important for the health and health outcomes of the mother and that of the child and in ensuring that both maternal and child deaths are prevented. The objective of the study is to inspire good quality maternal and child health care services in Swaziland. Specifically, is Swaziland on track with the 2015 Millennium Development Goals? Methods: The study used secondary data from the Swaziland Demographic and Health Survey 2006-07. This is an explorative and descriptive study which used pre-selected variables to study factors influencing the use of maternal and child healthcare services in Swaziland. Different types of examinations, such as univariate, bivariate, and multivariate statistical analysis were adopted. Results: The study findings showed a high use rate of antenatal care (97.3%) and delivery care (74.0%), and a low rate of postnatal care use (20.5%). The uptake childhood immunization is also high in the country, averaging more than 80.0%. Moreover, certain factors which were found to be influencing the use of maternal healthcare and childhood immunization include: woman’s age, parity, media exposure, maternal education, wealth status, and residence. The findings also revealed that these factors affect the use of maternal and child health differently. Conclusion: It is important to study factors related to maternal and child health uptake to inform relevant stakeholders about possible areas of improvement. Programs to educate families about the importance of maternal and child healthcare services should be implemented. Swaziland needs to work hard on child survival and maternal health care services, no doubt it is on track with the MDG 4 & 5.Keywords: maternal healthcare, antenatal care, delivery care, postnatal care, child health, immunization, socio-economic and demographic factors
Procedia PDF Downloads 49341197 Intensity Modulated Radiotherapy of Nasopharyngeal Carcinomas: Patterns of Loco Regional Relapse
Authors: Omar Nouri, Wafa Mnejja, Nejla Fourati, Fatma Dhouib, Wicem Siala, Ilhem Charfeddine, Afef Khanfir, Jamel Daoud
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Background and objective: Induction chemotherapy (IC) followed by concomitant chemo radiotherapy with intensity modulated radiation (IMRT) technique is actually the recommended treatment modality for locally advanced nasopharyngeal carcinomas (NPC). The aim of this study was to evaluate the prognostic factors predicting loco regional relapse with this new treatment protocol. Patients and methods: A retrospective study of 52 patients with NPC treated between June 2016 and July 2019. All patients received IC according to the protocol of the Head and Neck Radiotherapy Oncology Group (Gortec) NPC 2006 (3 TPF courses) followed by concomitant chemo radiotherapy with weekly cisplatin (40 mg / m2). Patients received IMRT with integrated simultaneous boost (SIB) of 33 daily fractions at a dose of 69.96 Gy for high-risk volume, 60 Gy for intermediate risk volume and 54 Gy for low-risk volume. Median age was 49 years (19-69) with a sex ratio of 3.3. Forty five tumors (86.5%) were classified as stages III - IV according to the 2017 UICC TNM classification. Loco regional relapse (LRR) was defined as a local and/or regional progression that occurs at least 6 months after the end of treatment. Survival analysis was performed according to Kaplan-Meier method and Log-rank test was used to compare anatomy clinical and therapeutic factors that may influence loco regional free survival (LRFS). Results: After a median follow up of 42 months, 6 patients (11.5%) experienced LRR. A metastatic relapse was also noted for 3 of these patients (50%). Target volumes coverage was optimal for all patient with LRR. Four relapses (66.6%) were in high-risk target volume and two (33.3%) were borderline. Three years LRFS was 85,9%. Four factors predicted loco regional relapses: histologic type other than undifferentiated (UCNT) (p=0.027), a macroscopic pre chemotherapy tumor volume exceeding 100 cm³ (p=0.005), a reduction in IC doses exceeding 20% (p=0.016) and a total cumulative cisplatin dose less than 380 mg/m² (p=0.0.34). TNM classification and response to IC did not impact loco regional relapses. Conclusion: For nasopharyngeal carcinoma, tumors with initial high volume and/or histologic type other than UCNT, have a higher risk of loco regional relapse. Therefore, they require a more aggressive therapeutic approaches and a suitable monitoring protocol.Keywords: loco regional relapse, modulation intensity radiotherapy, nasopharyngeal carcinoma, prognostic factors
Procedia PDF Downloads 11941196 Techno-Economic Analysis of Solar Energy for Cathodic Protection of Oil and Gas Buried Pipelines in Southwestern of Iran
Authors: M. Goodarzi, M. Mohammadi, A. Gharib
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Solar energy is a renewable energy which has attracted special attention in many countries. Solar cathodic protectionsystems harness the sun’senergy to protect underground pipelinesand tanks from galvanic corrosion. The object of this study is to design and the economic analysis a cathodic protection system by impressed current supplied with solar energy panels applied to underground pipelines. In the present study, the technical and economic analysis of using solar energy for cathodic protection system in southwestern of Iran (Khuzestan province) is investigated. For this purpose, the ecological conditions such as the weather data, air clearness and sunshine hours are analyzed. The economic analyses were done using computer code to investigate the feasibility analysis from the using of various energy sources in order to cathodic protection system. The overall research methodology is divided into four components: Data collection, design of elements, techno economical evaluation, and output analysis. According to the results, solar renewable energy systems can supply adequate power for cathodic protection system purposes.Keywords: renewable energy, solar energy, solar cathodic protection station, lifecycle cost method
Procedia PDF Downloads 53041195 Sensor Data Analysis for a Large Mining Major
Authors: Sudipto Shanker Dasgupta
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One of the largest mining companies wanted to look at health analytics for their driverless trucks. These trucks were the key to their supply chain logistics. The automated trucks had multi-level sub-assemblies which would send out sensor information. The use case that was worked on was to capture the sensor signal from the truck subcomponents and analyze the health of the trucks from repair and replacement purview. Open source software was used to stream the data into a clustered Hadoop setup in Amazon Web Services cloud and Apache Spark SQL was used to analyze the data. All of this was achieved through a 10 node amazon 32 core, 64 GB RAM setup real-time analytics was achieved on ‘300 million records’. To check the scalability of the system, the cluster was increased to 100 node setup. This talk will highlight how Open Source software was used to achieve the above use case and the insights on the high data throughput on a cloud set up.Keywords: streaming analytics, data science, big data, Hadoop, high throughput, sensor data
Procedia PDF Downloads 40041194 Distributed Perceptually Important Point Identification for Time Series Data Mining
Authors: Tak-Chung Fu, Ying-Kit Hung, Fu-Lai Chung
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In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is first introduced in 2001. This process originally works for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful cases, it is worth to further explore the opportunity to apply PIP in time series ‘Big Data’. However, the performance of PIP identification is always considered as the limitation when dealing with ‘Big’ time series data. In this paper, two distributed versions of PIP identification based on the Specialized Binary (SB) Tree are proposed. The proposed approaches solve the bottleneck when running the PIP identification process in a standalone computer. Improvement in term of speed is obtained by the distributed versions.Keywords: distributed computing, performance analysis, Perceptually Important Point identification, time series data mining
Procedia PDF Downloads 42441193 Evaluating Performance of an Anomaly Detection Module with Artificial Neural Network Implementation
Authors: Edward Guillén, Jhordany Rodriguez, Rafael Páez
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Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance.Keywords: network intrusion detection, machine learning, artificial neural network, anomaly detection module
Procedia PDF Downloads 33441192 Analysis of the Impact of Climate Change on Maize (Zea Mays) Yield in Central Ethiopia
Authors: Takele Nemomsa, Girma Mamo, Tesfaye Balemi
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Climate change refers to a change in the state of the climate that can be identified (e.g. using statistical tests) by changes in the mean and/or variance of its properties and that persists for an extended period, typically decades or longer. In Ethiopia; Maize production in relation to climate change at regional and sub- regional scales have not been studied in detail. Thus, this study was aimed to analyse the impact of climate change on maize yield in Ambo Districts, Central Ethiopia. To this effect, weather data, soil data and maize experimental data for Arganne hybrid were used. APSIM software was used to investigate the response of maize (Zea mays) yield to different agronomic management practices using current and future (2020s–2080s) climate data. The climate change projections data which were downscaled using SDSM were used as input of climate data for the impact analysis. Compared to agronomic practices the impact of climate change on Arganne in Central Ethiopia is minute. However, within 2020s-2080s in Ambo area; the yield of Arganne hybrid is projected to reduce by 1.06% to 2.02%, and in 2050s it is projected to reduce by 1.56 While in 2080s; it is projected to increase by 1.03% to 2.07%. Thus, to adapt to the changing climate; farmers should consider increasing plant density and fertilizer rate per hectare.Keywords: APSIM, downscaling, response, SDSM
Procedia PDF Downloads 37341191 Exploiting Kinetic and Kinematic Data to Plot Cyclograms for Managing the Rehabilitation Process of BKAs by Applying Neural Networks
Authors: L. Parisi
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Kinematic data wisely correlate vector quantities in space to scalar parameters in time to assess the degree of symmetry between the intact limb and the amputated limb with respect to a normal model derived from the gait of control group participants. Furthermore, these particular data allow a doctor to preliminarily evaluate the usefulness of a certain rehabilitation therapy. Kinetic curves allow the analysis of ground reaction forces (GRFs) to assess the appropriateness of human motion. Electromyography (EMG) allows the analysis of the fundamental lower limb force contributions to quantify the level of gait asymmetry. However, the use of this technological tool is expensive and requires patient’s hospitalization. This research work suggests overcoming the above limitations by applying artificial neural networks.Keywords: kinetics, kinematics, cyclograms, neural networks, transtibial amputation
Procedia PDF Downloads 43741190 Does Level of Countries Corruption Affect Firms Working Capital Management?
Authors: Ebrahim Mansoori, Datin Joriah Muhammad
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Recent studies in finance have focused on the effect of external variables on working capital management. This study investigates the effect of corruption indexes on firms' working capital management. A large data set that covers data from 2005 to 2013 from five ASEAN countries, namely, Malaysia, Indonesia, Singapore, Thailand, and the Philippines, was selected to investigate how the level of corruption in these countries affect working capital management. The results of panel data analysis include fixed effect estimations showed that a high level of countries' corruption indexes encourages managers to shorten the CCC length. Meanwhile, the managers reduce the level of investment in cash and cash equivalents when the levels of corruption indexes increase. Therefore, increasing the level of countries' corruption indexes encourages managers to select conservative working capital strategies by reducing the level of NLB.Keywords: ASEAN, corruption indexes, panel data analysis, working capital management
Procedia PDF Downloads 43241189 Indirect Regeneration and Somatic Embryogenesis from Leaf and Stem Explants of Crassula ovata 42-45 (Mill.) Druce: An Ornamental Medicinal Plant
Authors: A. B. A. Ahmed, D. I. Amar, R. M. Taha
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This research aims to investigate callus induction, somatic embryogenesis and indirect plant regeneration of Crassula ovata (Mill.) Druce – the famous ornamental plant. Experiment no.1: Callus induction was obtained from leaf and stem explants on Murashige and Skoog (MS) medium supplemented with various plant growth regulators (PGRs). Effects of different PGRs, plant regeneration and subsequent plantlet conversion were also assessed. Indirect plant regeneration was achieved from the callus of stem explants by the addition of 1.5 mg/L Kinetin (KN) alone. Best shoot induction was achieved (6.5 shoots/per explant) after 60 days. For successful rooting, regenerated plantlets were sub-cultured on the same MS media supplemented with 1.5 mg/L KN alone. The rooted plantlets were acclimatized and the survival rate was 90%. Experiment no.2: Results revealed that 0.5 mg/L 2,4-D alone and in combination with 1.0 mg/L 6-Benzyladenine (BA) gave 89.8% callus from the stem explants as compared to leaf explants. Callus proliferation and somatic embryo formation were also evaluated by ‘Double Staining Method’ and different stages of somatic embryogenesis were revealed by scanning electron microscope. Full Strength MS medium produced the highest number (49.6%) of cotyledonary stage somatic embryos (SEs). Mature cotyledonary stage SEs developed into plantlets after 12 weeks of culture. Well-rooted plantlets were successfully acclimatized at the survival rate of 85%. Indirectly regenerated plants did not show any detectable variation in morphological and growth characteristics when compared with the donor plant.Keywords: callus induction, indirect plant regeneration, double staining, somatic embryogenesis, Crassula ovata
Procedia PDF Downloads 37941188 Integration of Big Data to Predict Transportation for Smart Cities
Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin
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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system. The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.Keywords: big data, machine learning, smart city, social cost, transportation network
Procedia PDF Downloads 25041187 A Knee Modular Orthosis Design Based on Kinematic Considerations
Authors: C. Copilusi, C. Ploscaru
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This paper addresses attention to a research regarding the design of a knee orthosis in a modular form used on children walking rehabilitation. This research is focused on the human lower limb kinematic analysis which will be used as input data on virtual simulations and prototype validation. From this analysis, important data will be obtained and used as input for virtual simulations of the knee modular orthosis. Thus, a knee orthosis concept was obtained and validated through virtual simulations by using MSC Adams software. Based on the obtained results, the modular orthosis prototype will be manufactured and presented in this article.Keywords: human lower limb, children orthoses, kinematic analysis, knee orthosis
Procedia PDF Downloads 28041186 Government (Big) Data Ecosystem: Definition, Classification of Actors, and Their Roles
Authors: Syed Iftikhar Hussain Shah, Vasilis Peristeras, Ioannis Magnisalis
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Organizations, including governments, generate (big) data that are high in volume, velocity, veracity, and come from a variety of sources. Public Administrations are using (big) data, implementing base registries, and enforcing data sharing within the entire government to deliver (big) data related integrated services, provision of insights to users, and for good governance. Government (Big) data ecosystem actors represent distinct entities that provide data, consume data, manipulate data to offer paid services, and extend data services like data storage, hosting services to other actors. In this research work, we perform a systematic literature review. The key objectives of this paper are to propose a robust definition of government (big) data ecosystem and a classification of government (big) data ecosystem actors and their roles. We showcase a graphical view of actors, roles, and their relationship in the government (big) data ecosystem. We also discuss our research findings. We did not find too much published research articles about the government (big) data ecosystem, including its definition and classification of actors and their roles. Therefore, we lent ideas for the government (big) data ecosystem from numerous areas that include scientific research data, humanitarian data, open government data, industry data, in the literature.Keywords: big data, big data ecosystem, classification of big data actors, big data actors roles, definition of government (big) data ecosystem, data-driven government, eGovernment, gaps in data ecosystems, government (big) data, public administration, systematic literature review
Procedia PDF Downloads 15341185 Language Errors Used in “The Space between Us” Movie and Their Effects on Translation Quality: Translation Study toward Discourse Analysis Approach
Authors: Mochamad Nuruz Zaman, Mangatur Rudolf Nababan, M. A. Djatmika
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Both society and education areas teach to have good communication for building the interpersonal skills up. Everyone has the capacity to understand something new, either well comprehension or worst understanding. Worst understanding makes the language errors when the interactions are done by someone in the first meeting, and they do not know before it because of distance area. “The Space between Us” movie delivers the love-adventure story between Mars Boy and Earth Girl. They are so many missing conversations because of the different climate and environment. As the moviegoer also must be focused on the subtitle in order to enjoy well the movie. Furthermore, Indonesia subtitle and English conversation on the movie still have overlapping understanding in the translation. Translation hereby consists of source language -SL- (English conversation) and target language -TL- (Indonesia subtitle). These research gap above is formulated in research question by how the language errors happened in that movie and their effects on translation quality which is deepest analyzed by translation study toward discourse analysis approach. The research goal is to expand the language errors and their translation qualities in order to create a good atmosphere in movie media. The research is studied by embedded research in qualitative design. The research locations consist of setting, participant, and event as focused determined boundary. Sources of datum are “The Space between Us” movie and informant (translation quality rater). The sampling is criterion-based sampling (purposive sampling). Data collection techniques use content analysis and questioner. Data validation applies data source and method triangulation. Data analysis delivers domain, taxonomy, componential, and cultural theme analysis. Data findings on the language errors happened in the movie are referential, register, society, textual, receptive, expressive, individual, group, analogical, transfer, local, and global errors. Data discussions on their effects to translation quality are concentrated by translation techniques on their data findings; they are amplification, borrowing, description, discursive creation, established equivalent, generalization, literal, modulation, particularization, reduction, substitution, and transposition.Keywords: discourse analysis, language errors, The Space between Us movie, translation techniques, translation quality instruments
Procedia PDF Downloads 21241184 Text Analysis to Support Structuring and Modelling a Public Policy Problem-Outline of an Algorithm to Extract Inferences from Textual Data
Authors: Claudia Ehrentraut, Osama Ibrahim, Hercules Dalianis
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Policy making situations are real-world problems that exhibit complexity in that they are composed of many interrelated problems and issues. To be effective, policies must holistically address the complexity of the situation rather than propose solutions to single problems. Formulating and understanding the situation and its complex dynamics, therefore, is a key to finding holistic solutions. Analysis of text based information on the policy problem, using Natural Language Processing (NLP) and Text analysis techniques, can support modelling of public policy problem situations in a more objective way based on domain experts knowledge and scientific evidence. The objective behind this study is to support modelling of public policy problem situations, using text analysis of verbal descriptions of the problem. We propose a formal methodology for analysis of qualitative data from multiple information sources on a policy problem to construct a causal diagram of the problem. The analysis process aims at identifying key variables, linking them by cause-effect relationships and mapping that structure into a graphical representation that is adequate for designing action alternatives, i.e., policy options. This study describes the outline of an algorithm used to automate the initial step of a larger methodological approach, which is so far done manually. In this initial step, inferences about key variables and their interrelationships are extracted from textual data to support a better problem structuring. A small prototype for this step is also presented.Keywords: public policy, problem structuring, qualitative analysis, natural language processing, algorithm, inference extraction
Procedia PDF Downloads 58341183 Simulations to Predict Solar Energy Potential by ERA5 Application at North Africa
Authors: U. Ali Rahoma, Nabil Esawy, Fawzia Ibrahim Moursy, A. H. Hassan, Samy A. Khalil, Ashraf S. Khamees
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The design of any solar energy conversion system requires the knowledge of solar radiation data obtained over a long period. Satellite data has been widely used to estimate solar energy where no ground observation of solar radiation is available, yet there are limitations on the temporal coverage of satellite data. Reanalysis is a “retrospective analysis” of the atmosphere parameters generated by assimilating observation data from various sources, including ground observation, satellites, ships, and aircraft observation with the output of NWP (Numerical Weather Prediction) models, to develop an exhaustive record of weather and climate parameters. The evaluation of the performance of reanalysis datasets (ERA-5) for North Africa against high-quality surface measured data was performed using statistical analysis. The estimation of global solar radiation (GSR) distribution over six different selected locations in North Africa during ten years from the period time 2011 to 2020. The root means square error (RMSE), mean bias error (MBE) and mean absolute error (MAE) of reanalysis data of solar radiation range from 0.079 to 0.222, 0.0145 to 0.198, and 0.055 to 0.178, respectively. The seasonal statistical analysis was performed to study seasonal variation of performance of datasets, which reveals the significant variation of errors in different seasons—the performance of the dataset changes by changing the temporal resolution of the data used for comparison. The monthly mean values of data show better performance, but the accuracy of data is compromised. The solar radiation data of ERA-5 is used for preliminary solar resource assessment and power estimation. The correlation coefficient (R2) varies from 0.93 to 99% for the different selected sites in North Africa in the present research. The goal of this research is to give a good representation for global solar radiation to help in solar energy application in all fields, and this can be done by using gridded data from European Centre for Medium-Range Weather Forecasts ECMWF and producing a new model to give a good result.Keywords: solar energy, solar radiation, ERA-5, potential energy
Procedia PDF Downloads 20441182 The Role of Transport Investment and Enhanced Railway Accessibility in Regional Efficiency Improvement in Saudi Arabia: Data Envelopment Analysis
Authors: Saleh Alotaibi, Mohammed Quddus, Craig Morton, Jobair Bin Alam
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This paper explores the role of large-scale investment in transport sectors and the impact of increased railway accessibility on the efficiency of the regional economic productivity in the Kingdom of Saudi Arabia (KSA). There are considerable differences among the KSA regions in terms of their levels of investment and productivity due to their geographical scale and location, which in turn greatly affect their relative efficiency. The study used a non-parametric linear programming technique - Data Envelopment Analysis (DEA) - to measure the regional efficiency change over time and determine the drivers of inefficiency and their scope of improvement. In addition, Window DEA analysis is carried out to compare the efficiency performance change for various time periods. Malmquist index (MI) is also analyzed to identify the sources of productivity change between two subsequent years. The analysis involves spatial and temporal panel data collected from 1999 to 2018 for the 13 regions of the country. Outcomes reveal that transport investment and improved railway accessibility, in general, have significantly contributed to regional economic development. Moreover, the endowment of the new railway stations has spill-over effects. The DEA Window analysis confirmed the dynamic improvement in the average regional efficiency over the study periods. MI showed that the technical efficiency change was the main source of regional productivity improvement. However, there is evidence of investment allocation discrepancy among regions which could limit the achievement of development goals in the long term. These relevant findings will assist the Saudi government in developing better strategic decisions for future transport investments and their allocation at the regional level.Keywords: data envelopment analysis, transport investment, railway accessibility, efficiency
Procedia PDF Downloads 14541181 Measurement of Operational and Environmental Performance of the Coal-Fired Power Plants in India by Using Data Envelopment Analysis
Authors: Vijay Kumar Bajpai, Sudhir Kumar Singh
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In this study, the performance analyses of the twenty five coal-fired power plants (CFPPs) used for electricity generation are carried out through various data envelopment analysis (DEA) models. Three efficiency indices are defined and pursued. During the calculation of the operational performance, energy and non-energy variables are used as input, and net electricity produced is used as desired output. CO2 emitted to the environment is used as the undesired output in the computation of the pure environmental performance while in Model-3 CO2 emissions is considered as detrimental input in the calculation of operational and environmental performance. Empirical results show that most of the plants are operating in increasing returns to scale region and Mettur plant is efficient one with regards to energy use and environment. The result also indicates that the undesirable output effect is insignificant in the research sample. The present study will provide clues to plant operators towards raising the operational and environmental performance of CFPPs.Keywords: coal fired power plants, environmental performance, data envelopment analysis, operational performance
Procedia PDF Downloads 44841180 In Vitro Propagation of Vanilla Planifolia Using Nodal Explants and Varied Concentrations of Naphthaleneacetic acid (NAA) and 6-Benzylaminopurine (BAP).
Authors: Jessica Arthur, Duke Amegah, Kingsley Akenten Wiafe
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Background: Vanilla planifolia is the only edible fruit of the orchid family (Orchidaceae) among the over 35,000 Orchidaceae species found worldwide. In Ghana, Vanilla was discovered in the wild, but it is underutilized for commercial production, most likely due to a lack of knowledge on the best NAA and BAP combinations for in vitro propagation to promote successfully regenerated plant acclimatization. The growing interest and global demand for elite Vanilla planifolia plants and natural vanilla flavour emphasize the need for an effective industrial-scale micropropagation protocol. Tissue culture systems are increasingly used to grow disease-free plants and reliable in vitro methods can also produce plantlets with typically modest proliferation rates. This study sought to develop an efficient protocol for in vitro propagation of vanilla using nodal explants by testing different concentrations of NAA and BAP, for the proliferation of the entire plant. Methods: Nodal explants with dormant axillary buds were obtained from year-old laboratory-grown Vanilla planifolia plants. MS media was prepared with a nutrient stock solution (containing macronutrients, micronutrients, iron solution and vitamins) and semi-solidified using phytagel. It was supplemented with different concentrations of NAA and BAP to induce multiple shoots and roots (0.5mg/L BAP with NAA at 0, 0.5, 1, 1.5, 2.0mg/L and vice-versa). The explants were sterilized, cultured in labelled test tubes and incubated at 26°C ± 2°C with 16/8 hours light/dark cycle. Data on shoot and root growth, leaf number, node number, and survival percentage were collected over three consecutive two-week periods. The data were square root transformed and subjected to ANOVA and LSD at a 5% significance level using the R statistical package. Results: Shoots emerged at 8 days and roots at 12 days after inoculation with 94% survival rate. It was discovered that for the NAA treatments, MS media supplemented with 2.00 mg/l NAA resulted in the highest shoot length (10.45cm), maximum root number (1.51), maximum shoot number (1.47) and the highest number of leaves (1.29). MS medium containing 1.00 mg/l NAA produced the highest number of nodes (1.62) and root length (14.27cm). Also, a similar growth pattern for the BAP treatments was observed. MS medium supplemented with 1.50 mg/l BAP resulted in the highest shoot length (14.98 cm), the highest number of nodes (4.60), the highest number of leaves (1.75) and the maximum shoot number (1.57). MS medium containing 0.50 mg/l BAP and 1.0 mg/l BAP generated a maximum root number (1.44) and the highest root length (13.25cm), respectively. However, the best concentration combination for maximizing shoot and root was media containing 1.5mg/l BAP combined with 0.5mg/l NAA, and 1.0mg/l NAA combined with 0.5mg/l of BAP respectively. These concentrations were optimum for in vitro growth and production of Vanilla planifolia. Significance: This study presents a standardized protocol for labs to produce clean vanilla plantlets, enhancing cultivation in Ghana and beyond. It provides insights into Vanilla planifolia's growth patterns and hormone responses, aiding future research and cultivation.Keywords: Vanilla planifolia, In vitro propagation, plant hormones, MS media
Procedia PDF Downloads 5241179 Traditional Chinese Medicine Treatment for Coronary Heart Disease: a Meta-Analysis
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Traditional Chinese medicine has been used in the treatment of coronary heart disease (CHD) for centuries, and in recent years, the research data on the efficacy of traditional Chinese medicine through clinical trials has gradually increased to explore its real efficacy and internal pharmacology. However, due to the complexity of traditional Chinese medicine prescriptions, the efficacy of each component is difficult to clarify, and pharmacological research is challenging. This study aims to systematically review and clarify the clinical efficacy of traditional Chinese medicine in the treatment of coronary heart disease through a meta-analysis. Based on PubMed, CNKI database, Wanfang data, and other databases, eleven randomized controlled trials and 1091 CHD subjects were included. Two researchers conducted a systematic review of the papers and conducted a meta-analysis supporting the positive therapeutic effect of traditional Chinese medicine in the treatment of CHD.Keywords: coronary heart disease, Chinese medicine, treatment, meta-analysis
Procedia PDF Downloads 11541178 Agile Methodology for Modeling and Design of Data Warehouses -AM4DW-
Authors: Nieto Bernal Wilson, Carmona Suarez Edgar
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The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects. Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse
Procedia PDF Downloads 39741177 Studies of Rule Induction by STRIM from the Decision Table with Contaminated Attribute Values from Missing Data and Noise — in the Case of Critical Dataset Size —
Authors: Tetsuro Saeki, Yuichi Kato, Shoutarou Mizuno
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STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by simulation experiments specifying rules in advance, and by comparison with conventional methods. However, scope for future development remains before STRIM can be applied to the analysis of real-world data sets. The first requirement is to determine the size of the dataset needed for inducting true rules, since finding statistically significant rules is the core of the method. The second is to examine the capacity of rule induction from datasets with contaminated attribute values created by missing data and noise, since real-world datasets usually contain such contaminated data. This paper examines the first problem theoretically, in connection with the rule length. The second problem is then examined in a simulation experiment, utilizing the critical size of dataset derived from the first step. The experimental results show that STRIM is highly robust in the analysis of datasets with contaminated attribute values, and hence is applicable to realworld data.Keywords: rule induction, decision table, missing data, noise
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