Search results for: data envelopment analysis (DEA)

Authors: M. Mert, M. T. Ozkan

Abstract:

Pile load tests should be applied to check the bearing capacity calculations and to determine the settlement of the pile corresponding to test load. Strain gauges can be installed into pile in order to determine the shaft resistance of the piles for every soil layer respectively. Detailed results can be obtained by means of strain gauges placed at certain levels into test piles. In the scope of this study, pile load test data obtained from two different projects are examined.  Instrumented static pile load tests were applied on totally 7 test bored piles of different diameters (80 cm, 150 cm, and 200 cm) and different lengths (between 30-76 m) in two different project site. Settlement analysis of test piles is done by using some of load transfer methods and finite element method. Plaxis 3D which is a three-dimensional finite element program is also used for settlement analysis of the test piles. In this study, firstly bearing capacity of test piles are determined and compared with strain gauge data which is required for settlement analysis. Then, settlement values of the test piles are estimated by using load transfer methods developed in recent years and finite element method. The aim of this study is to show similarities and differences between the results obtained from settlement analysis methods and instrumented pile load tests.

Keywords: failure, finite element method, monitoring and instrumentation, pile, settlement

Procedia PDF Downloads 147

Authors: Amanda T. Badaró, Douglas F. Barbin, Sofia T. Garcia, Maria Teresa P. S. Clerici, Amanda R. Ferreira

Abstract:

Food fortification is the intentional addition of a nutrient in a food matrix and has been widely used to overcome the lack of nutrients in the diet or increasing the nutritional value of food. Fortified food must meet the demand of the population, taking into account their habits and risks that these foods may cause. Wheat and its by-products, such as semolina, has been strongly indicated to be used as a food vehicle since it is widely consumed and used in the production of other foods. These products have been strategically used to add some nutrients, such as fibers. Methods of analysis and quantification of these kinds of components are destructive and require lengthy sample preparation and analysis. Therefore, the industry has searched for faster and less invasive methods, such as Near-Infrared Spectroscopy (NIR). NIR is a rapid and cost-effective method, however, it is based on indirect measurements, yielding high amount of data. Therefore, NIR spectroscopy requires calibration with mathematical and statistical tools (Chemometrics) to extract analytical information from the corresponding spectra, as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is well suited for NIR, once it can handle many spectra at a time and be used for non-supervised classification. Advantages of the PCA, which is also a data reduction technique, is that it reduces the data spectra to a smaller number of latent variables for further interpretation. On the other hand, LDA is a supervised method that searches the Canonical Variables (CV) with the maximum separation among different categories. In LDA, the first CV is the direction of maximum ratio between inter and intra-class variances. The present work used a portable infrared spectrometer (NIR) for identification and classification of pure and fiber-fortified semolina samples. The fiber was added to semolina in two different concentrations, and after the spectra acquisition, the data was used for PCA and LDA to identify and discriminate the samples. The results showed that NIR spectroscopy associate to PCA was very effective in identifying pure and fiber-fortified semolina. Additionally, the classification range of the samples using LDA was between 78.3% and 95% for calibration and 75% and 95% for cross-validation. Thus, after the multivariate analysis such as PCA and LDA, it was possible to verify that NIR associated to chemometric methods is able to identify and classify the different samples in a fast and non-destructive way.

Keywords: Chemometrics, fiber, linear discriminant analysis, near-infrared spectroscopy, principal component analysis, semolina

Procedia PDF Downloads 190

Authors: Rossella Arcucci, Luisa D'Amore, Simone Celestino, Giuseppe Scotti, Giuliano Laccetti

Abstract:

This work is the first dowel in a rather wide research activity in collaboration with Euro Mediterranean Center for Climate Changes, aimed at introducing scalable approaches in Ocean Circulation Models. We discuss designing and implementation of a parallel algorithm for solving the Variational Data Assimilation (DA) problem on Graphics Processing Units (GPUs). The algorithm is based on the fully scalable 3DVar DA model, previously proposed by the authors, which uses a Domain Decomposition approach (we refer to this model as the DD-DA model). We proceed with an incremental porting process consisting of 3 distinct stages: requirements and source code analysis, incremental development of CUDA kernels, testing and optimization. Experiments confirm the theoretic performance analysis based on the so-called scale up factor demonstrating that the DD-DA model can be suitably mapped on GPU architectures.

Keywords: data assimilation, GPU architectures, ocean models, parallel algorithm

Procedia PDF Downloads 389

Authors: Y. K. Park, B. K. Kim, J. W. Lee, S. J. Lee

Abstract:

To assess the risk of the underground structures and surrounding ground, we collect basic data by the engineering method of measurement, exploration and surveys and, derive the risk through proper analysis and each assessment for urban railroad underground structures and surrounding ground including station inflow. Basic data are obtained by the fiber-optic sensors, MEMS sensors, water quantity/quality sensors, tunnel scanner, ground penetrating radar, light weight deflectometer, and are evaluated if they are more than the proper value or not. Based on these data, we analyze the risk level of urban railroad underground structures and surrounding ground. And we develop the risk management system to manage efficiently these data and to support a convenient interface environment at input/output of data.

Keywords: urban railroad, underground structures, ground subsidence, station inflow, risk

Procedia PDF Downloads 318

Authors: Anam Bhatti, Sumbal Arif, Mariam Mehar, Sohail Younas

Abstract:

CSR has become one of the imperative implements in satisfying customers. The impartial of this research is to calculate CSR, relationship marketing, and customer satisfaction. In Pakistan, there is not enough research work on the effect of CSR and relationship marketing on relationship maintainer and customer loyalty. To find out deductive approach and survey method is used as research approach and research strategy respectively. This research design is descriptive and quantitative study. For data, collection questionnaire method with semantic differential scale and seven point scales are adopted. Data has been collected by adopting the non-probability convenience technique as sampling technique and the sample size is 400. For factor confirmatory factor analysis, structure equation modeling and medication analysis, regression analysis Amos software were used. Strong empirical evidence supports that the customer’s perception of CSR performance is highly influenced by the values.

Keywords: CSR, Relationship marketing, Relationship maintainer, Customer loyalty, Customer satisfaction

Procedia PDF Downloads 448

Authors: Kai Chun Yang, Shao-Wen Chen, Jong-Rong Wang, Chunkuan Shih, Jung-Hua Yang, Hsiung-Chih Chen, Wen-Sheng Hsu

Abstract:

In this study, we build RADionuclide Transport, Removal And Dose Estimation/Symbolic Nuclear Analysis Package (SNAP/RADTRAD) model of Kuosheng Nuclear Power Plant which is based on the Final Safety Evaluation Report (FSAR) and other data of Kuosheng Nuclear Power Plant. It is used to estimate the radiation dose of the Exclusion Area Boundary (EAB), the Low Population Zone (LPZ), and the control room following ‘release from the containment’ case in Loss Of Coolant Accident (LOCA). The RADTRAD analysis result shows that the evaluation dose at EAB, LPZ, and the control room are close to the FSAR data, and all of the doses are lower than the regulatory limits. At last, we do a sensitivity analysis and observe that the evaluation doses increase as the intake rate of the control room increases.

Keywords: RADTRAD, radionuclide transport, removal and dose estimation, snap, symbolic nuclear analysis package, boiling water reactor, NPP, kuosheng

Procedia PDF Downloads 318

Authors: Nyoman Wijana, Chandra Arha

Abstract:

Disclosures of Intellectual Capital (IC) is a presentation of corporate information assets that are not recorded in the financial statements. This disclosures is very helpful because it provides inform corporate assets are intangible. In the new economic era, the company's intangible assets will determine company's competitive advantage. This study aimed to examine the effect of IC disclosures on market capitalization growth. Observational studies conducted over ten years in 2002-2011. The purpose of this study was to determine the effect for last ten years. One hundred samples of the company's largest market capitalization in 2011 traced back to last ten years. Data that used, are in 2011, 2008, 2005, and 2002 Method that’s used for acquiring the data is content analysis. The analytical method used is Ordinanary Least Square (OLS) and analysis tools are e views 7 This software using Pooled Least Square estimation parameters are specifically designed for panel data. The results of testing analysis showed inconsistent expression levels affect the growth of the market capitalization in each year of observation. The results of this study are expected to motivate the public company in Indonesia to do more voluntary IC disclosures and encourage regulators to make regulations in a comprehensive manner so that all categories of the IC must be disclosed by the company.

Keywords: IC disclosures, market capitalization growth, analytical method, OLS

Procedia PDF Downloads 323

Authors: Aliakbar Golshani, Seyyed Mehdi Poorhashemi, Mahsa Gharizadeh

Abstract:

The under passing tunnels are strongly influenced by the soils around. There are some complexities in the specification of real soil behavior, owing to the fact that lots of uncertainties exist in soil properties, and additionally, inappropriate soil constitutive models. Such mentioned factors may cause incompatible settlements in numerical analysis with the obtained values in actual construction. This paper aims to report a case study on a specific tunnel constructed by NATM. The tunnel has a depth of 11.4 m, height of 12.2 m, and width of 14.4 m with 2.5 lanes. The numerical modeling was based on a 2D finite element program. The soil material behavior was modeled by hardening soil model. According to the field observations, the numerical estimated settlement at the ground surface was approximately four times more than the measured one, after the entire installation of the initial lining, indicating that some unknown factors affect the values. Consequently, the geotechnical parameters are accurately revised by a numerical back-analysis using laboratory and field test data and based on the obtained monitoring data. The obtained result confirms that typically, the soil parameters are conservatively low-estimated. And additionally, the constitutive models cannot be applied properly for all soil conditions.

Keywords: NATM tunnel, initial lining, laboratory test data, numerical back-analysis

Procedia PDF Downloads 347

Authors: Małgorzata Cichy

Abstract:

Afghanistan is a source of conflicts that affect security on a global scale. The role of ethnic and national determinants in the peacebuilding process in this country remains an extremely important factor in this respect. Research methods include literature and data analysis (scientific literature, documents of governmental and non-governmental organizations, statistical data and media reports), institutional and legal analysis, as well as decision-making method. The main objective of the research is a comprehensive answer to the question of how ethnic and national factors affect the process of building peace in Afghanistan after 2021 and what impact it has on international security.

Keywords: Afghanistan, pashtuns, peace, taliban

Procedia PDF Downloads 62

Authors: M. Naidoo

Abstract:

Artificial intelligence often requires large amounts of good quality data. Within important fields, such as healthcare, the training of AI systems predominately relies on health and personal data; however, the usage of this data is complicated by various layers of law and ethics that seek to protect individuals’ privacy rights. This research seeks to establish the challenges AI and data sciences pose to (i) informational rights, (ii) privacy rights, and (iii) data protection. To solve some of the issues presented, various methods are suggested, such as embedding values in technological development, proper balancing of rights and interests, and others.

Keywords: artificial intelligence, data science, law, policy

Procedia PDF Downloads 89

Authors: Suzan Arslanturk, Mohammad-Reza Siadat, Theophilus Ogunyemi, Ananias Diokno

Abstract:

Missing data is a common challenge in statistical analyses of most clinical survey datasets. A variety of methods have been developed to enable analysis of survey data to deal with missing values. Imputation is the most commonly used among the above methods. However, in order to minimize the bias introduced due to imputation, one must choose the right imputation technique and apply it to the correct type of missing data. In this paper, we have identified different types of missing values: missing data due to skip pattern (SPMD), undetermined missing data (UMD), and genuine missing data (GMD) and applied rough set imputation on only the GMD portion of the missing data. We have used rough set imputation to evaluate the effect of such imputation on prediction by generating several simulation datasets based on an existing epidemiological dataset (MESA). To measure how well each dataset lends itself to the prediction model (logistic regression), we have used p-values from the Wald test. To evaluate the accuracy of the prediction, we have considered the width of 95% confidence interval for the probability of incontinence. Both imputed and non-imputed simulation datasets were fit to the prediction model, and they both turned out to be significant (p-value < 0.05). However, the Wald score shows a better fit for the imputed compared to non-imputed datasets (28.7 vs. 23.4). The average confidence interval width was decreased by 10.4% when the imputed dataset was used, meaning higher precision. The results show that using the rough set method for missing data imputation on GMD data improve the predictive capability of the logistic regression. Further studies are required to generalize this conclusion to other clinical survey datasets.

Keywords: rough set, imputation, clinical survey data simulation, genuine missing data, predictive index

Procedia PDF Downloads 145

Authors: Chante J. De Klerk

Abstract:

The potential of music to facilitate superior performance during high to elite level gymnastics conditioning instigated this research. A team of seven gymnasts completed a fixed conditioning programme eight times, alternating the two variable conditions. Four sessions of each condition were conducted: without music (session 1), with music (session 2), without music (3), with music (4), without music (5), and so forth. Quantitative data were collected in both conditions through physiological monitoring of the gymnasts, and administration of the Situational Motivation Scale (SIMS). Statistical analysis of the physiological data made it possible to quantify the presence as well as the magnitude of the musical intervention’s impact on various aspects of the gymnasts' physiological functioning during conditioning. The SIMS questionnaire results were used to evaluate if their motivation towards conditioning was altered by the intervention. Thematic analysis of qualitative data collected through semi-structured interviews revealed themes reflecting the gymnasts’ sentiments towards the data collection process. Gymnast-specific descriptions and experiences of the team as a whole were integrated with the quantitative data to facilitate greater dimension in establishing the impact of the intervention. The results showed positive physiological, motivational, and emotional effects. In the presence of music, superior sympathetic nervous activation, and energy efficiency, with more economic breathing, dominated the physiological data. Fatigue and arousal levels (emotional and physiological) were also conducive to improved conditioning outcomes compared to conventional conditioning (without music). Greater levels of positive affect and motivation emerged in analysis of both the SIMS and interview data sets. Overall, the intervention was found to promote psychophysiological coherence during the physical activity. In conclusion, a strategically constructed musical intervention, designed to accompany a gymnastics conditioning session for high to elite level gymnasts, has ergogenic potential.

Keywords: arousal, fatigue, gymnastics conditioning, motivation, musical intervention, psychophysiological coherence

Procedia PDF Downloads 72

Authors: Nidhin Dani Abraham, T. K. Sri Shilpa

Abstract:

Asset liability management is an important aspect in banking business. Moreover, the today’s banking is based on BASEL III which strictly regulates on the counterparty default. This paper focuses on prediction and analysis of counter party default risk, which is a type of risk occurs when the customers fail to repay the amount back to the lender (bank or any financial institutions). This paper proposes an approach to reduce the counterparty risk occurring in the financial institutions using an appropriate data mining technique and thus predicts the occurrence of NPA. It also helps in asset building and restructuring quality. Liability management is very important to carry out banking business. To know and analyze the depth of liability of bank, a suitable technique is required. For that a data mining technique is being used to predict the dormant behaviour of various deposit bank customers. Various models are implemented and the results are analyzed of saving bank deposit customers. All these data are cleaned using data cleansing approach from the bank data warehouse.

Keywords: data mining, asset liability management, BASEL III, banking

Procedia PDF Downloads 530

Authors: Moti Zwilling, Srečko Natek

Abstract:

This paper aims to present an innovative predictive analytics analysis in order to find the best combination between two consumers who strive to find their partner or in internet sites. The methodology shown in this paper is based on analysis of consumer preferences and involves data mining and machine learning search techniques. The study is composed of two parts: The first part examines by means of descriptive statistics the correlations between a set of parameters that are taken between man and women where they intent to meet each other through the social media, usually the internet. In this part several hypotheses were examined and statistical analysis were taken place. Results show that there is a strong correlation between the affiliated attributes of man and woman as long as concerned to how they present themselves in a social media such as "Facebook". One interesting issue is the strong desire to develop a serious relationship between most of the respondents. In the second part, the authors used common data mining algorithms to search and classify the most important and effective attributes that affect the response rate of the other side. Results exhibit that personal presentation and education background are found as most affective to achieve a positive attitude to one's profile from the other mate.

Keywords: dating sites, social networks, machine learning, decision trees, data mining

Procedia PDF Downloads 279

Authors: Nadia Sghaier

Abstract:

In this paper, we aim to identify the determinants of the life and non-life insurance premiums of different lines for the case of the Tunisian insurance market over a recent period from 1997 to 2019. The empirical analysis is conducted using the linear cointegration techniques in the panel data framework, which allow both long and short-run relationships. The obtained results show evidence of long-run relationship between premiums, losses, and financial variables (stock market indices and interest rate). Furthermore, we find that the short-run effect of explanatory variables differs across lines. This finding has important implications for insurance tarification and regulation.

Keywords: insurance premiums, lines, Tunisian insurance market, cointegration approach in panel data

Procedia PDF Downloads 173

Authors: K. Indra Gandhi

Abstract:

Wireless Sensor Networks (WSNs) have always been characterized for application-specific sensing, relaying and collection of information for further analysis. However, software development was not considered as a separate entity in this process of data collection which has posed severe limitations on the software development for WSN. Software development for WSN is a complex process since the components involved are data-driven, network-driven and application-driven in nature. This implies that there is a tremendous need for the separation of concern from the software development perspective. A layered approach for developing data acquisition design based on Model Driven Development (MDD) has been proposed as the sensed data collection process itself varies depending upon the application taken into consideration. This work focuses on the layered view of the data acquisition process so as to ease the software point of development. A metamodel has been proposed that enables reusability and realization of the software development as an adaptable component for WSN systems. Further, observing users perception indicates that proposed model helps in improving the programmer's productivity by realizing the collaborative system involved.

Keywords: data acquisition, model-driven development, separation of concern, wireless sensor networks

Procedia PDF Downloads 414

Authors: Tapan Jain, Davender Singh Saini

Abstract:

Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms.

Keywords: routing, hierarchical clustering, agglomerative, quantitative, wireless sensor network

Procedia PDF Downloads 573

Authors: Ana Paula Camelo, Keila Sanches

Abstract:

The Geographically Weighted Regression (GWR) was proposed in geography literature to allow relationship in a regression model to vary over space. In Brazil, the agricultural exploitation of the Cerrado Biome is the main cause of deforestation. In this study, we propose a methodology using geostatistical methods to characterize the spatial dependence of deforestation in the Cerrado based on agricultural production indicators. Therefore, it was used the set of exploratory spatial data analysis tools (ESDA) and confirmatory analysis using GWR. It was made the calibration a non-spatial model, evaluation the nature of the regression curve, election of the variables by stepwise process and multicollinearity analysis. After the evaluation of the non-spatial model was processed the spatial-regression model, statistic evaluation of the intercept and verification of its effect on calibration. In an analysis of Spearman’s correlation the results between deforestation and livestock was +0.783 and with soybeans +0.405. The model presented R²=0.936 and showed a strong spatial dependence of agricultural activity of soybeans associated to maize and cotton crops. The GWR is a very effective tool presenting results closer to the reality of deforestation in the Cerrado when compared with other analysis.

Keywords: deforestation, geographically weighted regression, land use, spatial analysis

Procedia PDF Downloads 338

Authors: Mohammed Abdullah Alshahrani

Abstract:

The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

Procedia PDF Downloads 27

Authors: Tarek S. Duzan, Abdulaziz F. Ettir

Abstract:

Wave velocities, Core and Log petrophysical data were collected from recently drilled four new wells scattered through-out the Dahra/Jofra (PL-5) Reservoir. The collected data were analyzed for the relationships of Wave Velocities with rock property such as Porosity, permeability and Bulk Density. Lots of Literature review reveals a number of differing results and conclusions regarding wave velocities (Compressional Waves (Vp) and Shear Waves (Vs)) versus rock petrophysical property relationships, especially in carbonate reservoirs. In this paper, we focused on the relationships between wave velocities (Vp , Vs) and the ratio Vp/Vs with rock properties for shallow marine libyan carbonate reservoir (Real Case). Upon data analysis, a relationship between petrophysical properties and wave velocities (Vp, Vs) and the ratio Vp/Vs has been found. Porosity and bulk density properties have shown exponential relationship with wave velocities, while permeability has shown a power relationship in the interested zone. It is also clear that wave velocities (Vp , Vs) seems to be a good indicator for the lithology change with true vertical depth. Therefore, it is highly recommended to use the output relationships to predict porosity, bulk density and permeability of the similar reservoir type utilizing the most recent seismic data.

Keywords: conventional core analysis (porosity, permeability bulk density) data, VS wave and P-wave velocities, shallow carbonate reservoir in D/J field

Procedia PDF Downloads 310

Authors: Praniil Nagaraj

Abstract:

This paper proposes a method to collect homeless data using crowdsourcing and presents an approach to analyze the data, demonstrating its potential to strengthen existing and future policies aimed at promoting socio-economic equilibrium. The 2022 Annual Homeless Assessment Report (AHAR) to Congress highlighted alarming statistics, emphasizing the need for effective decision-making and budget allocation within local planning bodies known as Continuums of Care (CoC). This paper's contributions can be categorized into three main areas. Firstly, a unique method for collecting homeless data is introduced, utilizing a user-friendly smartphone app (currently available for Android). The app enables the general public to quickly record information about homeless individuals, including the number of people and details about their living conditions. The collected data, including date, time, and location, is anonymized and securely transmitted to the cloud. It is anticipated that an increasing number of users motivated to contribute to society will adopt the app, thus expanding the data collection efforts. Duplicate data is addressed through simple classification methods, and historical data is utilized to fill in missing information. The second contribution of this paper is the description of data analysis techniques applied to the collected data. By combining this new data with existing information, statistical regression analysis is employed to gain insights into various aspects, such as distinguishing between unsheltered and sheltered homeless populations, as well as examining their correlation with factors like unemployment rates, housing affordability, and labor demand. Initial data is collected in San Francisco, while pre-existing information is drawn from three cities: San Francisco, New York City, and Washington D.C., facilitating the conduction of simulations. The third contribution focuses on demonstrating the practical implications of the data processing results. The challenges faced by key stakeholders, including charitable organizations and local city governments, are taken into consideration. Two case studies are presented as examples. The first case study explores improving the efficiency of food and necessities distribution, as well as medical assistance, driven by charitable organizations. The second case study examines the correlation between micro-geographic budget expenditure by local city governments and homeless information to justify budget allocation and expenditures. The ultimate objective of this endeavor is to enable the continuous enhancement of the quality of life for the underprivileged. It is hoped that through increased crowdsourcing of data from the public, the Generosity Curve and the Need Curve will intersect, leading to a better world for all.

Keywords: crowdsourcing, homelessness, socio-economic policies, statistical analysis

Procedia PDF Downloads 40

Authors: Nurfirdawati Muhamad Hanafi, Azmanirah Ab Rahman, Marina Ibrahim Mukhtar, Jamil Ahmad, Sarebah Warman

Abstract:

This study was conducted to generate empirical evidence on validity and reliability of the item of Competency Assessment Implementation (CAI) Instrument using Rasch Model for polythomous data aided by Winstep software version 3.68. The construct validity was examined by analyzing the point-measure correlation index (PTMEA), in fit and outfit MNSQ values; meanwhile the reliability was examined by analyzing item reliability index. A survey technique was used as the major method with the CAI instrument on 156 teachers from vocational schools. The results have shown that the reliability of CAI Instrument items were between 0.80 and 0.98. PTMEA Correlation is in positive values, in which the item is able to distinguish between the ability of the respondent. Statistical data obtained shows that out of 154 items, 12 items from the instrument suggested to be omitted. This study is hoped could bring a new direction to the process of data analysis in educational research.

Keywords: competency assessment, reliability, validity, item analysis

Procedia PDF Downloads 422

Authors: Jose Funes, Jeff Sauer, Laixiang Sun

Abstract:

This research examines determinants of multifamily housing development and spillovers in the District of Columbia. A range of socioeconomic factors related to income distribution, productivity, and land use policies are thought to influence the development in contemporary U.S. multifamily housing markets. The analysis leverages data from the American Community Survey to construct panel datasets spanning from 2010 to 2019. Using spatial regression, we identify several socioeconomic measures and land use policies both positively and negatively associated with new housing supply. We contextualize housing estimates related to race in relation to uneven development in the contemporary D.C. housing supply.

Keywords: neighborhood effect, sorting, spatial spillovers, multifamily housing

Procedia PDF Downloads 76

Authors: Inigo Beckett

Abstract:

In forensic investigations, it is often the case that the most potentially useful recorded evidence derives from coincidental imagery, recorded immediately before or during an incident, and that during the incident (e.g. a ‘failure’ or fire event), the evidence is changed or destroyed. To an image analysis expert involved in photogrammetric analysis for Civil or Criminal Proceedings, traditional computer vision methods involving calibrated cameras is often not appropriate because image metadata cannot be relied upon. This paper presents an approach for resolving this problem, considering in particular and by way of a case study, the angle of a simple ladder shown in a photograph. The UK Health and Safety Executive (HSE) guidance document published in 2014 (INDG455) advises that a leaning ladder should be erected at 75 degrees to the horizontal axis. Personal injury cases can arise in the construction industry because a ladder is too steep or too shallow. Ad-hoc photographs of such ladders in their incident position provide a basis for analysis of their angle. This paper presents a direct approach for ascertaining the position of the camera and the angle of the ladder simultaneously from the photograph(s) by way of a workflow that encompasses a novel application of projective geometry and matrix analysis. Mathematical analysis shows that for a given pixel ratio of directly measured collinear points (i.e. features that lie on the same line segment) from the 2D digital photograph with respect to a given viewing point, we can constrain the 3D camera position to a surface of a sphere in the scene. Depending on what we know about the ladder, we can enforce another independent constraint on the possible camera positions which enables us to constrain the possible positions even further. Experiments were conducted using synthetic and real-world data. The synthetic data modeled a vertical plane with a ladder on a horizontally flat plane resting against a vertical wall. The real-world data was captured using an Apple iPhone 13 Pro and 3D laser scan survey data whereby a ladder was placed in a known location and angle to the vertical axis. For each case, we calculated camera positions and the ladder angles using this method and cross-compared them against their respective ‘true’ values.

Keywords: image analysis, projective geometry, homography, photogrammetry, ladders, Forensics, Mathematical modeling, planar geometry, matrix analysis, collinear, cameras, photographs

Procedia PDF Downloads 27

Authors: Eszter Siposne Nandori

Abstract:

The paper describes how the subjective interpretation of poverty changed during the COVID-19 pandemic. The results of data collection at the end of 2020 are compared to the results of a similar survey from 2019. The methods of systematic data collection are used to collect data about the beliefs of the population about poverty. The analysis is carried out in Borsod-Abaúj-Zemplén County, one of the most backward areas in Hungary. The paper concludes that poverty is mainly linked to material values, and it did not change from 2019 to 2020. Some slight changes, however, highlight the effect of the pandemic: poverty is increasingly seen as a generational problem in 2020, and another important change is that isolation became more closely related to poverty.

Keywords: Hungary, interpretation of poverty, pandemic, systematic data collection, subjective poverty

Procedia PDF Downloads 102

Authors: Guilhermina Torrao

Abstract:

An extensive number of studies have been conducted to determine the factors which influence crash injury risk (CIR); however, uncertainties inherent to selected variables have been neglected. A review of existing literature is required to not only obtain an overview of the variables and measures but also ascertain the implications when comparing studies without a systematic view of variable taxonomy. Therefore, the aim of this literature review is to examine and report on peer-reviewed studies in the field of crash analysis and to understand the implications of broad variations in variable selection in CIR analysis. The objective of this study is to demonstrate the variance in variable selection and classification when modeling injury risk involving occupants of light vehicles by presenting an analytical review of the literature. Based on data collected from 64 journal publications reported over the past 21 years, the analytical review discusses the variables selected by each study across an organized list of predictors for CIR analysis and provides a better understanding of the contribution of accident and vehicle factors to injuries acquired by occupants of light vehicles. A cross-comparison analysis demonstrates that almost half the studies (48%) did not consider vehicle design specifications (e.g., vehicle weight), whereas, for those that did, the vehicle age/model year was the most selected explanatory variable used by 41% of the literature studies. For those studies that included speed risk factor in their analyses, the majority (64%) used the legal speed limit data as a ‘proxy’ of vehicle speed at the moment of a crash, imposing limitations for CIR analysis and modeling. Despite the proven efficiency of airbags in minimizing injury impact following a crash, only 22% of studies included airbag deployment data. A major contribution of this study is to highlight the uncertainty linked to explanatory variable selection and identify opportunities for improvements when performing future studies in the field of road injuries.

Keywords: crash, exploratory, injury, risk, variables, vehicle

Procedia PDF Downloads 106

Authors: Lucas A. Silva, Carla R. Aguiar

Abstract:

This work presents a proposal to perform contextual sentiment analysis using a supervised learning algorithm and disregarding the extensive training of annotators. To achieve this goal, a web platform was developed to perform the entire procedure outlined in this paper. The main contribution of the pipeline described in this article is to simplify and automate the annotation process through a system of analysis of congruence between the notes. This ensured satisfactory results even without using specialized annotators in the context of the research, avoiding the generation of biased training data for the classifiers. For this, a case study was conducted in a blog of entrepreneurship. The experimental results were consistent with the literature related annotation using formalized process with experts.

Keywords: sentiment analysis, untrained annotators, naive bayes, entrepreneurship, contextualized classifier

Procedia PDF Downloads 373

Authors: Pebri Nurhayati, Rozanah Ahlam Fadiyah

Abstract:

In the development of the city often use rural areas that can not be separated from the change in land use that lead to the degradation of urban green space in the city fringe. In the long run, the degradation of green open space this can impact on the decline of ecological, psychological and public health. Therefore, this research aims to (1) determine the relationship between the parameters of the degradation rate of urban development with green space, (2) develop a spatial model of the impact of urban development on the degradation of green open space with remote sensing techniques and Geographical Information Systems in an integrated manner. This research is a descriptive research with data collection techniques of observation and secondary data . In the data analysis, to interpret the direction of urban development and degradation of green open space is required in 2005-2010 ASTER image with NDVI. Of interpretation will generate two maps, namely maps and map development built land degradation green open space. Secondary data related to the rate of population growth, the level of accessibility, and the main activities of each city map is processed into a population growth rate, the level of accessibility maps, and map the main activities of the town. Each map is used as a parameter to map the degradation of green space and analyzed by non-parametric statistical analysis using Crosstab thus obtained value of C (coefficient contingency). C values were then compared with the Cmaximum to determine the relationship. From this research will be obtained in the form of modeling spatial map of the City Development Impact Degradation Green Space in Urban Fringe eastern city of Yogyakarta 2005-2010. In addition, this research also generate statistical analysis of the test results of each parameter to the degradation of green open space in the Urban Fringe eastern city of Yogyakarta 2005-2010.

Keywords: spatial analysis, urban development, degradation of green space, urban fringe

Procedia PDF Downloads 288

Authors: Olumuyiwa Ojo, Masengo Ilunga

Abstract:

Artificial neural network (ANN) has broken the bounds of the convention programming, which is actually a function of garbage in garbage out by its ability to mimic the human brain. Its ability to adopt, adapt, adjust, evaluate, learn and recognize the relationship, behavior, and pattern of a series of data set administered to it, is tailored after the human reasoning and learning mechanism. Thus, the study aimed at modeling wastewater treatment process in order to accurately diagnose water control problems for effective treatment. For this study, a stage ANN model development and evaluation methodology were employed. The source data analysis stage involved a statistical analysis of the data used in modeling in the model development stage, candidate ANN architecture development and then evaluated using a historical data set. The model was developed using historical data obtained from Daspoort Wastewater Treatment plant South Africa. The resultant designed dimensions and model for wastewater treatment plant provided good results. Parameters considered were temperature, pH value, colour, turbidity, amount of solids and acidity. Others are total hardness, Ca hardness, Mg hardness, and chloride. This enables the ANN to handle and represent more complex problems that conventional programming is incapable of performing.

Keywords: ANN, artificial neural network, wastewater treatment, model, development

Procedia PDF Downloads 132

Authors: Emma E. Hellawell, Susan J. Hughes

Abstract:

BBrownfield sites are currently being redeveloped for residential use. Information on soil contamination on these former industrial sites is collected as part of the planning process by the local government. This research project analyses this untapped resource of environmental data, using site investigation data submitted to a local Borough Council, in Surrey, UK. Over 150 site investigation reports were collected and interrogated to extract relevant information. This study involved three phases. Phase 1 was the development of a database for soil contamination information from local government reports. This database contained information on the source, history, and quality of the data together with the chemical information on the soil that was sampled. Phase 2 involved obtaining site investigation reports for development within the study area and extracting the required information for the database. Phase 3 was the data analysis and interpretation of key contaminants to evaluate typical levels of contaminants, their distribution within the study area, and relating these results to current guideline levels of risk for future site users. Preliminary results for a pilot study using a sample of the dataset have been obtained. This pilot study showed there is some inconsistency in the quality of the reports and measured data, and careful interpretation of the data is required. Analysis of the information has found high levels of lead in shallow soil samples, with mean and median levels exceeding the current guidance for residential use. The data also showed elevated (but below guidance) levels of potentially carcinogenic polyaromatic hydrocarbons. Of particular concern from the data was the high detection rate for asbestos fibers. These were found at low concentrations in 25% of the soil samples tested (however, the sample set was small). Contamination levels of the remaining chemicals tested were all below the guidance level for residential site use. These preliminary pilot study results will be expanded, and results for the whole local government area will be presented at the conference. The pilot study has demonstrated the potential for this extensive dataset to provide greater information on local contamination levels. This can help inform regulators and developers and lead to more targeted site investigations, improving risk assessments, and brownfield development.

Keywords: Brownfield development, contaminated land, local government planning data, site investigation

Procedia PDF Downloads 123