Search results for: mixture regression model

Authors: B. Hossen, Y. Helmut

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Soil salinity is a major problem for the coastal region of Bangladesh, which has been increasing for the last four decades. Determination of soil salinity is essential for proper land use planning for agricultural crop production. The aim of the research is to estimate and monitor the soil salinity in the study area. Remote sensing can be an effective tool for detecting soil salinity in data-scarce conditions. In the research, Landsat 8 is used, which required atmospheric and radiometric correction, and nine soil salinity indices are applied to develop a soil salinity map. Ground soil salinity data, i.e., EC value, is collected as a printed map which is then scanned and digitized to develop a point shapefile. Linear regression is made between satellite-based generated map and ground soil salinity data, i.e., EC value. The results show that maximum R² value is found for salinity index SI 7 = G*R/B representing 0.022. This minimal R² value refers that there is a negligible relationship between ground EC value and salinity index generated value. Hence, these indices are not appropriate to assess soil salinity though many studies used those soil salinity indices successfully. Therefore, further research is necessary to formulate a model for determining the soil salinity in the coastal of Bangladesh.

Keywords: soil salinity, EC, Landsat 8, salinity indices, linear regression, remote sensing

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Navid Kaboudi, Ali Shayanfar

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Introduction: Feeding infants with safe milk from the beginning of their life is an important issue. Drugs which are used by mothers can affect the composition of milk in a way that is not only unsuitable, but also toxic for infants. Consuming permeable drugs during that sensitive period by mother could lead to serious side effects to the infant. Due to the ethical restrictions of drug testing on humans, especially women, during their lactation period, computational approaches based on structural parameters could be useful. The aim of this study is to develop mechanistic models to predict the M/P ratio of drugs during breastfeeding period based on their structural descriptors. Methods: Two hundred and nine different chemicals with their M/P ratio were used in this study. All drugs were categorized into two groups based on their M/P value as Malone classification: 1: Drugs with M/P>1, which are considered as high risk 2: Drugs with M/P>1, which are considered as low risk Thirty eight chemical descriptors were calculated by ACD/labs 6.00 and Data warrior software in order to assess the penetration during breastfeeding period. Later on, four specific models based on the number of hydrogen bond acceptors, polar surface area, total surface area, and number of acidic oxygen were established for the prediction. The mentioned descriptors can predict the penetration with an acceptable accuracy. For the remaining compounds (N= 147, 158, 160, and 174 for models 1 to 4, respectively) of each model binary regression with SPSS 21 was done in order to give us a model to predict the penetration ratio of compounds. Only structural descriptors with p-value<0.1 remained in the final model. Results and discussion: Four different models based on the number of hydrogen bond acceptors, polar surface area, and total surface area were obtained in order to predict the penetration of drugs into human milk during breastfeeding period About 3-4% of milk consists of lipids, and the amount of lipid after parturition increases. Lipid soluble drugs diffuse alongside with fats from plasma to mammary glands. lipophilicity plays a vital role in predicting the penetration class of drugs during lactation period. It was shown in the logistic regression models that compounds with number of hydrogen bond acceptors, PSA and TSA above 5, 90 and 25 respectively, are less permeable to milk because they are less soluble in the amount of fats in milk. The pH of milk is acidic and due to that, basic compounds tend to be concentrated in milk than plasma while acidic compounds may consist lower concentrations in milk than plasma. Conclusion: In this study, we developed four regression-based models to predict the penetration class of drugs during the lactation period. The obtained models can lead to a higher speed in drug development process, saving energy, and costs. Milk/plasma ratio assessment of drugs requires multiple steps of animal testing, which has its own ethical issues. QSAR modeling could help scientist to reduce the amount of animal testing, and our models are also eligible to do that.

Keywords: logistic regression, breastfeeding, descriptors, penetration

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Authors: Mina Adel Shokry Fahim, Jūratė Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realisation often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter

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Authors: Haoming Ma, Guo Yu, Peiru Zhou

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Background: Previous studies show that dysglycemia, mostly hyperglycemia, hypoglycemia and glycemic variability(GV), are associated with excess mortality in critically ill patients, especially those without diabetes. Glycemic variability is an increasingly important measure of glucose control in the intensive care unit (ICU) due to this association. However, there is limited data pertaining to the relationship between different clinical factors and glycemic variability and clinical outcomes categorized by their DM status. This retrospective study of 958 intensive care unit(ICU) patients was conducted to investigate the relationship between GV and outcome in critically ill patients and further to determine the significant factors that contribute to the glycemic variability. Aim: We hypothesize that the factors contributing to mortality and the glycemic variability are different from critically ill patients with or without diabetes. And the primary aim of this study was to determine which dysglycemia (hyperglycemia\hypoglycemia\glycemic variability) is independently associated with an increase in mortality among critically ill patients in different groups (DM/Non-DM). Secondary objectives were to further investigate any factors affecting the glycemic variability in two groups. Method: A total of 958 diabetic and non-diabetic patients with severe diseases in the ICU were selected for this retrospective analysis. The glycemic variability was defined as the coefficient of variation (CV) of blood glucose. The main outcome was death during hospitalization. The secondary outcome was GV. The logistic regression model was used to identify factors associated with mortality. The relationships between GV and other variables were investigated using linear regression analysis. Results: Information on age, APACHE II score, GV, gender, in-ICU treatment and nutrition was available for 958 subjects. Predictors remaining in the final logistic regression model for mortality were significantly different in DM/Non-DM groups. Glycemic variability was associated with an increase in mortality in both DM(odds ratio 1.05; 95%CI:1.03-1.08,p＜0.001) or Non-DM group(odds ratio 1.07; 95%CI:1.03-1.11,p=0.002). For critically ill patients without diabetes, factors associated with glycemic variability included APACHE II score(regression coefficient, 95%CI:0.29,0.22-0.36,p＜0.001), Mean BG(0.73,0.46-1.01,p＜0.001), total parenteral nutrition(2.87,1.57-4.17,p＜0.001), serum albumin(-0.18,-0.271 to -0.082,p＜0.001), insulin treatment(2.18,0.81-3.55,p=0.002) and duration of ventilation(0.006,0.002-1.010,p=0.003).However, for diabetes patients, APACHE II score(0.203,0.096-0.310,p＜0.001), mean BG(0.503,0.138-0.869,p=0.007) and duration of diabetes(0.167,0.033-0.301,p=0.015) remained as independent risk factors of GV. Conclusion: We found that the relation between dysglycemia and mortality is different in the diabetes and non-diabetes groups. And we confirm that GV was associated with excess mortality in DM or Non-DM patients. Furthermore, APACHE II score, Mean BG, total parenteral nutrition, serum albumin, insulin treatment and duration of ventilation were significantly associated with an increase in GV in Non-DM patients. While APACHE II score, mean BG and duration of diabetes (years) remained as independent risk factors of increased GV in DM patients. These findings provide important context for further prospective trials investigating the effect of different clinical factors in critically ill patients with or without diabetes.

Keywords: diabetes, glycemic variability, predictors, severe disease

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Authors: Leo Carlo B. Rondina

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Self-image is an important factor to improve the self-esteem of the personnel. The purpose of the study is to determine the self-image of the police. The respondents were the 503 policemen assigned in different Police Station in Davao City, and they were chosen with the used of random sampling. With the used of Exploratory Factor Analysis (EFA), latent construct variables of police image were identified as follows; professionalism, obedience, morality and justice and fairness. Further, ordinal regression indicates statistical characteristics on ages 21-40 which means the age of the respondent statistically improves self-image.

Keywords: police image, exploratory factor analysis, ordinal regression, Galatea effect

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Authors: Wajid Ali Khan

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Extensive experimentation and numerical investigation are performed to predict the machining-induced residual stresses in the end milling of AISI 1045 steel, and an optimization code has been developed using the particle swarm optimization technique. Experiments were conducted using a single factor at a time and design of experiments approach. Regression analysis was done, and a mathematical model of the cutting process was developed, thus predicting the machining-induced residual stress with reasonable accuracy. The mathematical model served as the objective function to be optimized using particle swarm optimization. The relationship between the different cutting parameters and the output variables, force, and residual stresses has been studied. The combined effect of the process parameters, speed, feed, and depth of cut was examined, and it is understood that 85% of the variation of these variables can be attributed to these machining parameters under research. A 3D finite element model is developed to predict the cutting forces and the machining-induced residual stresses in end milling operation. The results were validated experimentally and against the Johnson-cook model available in the literature.

Keywords: residual stresses, end milling, 1045 steel, optimization

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Authors: Babak Safaei, A. M. Fattahi

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Polymer/carbon nanotube nanocomposites have a wide range of promising applications Due to their enhanced properties. In this work, free vibration analysis of single-walled carbon nanotube-reinforced composite plates is conducted in which carbon nanotubes are embedded in an amorphous polyethylene. The rule of mixture based on various types of plate model namely classical plate theory (CLPT), first-order shear deformation theory (FSDT), and higher-order shear deformation theory (HSDT) was employed to obtain fundamental frequencies of the nanocomposite plates. Generalized differential quadrature (GDQ) method was used to discretize the governing differential equations along with the simply supported and clamped boundary conditions. The material properties of the nanocomposite plates were evaluated using molecular dynamic (MD) simulation corresponding to both short-(10,10) SWCNT and long-(10,10) SWCNT composites. Then the results obtained directly from MD simulations were fitted with those calculated by the rule of mixture to extract appropriate values of carbon nanotube efficiency parameters accounting for the scale-dependent material properties. The selected numerical results are presented to address the influences of nanotube volume fraction and edge supports on the value of fundamental frequency of carbon nanotube-reinforced composite plates corresponding to both long- and short-nanotube composites.

Keywords: nanocomposites, molecular dynamics simulation, free vibration, generalized, differential quadrature (GDQ) method

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Authors: A. Ortuzar, I. Escondrillas, F. Mijangos

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There is a need for new systems that can be attached to drinking water treatment plants and have the required treatment capacity as well as the selectivity regarding components derived from anthropogenic activities. In a context of high volumes of water and low concentration of contaminants, adsorption/interchange processes are appealing since they meet the required features. Iron oxides such as siderite and molysite, which are respectively based on FeCO₃ and FeCl₃, can be found in nature. In this work, their observed performance, raw or roasted at different temperatures, as adsorbents of some inorganic anions is discussed. Roasted 1:1 FeCO₃: FeCl₃ mixture was very selective for arsenic and allowed a 100% removal of As from a 10 mg L^-1 As solution. Besides, the 1:1 FeCO₃ and FeCl₃ mixture roasted at 500 ºC showed good selectivity for, in order of preference, arsenate, bromate, phosphate, fluoride and nitrate anions with distribution coefficients of, respectively, 4200, 2800, 2500 0.4 and 0.03 L g^-1.

Keywords: drinking water, natural adsorbent materials, removal, selectivity

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Authors: Mehdi Moeinaddini, Zohreh Asadi-Shekari, Muhammad Zaly Shah, Amran Hamzah

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Currently, planners try to have more green travel options to decrease economic, social and environmental problems. Therefore, this study tries to find significant urban travel factors to be used to increase the usage of alternative urban travel modes. This paper attempts to identify the relationship between prominent urban mobility indicators and daily trips by public transport in 30 cities from various parts of the world. Different travel modes, infrastructures and cost indicators were evaluated in this research as mobility indicators. The results of multi-linear regression analysis indicate that there is a significant relationship between mobility indicators and the daily usage of public transport.

Keywords: green travel modes, urban travel indicators, daily trips by public transport, multi-linear regression analysis

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Authors: Yafri Hazbi, Zaenal Bachruddin, Nafiatul Umami, Lies Mira Yusiati

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The objective of this study was to identify the benefits of total mixture concentrate based on tofu waste silage (TMC-TWS) as ration containing lactic acid bacteria on performance of lambs. Fifteen weaning lambs (2-3 months old) were randomly divided into two treatment groups, treatment group I (TI) was fed with TMC-TWS as ration and treatment group II (TII) was fed with TMC-TWS fresh (without silage fermentation). The performance of lambs was evaluated on day 0, 15, and 30 to have data of body weight per day. Meanwhile, blood sampling and feces were made on the 30th day to get an analysis on the blood profile (erythrocytes (mill/ml), hemoglobin (g/dL), packed cell volume (%), and leukocytes (mill/ml)) and the number of worm eggs in feces. The results of this study showed no significant difference between the effect of different feed on the blood profile (erythrocytes (mill/ml), hemoglobin (g/dL), packed cell volume (%), as well as the number of worm eggs in the feces. However the results showed significant differences if it is low (P<0.05) due to the treatment group based on sex on body weight gain per day, feed conversion rate and the number of erythrocytes.

Keywords: lambs, total mixture concentrate, silage, acid lactid bacteria, blood profile, eggs worm in feces

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Authors: Hussaini Bala

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There are different opinions in the literature on the relationship between Audit Committee characteristics and earnings management. The mix of opinions makes the direction of their relationship ambiguous. This study investigated the relationship between Audit Committee characteristics and earnings management of listed food and beverages Firms in Nigeria. The study covered the period of six years from 2007 to 2012. Data for the study were extracted from the Firms’ annual reports and accounts. After running the OLS regression, a robustness test was conducted for the validity of statistical inferences. The dependent variable was generated using two steps regression in order to determine the discretionary accrual of the sample Firms. Multiple regression was employed to run the data of the study using Random Model. The results from the analysis revealed a significant association between audit committee characteristics and earnings management of the Firms. While audit committee size and committees’ financial expertise showed an inverse relationship with earnings management, committee’s independence, and frequency of meetings are positively and significantly related to earnings management. In line with the findings, the study recommended among others that listed food and beverages Firms in Nigeria should strictly comply with the provision of Companies and Allied Matters Act (CAMA) and SEC Code of Corporate Governance on the issues regarding Audit Committees. Regulators such as SEC should increase the minimum number of Audit Committee members with financial expertise and also have a statutory position on the maximum number of Audit Committees meetings, which should not be greater than four meetings in a year as SEC code of corporate governance is silent on this.

Keywords: audit committee, earnings management, listed Food and beverages size, leverage, Nigeria

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Ana Gabriela Diaz, Luis Fermín Córdova, Jr., Roberto Lamazares

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The island of Cuba, the largest of the Greater Antilles, is located in the tropical North Atlantic. It is annually affected by numerous weather events, which have caused severe damage to our coastal areas. In the same way that many other coastlines around the world, the beautiful beaches of the Hicacos Peninsula also suffer from erosion. This leads to a structural regression of the coastline. If measures are not taken, the hotels will be exposed to the advance of the sea, and it will be a serious problem for the economy. With the aim of studying the intensity of this type of activity, specialists of group of coastal and marine engineering from CIH, in the framework of the research conducted within the project MEGACOSTAS 2, provide their research to simulate extreme events and assess their impact in coastal areas, mainly regarding the definition of flood volumes and morphodynamic changes in sandy beaches. The main objective of this work is the evaluation of the process of Varadero beach erosion (the coastal sector has an important impact in the country's economy) on the Hicacos Peninsula for different paths of hurricanes. The mathematical model XBeach, which was integrated into the Coastal engineering system introduced by the project of MEGACOSTA 2 to determine the area and the more critical profiles for the path of hurricanes under study, was applied. The results of this project have shown that Center area is the greatest dynamic area in the simulation of the three paths of hurricanes under study, showing high erosion volumes and the greatest average length of regression of the coastline, from 15- 22 m.

Keywords: beach, erosion, mathematical model, coastal areas

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Authors: Mohan T. Harish, Masson Lauriane, Sreevalsa Kolathayar

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Environmental issues raise alarm in the constructional field which implies a need for exploring new construction materials derived from the waste and residual products. This paper presents a detailed review of the alternatives approaches employed in the construction field using plastic waste in mixture with mixed with fillers. A detailed analysis of the plastic waste used in concrete, with soil, sand, clay and natural residues like sawdust, rice husk etc are presented. The different process carried forward was also discussed along with the scrutiny of the change in mechanical properties. The effect of coupling agents in the proposed mixture has been appraised in detail which gives implications for its future application in the field of plastic waste with viable materials in composite construction blocks.

Keywords: plastic waste, composite materials, construction block, concrete, natural residue, coupling agent

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Authors: Hasan Taherkhani

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Increasingly accumulation of the solid waste materials has become a major environmental problem of communities. In addition to the protection of environment, the recycling and reusing of the waste materials are financially beneficial. Waste materials can be used in highway construction. This study aimed to investigate the applicability of recycled concrete, asphalt and steel slag powder, as a replacement of the primary mineral filler in asphalt concrete has been investigated. The primary natural siliceous aggregate filler, as control, has been replaced with the secondary recycled concrete, asphalt and steel slag powders, and some engineering properties of the mixtures have been evaluated. Marshal Stability, flow, indirect tensile strength, moisture damage, static creep and volumetric properties of the mixtures have been evaluated. The results show that, the Marshal Stability of the mixtures containing recycled powders is higher than that of the control mixture. The flow of the mixtures containing recycled steel slag is lower, and that of the mixtures containing recycled asphalt and cement concrete powder is found to be higher than that of the control mixture. It is also found that the resistance against moisture damage and permanent deformation of the mixture can be improved by replacing the natural filler with the recycled powders. The volumetric properties of the mixtures are not significantly influenced by replacing the natural filler with the recycled powders.

Keywords: filler, steel slag, recycled concrete, recycled asphalt concrete, tensile strength, moisture damage, creep

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: Mukesh Kumar, Onkar Singh, Naveen Kumar, Amar Deep

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The waste cooking oil is taken as feedstock for biodiesel production. For this research, waste cooking oil is collected from many hotels and restaurants, and then biodiesel is prepared for experimentation purpose. The prepared biodiesel is mixed with mineral diesel in the proportion of 10%, 20%, and 30% to perform tests on a diesel engine. The experimental analysis is carried out at different load conditions to analyze the impact of the blending ratio on the performance and emission parameters. When the blending proportion of biodiesel is increased, then the highest pressure reduces due to the fall in the calorific value of the blended mixture. Experimental analysis shows a promising decrease in nitrogen oxides (NOx). A mixture of 20% biodiesel and mineral diesel is the best negotiation, mixing ratio, and beyond that, a remarkable reduction in the outcome of the performance has been observed.

Keywords: alternative sources, diesel engine, emissions, performance

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Authors: Harpreet Kaur, Er. Arjun, Kirandeep Kaur, P. K. Mittal

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Simulation of a human body from 20% gelatin & 80% water mixture is examined from wound ballistics point of view. Parameters such as incapacitation energy & temporary to permanent cavity size & tools of hydrodynamics have been employed to arrive at a model of human body similar to the one adopted by NATO. Calculations using equations of motion yield a value of 339 µs in which a temporary cavity with maximum size settles down to permanent cavity. This occurs for a 10mm size bullets & settle down to permanent cavity in case of 4 different bullets i.e. 5.45, 5.56, 7.62,10 mm sizes The obtained results are in excellent agreement with the body as right circular cylinder of 15 cm height & 10 cm diameter. An effort is made here in this work to present a sound theoretical base to parameters commonly used in wound ballistics from field experience discussed by Col Coats & Major Beyer. Keywords. Gelatin, gunshot, hydrodynamic model, oscillation time, temporary cavity and permanent cavity, Wound Ballistic.

Keywords: gelatin, gunshot, wound, cavity

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Authors: Shan-Hong Ying, Ying-Fang Wang

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A surrogate approach was deployed for assessing exposures of multiple chemicals at the selected working area of coating processes and applied to assess the exposure concentration of similar exposed groups using the same chemicals but different formula ratios. For the selected area, 6 to 12 portable photoionization detector (PID) were placed uniformly in its workplace to measure its total VOCs concentrations (CT-VOCs) for 6 randomly selected workshifts. Simultaneously, one sampling strain was placed beside one of these portable PIDs, and the collected air sample was analyzed for individual concentration (CVOCi) of 5 VOCs (xylene, butanone, toluene, butyl acetate, and dimethylformamide). Predictive models were established by relating the CT-VOCs to CVOCi of each individual compound via simple regression analysis. The established predictive models were employed to predict each CVOCi based on the measured CT-VOC for each the similar working area using the same portable PID. Results show that predictive models obtained from simple linear regression analyses were found with an R2 = 0.83~0.99 indicating that CT-VOCs were adequate for predicting CVOCi. In order to verify the validity of the exposure prediction model, the sampling analysis of the above chemical substances was further carried out and the correlation between the measured value (Cm) and the predicted value (Cp) was analyzed. It was found that there is a good correction between the predicted value and measured value of each measured chemical substance (R2=0.83~0.98). Therefore, the surrogate approach could be assessed the exposure concentration of similar exposed groups using the same chemicals but different formula ratios. However, it is recommended to establish the prediction model between the chemical substances belonging to each coater and the direct-reading PID, which is more representative of reality exposure situation and more accurately to estimate the long-term exposure concentration of operators.

Keywords: exposure assessment, exposure prediction model, surrogate approach, TVOC

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

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

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

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Authors: Innocent Pangapanga, Eric Mungatana

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Tropical cyclones–related floods (TCRFs) in Malawi have devastating effects on smallholder agriculture, thereby threatening the food security agenda, which is already constrained by poor agricultural innovations, low use of improved varieties, and unaffordable inorganic fertilizers, and fragmenting landholding sizes. Accordingly, households have engineered and indigenously implemented sustainable landscape management (SLM) innovations to contain the adverse effects of TCRFs on farm productivity. This study, therefore, interrogated the efficacy of SLM adoption on farm productivity under varying TCRFs, while controlling for the potential selection bias and unobservable heterogeneity through the application of the Endogenous Switching Regression Model. In this study, we further investigated factors driving SLM adoption. Substantively, we found TCRFs reducing farm productivity by 31 percent, on the one hand, and influencing the adoption of SLM innovations by 27 percent, on the other hand. The study also observed that households that interacted SLM with TCRFs were more likely to enhance farm productivity by 24 percent than their counterparts. Interestingly, the study results further demonstrated that multiple adoptions of SLM-related innovations, including intercropping, agroforestry, and organic manure, enhanced farm productivity by 126 percent, suggesting promoting SLM adoption as a package to appropriately inform existing sustainable development goals’ agricultural productivity initiatives under intensifying TCRFs in the country.

Keywords: tropical cyclones–related floods, sustainable landscape management innovations, farm productivity, endogeneity, endogenous switching regression model, panel data, smallholder agriculture

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Authors: Selma Hamimed, Abdelwaheb Chatti

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The current study deals with the biological treatment of saline wastewaters generated by various agro-food industries using Yarrowia lipolytica. The ability of this yeast was studied on the mixture of olive mill wastewater and tuna wash processing wastewater. Results showed that the high proportion of olive mill wastewater in the mixture about (75:25) is the suitable one for the highest Y. lipolytica biomass production, reaching 11.3 g L⁻¹ after seven days. In addition, results showed significant removal of chemical oxygen demand (COD) and phosphorous of 97.49 % and 98.90 %, respectively. On the other hand, Y. lipolytica was found to be effective to desalinate all mixtures reaching a removal of 92.21 %. Moreover, the analytical results using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) confirmed the biosorption of NaCl on the surface of the yeast as nanocrystals form with a size of 47.3 nm.

Keywords: nanocrystallization of NaCl, desalination, wastewater treatment, yarrowia lipolytica

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: S. Bangphan, P. Bangphan, C. Ketsombun, T. Sammana

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Optimization of response surface methodology (RSM) was employed to study the effects of three factor (rubber of clearance, spindle of speed, and rice of moisture) in brown rice peeling machine of the optimal good rice yield (99.67, average of three repeats). The optimized composition derived from RSM regression was analyzed using Regression analysis and Analysis of Variance (ANOVA). At a significant level α=0.05, the values of Regression coefficient, R2 adjust were 96.55% and standard deviation were 1.05056. The independent variables are initial rubber of clearance, spindle of speed and rice of moisture parameters namely. The investigating responses are final rubber clearance, spindle of speed and moisture of rice.

Keywords: brown rice, response surface methodology (RSM), peeling machine, optimization, paddy husker

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Authors: Supriyono Supriyono, Sumardiyono Sumardiyono, Peni Pujiastuti, Dian Indriana Hapsari

Abstract:

Crude palm oil (CPO) is a vegetable oil that came from a palm tree bunch. The productivity of the oil is 12 ton/hectare/year. Thus palm oil tree was known as highest vegetable oil yield. It was grown across Equatorial County, especially in Malaysia and Indonesia. The greenish-red color on CPO was come from carotenoid. Carotenoid is one of the antioxidants that could be extracted. Carotenoid could be used as functional food and other purposes. Another antioxidant that also found in CPO is tocopherol. The aim of the research work is to find antioxidant activity on CPO comparing to the synthetic antioxidant that available in a market. In this research work, antioxidant was extracted by a mixture of acetone and n.hexane, while the activity of the antioxidant extract was determined by DPPH method. Antioxidant activity of the extracted compound about 46% compared to pure tocopherol. While the solvent mixture compose by 90% acetone and 10% n. hexane meet the best on the antioxidant activity.

Keywords: antioxidant, beta carotene, crude palm oil, DPPH, tocopherol

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Authors: Mukesh Khadka, Alexander Paz, Hanns de la Fuente-Mella

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Typically, pavement performance models are developed in two steps: (i) pavement segments with similar characteristics are grouped together to form a cluster, and (ii) the corresponding performance models are developed using statistical techniques. A challenge is to select the characteristics that define clusters and the segments associated with them. If inappropriate characteristics are used, clusters may include homogeneous segments with different performance behavior or heterogeneous segments with similar performance behavior. Prediction accuracy of performance models can be improved by grouping the pavement segments into more uniform clusters by including both characteristics and a performance measure. This grouping is not always possible due to limited information. It is impractical to include all the potential significant factors because some of them are potentially unobserved or difficult to measure. Historical performance of pavement segments could be used as a proxy to incorporate the effect of the missing potential significant factors in clustering process. The current state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and the associated performance models simultaneously. CLR incorporates the effect of significant factors as well as a performance measure. In this study, a mathematical program was formulated for CLR models including multiple explanatory variables. Pavement data collected recently over the entire state of Nevada were used. International Roughness Index (IRI) was used as a pavement performance measure because it serves as a unified standard that is widely accepted for evaluating pavement performance, especially in terms of riding quality. Results illustrate the advantage of the using CLR. Previous studies have used CLR along with experimental data. This study uses actual field data collected across a variety of environmental, traffic, design, and construction and maintenance conditions.

Keywords: clusterwise regression, pavement management system, performance model, optimization

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Authors: T. Yılmaz, Ş. Tavman

Abstract:

In this study, optimization of ultrasound assisted extraction (UAE) of hemicellulose based polysaccharides from plant waste material has been studied. Selected material is hazelnut skin. Extraction variables for the operation are extraction time, amplitude and application temperature. Optimum conditions have been evaluated depending on responses such as amount of wet crude polysaccharide, total carbohydrate content and dried sample. Pretreated hazelnut skin powders were used for the experiments. 10 grams of samples were suspended in 100 ml water in a jacketed vessel with additional magnetic stirring. Mixture was sonicated by immersing ultrasonic probe processor. After the extraction procedures, ethanol soluble and insoluble sides were separated for further examinations. The obtained experimental data were analyzed by analysis of variance (ANOVA). Second order polynomial models were developed using multiple regression analysis. The individual and interactive effects of applied variables were evaluated by Box Behnken Design. The models developed from the experimental design were predictive and good fit with the experimental data with high correlation coefficient value (R2 more than 0.95). Extracted polysaccharides from hazelnut skin are assumed to be pectic polysaccharides according to the literature survey of Fourier Transform Spectrometry (FTIR) analysis results. No more change can be observed between spectrums of different sonication times. Application of UAE at optimized condition has an important effect on extraction of hemicellulose from plant material by satisfying partial hydrolysis to break the bounds with other components in plant cell wall material. This effect can be summarized by varied intensity of microjets and microstreaming at varied sonication conditions.

Keywords: hazelnut skin, optimization, polysaccharide, ultrasound assisted extraction

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Authors: M. S. Gaafar, S. Y. Marzouk, I. S. Mahmoud, S. Al-Zobaidi

Abstract:

Makishima and Mackenzie model was used to simulation of acoustic properties (longitudinal and shear ultrasonic wave velocities, elastic moduli theoretically for many tellurite and borate glasses. The model was proposed mainly depending on the values of the experimentally measured density, which are obtained before. In this search work, we are trying to obtain the values of densities of amorphous glasses (as the density depends on the geometry of the network structure of these glasses). In addition, the problem of simulating the slope of linear regression between the experimentally determined bulk modulus and the product of packing density and experimental Young's modulus, were solved in this search work. The results showed good agreement between the experimentally measured values of densities and both ultrasonic wave velocities, and those theoretically determined.

Keywords: glasses, ultrasonic wave velocities, elastic modulus, Makishima & Mackenzie Model

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Authors: Bernhard Widhalm, Cornelia Rieder-Gradinger, Ewald Srebotnik

Abstract:

Pine wood (Pinus sylvestris L.) is the basic raw material for the production of Oriented Strand Boards (OSB) and the major source of volatile organic compounds, especially terpenes (like α- and β-pinene). To lower the total emission level of OSB, terpene metabolising microorganisms were therefore applied onto pine wood strands for the production of emission-reduced boards. Suitable microorganisms were identified during preliminary tests under laboratory conditions. At first, their terpene degrading potential was investigated in liquid culture, followed by laboratory tests using unsterile pine wood particles and strands. The main focus was laid on an adoptable terpene reduction in a short incubation time. An optimised bacterial mixture of Pseudomonas putida and Pseudomonas fluorescens showed the best results and was therefore used for further experiments on a larger scale. In an industry-compatible testing procedure, pine wood strands were incubated with the bacterial mixture for a period of 2 to 4 days. Incubation time was stopped by drying the strands. OSB were then manufactured from the pre-treated strands and emissions were measured by means of SPME/GC-MS analysis. Bacterial pre-treatment of strands resulted in a reduction of α-pinene- and β-pinene-emissions from OSB by 40% and 70%, respectively, even after only 2 days of incubation. The results of the investigation provide a basis for the application of microbial treatment within the industrial OSB production line, where shortest possible incubation times are required. For this purpose, the performance of the bacterial mixture will have to be further optimised.

Keywords: GC-MS, OSB, Pseudomonas sp., terpene degradation

Procedia PDF Downloads 266