Search results for: measurement accuracy

Authors: Nigar Sultanova

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Facial morphology is a constant topic of concern for clinicians. When anthropometric methods were introduced into clinical practice to quantify changes in the craniofacial framework, features distinguishing various ethnic group were discovered. Normative data of facial measurements are indispensable to precise determination of the degree of deviations from normal. Establish the reference range of facial proportions in Azerbaijan population by anthropometric measurements of craniofacial complex. The study group consisted of 350 healthy young subjects, 175 males and 175 females, 18 to 25 years of age, from 7 different regions of Azerbaijan. The anthropometric examination was performed according to L.Farkas's method with our modification. In order to determine the morphologic characteristics of seven regions of the craniofacial complex 42 anthropometric measurements were selected. The anthropometric examination. Included the usage of 33 anthropometric landmarks. The 80 indices of the facial proportions, suggested by Farkas and Munro, were calculated: head -10, face - 23, nose - 23, lips - 9, orbits - 11, ears - 4. The date base of the North American white population was used as a reference group. Anthropometric measurements of facial proportions in Azerbaijan population revealed a significant difference between mеn and womеn, according to sexual dimorphism. In comparison with North American whites, considerable differences of facial proportions were observed in the head, face, orbits, labio-oral, nose and ear region. However, in women of the Azerbaijani population, 29 out of 80 proportion indices were similar to the proportions of NAW women. In the men of the Azerbaijani population, 27 out of 80 proportion indices did not reveal a statistically significant difference from the proportions of NAW men. Estimation of the reference range of facial proportions in Azerbaijan population migth be helpful to formulate surgical plan in treatment of congenital or post-traumatic facial deformities successfully.

Keywords: facial morphology, anthropometry, indices of proportion, measurement

Procedia PDF Downloads 121

Authors: Chaiyaporn Yuksen

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Backgroud: Supraventricular tachycardia (SVT) is an abnormally fast atrial tachycardia characterized by narrow (≤ 120 ms) and constant QRS. Adenosine was the drug of choice; the first dose was 6 mg. It can be repeated with the second and third doses of 12 mg, with greater than 90% success. The study found that patients observed at 4 hours after normal sinus rhythm was no recurrence within 24 hours. The objective of this study was to investigate the factors that influence the recurrence of SVT after adenosine in the emergency department (ED). Method: The study was conducted retrospectively exploratory model, prognostic study at the Emergency Department (ED) in Faculty of Medicine, Ramathibodi Hospital, a university-affiliated super tertiary care hospital in Bangkok, Thailand. The study was conducted for ten years period between 2010 and 2020. The inclusion criteria were age > 15 years, visiting the ED with SVT, and treating with adenosine. Those patients were recorded with the recurrence SVT in ED. The multivariable logistic regression model developed the predictive model and prediction score for recurrence PSVT. Result: 264 patients met the study criteria. Of those, 24 patients (10%) had recurrence PSVT. Five independent factors were predictive of recurrence PSVT. There was age>65 years, heart rate (after adenosine) > 100 per min, structural heart disease, and dose of adenosine. The clinical risk score to predict recurrence PSVT is developed accuracy 74.41%. The score of >6 had the likelihood ratio of recurrence PSVT by 5.71 times Conclusion: The clinical predictive score of > 6 was associated with recurrence PSVT in ED.

Keywords: clinical prediction score, SVT, recurrence, emergency department

Procedia PDF Downloads 158

Authors: Farzad Danaei, Yilmaz Akkaya

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In many civil engineering applications, especially in the construction of large concrete structures, the early age behavior of concrete has shown to be a crucial problem. The uneven rise in temperature within the concrete in these constructions is the fundamental issue for quality control. Therefore, developing accurate and fast temperature prediction models is essential. The thermal properties of concrete fluctuate over time as it hardens, but taking into account all of these fluctuations makes numerical models more complex. Experimental measurement of the thermal properties at the laboratory conditions also can not accurately predict the variance of these properties at site conditions. Therefore, specific heat capacity and the heat conductivity coefficient are two variables that are considered constant values in many of the models previously recommended. The proposed equations demonstrate that these two quantities are linearly decreasing as cement hydrates, and their value are related to the degree of hydration. The effects of changing the thermal conductivity and specific heat capacity values on the maximum temperature and the time it takes for concrete to reach that temperature are examined in this study using numerical sensibility analysis, and the results are compared to models that take a fixed value for these two thermal properties. The current study is conducted in 7 different mix designs of concrete with varying amounts of supplementary cementitious materials (fly ash and ground granulated blast furnace slag). It is concluded that the maximum temperature will not change as a result of the constant conductivity coefficient, but variable specific heat capacity must be taken into account, also about duration when a concrete's central node reaches its max value again variable specific heat capacity can have a considerable effect on the final result. Also, the usage of GGBFS has more influence compared to fly ash.

Keywords: early-age concrete, mass concrete, specific heat capacity, thermal conductivity coefficient

Procedia PDF Downloads 82

Authors: El-Said. I. Shabana, Mohammad S. Tayeb, Maher M. T. Qutub, Abdulraheem A. Kinsara

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Usually, phosphate deposits contain ²³⁸U and ²³²Th in addition to their decay products. Due to their different pathways in the environment, the ²³⁸U/²³²Th activity concentration ratio usually found to be greater than unity in phosphate sediments. The presence of these radionuclides creates a potential need to control exposure of workers in the mining and processing activities of the phosphate minerals in accordance with IAEA safety standards. The greatest dose to workers comes from exposure to radon, especially ²²²Rn from the uranium series, and has to be controlled. In this regard, radon (²²²Rn) was measured in the atmosphere (indoor and outdoor) of Al-Jalamid phosphate-mines working area using a portable radon-measurement instrument RAD7, in a purpose of radiation protection. Radon was measured in 61 sites inside the open phosphate mines, the phosphate upgrading facility (offices and rooms of the workers, and in some open-air sites) and in the dwellings of the workers residence-village that lies at about 3 km from the mines working area. The obtained results indicated that the average indoor radon concentration was about 48.4 Bq/m³. Inside the upgrading facility, the average outdoor concentrations were 10.8 and 9.7 Bq/m³ in the concentrate piles and crushing areas, respectively. It was 12.3 Bq/m³ in the atmosphere of the open mines. These values are comparable with the global average values. Based on the average values, the annual effective dose due to radon inhalation was calculated and risk estimates have been done. The average annual effective dose to workers due to the radon inhalation was estimated by 1.32 mSv. The potential excess risk of lung cancer mortality that could be attributed to radon, when considering the lifetime exposure, was estimated by 53.0x10^-4. The results have been discussed in detail.

Keywords: dosimetry, environmental monitoring, phosphate deposits, radiation protection, radon

Procedia PDF Downloads 277

Authors: Yi-Guang Li, Suresh Sampath

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Gas turbine performance degrades over time, and the degradation is greatly affected by environmental, ambient, and operating conditions. The engines may degrade slowly under favorable conditions and result in a waste of engine life if a scheduled maintenance scheme is followed. They may also degrade fast and fail before a scheduled overhaul if the conditions are unfavorable, resulting in serious secondary damage, loss of engine availability, and increased maintenance costs. To overcome these problems, gas turbine owners are gradually moving from scheduled maintenance to condition-based maintenance, where condition monitoring is one of the key supporting technologies. This paper presents an integrated adaptive GPA diagnostics and performance monitoring system developed at Cranfield University for gas turbine gas path condition monitoring. It has the capability to predict the performance degradation of major gas path components of gas turbine engines, such as compressors, combustors, and turbines, using gas path measurement data. It is also able to predict engine key performance parameters for condition monitoring, such as turbine entry temperature that cannot be directly measured. The developed technology has been implemented into digital twin computer Software, Pythia, to support the condition monitoring of gas turbine engines. The capabilities of the integrated GPA condition monitoring system are demonstrated in three test cases using a model gas turbine engine similar to the GE aero-derivative LM2500 engine widely used in power generation and marine propulsion. It shows that when the compressor of the model engine degrades, the Adaptive GPA is able to predict the degradation and the changing engine performance accurately using gas path measurements. Such a presented technology and software are generic, can be applied to different types of gas turbine engines, and provide crucial engine health and performance parameters to support condition monitoring and condition-based maintenance.

Keywords: gas turbine, adaptive GPA, performance, diagnostics, condition monitoring

Procedia PDF Downloads 94

Authors: Satish Kumar, Spandan Sahu, Sarjati Sahoo, K. K. Khatua

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An experimental investigation has been carried out in a tilting flume of 2 m wide, 13 m long, and 0.3 m deep to study the effect of flow on the formation of braided channel pattern. Sediment flow is recirculated through the flume, which passes from the headgate to the sediment/water collecting tank through the tailgate. Further, without altering the geometry of the sand bed channel, the discharge is varied to study the effect of the formation of the braided pattern with time. Then the flow rate is varied to study the effect of flow on the formation of the braided pattern. Sediment transport rate is highly variable and was found to be a nonlinear function of flow rate, aspect ratio, longitudinal slope, and time. Total braided intensity (BIT) for each discharge case is found to be more than the active braided intensity (BIA). Both the parameters first increase and then decrease as the time progresses following a similar pattern for all the observed discharge cases. When the flow is increased, the movement of sediment also increases since the active braided intensity is found to adjust quickly. The measurement of velocity and boundary shear helps to study the erosion and sedimentation processes in the channel and formation of small meandering channels and then the braided channel for different discharge conditions of a sediment river. Due to regime properties of rivers, both total braided Intensity and active braided intensity become stable for a given channel and flow conditions. In the present case, the trend of the ratio of BIA to BIT is found to be asymptotic against the time with a value of 0.4. After the particular time elapses off the flow, new small channels are also found to be formed with changes in the sinuosity of the active channels, thus forming the braided network. This is due to the continuous erosion and sedimentation processes occurring for the flow process for the flow and sediment conditions.

Keywords: active braided intensity, bed load, sediment transport, shear stress, total braided intensity

Procedia PDF Downloads 133

Authors: Ghada Badr, Manar Hosny, Nuha Bintayyash, Eman Albilali, Souad Larabi Marie-Sainte

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The median problem is significantly applied to derive the most reasonable rearrangement phylogenetic tree for many species. More specifically, the problem is concerned with finding a permutation that minimizes the sum of distances between itself and a set of three signed permutations. Genomes with equal number of genes but different order can be represented as permutations. In this paper, an algorithm, namely BeamGA median, is proposed that combines a heuristic search approach (local beam) as an initialization step to generate a number of solutions, and then a Genetic Algorithm (GA) is applied in order to refine the solutions, aiming to achieve a better median with the smallest possible reversal distance from the three original permutations. In this approach, any genome rearrangement distance can be applied. In this paper, we use the reversal distance. To the best of our knowledge, the proposed approach was not applied before for solving the median problem. Our approach considers true biological evolution scenario by applying the concept of common intervals during the GA optimization process. This allows us to imitate a true biological behavior and enhance genetic approach time convergence. We were able to handle permutations with a large number of genes, within an acceptable time performance and with same or better accuracy as compared to existing algorithms.

Keywords: median problem, phylogenetic tree, permutation, genetic algorithm, beam search, genome rearrangement distance

Procedia PDF Downloads 267

Authors: Jinsiang Shaw, Sheng-Xiang Xu

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This paper presents the development of an autonomous automated guided vehicle (AGV) with a robot arm attached on top of it within the framework of robot operation system (ROS). ROS can provide libraries and tools, including hardware abstraction, device drivers, libraries, visualizers, message-passing, package management, etc. For this reason, this AGV can provide automatic navigation and parts transportation and pick-and-place task using robot arm for typical industrial production line use. More specifically, this AGV will be controlled by an on-board host computer running ROS software. Command signals for vehicle and robot arm control and measurement signals from various sensors are transferred to respective microcontrollers. Users can operate the AGV remotely through the TCP / IP protocol and perform SLAM (Simultaneous Localization and Mapping). An RGBD camera and LIDAR sensors are installed on the AGV, using these data to perceive the environment. For SLAM, Gmapping is used to construct the environment map by Rao-Blackwellized particle filter; and AMCL method (Adaptive Monte Carlo localization) is employed for mobile robot localization. In addition, current AGV position and orientation can be visualized by ROS toolkit. As for robot navigation and obstacle avoidance, A* for global path planning and dynamic window approach for local planning are implemented. The developed ROS AGV with a robot arm on it has been experimented in the university factory. A 2-D and 3-D map of the factory were successfully constructed by the SLAM method. Base on this map, robot navigation through the factory with and without dynamic obstacles are shown to perform well. Finally, pick-and-place of parts using robot arm and ensuing delivery in the factory by the mobile robot are also accomplished.

Keywords: automated guided vehicle, navigation, robot operation system, Simultaneous Localization and Mapping

Procedia PDF Downloads 152

Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

Procedia PDF Downloads 190

Authors: Mehrdad Hosseinishakib, Mozhgan Chakani, Gholamreza Babaei

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This study aims to investigate the relationship between the bilateral communication channels, communication technologies with effective communications and communication technologies, employee participation in motivated decision-making of employees using the EFQM excellence model in Education Organization of Area 4 in Karaj. This research is an applied research in terms of the purpose and is a descriptive survey research in terms of nature and method and assesses the current situation using field studies. The statistical population consists of all employees and managers of Education Organization of Area 4 in Karaj including 5442 persons and random sampling was used and sample size is 359 using Cochran formula. Measurement tool is a researcher-made questionnaire with 20 questions including two categories of expertise and general questions. The first category includes general questions about respondents' personal characteristics such as gender and level of education, work experience and courses of study. The second category includes expertise questions of the questionnaire that have been designed to test research hypotheses that its reliability was approved by Cronbach's alpha coefficient 0.916 and its validity was approved according to the vies of teachers and some senior managers of Education Organization of Area 4 in Karaj. The results of the analysis of the findings show that there is a significant relationship between mutual communication channels, communication technologies with effective communication between employees and management. There is also a significant relationship between communication technologies and employee motivation and employee participation in their motivated decision-making in Education Organization of Area 4 in Karaj.

Keywords: communication channels, effective communication, EFQM model, ANOVA

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Authors: Sharon Li, Zhonghang Xia

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Post-database search is an essential phase in peptide identification using tandem mass spectrometry (MS/MS) to refine peptide-spectrum matches (PSMs) produced by database search engines. These engines frequently face difficulty differentiating between correct and incorrect peptide assignments. Despite advances in statistical and machine learning methods aimed at improving the accuracy of peptide identification, challenges remain in selecting critical features for these models. In this study, two machine learning models—a random forest tree and a support vector machine—were applied to three datasets to enhance PSMs. SHAP values were utilized to determine the significance of each feature within the models. The experimental results indicate that the random forest model consistently outperformed the SVM across all datasets. Further analysis of SHAP values revealed that the importance of features varies depending on the dataset, indicating that a feature's role in model predictions can differ significantly. This variability in feature selection can lead to substantial differences in model performance, with false discovery rate (FDR) differences exceeding 50% between different feature combinations. Through SHAP value analysis, the most effective feature combinations were identified, significantly enhancing model performance.

Keywords: peptide identification, SHAP value, feature selection, random forest tree, support vector machine

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Authors: Berna Eren Tokgoz, Mahdi Safa, Seokyon Hwang

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Power distribution systems are essential and crucial infrastructures for the development and maintenance of a sustainable society. These systems are extremely vulnerable to various types of natural and man-made disasters. The assessment of resilience focuses on preparedness and mitigation actions under pre-disaster conditions. It also concentrates on response and recovery actions under post-disaster situations. The aim of this study is to present a methodology to assess the resilience of electric power distribution poles against wind-related events. The proposed methodology can improve the accuracy and rapidity of the evaluation of the conditions and the assessment of the resilience of poles. The methodology provides a metric for the evaluation of the resilience of poles under pre-disaster and post-disaster conditions. The metric was developed using mathematical expressions for physical forces that involve various variables, such as physical dimensions of the pole, the inclination of the pole, and wind speed. A three-dimensional imaging technology (photogrammetry) was used to determine the inclination of poles. Based on expert opinion, the proposed metric was used to define zones to visualize resilience. Visual representation of resilience is helpful for decision makers to prioritize their resources before and after experiencing a wind-related disaster. Multiple electric poles in the City of Beaumont, TX were used in a case study to evaluate the proposed methodology.  

Keywords: photogrammetry, power distribution systems, resilience metric, system resilience, wind-related disasters

Procedia PDF Downloads 222

Authors: Lubna Eljabu, Mohammad Etemad, Stan Matwin

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Port authorities have many challenges in congested ports to allocate their resources to provide a safe and secure loading/ unloading procedure for cargo vessels. Selecting a destination port is the decision of a vessel master based on many factors such as weather, wavelength and changes of priorities. Having access to a tool which leverages AIS messages to monitor vessel’s movements and accurately predict their next destination port promotes an effective resource allocation process for port authorities. In this research, we propose a method, namely, Reference Route of Trajectory (RRoT) to assist port authorities in predicting inflow and outflow traffic in their local environment by monitoring Automatic Identification System (AIS) messages. Our RRoT method creates a reference route based on historical AIS messages. It utilizes some of the best trajectory similarity measure to identify the destination of a vessel using their recent movement. We evaluated five different similarity measures such as Discrete Fr´echet Distance (DFD), Dynamic Time Warping (DTW), Partial Curve Mapping (PCM), Area between two curves (Area) and Curve length (CL). Our experiments show that our method identifies the destination port with an accuracy of 98.97% and an fmeasure of 99.08% using Dynamic Time Warping (DTW) similarity measure.

Keywords: spatial temporal data mining, trajectory mining, trajectory similarity, resource optimization

Procedia PDF Downloads 125

Authors: Rosemary Hiscock, Clive Sabel, David Manley, Sam Wren-Lewis

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As part of the Happy City Index Project, Happy City have developed a survey instrument to measure experienced wellbeing: how people are feeling and functioning in their everyday lives. The survey instrument, called the Happiness Pulse, was developed in partnership with the New Economics Foundation (NEF) with the dual aim of collecting citywide wellbeing data and engaging individuals and communities in the measurement and promotion of their own wellbeing. The survey domains and items were selected through a review of the academic literature and a stakeholder engagement process, including local policymakers, community organisations and individuals. The Happiness Pulse was included in the Bristol pilot of the Happy City Index (n=722). The experienced wellbeing items were subjected to factor analysis. A reduced number of items to be included in a revised scale for future data collection were again entered into a factor analysis. These revised factors were tested for reliability and validity. Among items to be included in a revised scale for future data collection three factors emerged: Be, Do and Connect. The Be factor had good reliability, convergent and criterion validity. The Do factor had good discriminant validity. The Connect factor had adequate reliability and good discriminant and criterion validity. Some age, gender and socioeconomic differentiation was found. The properties of a new scale to measure experienced wellbeing, intended for use by municipal authorities, are described. Happiness Pulse data can be combined with local data on wellbeing conditions to determine what matters for peoples wellbeing across a city and why.

Keywords: city wellbeing , community wellbeing, engaging individuals and communities, measuring wellbeing and happiness

Procedia PDF Downloads 267

Authors: Masoud Ghahvechi Pour

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Risk management is a systematic and regular process of identifying, analyzing and responding to risks throughout the project's life cycle in order to achieve the optimal level of elimination, reduction or control of risk. The purpose of project risk management is to increase the probability and effect of positive events and reduce the probability and effect of unpleasant events on the project. Risk management is one of the most fundamental parts of project management, so that unmanaged or untransmitted risks can be one of the primary factors of failure in a project. Effective risk management does not apply to risk regression, which is apparently the cheapest option of the activity. However, the main problem with this option is the economic sensitivity, because what is potentially profitable is by definition risky, and what does not pose a risk is economically interesting and does not bring tangible benefits. Therefore, in relation to the implemented project, effective risk management is finding a "middle ground" in its management, which includes, on the one hand, protection against risk from a negative direction by means of accurate identification and classification of risk, which leads to analysis And it becomes a comprehensive analysis. On the other hand, management using all mathematical and analytical tools should be based on checking the maximum benefits of these decisions. Detailed analysis, taking into account all aspects of the company, including stakeholder analysis, will allow us to add what will become tangible benefits for our project in the future to effective risk management. Identifying the risk of the project is based on the theory that which type of risk may affect the project, and also refers to specific parameters and estimating the probability of their occurrence in the project. These conditions can be divided into three groups: certainty, uncertainty, and risk, which in turn support three types of investment: risk preference, risk neutrality, specific risk deviation, and its measurement. The result of risk identification and project analysis is a list of events that indicate the cause and probability of an event, and a final assessment of its impact on the environment.

Keywords: risk, management, knowledge, risk management

Procedia PDF Downloads 72

Authors: Syarif Hidayat

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As millions of people with different backgrounds perform hajj every year in Saudi Arabia, it brings out several problems. Missing people is among many crucial problems need to be encountered. Some people might have had insufficient knowledge of using tracking system equipment. Other might become a victim of an accident, lose consciousness, or even died, prohibiting them to perform certain activity. For those reasons, people could not send proper SOS message. The major contribution of this paper is the application of the diverse alert method in pilgrims tracking system. It offers a simple yet robust solution to send SOS message by pilgrims during Hajj. Knowledge of context aware computing is assumed herein. This study presents four methods that could be utilized by pilgrims to send SOS. The first method is simple mobile application contains only a button. The second method is based on behavior analysis based off GPS location movement anomaly. The third method is by introducing pressing pattern to smartwatch physical button as a panic button. The fourth method is by identifying certain accelerometer pattern recognition as a sign of emergency situations. Presented method in this paper would be an important part of pilgrims tracking system. The discussion provided here includes easy to use design whilst maintaining tracking accuracy, privacy, and security of its users.

Keywords: context aware computing, emergency alert system, GPS, hajj pilgrim tracking, location-based services

Procedia PDF Downloads 220

Authors: Sky Chou, Joseph C. Chen

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This paper focuses on using six sigma methodologies to reach the desired shrinkage of a manufactured high-density polyurethane (HDPE) part produced by the injection molding machine. It presents a case study where the correct shrinkage is required to reduce or eliminate defects and to improve the process capability index Cp and Cpk for an injection molding process. To improve this process and keep the product within specifications, the six sigma methodology, design, measure, analyze, improve, and control (DMAIC) approach, was implemented in this study. The six sigma approach was paired with the Taguchi methodology to identify the optimized processing parameters that keep the shrinkage rate within the specifications by our customer. An L9 orthogonal array was applied in the Taguchi experimental design, with four controllable factors and one non-controllable/noise factor. The four controllable factors identified consist of the cooling time, melt temperature, holding time, and metering stroke. The noise factor is the difference between material brand 1 and material brand 2. After the confirmation run was completed, measurements verify that the new parameter settings are optimal. With the new settings, the process capability index has improved dramatically. The purpose of this study is to show that the six sigma and Taguchi methodology can be efficiently used to determine important factors that will improve the process capability index of the injection molding process.

Keywords: injection molding, shrinkage, six sigma, Taguchi parameter design

Procedia PDF Downloads 181

Authors: Kamrun N. Keya, Nasrin A. Kona, Ruhul A. Khan

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Pineapple leaf fiber (PALF) reinforced polypropylene (PP) based composites were fabricated by a conventional compression molding technique. In this investigation, PALF composites were manufactured using different percentages of fiber, which were varying from 25-50% on the total weight of the composites. To fabricate the PALF/PP composites, untreated and treated fibers were selected. A systematic study was done to observe the physical, mechanical and interfacial behavior of the composites. In this study, mechanical properties of the composites such as tensile, impact and bending properties were observed precisely. It was found that 45wt% of fiber composites showed better mechanical properties than others. Maximum tensile strength (TS) and bending strength (BS) was observed, 65 MPa and 50 MPa respectively, whereas the highest tensile modulus (TM) and bending modulus (BM) was examined, 1.7 GPa and 0.85 GPa respectively. The PALF/PP based composites were treated with irradiated under gamma radiation (the source strength 50 kCi Cobalt-60) of various doses (2.5 kGy to 10 kGy). The effect of gamma radiation on the composites was also investigated, and it found that the effect of 5.0 kGy (i.e. units for radiation measurement is 'gray', kGy=kilogray ) gamma dose showed better mechanical properties than other doses. The values of TS, BS, TM, and BM of the irradiated (5.0 kGy) composites were found to improve by 19%, 23%, 17% and 25 % over non-irradiated composites. After flexural testing, fracture sides of the untreated and treated both composites were studied by scanning electron microscope (SEM). SEM results of the treated PALF/PP based composites showed better fiber-matrix adhesion and interfacial bonding than untreated PALF/PP based composites. Water uptake and soil degradation tests of untreated and treated composites were also investigated.

Keywords: PALF, polypropylene, compression molding technique, gamma radiation, mechanical properties, scanning electron microscope

Procedia PDF Downloads 156

Authors: Adrian Hang Yue Siu, Matthew Holyland, Sharon Carey, Daniel Steffens, Nabila Ansari, Cherry E. Koh

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Introduction: Peritoneal malignancies are challenging cancers to manage. While cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS and HIPEC) may offer a cure, it’s considered radical and morbid. Pre-emptive identification of deconditioned patients for optimization may mitigate the risks of surgery. However, the difficulty lies in the scarcity of validated predictive tools to identify high-risk patients. In recent times, there has been growing interest in sarcopenia, which can occur as a result of malnutrition and malignancies. Therefore, the purpose of this study was to assess the utility of sarcopenia in predicting post-operative outcomes. Methods: A single quaternary-center retrospective study of CRS and HIPEC patients between 2017-2020 was conducted to determine the association between pre-operative sarcopenia and post-operative outcomes. Lumbar CT images were analyzed using Slice-o-matic® to measure sarcopenia. Results : Cohort (n=94) analysis found that 40% had sarcopenia, with a majority being female (53.2%) and a mean age of 55 years. Sarcopenia was statistically associated with decreased weight compared to non-sarcopenia patients, 72.7kg vs. 82.2kg (p=0.014) and shorter overall survival, 1.4 years vs. 2.1 years (p=0.032). Post-operatively, patients with sarcopenia experienced more post-operative complications (p=0.001). Conclusion: Complex procedures often require optimization to prevent complications and improve survival. While patient biomarkers – BMI and weight – are used for optimization, this research advocates for the identification of sarcopenia status for pre-operative planning. Sarcopenia may be an indicator of advanced disease requiring further treatment and is an emerging area of research. Larger studies are required to confirm these findings and to assess the reversibility of sarcopenia after surgery.

Keywords: sarcopaenia, cytoreductive surgery, hyperthermic intraperitoneal chemotherapy, surgical oncology

Procedia PDF Downloads 88

Authors: Rong Wang, Sandra Hasanefendic, Elizabeth von Hauff, Bart Bossink

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Technological learning and learning curve models have been continuously used to estimate the photovoltaics (PV) cost development over time for the climate mitigation targets. They can integrate a number of technological learning sources which influence the learning process. Yet the accuracy and realistic predictions for cost estimations of PV development are still difficult to achieve. This paper develops four hypothetical-alternative learning curve models by proposing different combinations of technological learning sources, including both local and global technology experience and the knowledge stock. This paper specifically focuses on the non-linear relationship between the costs and technological learning source and their dynamic interaction and uses the system dynamics approach to predict a more accurate PV cost estimation for future development. As the case study, the data from China is gathered and drawn to illustrate that the learning curve model that incorporates both the global and local experience is more accurate and realistic than the other three models for PV cost estimation. Further, absorbing and integrating the global experience into the local industry has a positive impact on PV cost reduction. Although the learning curve model incorporating knowledge stock is not realistic for current PV cost deployment in China, it still plays an effective positive role in future PV cost reduction.

Keywords: photovoltaic, system dynamics, technological learning, learning curve

Procedia PDF Downloads 101

Authors: Yahya Asanoglu, Celaletdin Ergun

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Ceramic-matrix composites (CMC) have significant prominence in various engineering applications because of their heat resistance associated with an ability to withstand the brittle type of catastrophic failure. In this study, specific raw materials have been chosen for the purpose of having suitable CMC material for high-temperature dielectric applications. CMC material will be manufactured through the polymer infiltration and pyrolysis (PIP) method. During the manufacturing process, vacuum infiltration and autoclave will be applied so as to decrease porosity and obtain higher mechanical properties, although this advantage leads to a decrease in the electrical performance of the material. Time and temperature adjustment in pyrolysis parameters provide a significant difference in the properties of the resulting material. The mechanical and thermal properties will be investigated in addition to the measurement of dielectric constant and tangent loss values within the spectrum of Ku-band (12 to 18 GHz). Also, XRD, TGA/PTA analyses will be employed to prove the transition of precursor to ceramic phases and to detect critical transition temperatures. Additionally, SEM analysis on the fracture surfaces will be performed to see failure mechanism whether there is fiber pull-out, crack deflection and others which lead to ductility and toughness in the material. In this research, the cost-effectiveness and applicability of the PIP method will be proven in the manufacture of CMC materials while optimization of pyrolysis time, temperature and cycle for specific materials is detected by experiment. Also, several resins will be shown to be a potential raw material for CMC radome and antenna applications. This research will be distinguished from previous related papers due to the fact that in this research, the combination of different precursors and fabrics will be experimented with to specify the unique cons and pros of each combination. In this way, this is an experimental sum of previous works with unique PIP parameters and a guide to the manufacture of CMC radome and antenna.

Keywords: CMC, PIP, precursor, quartz

Procedia PDF Downloads 163

Authors: Colin Smith, Linsey S Passarella

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Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

Procedia PDF Downloads 137

Authors: Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung

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Lightweight unmanned aerial vehicle (UAV) loading with novel sensors offers a low cost approach for data acquisition in complex environment. This study established a framework for applying UAV system in complex environment quick mapping and assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area Mai Po Inner Deep Bay Ramsar Site, Hong Kong. The study area was part of shallow bay with flat terrain and the major species including reedbed and four mangroves: Kandelia obovata, Aegiceras corniculatum, Acrostichum auerum and Acanthus ilicifolius. Other species involved in various graminaceous plants, tarbor, shrub and invasive species Mikania micrantha. In particular, invasive species climbed up to the mangrove canopy caused damage and morphology change which might increase species distinguishing difficulty. Hyperspectral images were acquired by Headwall Nano sensor with spectral range from 400nm to 1000nm and 0.06m spatial resolution image. A sequence of multi-view RGB images was captured with 0.02m spatial resolution and 75% overlap. Hyperspectral image was corrected for radiative and geometric distortion while high resolution RGB images were matched to generate maximum dense point clouds. Furtherly, a 5 cm grid digital surface model (DSM) was derived from dense point clouds. Multiple feature reduction methods were compared to identify the efficient method and to explore the significant spectral bands in distinguishing different species. Examined methods including stepwise discriminant analysis (DA), support vector machine (SVM) and minimum noise fraction (MNF) transformation. Subsequently, spectral subsets composed of the first 20 most importance bands extracted by SVM, DA and MNF, and multi-source subsets adding extra DSM to 20 spectrum bands were served as input in maximum likelihood classifier (MLC) and SVM classifier to compare the classification result. Classification results showed that feature reduction methods from best to worst are MNF transformation, DA and SVM. MNF transformation accuracy was even higher than all bands input result. Selected bands frequently laid along the green peak, red edge and near infrared. Additionally, DA found that chlorophyll absorption red band and yellow band were also important for species classification. In terms of 3D data, DSM enhanced the discriminant capacity among low plants, arbor and mangrove. Meanwhile, DSM largely reduced misclassification due to the shadow effect and morphological variation of inter-species. In respect to classifier, nonparametric SVM outperformed than MLC for high dimension and multi-source data in this study. SVM classifier tended to produce higher overall accuracy and reduce scattered patches although it costs more time than MLC. The best result was obtained by combining MNF components and DSM in SVM classifier. This study offered a precision species distribution survey solution for inaccessible wetland area with low cost of time and labour. In addition, findings relevant to the positive effect of DSM as well as spectral feature identification indicated that the utility of UAV-borne hyperspectral and photogrammetry deriving 3D data is promising in further research on wetland species such as bio-parameters modelling and biological invasion monitoring.

Keywords: digital surface model (DSM), feature reduction, hyperspectral, photogrammetric point cloud, species mapping, unmanned aerial vehicle (UAV)

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Authors: Clement Leroy, Guillaume Boitel

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This paper presents a comparative benchmarking review of a steady and transient three-dimensional (3D) flow computations in centrifugal pump using commercial (AnsysCFX) and open source (OpenFOAM) computational fluid dynamics (CFD) software. In centrifugal rotor-dynamic pump, the fluid enters in the impeller along to the rotating axis to be accelerated in order to increase the pressure, flowing radially outward into another stage, vaned diffuser or volute casing, from where it finally exits into a downstream pipe. Simulations are carried out at the best efficiency point (BEP) and part load, for single-phase flow with several turbulence models. The results are compared with overall performance report from experimental data. The use of CFD technology in industry is still limited by the high computational costs, and even more by the high cost of commercial CFD software and high-performance computing (HPC) licenses. The main objectives of the present study are to define OpenFOAM methodology for high-quality 3D steady and transient turbomachinery CFD simulation to conduct a thorough time-accurate performance analysis. On the other hand a detailed comparisons between computational methods, features on latest Ansys release 18 and OpenFOAM is investigated to assess the accuracy and industrial applications of those solvers. Finally an automated connected workflow (IoT) for turbine blade applications is presented.

Keywords: benchmarking, CFX, internet of things, openFOAM, time-accurate, turbomachinery

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Authors: Vishal Shetty, Pranjal Khasnis, Saptarshi Mandal

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This work explores the design and fabrication of a fundamental pulse detonation engine (PDE) prototype on the basis of pressure and temperature pulse obtained from numerical simulation of the same. PDE is an advanced propulsion system that utilizes detonation waves for thrust generation. PDEs use a fuel-air mixture ignited to create a supersonic detonation wave, resulting in rapid energy release, high pressures, and high temperatures. The operational cycle includes fuel injection, ignition, detonation, exhaust of combustion products, and purging of the chamber for the next cycle. This work presents details of the core operating principles of a PDE, highlighting its potential advantages over traditional jet engines that rely on continuous combustion. The design focuses on a straightforward, valve-controlled system for fuel and oxidizer injection into a detonation tube. The detonation was initiated using an electronically controlled spark plug or similar high-energy ignition source. Following the detonation, a purge valve was employed to expel the combusted gases and prepare the tube for the next cycle. Key considerations for the design include material selection for the detonation tube to withstand the high temperatures and pressures generated during detonation. Fabrication techniques prioritized readily available machining methods to create a functional prototype. This work detailed the testing procedures for verifying the functionality of the PDE prototype. Emphasis was given to the measurement of thrust generation and capturing of pressure data within the detonation tube. The numerical analysis presents performance evaluation and potential areas for future design optimization.

Keywords: pulse detonation engine, ignition, detonation, combustion

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Authors: Hemad Zareiforoush, Saeed Minaei, Ahmad Banakar, Mohammad Reza Alizadeh

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The quality inspection of rice (Oryza sativa L.) during its various processing stages is very important. In this research, an artificial intelligence-based model coupled with computer vision techniques was developed as a decision support system for qualitative grading of rice grains. For conducting the experiments, first, 25 samples of rice grains with different levels of percentage of broken kernels (PBK) and degree of milling (DOM) were prepared and their qualitative grade was assessed by experienced experts. Then, the quality parameters of the same samples examined by experts were determined using a machine vision system. A grading model was developed based on fuzzy logic theory in MATLAB software for making a relationship between the qualitative characteristics of the product and its quality. Totally, 25 rules were used for qualitative grading based on AND operator and Mamdani inference system. The fuzzy inference system was consisted of two input linguistic variables namely, DOM and PBK, which were obtained by the machine vision system, and one output variable (quality of the product). The model output was finally defuzzified using Center of Maximum (COM) method. In order to evaluate the developed model, the output of the fuzzy system was compared with experts’ assessments. It was revealed that the developed model can estimate the qualitative grade of the product with an accuracy of 95.74%.

Keywords: machine vision, fuzzy logic, rice, quality

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Authors: Anas Rao, Hao Duan, Fanhua Ma

Abstract:

The dependency on the fossil fuels can be minimized by using the hydrogen enriched compressed natural gas (HCNG) in the transportation vehicles. However, the NOx emissions of HCNG engines are significantly higher, and this turned to be its major drawback. Therefore, the study of NOx emission of HCNG engines is a very important area of research. In this context, the experiments have been performed at the different hydrogen percentage, ignition timing, air-fuel ratio, manifold-absolute pressure, load and engine speed. Afterwards, the simulation has been accomplished by the quasi-dimensional combustion model of HCNG engine. In order to investigate the NOx emission, the NO mechanism has been coupled to the quasi-dimensional combustion model of HCNG engine. The three NOx mechanism: the thermal NOx, prompt NOx and N2O mechanism have been used to predict NOx emission. For the validation purpose, NO curve has been transformed into NO packets based on the temperature difference of 100 K for the lean-burn and 60 K for stoichiometric condition. While, the width of the packet has been taken as the ratio of crank duration of the packet to the total burnt duration. The combustion chamber of the engine has been divided into three zones, with the zone equal to the product of summation of NO packets and space. In order to check the accuracy of the model, the percentage error of NOx emission has been evaluated, and it lies in the range of ±6% and ±10% for the lean-burn and stoichiometric conditions respectively. Finally, the percentage contribution of each NO formation has been evaluated.

Keywords: quasi-dimensional combustion , thermal NO, prompt NO, NO packet

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Authors: Mahdi Fakoor, Nabi Mehri Khansari, Ahmadreza Farokhi

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Evaluation of mode II fracture toughness (KIIC) in composite materials is very hard problem to be solved, since it can be affected by many mechanisms of dissipation. Furthermore, non-linearity in its behavior can offer an extra difficulty to obtain accuracy in the results. Different reported values for KIIC in various references can prove the mentioned assertion. In this research, some solutions proposed based on the form of necessary corrections that should be executed on the common test fixtures. Due to the fact that the common test fixtures are not able to active toughening mechanisms in pure Mode II correctly, we have employed some structural modifications on common fixtures. Particularly, the Iosipescu test is used as start point. The tests are applied on graphite/epoxy; PMMA and Western White Pine Wood. Also, mixed mode I/II fracture limit curves are used to indicate the scattering in test results are really relevant to the creation of Fracture Process Zone (FPZ). In the present paper, shear load consideration applied at the predicted shear zone by considering some significant structural amendments that can active mode II toughening mechanisms. Indeed, the employed empirical method causes significant developing in repeatability and reproducibility as well. Moreover, a 3D Finite Element (FE) is performed for verification of the obtained results. Eventually, it is figured out that, a remarkable precision can be obtained in common test fixture in comparison with the previous one.

Keywords: FPZ, shear test fixture, mode II fracture toughness, composite material, FEM

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Authors: Misbah Javaid, Vladimir Estivill-Castro, Rene Hexel

Abstract:

The field of robotics is advancing rapidly to the point where robots have become an integral part of the modern society. These robots collaborate and contribute productively with humans and compensate some shortcomings from human abilities and complement them with their skills. Effective teamwork of humans and robots demands to investigate the critical issue of trust. The field of human-computer interaction (HCI) has already examined trust humans place in technical systems mostly on issues like reliability and accuracy of performance. Early work in the area of expert systems suggested that automatic generation of explanations improved trust and acceptability of these systems. In this work, we augmented a robot with the user-invoked explanation generation proficiency. To measure explanations effect on human’s level of trust, we collected subjective survey measures and behavioral data in a human-robot team task into an interactive, adversarial and partial information environment. The results showed that with the explanation capability humans not only understand and recognize robot as an expert team partner. But, it was also observed that human's learning and human-robot team performance also significantly improved because of the meaningful interaction with the robot in the human-robot team. Moreover, by observing distinctive outcomes, we expect our research outcomes will also provide insights into further improvement of human-robot trustworthy relationships.

Keywords: explanation interface, adversaries, partial observability, trust building

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Authors: Pakawhat Khumkhreung, Yottana Khunatorn

Abstract:

The flow pattern inside rectangular intake air duct of 300 MW lignite coal-fired power plant is investigated in order to analyze and reduce overall inlet system pressure drop. The system consists of the 45-degree inlet elbow, the flow instrument, the 90-degree mitered elbow and fans, respectively. The energy loss in each section can be determined by Bernoulli’s equation and ASHRAE standard table. Hence, computational fluid dynamics (CFD) is used in this study based on Navier-Stroke equation and the standard k-epsilon turbulence modeling. Input boundary condition is 175 kg/s mass flow rate inside the 11-m² cross sectional duct. According to the inlet air flow rate, the Reynolds number of airstream is 2.7x10⁶ (based on the hydraulic duct diameter), thus the flow behavior is turbulence. The numerical results are validated with the real operation data. It is found that the numerical result agrees well with the operating data, and dominant loss occurs at the flow rate measurement device. Normally, the air flow rate is measured by the airfoil and it gets high pressure drop inside the duct. To overcome this problem, the airfoil is planned to be replaced with the other type measuring instrument, such as the average pitot tube which generates low pressure drop of airstream. The numerical result in case of average pitot tube shows that the pressure drop inside the inlet airstream duct is decreased significantly. It should be noted that the energy consumption of inlet air system is reduced too.

Keywords: airfoil, average pitot tube, combustion air, CFD, pressure drop, rectangular duct

Procedia PDF Downloads 160