Search results for: performance predicting formula

Authors: Zane Turner, Kevin Labille, Susan Gauch

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Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Zane Turner, Kevin Labille, Susan Gauch

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Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Jorge Gonzalez Camus, Valentin Keyantuo, Mahamadi Warma

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In this work, we obtain representation results for solutions of a time-fractional differential equation involving the discrete fractional Laplace operator in terms of generalized Wright functions. Such equations arise in the modeling of many physical systems, for example, chain processes in chemistry and radioactivity. The focus is on the linear problem of the simplified Moore - Gibson - Thompson equation, where the discrete fractional Laplacian and the Caputo fractional derivate of order on (0,2] are involved. As a particular case, we obtain the explicit solution for the discrete heat equation and discrete wave equation. Furthermore, we show the explicit solution for the equation involving the perturbed Laplacian by the identity operator. The main tool for obtaining the explicit solution are the Laplace and discrete Fourier transforms, and Stirling's formula. The methodology mainly is to apply both transforms in the equation, to find the inverse of each transform, and to prove that this solution is well defined, using Stirling´s formula.

Keywords: discrete fractional Laplacian, explicit representation of solutions, fractional heat and wave equations, fundamental

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Authors: T. J. Stepien, L. T. Stepien

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This abstract concerns an extremely fundamental issue. Namely, the fundamental problem of science is the issue of consistency. In this abstract, we present the theorem saying that the classical calculus of quantifiers is inconsistent in the traditional sense. At the beginning, we introduce a notation, and later we remind the definition of the consistency in the traditional sense. S1 is the set of all well-formed formulas in the calculus of quantifiers. RS1 denotes the set of all rules over the set S1. Cn(R, X) is the set of all formulas standardly provable from X by rules R, where R is a subset of RS1, and X is a subset of S1. The couple < R,X > is called a system, whenever R is a subset of RS1, and X is a subset of S1. Definition: The system < R,X > is consistent in the traditional sense if there does not exist any formula from the set S1, such that this formula and its negation are provable from X, by using rules from R. Finally, < R0+, L2 > denotes the classical calculus of quantifiers, where R0+ consists of Modus Ponens and the generalization rule. L2 is the set of all formulas valid in the classical calculus of quantifiers. The Main Result: The system < R0+, L2 > is inconsistent in the traditional sense.

Keywords: classical calculus of quantifiers, classical predicate calculus, generalization rule, consistency in the traditional sense, Modus Ponens

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Authors: M. Sirhan, S. Bekhor, A. Sidess

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In-service pavements deteriorate with time due to traffic wheel loads, environment, and climate conditions. Pavement deterioration leads to a reduction in their serviceability and structural behavior. Consequently, proper maintenance and rehabilitation (M&R) are necessary actions to keep the in-service pavement network at the desired level of serviceability. Due to resource and financial constraints, the pavement management system (PMS) prioritizes roads most in need of maintenance and rehabilitation action. It recommends a suitable action for each pavement based on the performance and surface condition of each road in the network. The pavement performance and condition are usually quantified and evaluated by different types of roughness-based and stress-based indices. Examples of such indices are Pavement Serviceability Index (PSI), Pavement Serviceability Ratio (PSR), Mean Panel Rating (MPR), Pavement Condition Rating (PCR), Ride Number (RN), Profile Index (PI), International Roughness Index (IRI), and Pavement Condition Index (PCI). PCI is commonly used in PMS as an indicator of the extent of the distresses on the pavement surface. PCI values range between 0 and 100; where 0 and 100 represent a highly deteriorated pavement and a newly constructed pavement, respectively. The PCI value is a function of distress type, severity, and density (measured as a percentage of the total pavement area). PCI is usually calculated iteratively using the 'Paver' program developed by the US Army Corps. The use of soft computing techniques, especially Artificial Neural Network (ANN), has become increasingly popular in the modeling of engineering problems. ANN techniques have successfully modeled the performance of the in-service pavements, due to its efficiency in predicting and solving non-linear relationships and dealing with an uncertain large amount of data. Typical regression models, which require a pre-defined relationship, can be replaced by ANN, which was found to be an appropriate tool for predicting the different pavement performance indices versus different factors as well. Subsequently, the objective of the presented study is to develop and train an ANN model that predicts the PCI values. The model’s input consists of percentage areas of 11 different damage types; alligator cracking, swelling, rutting, block cracking, longitudinal/transverse cracking, edge cracking, shoving, raveling, potholes, patching, and lane drop off, at three severity levels (low, medium, high) for each. The developed model was trained using 536,000 samples and tested on 134,000 samples. The samples were collected and prepared by The National Transport Infrastructure Company. The predicted results yielded satisfactory compliance with field measurements. The proposed model predicted PCI values with relatively low standard deviations, suggesting that it could be incorporated into the PMS for PCI determination. It is worth mentioning that the most influencing variables for PCI prediction are damages related to alligator cracking, swelling, rutting, and potholes.

Keywords: artificial neural networks, computer programming, pavement condition index, pavement management, performance prediction

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Authors: Sumachaya Harnsukworapanich, Tetsuo Ichimori

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The apportionment method is used by many countries, to calculate the distribution of seats in political bodies. For example, this method is used in the United States (U.S.) to distribute house seats proportionally based on the population of the electoral district. Famous apportionment methods include the divisor methods called the Adams Method, Dean Method, Hill Method, Jefferson Method and Webster Method. Sometimes the results from the implementation of these divisor methods are unfair and include errors. Therefore, it is important to examine the optimization of this method by using a bias measurement to figure out precise and fair results. In this research we investigate the bias of divisor methods in the U.S. Houses of Representatives toward large and small states by applying the Stolarsky Mean Method. We compare the bias of the apportionment method by using two famous bias measurements: The Balinski and Young measurement and the Ernst measurement. Both measurements have a formula for large and small states. The Third measurement however, which was created by the researchers, did not factor in the element of large and small states into the formula. All three measurements are compared and the results show that our measurement produces similar results to the other two famous measurements.

Keywords: apportionment, bias, divisor, fair, measurement

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Authors: Nopadol Nimsuwan, Sumet Prom-in

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The purpose of this study was to investigate the body types, size, and body compositions of softball players in the 39th National Games of Thailand. The population of this study was 352 softball players who participated in the 39th National Games of Thailand from which a sample size of 291 was determined using the Taro Yamane formula and selection is made with stratified sampling method. The data collected were weight, height, arm length, leg length, chest circumference, mid-upper arm circumference, calf circumference, subcutaneous fat in the upper arm area, the scapula bone area, above the pelvis area, and mid-calf area. Keys and Brozek formula was used to calculate the fat quantity, Kitagawa formula to calculate the muscle quantity, and Heath and Carter method was used to determine the values of body dimensions. The results of the study can be concluded as follows. The average body dimensions of the male softball players were the endo-mesomorph body type while the average body dimensions of female softball players were the meso-endomorph body type. When considered according to the softball positions, it was found that the male softball players in every position had the endo-mesomorph body type while the female softball players in every position had the meso-endomorph body type except for the center fielder that had the endo-ectomorph body type. The endo-mesomorph body type is suitable for male softball players, and the meso-endomorph body type is suitable for female softball players because these body types are suitable for the five basic softball skills which are: gripping, throwing, catching, hitting, and base running. Thus, people related to selecting softball players to play in sports competitions of different levels should consider factors in terms of body type, size, and body components of the players.

Keywords: body types, softball players, national games of Thailand, social sustainability

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Authors: Mais Khader, Xingjie Wei

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This research sheds light on female entrepreneurs by providing new insights on the survival predictions of companies led by females in the UK. This study aims to build a predictive machine learning model of the survival of female-led & co-led small & medium enterprises (SMEs) in the UK over the period 2000-2020. The predictive model built utilised a combination of financial and non-financial features related to both companies and their directors to predict SMEs' survival. These features were studied in terms of their contribution to the resultant predictive model. Five machine learning models are used in the modelling: Decision tree, AdaBoost, Naïve Bayes, Logistic regression and SVM. The AdaBoost model had the highest performance of the five models, with an accuracy of 73% and an AUC of 80%. The results show high feature importance in predicting companies' survival for company size, management experience, financial performance, industry, region, and females' percentage in management.

Keywords: company survival, entrepreneurship, females, machine learning, SMEs

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Authors: Yazid Alkraimeen

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Precise prediction of dielectric stresses and high voltages of power transformers require the accurate calculation of frequency-dependent parameters. A lack of accuracy can result in severe damages to transformer windings. Transient conditions is stuided by digital computers, which require the implementation of accurate models. This paper analyzes the computation of frequency-dependent skin and proximity losses included in the transformer winding model, using analytical equations and Finite Element Method (FEM). A modified formula to calculate the proximity and the skin losses is presented. The results of the frequency-dependent parameter calculations are verified using the Finite Element Method. The time-domain transient voltages are obtained using Numerical Inverse Laplace Transform. The results show that the classical formula for proximity losses is overestimating the transient voltages when compared with the results obtained from the modified method on a simple transformer geometry.

Keywords: fast front transients, proximity losses, transformer winding modeling, skin losses

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Authors: Ashi Gautam, Isha Shukla, Akhil Seghal

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Biosensors are analytical devices that use a biological sensing element to detect and measure a specific chemical substance or biomolecule in a sample. These devices are widely used in various fields, including medical diagnostics, environmental monitoring, and food analysis, due to their high specificity, sensitivity, and selectivity. In this research paper, a machine learning model is proposed for predicting the suitability of skin care products based on biosensor readings. The proposed model takes in features extracted from biosensor readings, such as biomarker concentration, skin hydration level, inflammation presence, sensitivity, and free radicals, and outputs the most appropriate skin care product for an individual. This model is trained on a dataset of biosensor readings and corresponding skin care product information. The model's performance is evaluated using several metrics, including accuracy, precision, recall, and F1 score. The aim of this research is to develop a personalised skin care product recommendation system using biosensor data. By leveraging the power of machine learning, the proposed model can accurately predict the most suitable skin care product for an individual based on their biosensor readings. This is particularly useful in the skin care industry, where personalised recommendations can lead to better outcomes for consumers. The developed model is based on supervised learning, which means that it is trained on a labeled dataset of biosensor readings and corresponding skin care product information. The model uses these labeled data to learn patterns and relationships between the biosensor readings and skin care products. Once trained, the model can predict the most suitable skin care product for an individual based on their biosensor readings. The results of this study show that the proposed machine learning model can accurately predict the most appropriate skin care product for an individual based on their biosensor readings. The evaluation metrics used in this study demonstrate the effectiveness of the model in predicting skin care products. This model has significant potential for practical use in the skin care industry for personalised skin care product recommendations. The proposed machine learning model for predicting the suitability of skin care products based on biosensor readings is a promising development in the skin care industry. The model's ability to accurately predict the most appropriate skin care product for an individual based on their biosensor readings can lead to better outcomes for consumers. Further research can be done to improve the model's accuracy and effectiveness.

Keywords: biosensors, data model, machine learning, skin care

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Authors: Mustafa M. Donma, Orkide Donma

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Metabolic syndrome (MetS) is a severe health problem which is common among obese individuals. The components of MetS are rather stable in adults compared to the components discussed for children. Due to the ambiguity in this group of the population, how to diagnose MetS in morbid obese (MO) children still constitutes a matter of discussion. For this purpose, a formula, which facilitates the diagnosis of MetS in MO children, was investigated. The aim of this study was to develop a formula which was capable of discriminating MO children with and without MetS findings. Study population comprised MO children. Age and sex-dependent body mass index (BMI) percentiles of the children were above 99. Metabolic syndrome components were also determined. Elevated systolic and diastolic blood pressures (SBP and DBP), elevated fasting blood glucose (FBG), elevated triglycerides (TRG), and/or depressed high density lipoprotein cholesterol (HDL-C) in addition to central obesity were listed as MetS components for each child. Presence of at least two of these components confirmed that the case was MetS. Two groups were constituted. In the first group, there were forty-two MO children without MetS components. Second group was composed of forty-four MO children with at least two MetS components. Anthropometric measurements, including weight, height, waist, and hip circumferences, were performed following physical examination. Body mass index and homeostatic model assessment of insulin resistance values were calculated. Informed consent forms were obtained from the parents of the children. Institutional Non-Interventional Ethics Committee approved the study design. Blood pressure values were recorded. Routine biochemical analysis, including FBG, insulin (INS), TRG, HDL-C were performed. The performance and the clinical utility of the Diagnostic Obesity Notation Model Assessment Metabolic Syndrome Index (DONMA MetS index) [(INS/FBG)/(HDL-C/TRG)*100] was tested. Appropriate statistical tests were applied to the study data. p value smaller than 0.05 was defined as significant. Metabolic syndrome index values were 41.6±5.1 in MO group and 104.4±12.8 in MetS group. Corresponding values for HDL-C values were 54.5±13.2 mg/dl and 44.2±11.5 mg/dl. There were statistically significant differences between the groups (p<0.001). Upon evaluation of the correlations between MetS index and HDL-C values, a much stronger negative correlation was found in MetS group (r=-0.515; p=0.001) in comparison with the correlation detected in MO group (r=-0.371; p=0.016). From these findings, it was concluded that the statistical significance degree of the difference between MO and MetS groups was highly acceptable for this recently introduced MetS index as expected. This was due to the involvement of all of the biochemically defined MetS components into the index. This is particularly important because each of these four parameters used in the formula is cardiac risk factor. Aside from discriminating MO children with and without MetS findings, MetS index introduced in this study is important from the cardiovascular risk point of view in MetS group of children.

Keywords: children, fasting blood glucose, high density lipoprotein cholesterol, index, insulin, metabolic syndrome, morbid obesity, triglycerides.

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Authors: A. El-S. Makled, M. K. Al-Tamimi

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A mathematical model to predict the performance parameters (thrusts, chamber pressures, fuel mass flow rates, mixture ratios, and regression rates during firing time) of hybrid rocket motor (HRM) is evaluated. The internal ballistic (IB) hybrid combustion model assumes that the solid fuel surface regression rate is controlled only by heat transfer (convective and radiative) from flame zone to solid fuel burning surface. A laboratory HRM is designed, manufactured, and tested for low thrust profile space missions (10-15 N) and for validating the mathematical model (computer program). The polymer material and gaseous oxidizer which are selected for this experimental work are polymethyle-methacrylate (PMMA) and polyethylene (PE) as solid fuel grain and gaseous oxygen (GO₂) as oxidizer. The variation of various operational parameters with time is determined systematically and experimentally in firing of up to 20 seconds, and an average combustion efficiency of 95% of theory is achieved, which was the goal of these experiments. The comparison between recording fire data and predicting analytical parameters shows good agreement with the error that does not exceed 4.5% during all firing time. The current mathematical (computer) code can be used as a powerful tool for HRM analytical design parameters.

Keywords: hybrid combustion, internal ballistics, hybrid rocket motor, performance parameters

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Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Behzad Niknam, Kourosh Shahriar, Hassan Madani

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This paper demonstrates the applicability of Hybrid Neural Networks that combine with back propagation networks (BPN) and Genetic Algorithms (GAs) for predicting the flame length of tunnel fire A hybrid neural network model has been developed to predict the flame length of tunnel fire based parameters such as Fire Heat Release rate, air velocity, tunnel width, height and cross section area. The network has been trained with experimental data obtained from experimental work. The hybrid neural network model learned the relationship for predicting the flame length in just 3000 training epochs. After successful learning, the model predicted the flame length.

Keywords: tunnel fire, flame length, ANN, genetic algorithm

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Authors: Wangxu Xia

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BACKGROUND BI-RADS 4 lesions raise the possibility of malignancy that warrant further clinical and radiologic work-up. This study aimed to evaluate the predictive performance of diffusion-weighted imaging(DWI) and microcalcifications on mammography for predicting malignancy of BI-RADS 4 lesions. In addition, the predictive performance of DWI combined with microcalcifications was alsocompared with the Kaiser score. METHODS During January 2021 and June 2023, 144 patients with 178 BI-RADS 4 lesions underwent conventional MRI, DWI, and mammography were included. The lesions were dichotomized intobenign or malignant according to the pathological results from core needle biopsy or surgical mastectomy. DWI was performed with a b value of 0 and 800s/mm2 and analyzed using theapparent diffusion coefficient, and a Kaiser score > 4 was considered to suggest malignancy. Thediagnostic performances for various diagnostic tests were evaluated with the receiver-operatingcharacteristic (ROC) curve. RESULTS The area under the curve (AUC) for DWI was significantly higher than that of the of mammography (0.86 vs 0.71, P<0.001), but was comparable with that of the Kaiser score (0.86 vs 0.84, P=0.58). However, the AUC for DWI combined with mammography was significantly highthan that of the Kaiser score (0.93 vs 0.84, P=0.007). The sensitivity for discriminating malignant from benign BI-RADS 4 lesions was highest at 89% for Kaiser score, but the highest specificity of 83% can be achieved with DWI combined with mammography. CONCLUSION DWI combined with microcalcifications on mammography could discriminate malignant BI-RADS4 lesions from benign ones with a high AUC and specificity. However, Kaiser score had a better sensitivity for discrimination.

Keywords: MRI, DWI, mammography, breast disease

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Authors: Meera Tandan, Mohan Timilsina, Martin Cormican, Akke Vellinga

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Background: In general practice, many infections are treated empirically without microbiological confirmation. Understanding susceptibility of antimicrobials during empirical prescribing can be helpful to reduce inappropriate prescribing. This study aims to apply a prediction model using a decision tree approach to predict the antimicrobial resistance (AMR) of urinary tract infections (UTI) based on non-clinical features of patients over 65 years. Decision tree models are a novel idea to predict the outcome of AMR at an initial stage. Method: Data was extracted from the database of the microbiological laboratory of the University Hospitals Galway on all antimicrobial susceptibility testing (AST) of urine specimens from patients over the age of 65 from January 2011 to December 2014. The primary endpoint was resistance to common antimicrobials (Nitrofurantoin, trimethoprim, ciprofloxacin, co-amoxiclav and amoxicillin) used to treat UTI. A classification and regression tree (CART) model was generated with the outcome ‘resistant infection’. The importance of each predictor (the number of previous samples, age, gender, location (nursing home, hospital, community) and causative agent) on antimicrobial resistance was estimated. Sensitivity, specificity, negative predictive (NPV) and positive predictive (PPV) values were used to evaluate the performance of the model. Seventy-five percent (75%) of the data were used as a training set and validation of the model was performed with the remaining 25% of the dataset. Results: A total of 9805 UTI patients over 65 years had their urine sample submitted for AST at least once over the four years. E.coli, Klebsiella, Proteus species were the most commonly identified pathogens among the UTI patients without catheter whereas Sertia, Staphylococcus aureus; Enterobacter was common with the catheter. The validated CART model shows slight differences in the sensitivity, specificity, PPV and NPV in between the models with and without the causative organisms. The sensitivity, specificity, PPV and NPV for the model with non-clinical predictors was between 74% and 88% depending on the antimicrobial. Conclusion: The CART models developed using non-clinical predictors have good performance when predicting antimicrobial resistance. These models predict which antimicrobial may be the most appropriate based on non-clinical factors. Other CART models, prospective data collection and validation and an increasing number of non-clinical factors will improve model performance. The presented model provides an alternative approach to decision making on antimicrobial prescribing for UTIs in older patients.

Keywords: antimicrobial resistance, urinary tract infection, prediction, decision tree

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Authors: Ahmed N. Shmroukh, Gamal Tag Abdel-Jaber, Rashed D. Aldughpassi

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The feasibility of improving the performance of the proposed solar still unit which operated in very hot climate is investigated numerically and verified with experimental data. This solar desalination unit with proposed auxiliary device as seawater preheating system using petrol based textherm oil was used to produce pure fresh water from seawater. The effective evaporation area of basin is about 1 m². The unit was tested in two main operation modes which are normal and with seawater preheating system. The results showed that, there is good agreement between the theoretical data and the experimental data; this means that the numerical model can be accurately dependable for predicting the proposed solar still performance and design parameters. The results also showed that the fresh water productivity of the solar still in the modified preheating case which is higher than normal case, leads to an increase in productivity of 42%.

Keywords: improving productivity, seawater desalination, solar stills, theoretical model

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Authors: Ajayi Olusola Olajide, Alonge Olaide Moses

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Predicting the outcome of soccer matches poses an interesting challenge for which it is realistically impossible to successfully do so for every match. Despite this, there are lots of resources that are being expended on the correct prediction of soccer matches weekly, and all over the world. Soccer Match Result Prediction System Model (SMRPSM) is a system that is proposed whereby the results of matches between two soccer teams are auto-generated, with the added excitement of giving users a chance to test their predictive abilities. Soccer teams from different league football are loaded by the application, with each team’s corresponding manager and other information like team location, team logo and nickname. The user is also allowed to interact with the system by selecting the match to be predicted and viewing of the results of completed matches after registering/logging in.

Keywords: predicting, soccer match, outcome, soccer, matches, result prediction, system, model

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Authors: Musa Najimu, Isam Aljundi

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In this study, the development of mixed matrix membrane (MMM) containing carbide-derived carbon (CDC) for the separation of CO₂ was investigated. MMM with four different loadings (0.1 to 2 wt%) were prepared by the dry/wet phase inversion technique. Prior to this, the formula of the control polysulfone (PSF) membrane was optimized in terms of the PSF concentration in a mixture of NMP/THF solvents and ethanol. Prepared samples were characterized and tested for CO₂ and CH₄ gas permeation. The optimization of the control PSF membrane revealed that 30 wt% PSF is the critical polymer concentration in the formulation. Characterization results unveiled reinforcement of thermal stability and improved polarity imparted by CDC in the MMM, in addition to uniform dispersion of filler up to 1 wt% loading. Furthermore, the incorporation of CDC in PSF membrane formulation enhanced both the CO₂ permeance and ideal selectivity over the control membrane. A CDC loading of 0.5 wt% resulted in the highest CO₂ permeance of 5.5 GPU corresponding to 120% increase in permeance while a CDC loading of 1 wt% resulted in the highest selectivity (CO₂ /CH₄) of 27 corresponding to 29% increase in selectivity. Studies of operating temperature effect showed that an optimum operating temperature for M1.0 membrane is 20 ⁰C. In addition, the feed pressure studies showed that high pressure feeds will favor high performance of the membrane and a good CO₂ /CH₄ separation.

Keywords: carbide derived carbon, mixed matrix membrane, CO₂ separation, polysulfone

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Authors: Fazıl Gökgöz, Fahrettin Filiz

Abstract:

Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Sahar El-Achkar, Mizan Ahmad

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Poor adaptation of developmental milestones over the lifespan can significantly impact emotional experiences and Satisfaction with Life (SWL) post-retirement. Thus, it is important to understand how adaptive behaviour over the life course can predict emotional experiences. Broadly emotional experiences are either Positive Affect (PA) or Negative Affect (NA). This study sought to explore the impact of successful adaptation of developmental milestones throughout one’s life on emotional experiences and satisfaction with life following retirement. A cross-sectional self-report survey was completed by 132 Australian retirees between the ages 55 and 70 years. Three hierarchical regression models were fitted, controlling for age and gender, to predict PA, NA, and SWL. The full model predicting PA was statistically significant overall, F (8, 121) = 17.97, p < .001, account for 57% of the variability in PA. Industry/Inferiority were significantly predictive of PA. The full model predicting NA was statistically significant overall, F (8, 121) = 12.00, p < .001, accounting for 51% of the variability in NA. Age and Trust/Mistrust were significantly predictive of NA. The full model predicting NA was statistically significant overall, F (8, 121) = 12.00, p < .001, accounting for 51% of the variability in NA. Age and Trust/Mistrust were significantly predictive of NA. The full model predicting SWL, F (8, 121) = 11.05, p < .001, accounting for 45% of the variability in SWL. Trust/Mistrust and Ego Integrity/Despair were significantly predictive of SWL. A sense of industry post-retirement is important in generating PA. These results highlight that individuals presenting with adaptation and identity issues are likely to present with adjustment challenges and unpleasant emotional experiences post-retirement. This supports the importance of identifying and understanding the benefits of successful adaptation and development throughout the lifespan and its significance for the self-concept. Most importantly, the quality of lives of many may be improved, and the future risk of continued poor emotional experiences and SWL post-retirement may be mitigated. Specifically, the clinical implications of these findings are that they support the promotion of successful adaption over the life course and healthy ageing.

Keywords: adaptation, development, negative affect, positive affect, retirement, satisfaction with life

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Authors: C. Fernandez-Plaza, I. Abad, E. Diaz, I. Diaz

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Births occurring before the 37th week of gestation are considered preterm births. A threat of preterm is defined as the beginning of regular uterine contractions, dilation and cervical effacement between 23 and 36 gestation weeks. To author's best knowledge, the factors that determine the beginning of the birth are not completely defined yet. In particular, the incidence of sleep habits on preterm births is weekly studied. The aim of this study is to develop a model to predict the factors affecting premature delivery on pregnancy, based on the above potential risk factors, including those derived from sleep habits and light exposure at night (introduced as 12 variables obtained by a telephone survey using two questionnaires previously used by other authors). Thus, three groups of variables were included in the study (maternal, fetal and sleep habits). The study was approved by Research Ethics Committee of the Principado of Asturias (Spain). An observational, retrospective and descriptive study was performed with 481 births between January 1, 2015 and May 10, 2016 in the University Central Hospital of Asturias (Spain). A statistical analysis using SPSS was carried out to compare qualitative and quantitative variables between preterm and term delivery. Chi-square test qualitative variable and t-test for quantitative variables were applied. Statistically significant differences (p < 0.05) between preterm vs. term births were found for primiparity, multi-parity, kind of conception, place of residence or premature rupture of membranes and interruption during nights. In addition to the statistical analysis, machine learning methods to look for a prediction model were tested. In particular, tree based models were applied as the trade-off between performance and interpretability is especially suitable for this study. C5.0, recursive partitioning, random forest and tree bag models were analysed using caret R-package. Cross validation with 10-folds and parameter tuning to optimize the methods were applied. In addition, different noise reduction methods were applied to the initial data using NoiseFiltersR package. The best performance was obtained by C5.0 method with Accuracy 0.91, Sensitivity 0.93, Specificity 0.89 and Precision 0.91. Some well known preterm birth factors were identified: Cervix Dilation, maternal BMI, Premature rupture of membranes or nuchal translucency analysis in the first trimester. The model also identifies other new factors related to sleep habits such as light through window, bedtime on working days, usage of electronic devices before sleeping from Mondays to Fridays or change of sleeping habits reflected in the number of hours, in the depth of sleep or in the lighting of the room. IF dilation < = 2.95 AND usage of electronic devices before sleeping from Mondays to Friday = YES and change of sleeping habits = YES, then preterm is one of the predicting rules obtained by C5.0. In this work a model for predicting preterm births is developed. It is based on machine learning together with noise reduction techniques. The method maximizing the performance is the one selected. This model shows the influence of variables related to sleep habits in preterm prediction.

Keywords: machine learning, noise reduction, preterm birth, sleep habit

Procedia PDF Downloads 111

Authors: Mustafa Metin Donma

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Obesity increases the risk of elevated blood pressure, which is a metabolic syndrome (MetS) component. Waist circumference (WC) is accepted as an indispensable parameter for the evaluation of these health problems. The close relationship of height with blood pressure values revealed the necessity of including height in tense indices. The association of tense indices with WC has also become an increasingly important topic. The purpose of this study was to develop a tense index that could contribute to differential diagnosis of MetS more than the indices previously introduced. One hundred and ninety-four children, aged 06-11 years, were considered to constitute four groups. The study was performed on normal weight (Group 1), overweight+obese (Group 2), morbid obese [without (Group 3) and with (Group 4) MetS findings] children. Children were included in the groups according to the recommendations of World Health Organization based on age- and gender dependent body mass index percentiles. For MetS group, MetS components well-established before were considered. Anthropometric measurements, as well as blood pressure values were taken. Tense indices were computed. The formula for the first tense index was (SP+DP)/2. The second index was Advanced Donma Tense Index (ADTI). The formula for this index was [(SP+DP)/2] * Height. Statistical calculations were performed. 0.05 was accepted as the p value indicating statistical significance. There were no statistically significant differences between the groups for pulse pressure, systolic-to-diastolic pressure ratio and tense index. Increasing values were observed from Group 1 to Group 4 in terms of mean arterial blood pressure and advanced Donma tense index (ADTI), which was highly correlated with WC in all groups except Group 1. Both tense index and ADTI exhibited significant correlations with WC in Group 3. However, in Group 4, ADTI, which includes height parameter in the equation, was unique in establishing a strong correlation with WC. In conclusion, ADTI was suggested as a tense index while investigating children with MetS.

Keywords: blood pressure, child, height, metabolic syndrome, waist circumference

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Authors: Piotr Ciuman, Barbara Lipska

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The aim of presented research was to improve numerical predictions of air parameters distribution in the actual natatorium by the selection of calculation formula of mass flux of moisture emitted from the pool. Selected correlation should ensure the best compliance of numerical results with the measurements' results of these parameters in the facility. The numerical model of the natatorium was developed, for which boundary conditions were prepared on the basis of measurements' results carried out in the actual facility. Numerical calculations were carried out with the use of ANSYS CFX software, with six formulas being implemented, which in various ways made the moisture emission dependent on water surface temperature and air parameters in the natatorium. The results of calculations with the use of these formulas were compared for air parameters' distributions: Specific humidity, velocity and temperature in the facility. For the selection of the best formula, numerical results of these parameters in occupied zone were validated by comparison with the measurements' results carried out at selected points of this zone.

Keywords: experimental validation, indoor swimming pool, moisture emission, natatorium, numerical calculations CFD, thermal and humidity conditions, ventilation

Procedia PDF Downloads 372

Authors: Indira priyadarshini, B. Tulja Lal, K. Anusha, P. Sai Varun

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Formula One cars are the fastest road course racing cars in the world, owing to very high cornering speeds achieved through the generation of large amounts of aerodynamic downforce. The main intentions and goals of this paper are to reduce the accidents and improving the safety without affecting the visibility of the driver by redesigning Halo that was developed by Mercedes in conjunction with the FIA to deflect flying debris, such as a loose wheel, away from a driver’s head while the hinged locking mechanism can quickly be removed for easy access. Halo design has been modified in order to reduce the weight without affecting the aerodynamics of the car. CFD simulation is carried out to observe the flow over the Halo. The velocity profile and pressure contours were analyzed. Halo is designed using SOLIDWORKS Furthermore, using the software ANSYS FLUENT 3D simulation of the airflow contour around the Halo in order to make changes in the geometry to improve the design by reducing air resistance and improving aerodynamics. According to our assumption, new 3D Halo model has better aerodynamic properties in order to analyse possible improvements compared to the initial design. Structural analysis is also done by using ANSYS by making an F1 tire colliding with Halo at 225 kmph in order to know the deflections in the structure.

Keywords: aerodynamics, Halo, safety, visibility

Procedia PDF Downloads 322

Authors: Bita Mashayekhi, Amin Jahangard, Milad Samavat, Saeid Homayoun

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In the modern competitive business environment, one cannot overstate the importance of corporate social responsibility. The controversial link between the social and financial performance of firms has become a topic of interest for scholars. Hence, this study examines the social and financial performance link by taking into account the mediating role of innovation performance. We conducted the Covariance-based Structural Equation Modeling (CB-SEM) method on an international sample of firms provided by the ASSET4 database. In this research, to explore the black box of the social and financial performance relationship, we first examined the effect of social performance separately on financial performance and innovation; then, we measured the mediation role of innovation in the social and financial performance link. While our results indicate the positive effect of social performance on financial performance and innovation, we cannot document the positive mediating role of innovation. This possibly relates to the long-term nature of benefits from investments in innovation.

Keywords: ESG, financial performance, innovation, social performance, structural equation modeling

Procedia PDF Downloads 63

Authors: Frank Kuebler, Rolf Steinhilper

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Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

Procedia PDF Downloads 492

Authors: Yu-Chuan Huang

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Introduction: There is a higher mortality rate for unplanned transfer to intensive care units. It also needs a longer length of stay and makes the intensive care unit beds cannot be effectively used. It affects the immediate medical treatment of critically ill patients, resulting in a drop in the quality of medical care. Purpose: The purpose of this study was using SOFA and APACHEII score to analyze the mortality rate of the cases transferred from ED to ICU. According to the score that should be provide an appropriate care as early as possible. Methods: This study was a descriptive experimental design. The sample size was estimated at 220 to reach a power of 0.8 for detecting a medium effect size of 0.30, with a 0.05 significance level, using G-power. Considering an estimated follow-up loss, the required sample size was estimated as 242 participants. Data were calculated by medical system of SOFA and APACHEII score that cases transferred from ED to ICU in 2016. Results: There were 233 participants meet the study. The medical records showed 33 participants’ mortality. Age and sex with QSOFA , SOFA and sex with APACHEII showed p>0.05. Age with APCHHII in ED and ICU showed r=0.150, 0,268 (p < 0.001**). The score with mortality risk showed: ED QSOFA is r=0.235 (p < 0.001**), exp(B)=1.685(p = 0.007); ICU SOFA 0.78 (p < 0.001**), exp(B)=1.205(p < 0.001). APACHII in ED and ICU showed r= 0.253, 0.286 (p < 0.001**), exp(B) = 1.041,1.073(p = 0.017,0.001). For SOFA, a cutoff score of above 15 points was identified as a predictor of the 95% mortality risk. Conclusions: The SOFA and APACHE II were calculated based on initial laboratory data in the Emergency Department, and during the first 24 hours of ICU admission. In conclusion, the SOFA and APACHII score is significantly associated with mortality and strongly predicting mortality. Early predictors of morbidity and mortality, which we can according the predicting score, and provide patients with a detail assessment and proper care, thereby reducing mortality and length of stay.

Keywords: SOFA, APACHEII, mortality, ICU

Procedia PDF Downloads 125

Authors: Sairi Satari

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Introduction: Six-sigma is a metric that quantifies the performance of processes as a rate of Defects-Per-Million Opportunities. Sigma methodology can be applied in chemical pathology laboratory for evaluating process performance with evidence for process improvement in quality assurance program. In the laboratory, these methods have been used to improve the timeliness of troubleshooting, reduce the cost and frequency of quality control and minimize pre and post-analytical errors. Aim: The aim of this study is to evaluate the sigma values of the Cobas 8000 analyzer based on the minimum requirement of the specification. Methodology: Twenty-one analytes were chosen in this study. The analytes were alanine aminotransferase (ALT), albumin, alkaline phosphatase (ALP), Amylase, aspartate transaminase (AST), total bilirubin, calcium, chloride, cholesterol, HDL-cholesterol, creatinine, creatinine kinase, glucose, lactate dehydrogenase (LDH), magnesium, potassium, protein, sodium, triglyceride, uric acid and urea. Total error was obtained from Clinical Laboratory Improvement Amendments (CLIA). The Bias was calculated from end cycle report of Royal College of Pathologists of Australasia (RCPA) cycle from July to December 2016 and coefficient variation (CV) from six-month internal quality control (IQC). The sigma was calculated based on the formula :Sigma = (Total Error - Bias) / CV. The analytical performance was evaluated based on the sigma, sigma > 6 is world class, sigma > 5 is excellent, sigma > 4 is good and sigma < 4 is satisfactory and sigma < 3 is poor performance. Results: Based on the calculation, we found that, 96% are world class (ALT, albumin, ALP, amylase, AST, total bilirubin, cholesterol, HDL-cholesterol, creatinine, creatinine kinase, glucose, LDH, magnesium, potassium, triglyceride and uric acid. 14% are excellent (calcium, protein and urea), and 10% ( chloride and sodium) require more frequent IQC performed per day. Conclusion: Based on this study, we found that IQC should be performed frequently for only Chloride and Sodium to ensure accurate and reliable analysis for patient management.

Keywords: sigma matrics, analytical performance, total error, bias

Procedia PDF Downloads 147