Search results for: validation indexes

Authors: Saowaluck Ukrisdawithid

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The thermal conductivity of thermal insulation materials are measured by Heat Flow Meter (HFM) apparatus. The components of uncertainty are complex and difficult on routine measurement by modelling approach. In this study, uncertainty of thermal conductivity measurement was estimated by single laboratory validation approach. The within-laboratory reproducibility was 1.1%. The standard uncertainty of method and laboratory bias by using SRM1453 expanded polystyrene board was dominant at 1.4%. However, it was assessed that there was no significant bias. For sample measurement, the sources of uncertainty were repeatability, density of sample and thermal conductivity resolution of HFM. From this approach to sample measurements, the combined uncertainty was calculated. In summary, the thermal conductivity of sample, polystyrene foam, was reported as 0.03367 W/m·K ± 3.5% (k = 2) at mean temperature 23.5 °C. The single laboratory validation approach is simple key of routine testing laboratory for estimation uncertainty of thermal conductivity measurement by using HFM, according to ISO/IEC 17025-2017 requirements. These are meaningful for laboratory competent improvement, quality control on products, and conformity assessment.

Keywords: single laboratory validation approach, within-laboratory reproducibility, method and laboratory bias, certified reference material

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Authors: Darren Zou

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Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: BART, Bayesian, predict, stock

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Authors: Yoonsuh Jung

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As DNA microarray data contain relatively small sample size compared to the number of genes, high dimensional models are often employed. In high dimensional models, the selection of tuning parameter (or, penalty parameter) is often one of the crucial parts of the modeling. Cross-validation is one of the most common methods for the tuning parameter selection, which selects a parameter value with the smallest cross-validated score. However, selecting a single value as an "optimal" value for the parameter can be very unstable due to the sampling variation since the sample sizes of microarray data are often small. Our approach is to choose multiple candidates of tuning parameter first, then average the candidates with different weights depending on their performance. The additional step of estimating the weights and averaging the candidates rarely increase the computational cost, while it can considerably improve the traditional cross-validation. We show that the selected value from the suggested methods often lead to stable parameter selection as well as improved detection of significant genetic variables compared to the tradition cross-validation via real data and simulated data sets.

Keywords: cross validation, parameter averaging, parameter selection, regularization parameter search

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Authors: Karim Kootahi, Seyed Abolhasan Naeini

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The soil-water characteristic curve (SWCC), which represents the relationship between suction and water content (or degree of saturation), is an important property of unsaturated soils. The conventional method for determining SWCC is through specialized testing procedures. Since these procedures require specialized unsaturated soil testing apparatus and lengthy testing programs, several index properties-based correlations have been developed for estimating the SWCC of fine-grained soils. There are, however, considerable inconsistencies among the published correlations and there is no validation study on the predictive ability of existing correlations. In the present study, all existing index properties-based correlations are evaluated using a high quality worldwide database. The performances of existing correlations are assessed both graphically and quantitatively using statistical measures. The results of the validation indicate that most of the existing correlations provide unacceptable estimates of degree of saturation but the most recent model appears to be promising.

Keywords: SWCC, correlations, index properties, validation

Procedia PDF Downloads 152

Authors: Emine Feyza Şükür, Mevlüt Türköz, Murat Dilmeç, Hüseyin Selçuk Halkacı

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In this study, a warm bulge test system was designed, built and experimentally validated to perform warm bulge tests with all necessary systems. In addition, performance of each sub-system is validated through repeated production and/or test runs as well as through part quality measurements. Validation and performance tests were performed to characterize the repeatability of the system. As a result of these tests, the desired temperature distribution on the sheet metal was obtained by the heating systems and the good repeatability of the bulge tests was obtained. Consequently, this study is expected to provide other researchers and manufacturer with a set of design and process guidelines to develop similar systems.

Keywords: design, test unit, warm bulge test unit, validation test

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Authors: Soon-Mi Park, Ihn Sook Jeong

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This retrospective study was aimed to identify risk factors of intravenous (IV) infiltration for hospitalized children. The participants were 1,174 children for test and 424 children for validation, who admitted to a general hospital, received peripheral intravenous injection therapy at least once and had complete records. Data were analyzed with frequency and percentage or mean and standard deviation were calculated, and decision tree analysis was used to screen for the most important risk factors for IV infiltration for hospitalized children. The decision tree analysis showed that the most important traditional risk factors for IV infiltration were the use of ampicillin/sulbactam, IV insertion site (lower extremities), and medical department (internal medicine) both in the test sample and validation sample. The correct classification was 92.2% in the test sample and 90.1% in the validation sample. More careful attention should be made to patients who are administered ampicillin/sulbactam, have IV site in lower extremities and have internal medical problems to prevent or detect infiltration occurrence.

Keywords: decision tree analysis, intravenous infiltration, child, validation

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Authors: Shahadut Hossain

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Measurement errors in continuous covariates and/or misclassifications in categorical covariates are common in epidemiological studies. Regression analysis ignoring such mismeasurements seriously biases the estimated main and interaction effects of covariates on the outcome of interest. Thus, adjustments for such mismeasurements are necessary. In this research, we propose a Bayesian parametric framework for eliminating deleterious impacts of covariate mismeasurements in logistic regression. The proposed adjustment method is unified and thus can be applied to any generalized linear and non-linear regression models. Furthermore, adjustment for covariate mismeasurements requires validation data usually in the form of either gold standard measurements or replicates of the mismeasured covariates on a subset of the study population. Initial investigation shows that adequacy of such adjustment depends on the sizes of main and validation samples, especially when prevalences of the categorical covariates are low. Thus, we investigate the impact of main and validation sample sizes on the adjusted estimates, and provide a general guideline about these sample sizes based on simulation studies.

Keywords: measurement errors, misclassification, mismeasurement, validation sample, Bayesian adjustment

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Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

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Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

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Authors: Sergio Andres Rojas, Julian Benavides Franco, Juan Tomas Sayago

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An increasing amount of research demonstrates how market mood affects financial markets, but their primary goal is to demonstrate how Trump's tweets impacted US interest rate volatility. Following that lead, this work evaluates the effect that Trump's tweets had during his presidency on local and international stock markets, considering not just volatility but the direction of the movement. Three indexes for Trump's tweets were created relating his activity with movements in the S&P500 using natural language analysis and machine learning algorithms. The indexes consider Trump's tweet activity and the positive or negative market sentiment they might inspire. The first explores the relationship between tweets generating negative movements in the S&P500; the second explores positive movements, while the third explores the difference between up and down movements. A pseudo-investment strategy using the indexes produced statistically significant above-average abnormal returns. The findings also showed that the pseudo strategy generated a higher return in the local market if applied to intraday data. However, only a negative market sentiment caused this effect on daily data. These results suggest that the market reacted primarily to a negative idea reflected in the negative index. In the international market, it is not possible to identify a pervasive effect. A rolling window regression model was also performed. The result shows that the impact on the local and international markets is heterogeneous, time-changing, and differentiated for the market sentiment. However, the negative sentiment was more prone to have a significant correlation most of the time.

Keywords: market sentiment, Twitter market sentiment, machine learning, natural dialect analysis

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Authors: Xu Yifei, Le Yining, Yang Qingluan, Tu Yanjie

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Objective: This study aims to investigate the distribution features of Traditional Chinese Medicine (TCM) syndromes and syndrome elements in Epstein-Barr virus-associated diseases and then explores the relations between TCM syndromes or syndrome elements and laboratory indicators of Epstein-Barr virus-associated diseases. Methods: A cross-sectional study of 70 patients with EBV infection was described. We assessed the diagnostic information and laboratory indicators of these patients from Huashan Hospital Affiliated to Fudan University between November 2017 and July 2019. The disease diagnosis and syndrome differentiation were based on the diagnostic criteria of EBV-associated diseases and the theory of TCM respectively. Confidence correlation analysis, logistic regression analysis, cluster analysis, and the Sankey diagram were used to analyze the correlation between the data. Results: The differentiation of the 4 primary TCM syndromes in the collected patients was correlated with the indexes of immune function, liver function, inflammation, and anemia, especially the relationship between Qifen syndrome and high lactic acid dehydrogenase level. The common 11 TCM syndrome elements were associated with the increased CD3+ T cell rate, low hemoglobin level, high procalcitonin level, high lactic acid dehydrogenase level, and low albumin level. Conclusion: The changes in immune function indexes, procalcitonin, and liver function-related indexes in patients with EBV-associated diseases were consistent with the evolution law of TCM syndromes. This study provides a reference for judging the pathological stages of these kinds of diseases, predicting their prognosis, and guiding subsequent treatment strategies based on TCM syndrome type.

Keywords: EBV-associated diseases, traditional Chinese medicine syndrome, syndrome element, diagnostics

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Authors: E. E. Marschalko, K. Kalcza-Janosi, I. Kotta, B. Bibok

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Cultural moderation aspects have been highlighted in the literature on self-determination behavior in some cultures, including in the Hungarian population. There is a lack of validated instruments in Hungarian for the assessment of self-determination related behaviors. In order to fill in this gap, the aim of this study was the translation, cultural adaptation and validation of Self Determination Scale (Sheldon, 1995) for the Hungarian population. A total of 4335 adults participated in the study. The mean age of the participants was 27.97 (SD=9.60). The sample consisted mostly from females, less than 20% were males. Exploratory and confirmatory factor analyses were performed for adequacy checking. Cronbach’s alpha was used to examine the reliability of the factors. Our results revealed that the Hungarian version of SDS has good psychometric properties and it is a reliable tool for psychologist who would like to study or assess self-determination in their clients. The final, adapted and validated SDS items are presented in this paper.

Keywords: self-determination scale, Hungarian, adaptation, validation, reliability

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Authors: Ian Phil Canlas, Mageswary Karpudewan, Joyce Magtolis, Rosario Canlas

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This study reported the development and validation of the Awareness to Disaster Risk Reduction Questionnaire for Teachers (ADRRQT). The questionnaire is a combination of Likert scale and open-ended questions that were grouped into two parts. The first part included questions relating to the general awareness on disaster risk reduction. Whereas, the second part comprised questions regarding the integration of disaster risk reduction in the teaching process. The entire process of developing and validating of the ADRRQT was described in this study. Statistical and qualitative findings revealed that the ADRRQT is significantly valid and reliable and has the potential of measuring awareness to disaster risk reduction of stakeholders in the field of teaching. Moreover, it also shows the potential to be adopted in other fields.

Keywords: awareness, development, disaster risk reduction, questionnaire, validation

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Authors: Carl van Walraven, Meltem Tuna

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Background: Network meta-analysis (NMA) quantifies the relative efficacy of 3 or more interventions from studies containing a subgroup of interventions. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct inclusion criteria that are evaluated on a common population (‘concurrent external validation’). Methods: We simulated binary events in 5000 patients using a known risk function. We biased the risk function and modified its precision by pre-specified amounts to create 15 prediction models with varying accuracy and distinct patient applicability. Prediction model accuracy was measured using the Scaled Brier Score (SBS). Overall prediction model accuracy was measured using fixed-effects methods that accounted for model applicability patterns. Prediction model accuracy was summarized as the Network Relative Model Accuracy (NeRMA) Score which ranges from -∞ through 0 (accuracy of random guessing) to 1 (accuracy of most accurate model in concurrent external validation). Results: The unbiased prediction model had the highest SBS. The NeRMA score correctly ranked all simulated prediction models by the extent of bias from the known risk function. A SAS macro and R-function was created to implement the NeRMA Score. Conclusions: The NeRMA Score makes it possible to quantify the accuracy of binomial prediction models having distinct inclusion criteria in a concurrent external validation.

Keywords: prediction model accuracy, scaled brier score, fixed effects methods, concurrent external validation

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Authors: Mohammadreza Mohebbi, Masoumeh Sanagou

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The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed.

Keywords: clinical prediction models, clinical decision rule, prognosis, external validation, model calibration, biostatistics

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Authors: Pragathi Gurumurthy, Christina Hagen, Patricia Ulloa, Martin A. Koch, Thorsten M. Buzug

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Obstructive sleep apnea (OSA) is a sleep disorder where the patient suffers a disturbed airflow during sleep due to partial or complete occlusion of the pharyngeal airway. Recently, numerical simulations have been used to better understand the mechanism of pharyngeal collapse. However, to gain confidence in the solutions so obtained, an experimental validation is required. Therefore, in this study an experimental validation of computational fluid dynamics (CFD) used for the study of human pharyngeal flow patterns during OSA is performed. A stationary incompressible Navier-Stokes equation solved using the finite element method was used to numerically study the flow patterns in a computed tomography-based human pharynx model. The inlet flow rate was set to 250 ml/s and such that a flat profile was maintained at the inlet. The outlet pressure was set to 0 Pa. The experimental technique used for the validation of CFD of fluid flow patterns is phase contrast-MRI (PC-MRI). Using the same computed tomography data of the human pharynx as in the simulations, a phantom for the experiment was 3 D printed. Glycerol (55.27% weight) in water was used as a test fluid at 25°C. Inflow conditions similar to the CFD study were simulated using an MRI compatible flow pump (CardioFlow-5000MR, Shelley Medical Imaging Technologies). The entire experiment was done on a 3 T MR system (Ingenia, Philips) with 108 channel body coil using an RF-spoiled, gradient echo sequence. A comparison of the axial velocity obtained in the pharynx from the numerical simulations and PC-MRI shows good agreement. The region of jet impingement and recirculation also coincide, therefore validating the numerical simulations. Hence, the experimental validation proves the reliability and correctness of the numerical simulations.

Keywords: computational fluid dynamics, experimental validation, phase contrast-MRI, obstructive sleep apnea

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Authors: Hanadi Mohamed Shawgi Gamal, Claus Thomas Bues

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Wood density is a variable influencing many of the technological and quality properties of wood. Understanding the pattern of wood density radial variation is important for its end-use. The X-ray technique, traditionally applied to softwood species to assess the wood quality properties, due to its simple and relatively uniform wood structure. On the other hand, very limited information is available about the validation of using this technique for hardwood species. The suitability of using the X-ray technique for the determination of hardwood density has a special significance in countries like Sudan, where only a few timbers are well known. This will not only save the time consumed by using the traditional methods, but it will also enhance the investigations of the great number of the lesser known species, the thing which will fill the huge cap of lake information of hardwood species growing in Sudan. The current study aimed to evaluate the validation of using the X-ray densitometry technique to determine the radial variation of wood density of Acacia seyal var. seyal. To this, a total of thirty trees were collected randomly from four states in Sudan. The wood density radial trend was determined using the basic density as well as density obtained by the X-ray densitometry method in order to assess the validation of X-ray technique in wood density radial variation determination. The results showed that the pattern of radial trend of density obtained by X-ray technique is very similar to that achieved by basic density. These results confirmed the validation of using the X-ray technique for Acacia seyal var. seyal density radial trend determination. It also promotes the suitability of using this method in other hardwood species.

Keywords: x-ray densitometry, wood density, Acacia seyal var. seyal, radial variation

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Authors: P. Zarfam, M. Mansouri Baghbaderani

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In seismic design, the proper reaction to the earthquake and the correct and accurate prediction of its subsequent effects on the structure are critical. Choose a proper probability distribution, which gives a more realistic probability of the structure's damage rate, is essential in damage discussions. With the development of design based on performance, analytical method of modal push over as an inexpensive, efficacious, and quick one in the estimation of the structures' seismic response is broadly used in engineering contexts. In this research three concrete frames of 3, 6, and 13 stories are analyzed in non-linear modal push over by 30 different earthquake records by OpenSEES software, then the detriment indexes of roof's displacement and relative displacement ratio of the stories are calculated by two parameters: peak ground acceleration and spectra acceleration. These indexes are used to establish the value of damage relations with log-normal distribution and logistics distribution. Finally the value of these relations is compared and the effect of height on the mentioned damage relations is studied, too.

Keywords: modal pushover analysis, concrete structure, seismic damage, log-normal distribution, logistic distribution

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Authors: Taghreed A. Ibrahim, Atef A. El-Hela, Hala M. El-Hefnawy

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The chromatographic profile of methanol extract of Hypericum sinaicum was determined using HPLC-DAD. Apigenin was used as an external standard in the development and validation of the HPLC method. The proposed method is simple, rapid and reliable and can be successfully applied for standardization of Hypericum sinaicum methanol extract.

Keywords: quality control, standardization, falvonoids, methanol extract

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Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Ghazal Faraj, András Micsik

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Shapes Constraint Language (SHACL), a World Wide Web Consortium (W3C) language, provides well-defined shapes and RDF graphs, named "shape graphs". These shape graphs validate other resource description framework (RDF) graphs which are called "data graphs". The structural features of SHACL permit generating a variety of conditions to evaluate string matching patterns, value type, and other constraints. Moreover, the framework of SHACL supports high-level validation by expressing more complex conditions in languages such as SPARQL protocol and RDF Query Language (SPARQL). SHACL includes two parts: SHACL Core and SHACL-SPARQL. SHACL Core includes all shapes that cover the most frequent constraint components. While SHACL-SPARQL is an extension that allows SHACL to express more complex customized constraints. Validating the efficacy of dataset mapping is an essential component of reconciled data mechanisms, as the enhancement of different datasets linking is a sustainable process. The conventional validation methods are the semantic reasoner and SPARQL queries. The former checks formalization errors and data type inconsistency, while the latter validates the data contradiction. After executing SPARQL queries, the retrieved information needs to be checked manually by an expert. However, this methodology is time-consuming and inaccurate as it does not test the mapping model comprehensively. Therefore, there is a serious need to expose a new methodology that covers the entire validation aspects for linking and mapping diverse datasets. Our goal is to conduct a new approach to achieve optimal validation outcomes. The first step towards this goal is implementing SHACL to validate the mapping between the International Committee for Documentation (CIDOC) conceptual reference model (CRM) and one of its ontologies. To initiate this project successfully, a thorough understanding of both source and target ontologies was required. Subsequently, the proper environment to run SHACL and its shape graphs were determined. As a case study, we performed SHACL over a CIDOC-CRM dataset after running a Pellet reasoner via the Protégé program. The applied validation falls under multiple categories: a) data type validation which constrains whether the source data is mapped to the correct data type. For instance, checking whether a birthdate is assigned to xsd:datetime and linked to Person entity via crm:P82a_begin_of_the_begin property. b) Data integrity validation which detects inconsistent data. For instance, inspecting whether a person's birthdate occurred before any of the linked event creation dates. The expected results of our work are: 1) highlighting validation techniques and categories, 2) selecting the most suitable techniques for those various categories of validation tasks. The next plan is to establish a comprehensive validation model and generate SHACL shapes automatically.

Keywords: SHACL, CIDOC-CRM, SPARQL, validation of ontology mapping

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Authors: R. Bouhennache, T. Bouden, A. A. Taleb, A. Chaddad

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In this paper we propose a method to map the urban areas. The method uses an arithmetic calculation processed from the land features indexes and Tasseled cap transformation TC of multi spectral Thematic Mapper Landsat TM image. For this purpose the derived indexes image from the original image such SAVI the soil adjusted vegetation index, UI the urban Index, and EBBI the enhanced built up and bareness index were staked to form a new image and the bands were uncorrelated, also the Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) supervised classification approaches were first applied on the new image TM data using the reference spectra of the spectral library and subsequently the four urban, vegetation, water and soil land cover categories were extracted with their accuracy assessment.The urban features were represented using a logic calculation applied to the brightness, UI-SAVI, NDBI-greenness and EBBI- brightness data sets. The study applied to Blida and mentioned that the urban features can be mapped with an accuracy ranging from 92 % to 95%.

Keywords: EBBI, SAVI, Tasseled Cap Transformation, UI

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Authors: Rama Devi Kyatham, D. Ashok, K. S. K. Rao Patnaik, Raju Bathula

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We have shown the importance of pyrazoles as anti-microbial chemical entities. These compounds have generally been considered significant due to their wide range of pharmacological acivities and their discovery motivates new avenues of research.The proposed pyrazoles were synthesized and evaluated for their anti-microbial activities. The Synthesized compounds were analyzed by different spectroscopic methods.

Keywords: pyrazoles, validation, resistant microbial strains, anti-microbial activities

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Authors: Vladyslav Povoroznyuk, Larysa Martynyuk, Iryna Syzonenko, Liliya Martynyuk

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Medical, social, and economic relevance of osteoporosis is caused by reducing quality of life, increasing disability and mortality of the patients as a result of fractures due to the low-energy trauma. This study is aimed to examine the associations of metabolic syndrome components, bone mineral density (BMD) and trabecular bone score (TBS) in menopausal women with non-vertebral fractures. 1161 menopausal women aged 50-79 year-old were examined and divided into three groups: A included 419 women with increased body weight (BMI - 25.0-29.9 kg/m²), B – 442 females with obesity (BMI >29.9 kg/m²)i and C – 300 women with metabolic syndrome (diagnosis according to IDF criteria, 2005). BMD of lumbar spine (L1-L4), femoral neck, total body and forearm was investigated with usage of dual-energy X-ray absorptiometry. The bone quality indexes were measured according to Med-Imaps installation. All analyses were performed using Statistical Package 6.0. BMD of lumbar spine (L1-L4), femoral neck, total body, and ultradistal radius was significant higher in women with obesity and metabolic syndrome compared to the pre-obese ones (p<0.001). TBS was significantly higher in women with increased body weight compared to obese and metabolic syndrome patients. Analysis showed significant positive correlation between waist circumference, triglycerides level and BMD of lumbar spine and femur. Significant negative association between serum HDL level and BMD of investigated sites was established. The TBS (L1-L4) indexes positively correlated with HDL (high-density lipoprotein) level. Despite the fact that BMD indexes were better in women with metabolic syndrome, the frequency of non-vertebral fractures was significantly higher in this group of patients.

Keywords: bone mineral density, trabecular bone score, metabolic syndrome, fracture

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Authors: S. Benchelha, H. C. Aoudjehane, M. Hakdaoui, R. El Hamdouni, H. Mansouri, T. Benchelha, M. Layelmam, M. Alaoui

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The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901).

Keywords: landslide susceptibility mapping, regression logistic, multivariate adaptive regression spline, Oudka, Taounate

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Authors: Julio Macedo

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Several methodologies are now available to conceive the improvements of a process so that it becomes competitive as for example total quality, process reengineering, six sigma, define measure analysis improvement control method. These improvements are of different nature and can be external to the process represented by an optimization model or a discrete simulation model. In addition, the process stakeholders are several and have different desired performances for the process. Hence, the methodologies above do not have a tool to aid in the conception of the required improvements. In order to fill this void we suggest the use of intelligent reference models. A reference model is a set of qualitative differential equations and an objective function that minimizes the gap between the current and the desired performance indexes of the process. The reference models are intelligent so when they receive the current state of the problematic process and the desired performance indexes they generate the required improvements for the problematic process. The reference models are fuzzy cognitive maps added with an objective function and trained using the improvements implemented by the high performance firms. Experiments done in a set of students show the reference models allow them to conceive more improvements than students that do not use these models.

Keywords: continuous improvement, fuzzy cognitive maps, process competitiveness, qualitative simulation, system dynamics

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Authors: Onur Arugaslan, Ajay Samant, Devrim Yaman

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Financial advisors and investors seek new assets which could potentially increase portfolio returns and decrease portfolio risk. Cryptocurrencies represent a relatively new asset class which could serve in both these roles. There has been very little research done in the area of the risk/return tradeoff in a portfolio consisting of fixed income assets, stocks, and cryptocurrency. The objective of this study is a rigorous examination of this issue. The data used in the study are the monthly returns on 4-week US Treasury Bills, S&P Investment Grade Corporate Bond Index, Bitcoin and the S&P 500 Stock Index. The methodology used in the study is the application Modern Portfolio Theory to evaluate the risk-adjusted returns of portfolios with varying combinations of these assets, using Sharpe, Treynor and Jensen Indexes, as well as the Sortino and Modigliani measures. The results of the study would include the ranking of various investment portfolios based on their risk/return characteristics. The conclusions of the study would include objective empirical inference for investors who are interested in including cryptocurrency in their asset portfolios but are unsure of the risk/return implications.

Keywords: financial economics, portfolio diversification, fixed income securities, cryptocurrency, stock indexes

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Authors: Rajaian Hoonejani Mohammad, Eshraghi Pegah, Zomorodian Zahra Sadat, Tahsildoost Mohammad

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

Procedia PDF Downloads 42

Authors: Leonardo C. Pacheco-Londoño, Nataly J Galan-Freyle, Lisandro Pacheco-Lugo, Antonio Acosta-Hoyos, Elkin Navarro, Gustavo Aroca-Martinez, Karin Rondón-Payares, Alberto C. Espinosa-Garavito, Samuel P. Hernández-Rivera

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At the Life Science Research Center at Simon Bolivar University, a primary focus is the diagnosis of various diseases, and the use of gold nanoparticles (Au-NPs) in diverse biomedical applications is continually expanding. In the present study, Au-NPs were employed as substrates for Surface-Enhanced Raman Spectroscopy (SERS) aimed at diagnosing kidney diseases arising from Lupus Nephritis (LN), preeclampsia (PC), and Hypertension (H). Discrimination models were developed for distinguishing patients with and without kidney diseases based on the SERS signals from urine samples by partial least squares-discriminant analysis (PLS-DA). A comparative study of the Raman signals across the three conditions was conducted, leading to the identification of potential metabolite signals. Model performance was assessed through cross-validation and external validation, determining parameters like sensitivity and specificity. Additionally, a secondary analysis was performed using machine learning (ML) models, wherein different ML algorithms were evaluated for their efficiency. Models’ validation was carried out using cross-validation and external validation, and other parameters were determined, such as sensitivity and specificity; the models showed average values of 0.9 for both parameters. Additionally, it is not possible to highlight this collaborative effort involved two university research centers and two healthcare institutions, ensuring ethical treatment and informed consent of patient samples.

Keywords: SERS, Raman, PLS-DA, kidney diseases

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Authors: Soheil Roumi, Fan Zhang, Rodney Stewart

Abstract:

Indoor environmental quality (IEQ) indexes represented the suitability of a place to study, work, and live. Many indexes have been introduced based on the physical measurement or occupant surveys in commercial buildings. The earlier studies did not elaborate on the relationship between energy consumption and IEQ in office buildings. Such a relationship can provide a comprehensive overview of the building's performance. Also, it would find the potential of already constructed buildings under the upcoming climate change. A commercial building in southeast Queensland, Australia, was evaluated in this study. Physical measurements of IEQ and Energy areconducted, and their relationship will be determined using statistical analysis. The case study building is modelled in TRNSys software, and it will be validatedusingthe actual building's BMS data. Then, the modelled buildingwill be simulated by predicted weather data developed by the commonwealth scientific and industrial research organisation of Australia to investigate the occupant resilience and energy consumption. Finally, recommendations will be presented to consume less energy while providinga proper indoor environment for office occupants.

Keywords: IEQ, office buildings, thermal comfort, occupant resilience

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Authors: Nizar Tayem, AbuMuhammad Moinuddeen, Ahmed A. Hussain, Redha M. Radaydeh

Abstract:

This research paper introduces an approach for estimating the direction of arrival (DOA) of multiple RF noncoherent sources in a uniform linear array (ULA). The proposed method utilizes a Capon-like estimation algorithm and incorporates LU decomposition to enhance the accuracy of DOA estimation while significantly reducing computational complexity compared to existing methods like the Capon method. Notably, the proposed method does not require prior knowledge of the number of sources. To validate its effectiveness, the proposed method undergoes validation through both software simulations and practical experimentation on a prototype testbed constructed using a software-defined radio (SDR) platform and GNU Radio software. The results obtained from MATLAB simulations and real-time experiments provide compelling evidence of the proposed method's efficacy.

Keywords: DOA estimation, real-time validation, software defined radio, computational complexity, Capon's method, GNU radio

Procedia PDF Downloads 51