Search results for: multivariate regression tree

Authors: Sadiq Sani, Muhammad B. Ibrahim

Abstract:

Watermelon rinds powder (WRP) and neem-tree leaves powder (NLP) were used as adsorbents for equilibrium adsorption isotherms studies for detoxification of methyl orange dye (MO) from simulated wastewater. The applicability of the process to various isotherm models was tested. All isotherms from the experimental data showed excellent linear reliability (R2: 0.9487-0.9992) but adsorptions onto WRP were more reliable (R2: 0.9724-0.9992) than onto NLP (R2: 0.9487-0.9989) except for Temkin’s Isotherm where reliability was better onto NLP (R2: 0.9937) than onto WRP (R2: 0.9935). Dubinin-Radushkevich’s monolayer adsorption capacities for both WRP and NLP (qD: 20.72 mg/g, 23.09 mg/g) were better than Langmuir’s (qm: 18.62 mg/g, 21.23 mg/g) with both capacities higher for adsorption onto NLP (qD: 23.09 mg/g; qm: 21.23 mg/g) than onto WRP (qD: 20.72 mg/g; qm: 18.62 mg/g). While values for Langmuir’s separation factor (RL) for both adsorbents suggested unfavourable adsorption processes (RL: -0.0461, -0.0250), Freundlich constant (nF) indicated favourable process onto both WRP (nF: 3.78) and NLP (nF: 5.47). Adsorption onto NLP had higher Dubinin-Radushkevich’s mean free energy of adsorption (E: 0.13 kJ/mol) than WRP (E: 0.08 kJ/mol) and Temkin’s heat of adsorption (bT) was better onto NLP (bT: -0.54 kJ/mol) than onto WRP (bT: -0.95 kJ/mol) all of which suggested physical adsorption.

Keywords: adsorption isotherms, methyl orange, neem leaves, watermelon rinds

Procedia PDF Downloads 274

Authors: T. J. Laws-Nicola

Abstract:

Henry Russell was best known as a composer of more than 300 songs. Many of his compositions were popular for both their sentimental texts, as in ‘The Old Armchair,’ and those of a more political nature, such as ‘Woodsman, Spare That Tree!’ Indeed, Russell had written such songs of advocacy as those associated with abolitionism (‘The Slave Ship’) and environmentalism (‘Woodsman, Spare that Tree!’). ‘The Maniac’ is his only composition addressing the issue of institutionalization. The text is borrowed and adapted from the monodrama The Captive by M.G. ‘Monk’ Lewis. Through an analysis of form, harmony, melody, text, and thematic development and interactions between text and music we can approach a clearer understanding of ‘The Maniac’ and how the text and music interact. Select periodicals, such as The London Times, provide contemporary critical review for ‘The Maniac.’ Additional nineteenth century songs whose texts focus on madness and/or institutionalization will assist in building a stylistic and cultural context for ‘The Maniac.’ Through comparative analyses of ‘The Maniac’ with a body of songs that focus on similar topics, we can approach a clear understanding of the song as a vehicle for deinstitutionalization.

Keywords: 19th century song, institutionalization, M. G. Lewis, Henry Russell

Procedia PDF Downloads 536

Authors: Brahim Boukabcha, Amar Rebbouh

Abstract:

The bootstrap method based on the idea of exploiting all the information provided by the initial sample, allows us to study the properties of estimators. In this article we will present a theoretical study on the different methods of bootstrapping and using the technique of re-sampling in statistics inference to calculate the standard error of means of an estimator and determining a confidence interval for an estimated parameter. We apply these methods tested in the regression models and Pareto model, giving the best approximations.

Keywords: bootstrap, error standard, bias, jackknife, mean, median, variance, confidence interval, regression models

Procedia PDF Downloads 381

Authors: Shahin Mirshekari, Mohammadreza Moradi, Hossein Jafari, Mehdi Jafari, Mohammad Ensaf

Abstract:

This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matern, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R² score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.

Keywords: Gaussian process regression, ensemble kernels, bayesian optimization, pharmaceutical sales analysis, time series forecasting, data analysis

Procedia PDF Downloads 71

Authors: Cristina Giosuè, Federica Biondo, Sergio Vitale

Abstract:

In the last years, the consumers' awareness for environmental concerns has been increasing, and seafood eco-labels are considered as a possible instrument to improve both seafood markets and sustainable fishing management. In this direction, the aim of this study was to carry out a meta-analysis on consumers’ willingness to pay (WTP) for eco-labeled wild seafood, by a meta-regression. Therefore, only papers published on ISI journals were searched on “Web of Knowledge” and “SciVerse Scopus” platforms, using the combinations of the following key words: seafood, ecolabel, eco-label, willingness, WTP and premium. The dataset was built considering: paper’s and survey’s codes, year of publication, first author’s nationality, species’ taxa and family, sample size, survey’s continent and country, data collection (where and how), gender and age of consumers, brand and ΔWTP. From analysis the interest on eco labeled seafood emerged clearly, in particular in developed countries. In general, consumers declared greater willingness to pay than that actually applied for eco-label products, with difference related to taxa and brand.

Keywords: eco label, meta regression, seafood, willingness to pay

Procedia PDF Downloads 123

Authors: Nancy Bartekian, Kaelan Vazquez, Christine Reyna

Abstract:

Immigrants coming into the United States of America may justify the American system (political, economic, healthcare, criminal justice) and see it as functional. This may be explained because they may come from countries that are even more unstable than the U.S. and/or come here to benefit from the promise of the “American dream” -a narrative that they might be more likely to believe in if they were willing to undergo the costly and sometimes dangerous process to immigrate. Conversely, native-born Americans, as well as immigrants who may have lived in America for a longer period of time, would have more experiences with the various broken systems in America that are dysfunctional, fail to provide adequate services equitably, and/or are steeped in systemic racism and other biases that disadvantage lower-status groups. Thus, our research expects that system justification would decrease, and condemnation would increase with more time spent in the U.S. for immigrant groups. We predict that a) those not born in the U.S. will be more likely to justify the system, b) they will also be less likely to condemn the system, and c) the longer an immigrant has been in the U.S. the less likely they will to justify, and more they will to condemn the system. We will use a mixed-model multivariate analysis of covariance (MANCOVA) and control for race, income, and education. We will also run linear regression models to test if there is a relationship between the length of time in the United States and a decrease in system justification, and length of time and an increase in system condemnation for those not born in the U.S. We will also conduct exploratory analyses to see if the predicted patterns are more likely within certain systems over other systems (political, economic, healthcare, criminal justice).

Keywords: immigration, system justification, system condemnation, system qualification

Procedia PDF Downloads 107

Authors: Seemal Mazhar Khan, Tahira Mubashar

Abstract:

The present research aimed to investigate the relationship in perceived organizational justice, organizational trust and employee engagement in bank employees. It was hypothesized: there is likely to be a relationship in perceived organizational justices, organizational trust and employee engagement; perceived organizational justice and organizational trust are likely to predict employee engagement; there is likely to be effect of bank type and designation on perceived organizational justice, organizational trust and employee engagement. The sample consisted of 150 bank employees (50 from government, 50 from private and 50 from privatized banks) selected from different banks in Lahore, Pakistan. Correlational research design was used to conduct this study. Perceived Organizational Justices Questionnaire, Organizational Trust Questionnaire and Employee Engagement Scale were used for assessment. Pearson product moment correlation, hierarchical regression and multivariate analysis of covariance were applied. Results showed a positive significant relationship in perceived organizational justice and organizational engagement and there were also a positive significant relation between organizational trust and job and organizational engagement. Results showed that organizational trust predicts organizational engagement after controlling the effect of age, marital status and socio-economic status and there is a significant interaction effect of bank type and designation level on organizational trust in bank employees. The findings of the research can serve as a platform for the awareness of important antecedents of employee engagement and organizations can inculcate trust for better and improved engagement of its employees, thereby, enhancing the productivity of their employees.

Keywords: bank employees, organizational engagement, perceived organizational justice, trust

Procedia PDF Downloads 399

Authors: Yina F. Muñoz, Alexander Paz, Hanns De La Fuente-Mella, Joaquin V. Fariña, Guilherme M. Sales

Abstract:

The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results.

Keywords: concrete bridges, deterioration, Markov chains, probability matrix

Procedia PDF Downloads 337

Authors: Kemal Polat

Abstract:

In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

Procedia PDF Downloads 327

Authors: Stephanie Chen, Mitja Echim, Christof Büskens

Abstract:

To describe and propagate the behavior of a system mathematical models are formulated. Parameter identification is used to adapt the coefficients of the underlying laws of science. For complex systems this approach can be incomplete and hence imprecise and moreover too slow to be computed efficiently. Therefore, these models might be not applicable for the numerical optimization of real systems, since these techniques require numerous evaluations of the models. Moreover not all quantities necessary for the identification might be available and hence the system must be adapted manually. Therefore, an approach is described that generates models that overcome the before mentioned limitations by not focusing on physical laws, but on measured (sensor) data of real systems. The approach is more general since it generates models for every system detached from the scientific background. Additionally, this approach can be used in a more general sense, since it is able to automatically identify correlations in the data. The method can be classified as a multivariate data regression analysis. In contrast to many other data regression methods this variant is also able to identify correlations of products of variables and not only of single variables. This enables a far more precise and better representation of causal correlations. The basis and the explanation of this method come from an analytical background: the series expansion. Another advantage of this technique is the possibility of real-time adaptation of the generated models during operation. Herewith system changes due to aging, wear or perturbations from the environment can be taken into account, which is indispensable for realistic scenarios. Since these data driven models can be evaluated very efficiently and with high precision, they can be used in mathematical optimization algorithms that minimize a cost function, e.g. time, energy consumption, operational costs or a mixture of them, subject to additional constraints. The proposed method has successfully been tested in several complex applications and with strong industrial requirements. The generated models were able to simulate the given systems with an error in precision less than one percent. Moreover the automatic identification of the correlations was able to discover so far unknown relationships. To summarize the above mentioned approach is able to efficiently compute high precise and real-time-adaptive data-based models in different fields of industry. Combined with an effective mathematical optimization algorithm like WORHP (We Optimize Really Huge Problems) several complex systems can now be represented by a high precision model to be optimized within the user wishes. The proposed methods will be illustrated with different examples.

Keywords: adaptive modeling, automatic identification of correlations, data based modeling, optimization

Procedia PDF Downloads 409

Authors: Pomila Sharma

Abstract:

Rajasthan is among regions with greatest climate sensitivity and lowest adaptive capabilities. In many parts of the Rajasthan surface water which can be highly turbid and contaminated with fecal coliform bacteria is used for drinking purposes. The majority rely almost exclusively upon traditional sources of highly turbid and untreated pathogenic surface water for their domestic water needs. In many parts of rural areas of Rajasthan, it is still difficult to obtain clean water, especially remote habitations with no groundwater due to quality issues or depletion and limited feasibility to connect with surface water schemes due to low density of population in these areas to justify large infrastructure investment. The most viable sources are rain water harvesting, community managed open wells, private wells, ponds and small-scale irrigation reservoirs have often been the main traditional sources of rural drinking water. Turbidity is conventionally removed by treating the water with expensive chemicals. This study has to investigate the use of crushed seeds from the tree Moringa oleifera (drumstick) as a natural alternative to conventional coagulant chemicals. The use of Moringa oleifera seed powder can produce potable water of higher quality than the original source. Moringa oleifera a native species of northern India, the tree is now grown extensively throughout the tropics and found in many countries of Africa, Asia & South America. The seeds of tree contains significant quantities of low molecular weight, water soluble proteins which carries the positive charge when the crushed seeds are added to water. This protein binds in raw water with negatively charged turbid water with bacteria, clay, algae, etc. Under proper mixing, these particles make flocks, which may be left to settle by gravity or be removed by filtration. Using Moringa oleifera as a replacement coagulation in such surface sources of arid and semi-arid areas can meet the need for water purification in remote places of Rajasthan state of India. The present study accesses to find out laboratory based investigation of the effect of seeds of Moringa tree on its coagulation effectiveness (purification) using turbid water samples of surface source of the Rajasthan state. In this study, moringa seed powder showed that filtering with seed powder may diminish water pollution and bacterial counts. Results showed Moringa oleifera seeds coagulate 90-95% of turbidity and color efficiently leading to an aesthetically clear supernatant & reduced about 85-90% of bacterial load reduction in samples.

Keywords: bacterial load, coagulant, turbidity, water purification

Procedia PDF Downloads 146

Authors: Fidelis E. Uwumiro, Chimaobi O. Nwevo, Favour O. Osemwota, Victory O. Okpujie, Emeka S. Obi, Omamuyovbi F. Nwoagbe, Ejiroghene Tejere, Joycelyn Adjei-Mensah, Christopher N. Ekeh, Charles T. Ogbodo

Abstract:

Several risk factors associated with cardiovascular events have been identified as specific to Chronic Kidney Disease (CKD). This study endeavors to validate these CKD-specific risk factors using up-to-date national-level data, thereby highlighting the crucial significance of confirming the validity and generalizability of findings obtained from previous studies conducted on smaller patient populations. The study utilized the nationwide inpatient sample database to identify adult hospitalizations for CKD from 2016 to 2020, employing validated ICD-10-CM/PCS codes. A comprehensive literature review was conducted to identify both traditional and CKD-specific risk factors associated with cardiovascular events. Risk factors and cardiovascular events were defined using a combination of ICD-10-CM/PCS codes and statistical commands. Only risk factors with specific ICD-10 codes and hospitalizations with complete data were included in the study. Cardiovascular events of interest included cardiac arrhythmias, sudden cardiac death, acute heart failure, and acute coronary syndromes. Univariate and multivariate regression models were employed to evaluate the association between chronic kidney disease-specific risk factors and cardiovascular events while adjusting for the impact of traditional CV risk factors such as old age, hypertension, diabetes, hypercholesterolemia, inactivity, and smoking. A total of 690,375 hospitalizations for CKD were included in the analysis. The study population was predominantly male (375,564, 54.4%) and primarily received care at urban teaching hospitals (512,258, 74.2%). The mean age of the study population was 61 years (SD 0.1), and 86.7% (598,555) had a CCI of 3 or more. At least one traditional risk factor for CV events was present in 84.1% of all hospitalizations (580,605), while 65.4% (451,505) included at least one CKD-specific risk factor for CV events. The incidence of CV events in the study was as follows: acute coronary syndromes (41,422; 6%), sudden cardiac death (13,807; 2%), heart failure (404,560; 58.6%), and cardiac arrhythmias (124,267; 18%). 91.7% (113,912) of all cardiac arrhythmias were atrial fibrillations. Significant odds of cardiovascular events on multivariate analyses included: malnutrition (aOR: 1.09; 95% CI: 1.06–1.13; p<0.001), post-dialytic hypotension (aOR: 1.34; 95% CI: 1.26–1.42; p<0.001), thrombophilia (aOR: 1.46; 95% CI: 1.29–1.65; p<0.001), sleep disorder (aOR: 1.17; 95% CI: 1.09–1.25; p<0.001), and post-renal transplant immunosuppressive therapy (aOR: 1.39; 95% CI: 1.26–1.53; p<0.001). The study validated malnutrition, post-dialytic hypotension, thrombophilia, sleep disorders, and post-renal transplant immunosuppressive therapy, highlighting their association with increased risk for cardiovascular events in CKD patients. No significant association was observed between uremic syndrome, hyperhomocysteinemia, hyperuricemia, hypertriglyceridemia, leptin levels, carnitine deficiency, anemia, and the odds of experiencing cardiovascular events.

Keywords: cardiovascular events, cardiovascular risk factors in CKD, chronic kidney disease, nationwide inpatient sample

Procedia PDF Downloads 82

Authors: Mahsa Tavakoli, Seyed Mohammad Hojjati, Yahya Kooch

Abstract:

Considering that coal mining is one of the important industrial activities, it may cause damages to environment. According to the author’s best knowledge, the effect of traditional coal mining activities on plant biodiversity has not been investigated in the Hyrcanian forests. Therefore, in this study, the effect of coal mining activities on vegetation and tree diversity was investigated in Hyrcanian forest, North Iran. After filed visiting and determining the mine, 16 plots (20×20 m²) were established by systematic-randomly (60×60 m²) in an area of 4 ha (200×200 m²-mine entrance placed at center). An area adjacent to the mine was not affected by the mining activity, and it is considered as the control area. In each plot, the data about trees such as number and type of species were recorded. The biodiversity of vegetation cover was considered 5 square sub-plots (1 m²) in each plot. PAST software and Ecological Methodology were used to calculate Biodiversity indices. The value of Shannon Wiener and Simpson diversity indices for tree cover in control area (1.04±0.34 and 0.62±0.20) was significantly higher than mining area (0.78±0.27 and 0.45±0.14). The value of evenness indices for tree cover in the mining area was significantly lower than that of the control area. The value of Shannon Wiener and Simpson diversity indices for vegetation cover in the control area (1.37±0.06 and 0.69±0.02) was significantly higher than the mining area (1.02±0.13 and 0.50±0.07). The value of evenness index in the control area was significantly higher than the mining area. Plant communities are a good indicator of the changes in the site. Study about changes in vegetation biodiversity and plant dynamics in the degraded land can provide necessary information for forest management and reforestation of these areas.

Keywords: vegetation biodiversity, species composition, traditional coal mining, Caspian forest

Procedia PDF Downloads 183

Authors: Ovidiu Domşa, Nicolae Bold

Abstract:

Using Information and Communication Technologies (ICT) notions in education and three basic processes of education (teaching, learning and assessment) can bring benefits to the pupils and the professional development of teachers. In this matter, we refer to these notions as concepts taken from the informatics area and apply them to the domain of education. These notions refer to genetic algorithms and arborescent structures, used in the specific process of assessment or evaluation. This paper uses these kinds of notions to generate subtrees from a main tree of tests related between them by their degree of difficulty. These subtrees must contain the highest number of connections between the nodes and the lowest number of missing edges (which are subtrees of the main tree) and, in the particular case of the non-existence of a subtree with no missing edges, the subtrees which have the lowest (minimal) number of missing edges between the nodes, where a node is a test and an edge is a direct connection between two tests which differs by one degree of difficulty. The subtrees are represented as sequences. The tests are the same (a number coding a test represents that test in every sequence) and they are reused for each sequence of tests.

Keywords: chromosome, genetic algorithm, subtree, test

Procedia PDF Downloads 325

Authors: Renata Konadu

Abstract:

In Ghana, electricity has become the main form of energy which all sectors of the economy rely on for their businesses. Therefore, as the economy grows, the demand and consumption of electricity also grow alongside due to the heavy dependence on it. However, since the supply of electricity has not increased to match the demand, there has been frequent power outages and load shedding affecting business performances. To solve this problem and advance policies to secure electricity in Ghana, it is imperative that those factors that cause consumption to increase be analysed by considering the three classes of consumers; residential, industrial and non-residential. The main argument, however, is that, export of electricity to other neighbouring countries should be included in the electricity consumption model and considered as one of the significant factors which can decrease or increase consumption. The author made use of multivariate time series data from 1980-2010 and econometric models such as Ordinary Least Squares (OLS) and Vector Error Correction Model. Findings show that GDP growth, urban population growth, electricity exports and industry value added to GDP were cointegrated. The results also showed that there is unidirectional causality from electricity export and GDP growth and Industry value added to GDP to electricity consumption in the long run. However, in the short run, there was found to be a directional causality among all the variables and electricity consumption. The results have useful implication for energy policy makers especially with regards to electricity consumption, demand, and supply.

Keywords: electricity consumption, energy policy, GDP growth, vector error correction model

Procedia PDF Downloads 437

Authors: Thota Sai Prakash, B. Yaswanth, Jhade Bhuvaneswar, Marreddy Divakar Reddy, Shyam Ji Gupta

Abstract:

Across the globe, there are a lot of chronic diseases, and heart disease stands out as one of the most perilous. Sadly, many lives are lost to this condition, even though early intervention could prevent such tragedies. However, identifying heart disease in its initial stages is not easy. To address this challenge, we propose an automated system aimed at predicting the presence of heart disease using advanced techniques. By doing so, we hope to empower individuals with the knowledge needed to take proactive measures against this potentially fatal illness. Our approach towards this problem involves meticulous data preprocessing and the development of predictive models utilizing classification algorithms such as Support Vector Machines (SVM), Decision Tree, and Random Forest. We assess the efficiency of every model based on metrics like accuracy, ensuring that we select the most reliable option. Additionally, we conduct thorough data analysis to reveal the importance of different attributes. Among the models considered, Random Forest emerges as the standout performer with an accuracy rate of 96.04% in our study.

Keywords: support vector machines, decision tree, random forest

Procedia PDF Downloads 42

Authors: Jiajia Peng, Jianqing Qiu, Jianjun Ren, Yu Zhao

Abstract:

Background: We aimed to compare clinical and survival differences between B-cell (B-NHL) and NKT-cell non-Hodgkin lymphomas (NKT-NHL) located in the nasal cavity, nasopharynx and paranasal sinuses, which are always categorized as one sinonasal type. Methods: Patients diagnosed with primary B-NHL and NKT-NHL in the nasal cavity, nasopharynx, and paranasal sinuses from the SEER database were included. We identified these patients based on histological types and anatomical sites and subsequently conducted univariate and multivariate Cox regression and Kaplan–Meier analyses to examine cancer-special survival (CSS) outcomes. Results: Overall, most B-NHL cases originated from the nasopharynx, while the majority of NKT-NHL cases occurred in the nasal cavity. Notably, the CSS outcomes improved significantly in all sinonasal B-NHL cases over time, whereas no such improvement trend was observed in each sinonasal NKT-NHL type. Additionally, increasing age was linked with an elevated risk of death in B-NHL, particularly in the nasal cavity (HR:3.37), rather than in NKT-NHL. Compared with B-NHL, the adverse effect of the higher stage on CSS was more evident in NKT-NHL, particularly in its nasopharynx site (HR: 5.12). Furthermore, radiotherapy was beneficial for survival in patients with sinonasal B-NHL and NKT-NHL, except in those with NKT-NHL in the nasopharynx site. However, chemotherapy has only been beneficial for CSS in patients with B-NHL in paranasal sinuses (HR: 0.42) since 2010, rather than in other types of B-NHL or NKT-NHL. Conclusions: Although B-NHL and NKT-NHL in the nasal cavity, nasopharynx and paranasal sinuses have similar anatomical locations, their clinic demographics and prognoses are largely different and should be treated and studied as distinct diseases.

Keywords: B-cell non-Hodgkin lymphomas, NKT-cell non-Hodgkin lymphomas, nasal cavity lymphomas, nasal sinuses lymphomas, nasopharynx lymphomas

Procedia PDF Downloads 106

Authors: Anandita Mitro, Archana Srivastava, Bidisha Banerjee

Abstract:

The present paper attempts to analyze the hierarchy and interconnection of factors responsible for the uptake of BCG vaccination in India. The study uses National Family Health Survey (NFHS-5) data which was conducted during 2019-21. The univariate logistic regression method is used to understand the univariate effects while the interconnection effects have been studied using the Categorical Inference Tree (CIT) which is a non-parametric Machine Learning (ML) model. The hierarchy of the factors is further established using Conditional Inference Forest which is an extension of the CIT approach. The results suggest that BCG vaccination coverage was influenced more by system-level factors and awareness than education or socio-economic status. Factors such as place of delivery, antenatal care, and postnatal care were crucial, with variations based on delivery location. Region-specific differences were also observed which could be explained by the factors. Awareness of the disease was less impactful along with the factor of wealth and urban or rural residence, although awareness did appear to substitute for inadequate ANC. Thus, from the policy point of view, it is revealed that certain subpopulations have less prevalence of vaccination which implies that there is a need for population-specific policy action to achieve a hundred percent coverage.

Keywords: vaccination, NFHS, machine learning, public health

Procedia PDF Downloads 60

Authors: Juan Francisco Reinoso Gordo, Francisco Javier Ariza-López, José Rodríguez Avi, Domingo Barrera Rosillo

Abstract:

The evaluation of the quality of a data product is based on the comparison of the product with a reference of greater accuracy. In the case of MDE data products, quality assessment usually focuses on positional accuracy and few studies consider other terrain characteristics, such as slope and orientation. The proposal that is made consists of evaluating the similarity of two DEMs (a product and a reference), through the joint analysis of the distribution functions of the variables of interest, for example, elevations, slopes and orientations. This is a multivariable approach that focuses on distribution functions, not on single parameters such as mean values or dispersions (e.g. root mean squared error or variance). This is considered to be a more holistic approach. The use of the Kolmogorov-Smirnov test is proposed due to its non-parametric nature, since the distributions of the variables of interest cannot always be adequately modeled by parametric models (e.g. the Normal distribution model). In addition, its application to the multivariate case is carried out jointly by means of a single test on the convolution of the distribution functions of the variables considered, which avoids the use of corrections such as Bonferroni when several statistics hypothesis tests are carried out together. In this work, two DEM products have been considered, DEM02 with a resolution of 2x2 meters and DEM05 with a resolution of 5x5 meters, both generated by the National Geographic Institute of Spain. DEM02 is considered as the reference and DEM05 as the product to be evaluated. In addition, the slope and aspect derived models have been calculated by GIS operations on the two DEM datasets. Through sample simulation processes, the adequate behavior of the Kolmogorov-Smirnov statistical test has been verified when the null hypothesis is true, which allows calibrating the value of the statistic for the desired significance value (e.g. 5%). Once the process has been calibrated, the same process can be applied to compare the similarity of different DEM data sets (e.g. the DEM05 versus the DEM02). In summary, an innovative alternative for the comparison of DEM data sets based on a multinomial non-parametric perspective has been proposed by means of a single Kolmogorov-Smirnov test. This new approach could be extended to other DEM features of interest (e.g. curvature, etc.) and to more than three variables

Keywords: data quality, DEM, kolmogorov-smirnov test, multivariate DEM comparison

Procedia PDF Downloads 115

Authors: Tesfay Gidey, Emiru Birhane, Ashenafi Manaye, Hailemariam Kassa, Tesfay Atsbha, Negasi Solomon, Hadgu Hishe, Aklilu Negussie, Petr Madera, Jose G. Borges

Abstract:

The globally endangered Dracaena ombet is one of the ten dragon multipurpose tree species in arid ecosystems. Anthropogenic and natural factors are now impacting the sustainability of the species. This study was conducted to prioritize criteria and alternative strategies for the conservation of the species using the analytical hierarchy process (AHP) model by involving all relevant stakeholders in the Desa'a dry Afromontane forest in northern Ethiopia. Information about the potential alternative strategies and the criteria for their evaluation was first collected from experts, personal experiences, and literature reviews. Afterward, they were validated using stakeholders' focus group discussions. Five candidate strategies with three evaluation criteria were considered for prioritization using the AHP techniques. The overall priority ranking value of the stakeholders showed that the ecological criterion was deemed as the most essential factor for the choice of alternative strategies, followed by the economic and social criteria. The minimum cut-off strategy, combining exclosures with the collection of only 5% of plant parts from the species, soil and water conservation, and silviculture interventions, was selected as the best alternative strategy for sustainable D. ombet conservation. The livelihood losses due to the selected strategy should be compensated by the collection of non-timber forest products, poultry farming, home gardens, rearing small ruminants, beekeeping, and agroforestry. This approach may be extended to study other dragon tree species and explore strategies for the conservation of other arid ecosystems.

Keywords: conservation strategies, analytical hierarchy process model, Desa'a forest, endangered species, Ethiopia, overexploitation

Procedia PDF Downloads 93

Authors: Encarnación Álvarez, Rosa M. García-Fernández, Juan F. Muñoz

Abstract:

Estimation of a proportion has many applications in economics and social studies. A common application is the estimation of the low income proportion, which gives the proportion of people classified as poor into a population. In this paper, we present this poverty indicator and propose to use the logistic regression estimator for the problem of estimating the low income proportion. Various sampling designs are presented. Assuming a real data set obtained from the European Survey on Income and Living Conditions, Monte Carlo simulation studies are carried out to analyze the empirical performance of the logistic regression estimator under the various sampling designs considered in this paper. Results derived from Monte Carlo simulation studies indicate that the logistic regression estimator can be more accurate than the customary estimator under the various sampling designs considered in this paper. The stratified sampling design can also provide more accurate results.

Keywords: poverty line, risk of poverty, auxiliary variable, ratio method

Procedia PDF Downloads 457

Authors: Mohamed Mouloud Haddak, Bouthayna Hayou

Abstract:

Road accidents in French overseas territories have been understudied, with relevant data often collected late and incompletely. Although these territories account for only 3% to 4% of road traffic injuries in France, their unique characteristics merit closer attention. Despite lower mobility and, consequently, lower exposure to road risks, the actual road risk in Overseas France is as high or even higher than in Metropolitan France. Significant disparities exist not only between Metropolitan France and Overseas territories but also among the overseas territories themselves. The varying population densities in these regions do not fully explain these differences, as each territory has its own distinct vulnerabilities and road safety challenges. This analysis, based on BAAC data files from 2005 to 2018 for both Metropolitan France and the overseas departments and regions, examines key variables such as gender, age, type of road user, type of obstacle hit, type of trip, road category, traffic conditions, weather, and location of accidents. Logistic regression models were built for each region to investigate the risk factors associated with fatal road accidents, focusing on the probability of being killed versus injured. Due to insufficient data, Mayotte and the Overseas Communities (French Polynesia and New Caledonia) were not included in the models. The findings reveal that road safety is worse in the overseas territories compared to Metropolitan France, particularly for vulnerable road users such as pedestrians and motorized two-wheelers. These territories present an accident profile that sits between that of Metropolitan France and middle-income countries. A pressing need exists to standardize accident data collection between Metropolitan and Overseas France to allow for more detailed comparative analyses. Further epidemiological studies could help identify the specific road safety issues unique to each territory, particularly with regards to socio-economic factors such as social cohesion, which may influence road safety outcomes. Moreover, the lack of data on new modes of travel, such as electric scooters, and the absence of socio-economic details of accident victims complicate the evaluation of emerging risk factors. Additional research, including sociological and psychosocial studies, is essential for understanding road users' behavior and perceptions of road risk, which could also provide valuable insights into accident trends in peri-urban areas in France.

Keywords: multivariate logistic regression, french overseas regions, road safety, road traffic accidents, territorial inequalities

Procedia PDF Downloads 14

Authors: G. Rajeshwari, V. D. M. Jabez Daniel

Abstract:

Radio Frequency Identification systems are used for automated identification in various applications such as automobiles, health care and security. It is also called as the automated data collection technology. RFID readers are placed in any area to scan large number of tags to cover a wide distance. The placement of the RFID elements may result in several types of collisions. A major challenge in RFID system is collision avoidance. In the previous works the collision was avoided by using algorithms such as ALOHA and tree algorithm. This work proposes collision reduction and increased throughput through reading enhancement method with tree algorithm. The reading enhancement is done by improving interrogation procedure and increasing the data handling capacity of RFID reader with parallel processing. The work is simulated using Xilinx ISE 14.5 verilog language. By implementing this in the RFID system, we can able to achieve high throughput and avoid collision in the reader at a same instant of time. The overall system efficiency will be increased by implementing this.

Keywords: antenna, anti-collision protocols, data management system, reader, reading enhancement, tag

Procedia PDF Downloads 306

Authors: Naragain Phumchusri, Kittitach Pongpairoj

Abstract:

Overbooking is a very useful revenue management technique that could help reduce costs caused by either undersales or oversales. In this paper, we propose an overbooking model for two types of cars that can minimize the total cost for car rental service. With two types of cars, there is an upgrade possibility for lower type to upper type. This makes the model more complex than one type of cars scenario. We have found that convexity can be proved in this case. Sensitivity analysis of the parameters is conducted to observe the effects of relevant parameters on the optimal solution. Model simplification is proposed using multiple linear regression analysis, which can help estimate the optimal overbooking level using appropriate independent variables. The results show that the overbooking level from multiple linear regression model is relatively close to the optimal solution (with the adjusted R-squared value of at least 72.8%). To evaluate the performance of the proposed model, the total cost was compared with the case where the decision maker uses a naïve method for the overbooking level. It was found that the total cost from optimal solution is only 0.5 to 1 percent (on average) lower than the cost from regression model, while it is approximately 67% lower than the cost obtained by the naïve method. It indicates that our proposed simplification method using regression analysis can effectively perform in estimating the overbooking level.

Keywords: overbooking, car rental industry, revenue management, stochastic model

Procedia PDF Downloads 173

Authors: Matthew Buns

Abstract:

Training specificity is imperative for successful performance of the elite athlete. Virtual reality (VR) has been successfully applied to a broad range of training domains. However, to date there is little research investigating the use of VR for sport training. The purpose of this study was to address the question of whether virtual reality (VR) training can improve real world hockey shooting performance. Twenty four volunteers were recruited and randomly selected to complete the virtual training intervention or enter a control group with no training. Four primary types of data were collected: 1) participant’s experience with video games and hockey, 2) participant’s motivation toward video game use, 3) participants technical performance on real-world hockey, and 4) participant’s technical performance in virtual hockey. One-way multivariate analysis of variance (ANOVA) indicated that that the intervention group demonstrated significantly more real-world hockey accuracy [F(1,24) =15.43, p <.01, E.S. = 0.56] while shooting on goal than their control group counterparts [intervention M accuracy = 54.17%, SD=12.38, control M accuracy = 46.76%, SD=13.45]. One-way multivariate analysis of variance (MANOVA) repeated measures indicated significantly higher outcome scores on real-world accuracy (35.42% versus 54.17%; ES = 1.52) and velocity (51.10 mph versus 65.50 mph; ES=0.86) of hockey shooting on goal. This research supports the idea that virtual training is an effective tool for increasing real-world hockey skill.

Keywords: virtual training, hockey skills, video game, esports

Procedia PDF Downloads 147

Authors: Bidyadhar Dehury, Sanjay Kumar Mohanty

Abstract:

According to the Right to Education Act of India, education is the fundamental right of all children of age group 6-14 year irrespective of their status. Using the unit level data from India Human Development Survey (IHDS), we tried to understand the inter-relationship between the level of poverty and the academic performance of the children aged 8-11 years. The level of multidimensional poverty is measured using five dimensions and 10 indicators using Alkire-Foster approach. The weighted deprivation score was obtained by giving equal weight to each dimension and indicators within the dimension. The weighted deprivation score varies from 0 to 1 and grouped into four categories as non-poor, vulnerable, multidimensional poor and sever multidimensional poor. The academic performance index was measured using three variables reading skills, math skills and writing skills using PCA. The bivariate and multivariate analysis was used in the analysis. The outcome variable was ordinal. So the predicted probabilities were calculated using the ordinal logistic regression. The predicted probabilities of good academic performance index was 0.202 if the child was sever multidimensional poor, 0.235 if the child was multidimensional poor, 0.264 if the child was vulnerable, and 0.316 if the child was non-poor. Hence, if the level of poverty among the children decreases from sever multidimensional poor to non-poor, the probability of good academic performance increases.

Keywords: multidimensional poverty, academic performance index, reading skills, math skills, writing skills, India

Procedia PDF Downloads 595

Authors: Chaudhuri Manoj Kumar Swain, Susmita Das

Abstract:

This paper explores a detailed procedure of predicting a path loss (PL) model and its application in estimating the coverage probability in a WiMAX network. For this a hybrid approach is followed in predicting an empirical PL model of a 2.65 GHz WiMAX network deployed in a suburban environment. Data collection, statistical analysis, and regression analysis are the phases of operations incorporated in this approach and the importance of each of these phases has been discussed properly. The procedure of collecting data such as received signal strength indicator (RSSI) through experimental set up is demonstrated. From the collected data set, empirical PL and RSSI models are predicted with regression technique. Furthermore, with the aid of the predicted PL model, essential parameters such as PL exponent as well as the coverage probability of the network are evaluated. This research work may assist in the process of deployment and optimisation of any cellular network significantly.

Keywords: WiMAX, RSSI, path loss, coverage probability, regression analysis

Procedia PDF Downloads 179

Authors: Esmail Sadipour

Abstract:

The use of social networks is increasing day by day in all societies. The purpose of this research was to know the relationship between the use of social networks (Instagram, WhatsApp, and Telegram) with executive functions and academic performance in first-year female high school students. This research was applied in terms of purpose, quantitative in terms of data type, and correlational in terms of technique. The population of this research consisted of all female high school students in the first year of district 2 of Tehran. Using Green's formula, the sample size of 150 people was determined and selected by cluster random method. In this way, from all 17 high schools in district 2 of Tehran, 5 high schools were selected by a simple random method and then one class was selected from each high school, and a total of 155 students were selected. To measure the use of social networks, a researcher-made questionnaire was used, the Barclay test (2012) was used for executive functions, and last semester's GPA was used for academic performance. Pearson's correlation coefficient and multivariate regression were used to analyze the data. The results showed that there is a negative relationship between the amount of use of social networks and self-control, self-motivation and time self-management. In other words, the more the use of social networks, the fewer executive functions of students, self-control, self-motivation, and self-management of their time. Also, with the increase in the use of social networks, the academic performance of students has decreased.

Keywords: social networks, executive function, academic performance, working memory

Procedia PDF Downloads 97

Authors: Bouthayna Hayou, Mohamed Mouloud Haddak

Abstract:

In road accidents in French overseas territories have been understudied, with relevant data often collected late and incompletely. Although these territories account for only 3% to 4% of road traffic injuries in France, their unique characteristics merit closer attention. Despite lower mobility and, consequently, lower exposure to road risks, the actual road risk in Overseas France is as high or even higher than in Metropolitan France. Significant disparities exist not only between Metropolitan France and Overseas territories but also among the overseas territories themselves. The varying population densities in these regions do not fully explain these differences, as each territory has its own distinct vulnerabilities and road safety challenges. This analysis, based on BAAC data files from 2005 to 2018 for both Metropolitan France and the overseas departments and regions, examines key variables such as gender, age, type of road user, type of obstacle hit, type of trip, road category, traffic conditions, weather, and location of accidents. Logistic regression models were built for each region to investigate the risk factors associated with fatal road accidents, focusing on the probability of being killed versus injured. Due to insufficient data, Mayotte and the Overseas Communities (French Polynesia and New Caledonia) were not included in the models. The findings reveal that road safety is worse in the overseas territories compared to Metropolitan France, particularly for vulnerable road users such as pedestrians and motorized two-wheelers. These territories present an accident profile that sits between that of Metropolitan France and middle-income countries. A pressing need exists to standardize accident data collection between Metropolitan and Overseas France to allow for more detailed comparative analyses. Further epidemiological studies could help identify the specific road safety issues unique to each territory, particularly with regard to socio-economic factors such as social cohesion, which may influence road safety outcomes. Moreover, the lack of data on new modes of travel, such as electric scooters, and the absence of socio-economic details of accident victims complicate the evaluation of emerging risk factors. Additional research, including sociological and psychosocial studies, is essential for understanding road users' behavior and perceptions of road risk, which could also provide valuable insights into accident trends in peri-urban areas in France.

Keywords: multivariate logistic regression, overseas France, road safety, road traffic accident, territorial inequalities

Procedia PDF Downloads 13

Authors: Goksel Ezgi Guzey, Bihrat Onoz

Abstract:

The scarcity of streamflow gauging stations and the increasing effects of global warming cause designing water management systems to be very difficult. This study is a significant contribution to assessing regional regression models for estimating streamflow. In this study, simulated meteorological data was related to the observed streamflow data from 1971 to 2020 for 33 stream gauging stations of the Euphrates-Tigris Basin. Ordinary least squares regression was used to predict flow for 2020-2100 with the simulated meteorological data. CORDEX- EURO and CORDEX-MENA domains were used with 0.11 and 0.22 grids, respectively, to estimate climate conditions under certain climate scenarios. Twelve meteorological variables simulated by two regional climate models, RCA4 and RegCM4, were used as independent variables in the ordinary least squares regression, where the observed streamflow was the dependent variable. The variability of streamflow was then calculated with 5-6 meteorological variables and watershed characteristics such as area and height prior to the application. Of the regression analysis of 31 stream gauging stations' data, the stations were subjected to a clustering analysis, which grouped the stations in two clusters in terms of their hydrometeorological properties. Two streamflow equations were found for the two clusters of stream gauging stations for every domain and every regional climate model, which increased the efficiency of streamflow estimation by a range of 10-15% for all the models. This study underlines the importance of homogeneity of a region in estimating streamflow not only in terms of the geographical location but also in terms of the meteorological characteristics of that region.

Keywords: hydrology, streamflow estimation, climate change, hydrologic modeling, HBV, hydropower

Procedia PDF Downloads 129