Search results for: corrosion prediction ductile fracture

Authors: Nato Kutaladze, George Mikuchadze, Giorgi Sokhadze

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Precipitation forecasts are one of the most demanding applications in numerical weather prediction (NWP). Georgia, as the whole Caucasian region, is characterized by very complex topography. The country territory is prone to flash floods and mudflows, quantitative precipitation estimation (QPE) and quantitative precipitation forecast (QPF) at any leading time are very important for Georgia. In this study, advanced research weather forecasting model’s skill in QPF is investigated over Georgia’s territory. We have analyzed several convection parameterization and microphysical scheme combinations for different rainy episodes and heavy rainy phenomena. We estimate errors and biases in accumulated 6 h precipitation using different spatial resolution during model performance verification for 12-hour and 24-hour lead time against corresponding rain gouge observations and satellite data. Various statistical parameters have been calculated for the 8-month comparison period, and some skills of model simulation have been evaluated. Our focus is on the formation and organization of convective precipitation systems in a low-mountain region. Several problems in connection with QPF have been identified for mountain regions, which include the overestimation and underestimation of precipitation on the windward and lee side of the mountains, respectively, and a phase error in the diurnal cycle of precipitation leading to the onset of convective precipitation in model forecasts several hours too early.

Keywords: extremal dependence index, false alarm, numerical weather prediction, quantitative precipitation forecasting

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Authors: Surjit Angra, Gajanan Rane, Vinod Kumar, Sushma Rani

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This paper presents metallurgical investigation of failed helical gear of diesel engine gear box used in a car. The failure had occurred near the bottomland of the tooth spacing. The failed surface was studied under Scanning Electron Microscope (SEM) and also visually investigated. The images produced through SEM at various magnifications were studied. Detailed metallurgical study indicates that failure was due to foreign material inclusion which is a casting defect. Further study also revealed pitting, spalling and inter-granular fracture as the causes of gear failure.

Keywords: helical gear, scanning electron microscope, casting defect, pitting

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Authors: Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong

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In recent years, real estate prediction or valuation has been a topic of discussion in many developed countries. Improper hype created by investors leads to fluctuating prices of real estate, affecting many consumers to purchase their own homes. Therefore, scholars from various countries have conducted research in real estate valuation and prediction. With the back-propagation neural network that has been popular in recent years and the orthogonal array in the Taguchi method, this study aimed to find the optimal parameter combination at different levels of orthogonal array after the system presented different parameter combinations, so that the artificial neural network obtained the most accurate results. The experimental results also demonstrated that the method presented in the study had a better result than traditional machine learning. Finally, it also showed that the model proposed in this study had the optimal predictive effect, and could significantly reduce the cost of time in simulation operation. The best predictive results could be found with a fewer number of experiments more efficiently. Thus users could predict a real estate transaction price that is not far from the current actual prices.

Keywords: artificial neural network, Taguchi method, real estate valuation model, investors

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Authors: Yan Yusupov, Aleksandra Soldatova, Yaroslav Zaglyadin

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The object of this work is Bazhenov-Abalak unconventional formation (BAUF) of Western Siberia. On the base of the Geomechanical model (GMM), a methodology was developed for sweet spot intervals and zones for drilling horizontal wells with hydraulic fracturing. Based on mechanical rock typification, eight mechanical rock types (MRT) have been identified. Sweet spot intervals are represented by siliceous-carbonate (2), siliceous (5) and carbonate (8) MRT that have the greatest brittleness index (BRIT). A correlation has been established between the thickness of brittle intervals and the initial well production rates, which makes it possible to identify sweet spot zones for drilling horizontal wells with hydraulic fracturing. Brittle and ductile intervals are separated by a BRIT cut-off of 0.4 since wells located at points with BRIT < 0.4 have insignificant rates (less than 2 m³/day). Wells with an average BRIT in BAUF of more than 0.4 reach industrial production rates. The next application of GMM is associated with the instability of the overburdened clay formation above the top of the BAUF. According to the wellbore stability analysis, the recommended mud weight for this formation must be not less than 1.53–1.55 g/cc. The optimal direction for horizontal wells corresponds to the azimuth of Shmin equal to 70-80°.

Keywords: unconventional reservoirs, geomechanics, sweet spot zones, borehole stability

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Authors: Javier E. García-Gallo, Nelson J. Fonseca-Ruiz, John F. Duitama-Munoz

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Sepsis is a syndrome that occurs with physiological and biochemical abnormalities induced by severe infection and carries a high mortality and morbidity, therefore the severity of its condition must be interpreted quickly. After patient admission in an intensive care unit (ICU), it is necessary to synthesize the large volume of information that is collected from patients in a value that represents the severity of their condition. Traditional severity of illness scores seeks to be applicable to all patient populations, and usually assess in-hospital mortality. However, the use of machine learning techniques and the data of a population that shares a common characteristic could lead to the development of customized mortality prediction scores with better performance. This study presents the development of a score for the one-year mortality prediction of the patients that are admitted to an ICU with a sepsis diagnosis. 5650 ICU admissions extracted from the MIMICIII database were evaluated, divided into two groups: 70% to develop the score and 30% to validate it. Comorbidities, demographics and clinical information of the first 24 hours after the ICU admission were used to develop a mortality prediction score. LASSO (least absolute shrinkage and selection operator) and SGB (Stochastic Gradient Boosting) variable importance methodologies were used to select the set of variables that make up the developed score; each of this variables was dichotomized and a cut-off point that divides the population into two groups with different mean mortalities was found; if the patient is in the group that presents a higher mortality a one is assigned to the particular variable, otherwise a zero is assigned. These binary variables are used in a logistic regression (LR) model, and its coefficients were rounded to the nearest integer. The resulting integers are the point values that make up the score when multiplied with each binary variables and summed. The one-year mortality probability was estimated using the score as the only variable in a LR model. Predictive power of the score, was evaluated using the 1695 admissions of the validation subset obtaining an area under the receiver operating characteristic curve of 0.7528, which outperforms the results obtained with Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS) and Simplified Acute Physiology Score II (SAPSII) scores on the same validation subset. Observed and predicted mortality rates within estimated probabilities deciles were compared graphically and found to be similar, indicating that the risk estimate obtained with the score is close to the observed mortality, it is also observed that the number of events (deaths) is indeed increasing as the outcome go from the decile with the lowest probabilities to the decile with the highest probabilities. Sepsis is a syndrome that carries a high mortality, 43.3% for the patients included in this study; therefore, tools that help clinicians to quickly and accurately predict a worse prognosis are needed. This work demonstrates the importance of customization of mortality prediction scores since the developed score provides better performance than traditional scoring systems.

Keywords: intensive care, logistic regression model, mortality prediction, sepsis, severity of illness, stochastic gradient boosting

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Authors: Parisa Hasanpour, Bahram Borooghani, Vahid Asadi

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In the current research, a catastrophic failure of a 304 stainless steel flange at pipeline transportation of ethylene in a petrochemical refinery was studied. Cracking was found in the flange after about 78840h service. Through the chemical analysis, tensile tests in addition to microstructural analysis such as optical microscopy and Scanning Electron Microscopy (SEM) on the failed part, it found that the fatigue was responsible for the fracture of the flange, which originated from bumps and depressions on the outer surface and propagated by vibration caused by the working condition.

Keywords: failure analysis, 304 stainless steel, fatigue, flange, petrochemical refinery

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Authors: Hee Yong Kang, Hyeon Ho Shin, Jung Cheol Yoo, Il Taek Lee, Sung Mo Yang

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Lightweight technology using composites is being developed for vehicle seat structures, and its design must meet the safety requirements. According to the Federal Motor Vehicle Safety Standard (FMVSS) 207 seating systems test procedure, the back moment load is applied to the seat back frame structure for the safety evaluation of the vehicle seat. The seat back frame using the composites is divided into three parts: upper part frame, and left- and right-side frame parts following the manufacturing process. When a rear moment load is applied to the seat back frame, the side frame receives the bending load and the torsional load at the same time. This results in the largest loaded strength. Therefore, strength test of the component unit is required. In this study, a component test method based on the FMVSS 207 seating systems test procedure was proposed for the strength analysis of bending load and torsional load of the automotive Bulk Molding Compound (BMC) Seat Back Side Frame. Moreover, strength evaluation according to the carbon band reinforcement was performed. The back-side frame parts of the seat that are applied to the test were manufactured through BMC that is composed of vinyl ester Matrix and short carbon fiber. Then, two kinds of reinforced and non-reinforced parts of carbon band were formed through a high-temperature compression molding process. In addition, the structure that is applied to the component test was constructed by referring to the FMVSS 207. Then, the bending load and the torsional load were applied through the displacement control to perform the strength test for four load conditions. The results of each test are shown through the load-displacement curves of the specimen. The failure strength of the parts caused by the reinforcement of the carbon band was analyzed. Additionally, the fracture characteristics of the parts for four strength tests were evaluated, and the weakness structure of the back-side frame of the seat structure was confirmed according to the test conditions. Through the bending and torsional strength test methods, we confirmed the strength and fracture characteristics of BMC Seat Back Side Frame according to the carbon band reinforcement. And we proposed a method of testing the part strength of a seat back frame for vehicles that can meet the FMVSS 207.

Keywords: seat back frame, bending and torsional strength, BMC (Bulk Molding Compound), FMVSS 207 seating systems

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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: Mia Françoise

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This report aims to develop a strategy for assisting short-term investors to benefit from the current economic climate in South Africa by utilizing technical analysis techniques and predictive analytics. As part of this research, value investing and technical analysis principles will be combined to maximize returns for South African investors while optimizing volatility. As an emerging market, South Africa offers many opportunities for high growth in sectors where other developed countries cannot grow at the same rate. Investing in South African companies with significant growth potential can be extremely rewarding. Although the risk involved is more significant in countries with less developed markets and infrastructure, there is more room for growth in these countries. According to recent research, the offshore market is expected to outperform the local market over the long term; however, short-term investments in the local market will likely be more profitable, as the Johannesburg Stock Exchange is predicted to outperform the S&P500 over the short term. The instabilities in the economy contribute to increased market volatility, which can benefit investors if appropriately utilized. Price prediction and portfolio optimization comprise the two primary components of this methodology. As part of this process, statistics and other predictive modeling techniques will be used to predict the future performance of stocks listed on the Johannesburg Stock Exchange. Following predictive data analysis, Modern Portfolio Theory, based on Markowitz's Mean-Variance Theorem, will be applied to optimize the allocation of assets within an investment portfolio. By combining different assets within an investment portfolio, this optimization method produces a portfolio with an optimal ratio of expected risk to expected return. This methodology aims to provide a short-term investment with a stock portfolio that offers the best risk-to-return profile for stocks listed on the JSE by combining price prediction and portfolio optimization.

Keywords: financial stocks, optimized asset allocation, prediction modelling, South Africa

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Authors: Tobias Strübing, Fatemeh Ziaeetabar

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Humans usually manipulate objects with their hands. To represent these actions in a simple and understandable way, we need to use a semantic framework. For this purpose, the Semantic Event Chain (SEC) method has already been presented which is done by consideration of touching and non-touching relations between manipulated objects in a scene. This method was improved by a computational model, the so-called enriched Semantic Event Chain (eSEC), which incorporates the information of static (e.g. top, bottom) and dynamic spatial relations (e.g. moving apart, getting closer) between objects in an action scene. This leads to a better action prediction as well as the ability to distinguish between more actions. Each eSEC manipulation descriptor is a huge matrix with thirty rows and a massive set of the spatial relations between each pair of manipulated objects. The current eSEC framework has so far only been used in the category of manipulation actions, which eventually involve two hands. Here, we would like to extend this approach to a whole body action descriptor and make a conjoint activity representation structure. For this purpose, we need to do a statistical analysis to modify the current eSEC by summarizing while preserving its features, and introduce a new version called Enhanced eSEC or (e2SEC). This summarization can be done from two points of the view: 1) reducing the number of rows in an eSEC matrix, 2) shrinking the set of possible semantic spatial relations. To achieve these, we computed the importance of each matrix row in an statistical way, to see if it is possible to remove a particular one while all manipulations are still distinguishable from each other. On the other hand, we examined which semantic spatial relations can be merged without compromising the unity of the predefined manipulation actions. Therefore by performing the above analyses, we made the new e2SEC framework which has 20% fewer rows, 16.7% less static spatial and 11.1% less dynamic spatial relations. This simplification, while preserving the salient features of a semantic structure in representing actions, has a tremendous impact on the recognition and prediction of complex actions, as well as the interactions between humans and robots. It also creates a comprehensive platform to integrate with the body limbs descriptors and dramatically increases system performance, especially in complex real time applications such as human-robot interaction prediction.

Keywords: enriched semantic event chain, semantic action representation, spatial relations, statistical analysis

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Authors: Emre Ozaslan, Bulent Acar, Mehmet Ali Guler

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Unidirectional composites are very popular structural materials used in aerospace, marine, energy and automotive industries thanks to their superior material properties. However, the mechanical behavior of composite materials is more complicated than isotropic materials because of their anisotropic nature. Also, a stress concentration availability on the structure, like a hole, makes the problem further complicated. Therefore, enormous number of tests require to understand the mechanical behavior and strength of composites which contain stress concentration. Accurate finite element analysis and analytical models enable to understand mechanical behavior and predict the strength of composites without enormous number of tests which cost serious time and money. In this study, unidirectional Carbon/Epoxy composite specimens with central circular hole were investigated in terms of stress concentration factor and strength prediction. The composite specimens which had different specimen wide (W) to hole diameter (D) ratio were tested to investigate the effect of hole size on the stress concentration and strength. Also, specimens which had same specimen wide to hole diameter ratio, but varied sizes were tested to investigate the size effect. Finite element analysis was performed to determine stress concentration factor for all specimen configurations. For quasi-isotropic laminate, it was found that the stress concentration factor increased approximately %15 with decreasing of W/D ratio from 6 to 3. Point stress criteria (PSC), inherent flaw method and progressive failure analysis were compared in terms of predicting the strength of specimens. All methods could predict the strength of specimens with maximum %8 error. PSC was better than other methods for high values of W/D ratio, however, inherent flaw method was successful for low values of W/D. Also, it is seen that increasing by 4 times of the W/D ratio rises the failure strength of composite specimen as %62.4. For constant W/D ratio specimens, all the strength prediction methods were more successful for smaller size specimens than larger ones. Increasing the specimen width and hole diameter together by 2 times reduces the specimen failure strength as %13.2.

Keywords: failure, strength, stress concentration, unidirectional composites

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Authors: Osayande Pascal Omondiagbe, Sherlock a Licorish

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Stack Overflow is a popular community question and answer portal which is used by practitioners to solve technology-related challenges during software development. Previous studies have shown that this forum is becoming a substitute for official software programming languages documentation. While tools have looked to aid developers by presenting interfaces to explore Stack Overflow, developers often face challenges searching through many possible answers to their questions, and this extends the development time. To this end, researchers have provided ways of predicting acceptable Stack Overflow answers by using various modeling techniques. However, less interest is dedicated to examining the performance and quality of typically used modeling methods, and especially in relation to models’ and features’ complexity. Such insights could be of practical significance to the many practitioners that use Stack Overflow. This study examines the performance and quality of various modeling methods that are used for predicting acceptable answers on Stack Overflow, drawn from 2014, 2015 and 2016. Our findings reveal significant differences in models’ performance and quality given the type of features and complexity of models used. Researchers examining classifiers’ performance and quality and features’ complexity may leverage these findings in selecting suitable techniques when developing prediction models.

Keywords: feature selection, modeling and prediction, neural network, random forest, stack overflow

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Authors: Razali Bin Abidin

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This paper presents the development of a portable water jet cutter for soft materials such as meat. Twelve geometries of nozzles were simulated using finite element method. Water pressure was set to 1500 lb/in². Through the simulation, highest average water output speed was 133.04 m/s. The nozzle was fabricated from Al - alloy 5052 with the Factor of Safety~ 3. This indicates that the nozzle made of Al-alloy 5052 is capable of performing the cutting process without any fracture. Preliminary design of mobile water jet hand tool is presented at the end of this paper.

Keywords: water jet, finite element, Al-alloy 5052, nozzle geometry

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Authors: Surajit Bhattacharya, Daniel Veltri, Atit A. Patel, Daniel N. Cox

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miRNAs have emerged as key post-transcriptional regulators of gene expression, however identification of biologically-relevant target genes for this epigenetic regulatory mechanism remains a significant challenge. To address this knowledge gap, we have developed a novel tool in R, Intra-miR-ExploreR, that facilitates integrated discovery of miRNA targets by incorporating target databases and novel target prediction algorithms, using statistical methods including Pearson and Distance Correlation on microarray data, to arrive at high confidence intragenic miRNA target predictions. We have explored the efficacy of this tool using Drosophila melanogaster as a model organism for bioinformatics analyses and functional validation. A number of putative targets were obtained which were also validated using qRT-PCR analysis. Additional features of the tool include downloadable text files containing GO analysis from DAVID and Pubmed links of literature related to gene sets. Moreover, we are constructing interaction maps of intragenic miRNAs, using both micro array and RNA-seq data, focusing on neural tissues to uncover regulatory codes via which these molecules regulate gene expression to direct cellular development.

Keywords: miRNA, miRNA:mRNA target prediction, statistical methods, miRNA:mRNA interaction network

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Authors: Yongseok Kim, Jeong-Min Lee, Hyunwoo Song, Junghan Yun, Jungin Byun, Jae-Mean Koo, Chang-Sung Seok

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Thermal barrier coating(TBC) is applied for gas turbine components to protect the components from extremely high temperature condition. Since metallic substrate cannot endure such severe condition of gas turbines, delamination of TBC can cause failure of the system. Thus, delamination life of TBC is one of the most important issues for designing the components operating at high temperature condition. Thermal stress caused by thermally grown oxide(TGO) layer is known as one of the major failure mechanisms of TBC. Thermal stress by TGO mainly occurs at the interface between TGO layer and ceramic top coat layer, and it is strongly influenced by the thickness and shape of TGO layer. In this study, Isothermal oxidation is conducted on coin-type TBC specimens prepared by APS(air plasma spray) method. After the isothermal oxidation at various temperature and time condition, the thickness and shape(rumpling shape) of the TGO is investigated, and the test data is processed by numerical analysis. Finally, the test data is arranged into a mathematical prediction model with two variables(temperature and exposure time) which can predict the thickness and rumpling shape of TGO.

Keywords: thermal barrier coating, thermally grown oxide, thermal stress, isothermal oxidation, numerical analysis

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Authors: Hong Je Kang, Young Chae Choi, Jin Sung Park, Isac Kim

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Purpose: Osteoporotic fractures are an important among postmenopausal women. When osteoporotic distal radial fractures occur, osteoporosis must be treated to prevent the hip and spine fractures. Intravenous injection of Zoledronic acid is expected to improve preventing osteoporotic fractures. Many articles reported the effect of intravenous Zoledronic acid to BMD of the hip and spine fracture or non-fracture patients with low BMD. However, that with distal radial fractures has rarely been reported. Therefore, the authors decided to study the effect of Zoledronic acid in BMD score, bone union, and bone turnover markers in the patients who underwent volar plating due to osteoporotic distal radial fractures. Materials: From April 2018 to May 2022, postmenopausal women aged 55 years or older who had osteoporotic distal radial fractures and who underwent surgical treatment using volar plate fixation were included. Zoledronic acid (5mg) was injected intravenously between 3 and 5 days after surgery. BMD scores after 1 year of operation were compared with the initial scores. Bone turnover markers were measured before surgery, after 3 months, and after 1 year. Radiological follow-up was performed every 2 weeks until the bone union and at 1 year postoperatively. Clinical outcome indicators were measured one year after surgery, and the occurrence of side effects was observed. Result: Total of 23 patients were included, with a lumbar BMD T score of -2.89±0.2 before surgery to -2.27±0.3 one year after surgery (p=0.012) and a femoral neck BMD T score of -2.45±0.3 before surgery to -2.36±0.3 (p=0.041) after one year, and all were statistically significant. Measured as bone resorption markers, serum CTX-1 was 337.43±10.4 pg/mL before surgery, 160.86±8.7 pg/mL (p=0.022) after three months, and 250.12±12.7 pg/mL (p=0.031) after one year. Urinary NTX-1 was 39.24±2.2 ng/mL before surgery, 24.46±1.2 ng/mL (p=0.014) after three months and 30.35±1.6 ng/mL (p=0.042) after one year. Measured as bone formation markers, serum osteocalcin was 13.04±1.1 ng/mL before surgery, 8.84±0.7 ng/mL (p=0.037) after 3 months and 11.1±0.4 ng/mL (p=0.026) after one year. Serum bone-specific ALP was 11.24±0.9 IU/L before surgery, 8.25±0.9 IU/L (p=0.036) after three months, and 10.2±0.9 IU/L (p=0.027) after one year. All were statistically significant. All cases showed bone union within an average of 6.91±0.3 weeks without any signs of failure. Complications were found in 5 out of 23 cases (21.7%), such as headache, nausea, muscle pain, and fever. Conclusion: When Zoledronic acid was used, BMD was improved in both the spine and femoral neck. This may reduce the likelihood and subsequent morbidity of additional osteoporotic fractures. This study is meaningful in that there was no difference in the duration of bone union and radiological characteristics in patients with distal radial fractures administrated with intravenous BP early after the fractures, and improvement in BMD and bone turnover indicators was measured.

Keywords: zeoldreonic acid, BMD, osteoporosis, distal radius

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Roseline Oghogho Osaseri, Osaseri E. I.

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Lassa fever is an epidemic hemorrhagic fever caused by the Lassa virus, an extremely virulent arena virus. This highly fatal disorder kills 10% to 50% of its victims, but those who survive its early stages usually recover and acquire immunity to secondary attacks. One of the major challenges in giving proper treatment is lack of fast and accurate diagnosis of the disease due to multiplicity of symptoms associated with the disease which could be similar to other clinical conditions and makes it difficult to diagnose early. This paper proposed an Adaptive Neuro Fuzzy Inference System (ANFIS) for the prediction of Lass Fever. In the design of the diagnostic system, four main attributes were considered as the input parameters and one output parameter for the system. The input parameters are Temperature on admission (TA), White Blood Count (WBC), Proteinuria (P) and Abdominal Pain (AP). Sixty-one percent of the datasets were used in training the system while fifty-nine used in testing. Experimental results from this study gave a reliable and accurate prediction of Lassa fever when compared with clinically confirmed cases. In this study, we have proposed Lassa fever diagnostic system to aid surgeons and medical healthcare practictionals in health care facilities who do not have ready access to Polymerase Chain Reaction (PCR) diagnosis to predict possible Lassa fever infection.

Keywords: anfis, lassa fever, medical diagnosis, soft computing

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Authors: Xiang-Yao Zheng, Jen-Cheng Wang, Joe-Air Jiang

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Improving the line ampacity while using existing power grids is an important issue that electricity dispatchers are now facing. Using the information provided by the dynamic thermal rating (DTR) of transmission lines, an overhead power grid can operate safely. However, dispatchers usually lack real-time DTR information. Thus, this study proposes a long-short-term memory (LSTM)-based method, which is one of the neural network models. The LSTM-based method predicts the DTR of lines using the weather data provided by Central Weather Bureau (CWB) of Taiwan. The possible thermal bottlenecks at different locations along the line and the margin of line ampacity can be real-time determined by the proposed LSTM-based prediction method. A case study that targets the 345 kV power grid of TaiPower in Taiwan is utilized to examine the performance of the proposed method. The simulation results show that the proposed method is useful to provide the information for the smart grid application in the future.

Keywords: electricity dispatch, line ampacity prediction, dynamic thermal rating, long-short-term memory neural network, smart grid

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Authors: Siti Noor Syahirah Mohd Sabri

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The oil and gas industry is committed to net zero carbon emissions because the consequences of climate change could be catastrophic unless responded to very soon. One way of reducing CO₂ emissions is to inject it into a depleted reservoir buried underground. This greenhouse gas reduction technique significantly reduces CO₂ released into the atmosphere. In general, depleted oil and gas reservoirs provide readily available sites for the storage of CO₂ in offshore areas. This is mainly due to the hydrocarbons have been optimally produced and the existence of voids for effective CO₂ storage. Hence, make it a good candidate for a CO₂ well injector location. Geological storage sites are often evaluated in terms of capacity, injectivity and containment. Leakage through the cap rock or existing well is the main concern in the depleted fields. In order to develop these fields as CO₂ storage sites, the long-term integrity of wells drilled in these oil & gas fields must be ascertained to ensure good CO₂ containment. Well, integrity is often defined as the ability to contain fluids without significant leakage through the project lifecycle. Most plugged and abandoned (P & A) wells in Peninsular Malaysia have drilled 20 – 30 years ago and were not designed to withstand downhole conditions having >50%vol CO₂ and CO₂/H₂O mixture. In addition, Corrosive-Resistant Alloy (CRA) tubular and CO₂-resistant cement was not used during good construction. The reservoir pressure and temperature conditions may have further degraded the material strength and elevated the corrosion rate. Understanding all the uncertainties that may have affected cement-casing bonds, such as the quality of cement behind the casing, subsidence effect, corrosion rate, etc., is the first step toward well integrity evaluation. Secondly, proper quantification of all the uncertainties involved needs to be done to ensure long-term underground storage objectives of CO₂ are achieved. This paper will discuss challenges associated with estimating the performance of well barrier elements in existing P&A wells. Risk ranking of the existing P&A wells is to be carried out in order to ensure the integrity of the storage site is maintained for long-term CO₂ storage. High-risk existing P&A wells are to be re-entered to restore good integrity and to reduce future leakage that may happen. In addition, the requirement to design a fit-for-purpose monitoring and mitigation technology package for potential CO₂ leakage/seepage in the marine environment will be discussed accordingly. The holistic approach will ensure that the integrity is maintained, and CO₂ is contained underground for years to come.

Keywords: CCUS, well integrity, co₂ storage, offshore

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Authors: Carlos Theran, Yohn Parra Bautista, Victor Adankai, Richard Alo, Jimwi Liu, Clement G. Yedjou

Abstract:

Alzheimer's disease (AD) is a neurodegenerative disorder primarily characterized by deteriorating cognitive functions. AD has gained relevant attention in the last decade. An estimated 24 million people worldwide suffered from this disease by 2011. In 2016 an estimated 40 million were diagnosed with AD, and for 2050 is expected to reach 131 million people affected by AD. Therefore, detecting and confirming AD at its different stages is a priority for medical practices to provide adequate and accurate treatments. Recently, Machine Learning (ML) models have been used to study AD's stages handling missing values in multiclass, focusing on the delineation of Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and normal cognitive (CN). But, to our best knowledge, robust performance information of these models and the missing data analysis has not been presented in the literature. In this paper, we propose studying the performance of five different machine learning models for AD's stages multiclass prediction in terms of accuracy, precision, and F1-score. Also, the analysis of three imputation methods to handle the missing value problem is presented. A framework that integrates ML model for AD's stages multiclass prediction is proposed, performing an average accuracy of 84%.

Keywords: alzheimer's disease, missing value, machine learning, performance evaluation

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Authors: Khalid Kaddoura, Ahmed Assad, Tarek Zayed

Abstract:

Sewer pipelines are prone to deterioration over-time. In fact, their deterioration does not follow a fixed downward pattern. This is in fact due to the defects that propagate through their service life. Sewer pipeline defects are categorized into distinct groups. However, the main two groups are the structural and operational defects. By definition, the structural defects influence the structural integrity of the sewer pipelines such as deformation, cracks, fractures, holes, etc. However, the operational defects are the ones that affect the flow of the sewer medium in the pipelines such as: roots, debris, attached deposits, infiltration, etc. Yet, the process for each defect to emerge follows a cause and effect relationship. Deformation, which is the change of the sewer pipeline geometry, is one type of an influencing defect that could be found in many sewer pipelines due to many surrounding factors. This defect could lead to collapse if the percentage exceeds 15%. Therefore, it is essential to predict the deformation percentage before confronting such a situation. Accordingly, this study will predict the percentage of the deformation defect in sewer pipelines adopting the multiple regression analysis. Several factors will be considered in establishing the model, which are expected to influence the defamation defect severity. Besides, this study will construct a time-based curve to understand how the defect would evolve overtime. Thus, this study is expected to be an asset for decision-makers as it will provide informative conclusions about the deformation defect severity. As a result, inspections will be minimized and so the budgets.

Keywords: deformation, prediction, regression analysis, sewer pipelines

Procedia PDF Downloads 187

Authors: Hadi Rouhi Belvirdi

Abstract:

Reinforced concrete structures, in addition to bearing service loads and seismic effects, may also be subjected to impact loads resulting from unforeseen incidents. Understanding the behavior of these structures is crucial, as they serve to protect against such sudden loads and can significantly reduce damage and destruction. In examining the behavior of structures under such loading conditions, a total of eight specimens of single-layer reinforced concrete slabs were subjected to impact loading through the free fall of weights from specified heights. The weights and dimensions of the specimens were uniform, and the amount of reinforcement was consistent. By altering the slabs' overall shape and the reinforcement details, efforts were made to optimize the behavior of the slabs against impact loads. The results indicated that utilizing ductile features in the slabs increased their resistance to impact loading. However, the compressive strength of the reinforcement did not significantly enhance the flexural resistance. Assuming a constant amount of longitudinal steel, changes in the placement of tensile reinforcement led to a decrease in resistance. With a fixed amount of transverse steel, merely adjusting the angle of the transverse reinforcement could help control cracking and mitigate premature failures. An increase in compressive resistance beyond a certain limit resulted in local buckling of the compressive zone, subsequently decreasing the impact resistance.

Keywords: reinforced concrete slab, high-strength concrete, impact loading, impact resistance

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Authors: M. K. Manoj, V. Pancholi, S. K. Nath

Abstract:

Microalloyed dual phase steels have been prepared by intercritical austenitisation (ICA) treatment of normalized steel at different temperature and time. Water quenching wad carried to obtain different martensite volume fraction (MVF) in DP steels. DP steels and normalized steels have been characterized by optical and scanning electron microscopy, Vickers hardness measurements and tensile properties determination. The effect of MVF and martensite morphology on mechanical properties and fracture behavior of microalloyed dual phase steels have been explained in the present work.

Keywords: dual phase steel, martensite morphology, hardness, tensile strength

Procedia PDF Downloads 328

Authors: Aminah Muchdar, Nuraeni, Eddy

Abstract:

The aims of the research are: (1) to verify the cropsyst plant model of experimental data in the field of soybean plants and (2) to predict planting time and potential yield soybean plant with the use of cropsyst model. This research is divided into several stages: (1) first calibration stage which conducted in the field from June until September 2015.(2) application models stage, where the data obtained from calibration in the field will be included in cropsyst models. The required data models are climate data, ground data/soil data,also crop genetic data. The relationship between the obtained result in field with simulation cropsyst model indicated by Efficiency Index (EF) which the value is 0,939.That is showing that cropsyst model is well used. From the calculation result RRMSE which the value is 1,922%.That is showing that comparative fault prediction results from simulation with result obtained in the field is 1,92%. The conclusion has obtained that the prediction of soybean planting time cropsyst based models that have been made valid for use. and the appropriate planting time for planting soybeans mainly on rain-fed land is at the end of the rainy season, in which the above study first planting time (June 2, 2015) which gives the highest production, because at that time there was still some rain. Tanggamus varieties more resistant to slow planting time cause the percentage decrease in the yield of each decade is lower than the average of all varieties.

Keywords: soybean, Cropsyst, calibration, efficiency Index, RRMSE

Procedia PDF Downloads 179

Authors: Won Jin Ryu, Mok Tan Ahn, Hyeok Choi, Joon Hong Park, Sung Min Kim, Jong Bae Park

Abstract:

Pipes for offshore plants require specifications that satisfy both high strength and high corrosion resistance. Therefore, currently, clad pipes are used in offshore plants. Clad pipes can be made using either overlay welding or clad plates. The present study was intended to figure out the effects of friction between two materials, which is a factor that affects two materials, were figured out using FEM to make clad pipes through heterogenous material drawing instead of the two methods mentioned above. Therefore, FEM has conducted while all other variables that the variable friction was fixed. The experimental results showed increases in pullout force along with increases in the friction in the boundary layer.

Keywords: clad pipe, FEM, friction, pullout force

Procedia PDF Downloads 494

Authors: Vinod Kumar, Surjit Angra

Abstract:

The objective of this theoretical study is to develop simple design formulas for the prediction of minimum film thickness and maximum mean film temperature rise in lightly loaded high-speed rolling/sliding lubricated elliptical contacts incorporating starvation effect. Herein, the reported numerical analysis focuses on thermoelastohydrodynamically lubricated rolling/sliding elliptical contacts, considering the Newtonian rheology of lubricant for wide range of operating parameters, namely load characterized by Hertzian pressure (PH = 0.01 GPa to 0.10 GPa), rolling speed (>10 m/s), slip parameter (S varies up to 1.0), and ellipticity ratio (k = 1 to 5). Starvation is simulated by systematically reducing the inlet supply. This analysis reveals that influences of load, rolling speed, and level of starvation are significant on the minimum film thickness. However, the maximum mean film temperature rise is strongly influenced by slip in addition to load, rolling speed, and level of starvation. In the presence of starvation, reduction in minimum film thickness and increase in maximum mean film temperature are observed. Based on the results of this study, empirical relations are developed for the prediction of dimensionless minimum film thickness and dimensionless maximum mean film temperature rise at the contacts in terms of various operating parameters.

Keywords: starvation, lubrication, elliptical contact, traction, minimum film thickness

Procedia PDF Downloads 392

Authors: Zeynep Küçükakça, Nezaket Parlak, Mesut Gür, Tahsin Engin, Hasan Küçük

Abstract:

In the current research, the single phase fluid flow and heat transfer characteristics are experimentally investigated. The experiments are conducted to cover transition zone for the Reynolds numbers ranging from 100 to 4800 by fused silica and stainless steel microtubes having diameters of 103-180 µm. The applicability of the Logarithmic Mean Temperature Difference (LMTD) method is revealed and an experimental method is developed to calculate the heat transfer coefficient. Heat transfer is supplied by a water jacket surrounding the microtubes and heat transfer coefficients are obtained by LMTD method. The results are compared with data obtained by the correlations available in the literature in the study. The experimental results indicate that the Nusselt numbers of microtube flows do not accord with the conventional results when the Reynolds number is lower than 1000. After that, the Nusselt number approaches the conventional theory prediction. Moreover, the scaling effects in micro scale such as axial conduction, viscous heating and entrance effects are discussed. On the aspect of fluid characteristics, the friction factor is well predicted with conventional theory and the conventional friction prediction is valid for water flow through microtube with a relative surface roughness less than about 4 %.

Keywords: microtube, laminar flow, friction factor, heat transfer, LMTD method

Procedia PDF Downloads 460

Authors: J. Ismail, F. Zaïri, M. Naït-Abdelaziz, Z. Azari

Abstract:

In this work, a combined approach of continuum damage mechanics (CDM) and fracture mechanics is applied to model a glass plate behavior under static indentation. A spherical indenter is used and a CDM based constitutive model with an anisotropic damage tensor was selected and implemented into a finite element code to study the damage of glass. Various regions with critical damage values were predicted in good agreement with the experimental observations in the literature. In these regions, the directions of crack propagation, including both cracks initiating on the surface as well as in the bulk, were predicted using the strain energy density factor.

Keywords: finite element modeling, continuum damage mechanics, indentation, cracks

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Authors: Reza Dinarvand, Mahdi Sadeghian, Somaye Sadeghian

Abstract:

Prediction of the ultimate bearing capacity of piles (Qu) is one of the basic issues in geotechnical engineering. So far, several methods have been used to estimate Qu, including the recently developed artificial intelligence methods. In recent years, optimization algorithms have been used to minimize artificial network errors, such as colony algorithms, genetic algorithms, imperialist competitive algorithms, and so on. In the present research, artificial neural networks based on colonial competition algorithm (ANN-ICA) were used, and their results were compared with other methods. The results of laboratory tests of short piles in clayey soils with parameters such as pile diameter, pile buried length, eccentricity of load and undrained shear resistance of soil were used for modeling and evaluation. The results showed that ICA-based artificial neural networks predicted lateral bearing capacity of short piles with a correlation coefficient of 0.9865 for training data and 0.975 for test data. Furthermore, the results of the model indicated the superiority of ICA-based artificial neural networks compared to back-propagation artificial neural networks as well as the Broms and Hansen methods.

Keywords: artificial neural network, clayey soil, imperialist competition algorithm, lateral bearing capacity, short pile

Procedia PDF Downloads 152