Search results for: features of structural

Authors: Mohammadsadegh Zanganehfar

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Since the introduction of fractal geometry in 1970, numerous efforts have been made by architects and researchers to transfer this area of mathematical knowledge in the discipline of architecture and postmodernist discourse. The discourse of complexity and architecture is one of the most significant ongoing discourses in the discipline of architecture from the '70s until today and has generated significant styles such as deconstructivism and parametrism in architecture. During these years, several projects were designed and presented by designers and architects using fractal geometry, but due to the lack of sufficient knowledge and appropriate comprehension of the features and characteristics of this nonlinear geometry, none of the fractal-based designs have been successful and satisfying. Fractal geometry as a geometric technology has a long presence in the history of architecture. The current research attempts to identify and discover the characteristics, features, potentials, and functionality of fractals despite their aesthetic aspect by examining case studies of pre-modern architecture in Asia and investigating the function of fractals.

Keywords: Asian architecture, fractal geometry, fractal technique, geometric properties

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Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

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Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

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Authors: I. Slim, H. Akkari, A. Ben Abdallah, I. Bhouri, M. Hedi Bedoui

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Osteoporosis is a common disease characterized by low bone mass and deterioration of micro-architectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The aim was to analyze according to clinical research a group of 174 subjects: 87 osteoporotic patients (OP) with various bone fracture types and 87 control cases (CC). To characterize osteoporosis, Fractal and MultiFractal (MF) methods were applied to images for features (attributes) extraction. In order to improve the results, a new method of MF spectrum based on the q-stucture function calculation was proposed and a combination of Fractal and MF attributes was used. The Support Vector Machines (SVM) was applied as a classifier to distinguish between OP patients and CC subjects. The features fusion (fractal and MF) allowed a good discrimination between the two groups with an accuracy rate of 96.22%.

Keywords: fractal, micro-architecture analysis, multifractal, osteoporosis, SVM

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Authors: Shin-Hyung Cho, Dong-Kyu Kim, Seung-Young Kho

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Truck-involved crashes have higher crash severity than non-truck-involved crashes. There have been many studies about the frequency of crashes and the development of severity models, but those studies only analyzed the relationship between observed variables. To identify why more people are injured or killed when trucks are involved in the crash, we must examine to quantify the complex causal relationship between severity of the crash and risk factors by adopting the latent factors of crashes. The aim of this study was to develop a structural equation or model based on truck-involved and non-truck-involved crashes, including five latent variables, i.e. a crash factor, environmental factor, road factor, driver’s factor, and severity factor. To clarify the unique characteristics of truck-involved crashes compared to non-truck-involved crashes, a confirmatory analysis method was used. To develop the model, we extracted crash data from 10,083 crashes on Korean freeways from 2008 through 2014. The results showed that the most significant variable affecting the severity of a crash is the crash factor, which can be expressed by the location, cause, and type of the crash. For non-truck-involved crashes, the crash and environment factors increase severity of the crash; conversely, the road and driver factors tend to reduce severity of the crash. For truck-involved crashes, the driver factor has a significant effect on severity of the crash although its effect is slightly less than the crash factor. The multiple group analysis employed to analyze the differences between the heterogeneous groups of drivers.

Keywords: crash severity, structural structural equation modeling (SEM), truck-involved crashes, multiple group analysis, crash on freeway

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Authors: Arunroong Wongkungwan

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This study aimed to investigate the influence of selected antecedents, which were tourists’ satisfaction towards attractions in Bangkok, perceived value of the attractions, feelings of engagement with the attractions, acquaintance with the attractions, push factors, pull factors and motivation to seek novelty, on foreign tourist’s loyalty towards tourist attractions in Bangkok. By using multi stage sampling technique, 400 international tourists were sampled. After that, Semi Structural Equation Model was utilized in the analysis stage by LISREL. The Semi Structural Equation Model of the selected antecedents of tourist’s loyalty attractions had a correlation with the empirical data through the following statistical descriptions: Chi- square = 3.43, df = 4, P- value = 0.48893; RMSEA = 0.000; CFI = 1.00; CN = 1539.75; RMR = 0.0022; GFI = 1.00 and AGFI = 0.98. The findings indicated that all antecedents were able together to predict the loyalty of the foreign tourists who visited Bangkok at 73 percent.

Keywords: antecedent, Bangkok, foreign tourists, loyalty, tourist attractions

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Authors: Jongho Park, Taekyun Kim, Jungbhin You, Sungnam Hong, Sun-Kyu Park

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Due to the aging of bridges, increasing of maintenance costs and decreasing of structural safety is occurred. The steel corrosion of reinforced concrete bridge is the most common problem and this phenomenon is accelerating due to abnormal weather and increasing CO₂ concentration due to climate change. To solve these problems, composite members using textile have been studied. A textile reinforced concrete can reduce carbon emissions by reduced concrete and without steel bars, so a lot of structural behavior studies are needed. Therefore, in this study, textile reinforced concrete beam was made and flexural test was performed. Also, the change of flexural strength according to the prestressing was conducted. As a result, flexural strength of TRC with prestressing was increased compared and flexural behavior was shown as reinforced concrete.

Keywords: AR-glass, flexural strength, prestressing, textile reinforced concrete

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Authors: Housein Deli, Loui Al-Shrouf, Hammoud Al Joumaa, Mohieddine Jelali

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When forming metallic materials, fluctuations in material properties, process conditions, and wear lead to deviations in the component geometry. Several hundred features sometimes need to be measured, especially in the case of functional and safety-relevant components. These can only be measured offline due to the large number of features and the accuracy requirements. The risk of producing components outside the tolerances is minimized but not eliminated by the statistical evaluation of process capability and control measurements. The inspection intervals are based on the acceptable risk and are at the expense of productivity but remain reactive and, in some cases, considerably delayed. Due to the considerable progress made in the field of condition monitoring and measurement technology, permanently installed sensor systems in combination with machine learning and artificial intelligence, in particular, offer the potential to independently derive forecasts for component geometry and thus eliminate the risk of defective products - actively and preventively. The reliability of forecasts depends on the quality, completeness, and timeliness of the data. Measuring all geometric characteristics is neither sensible nor technically possible. This paper, therefore, uses the example of car seat rail production to discuss the necessary first step of feature selection and reduction by correlation analysis, as otherwise, it would not be possible to forecast components in real-time and inline. Four different car seat rails with an average of 130 features were selected and measured using a coordinate measuring machine (CMM). The run of such measuring programs alone takes up to 20 minutes. In practice, this results in the risk of faulty production of at least 2000 components that have to be sorted or scrapped if the measurement results are negative. Over a period of 2 months, all measurement data (> 200 measurements/ variant) was collected and evaluated using correlation analysis. As part of this study, the number of characteristics to be measured for all 6 car seat rail variants was reduced by over 80%. Specifically, direct correlations for almost 100 characteristics were proven for an average of 125 characteristics for 4 different products. A further 10 features correlate via indirect relationships so that the number of features required for a prediction could be reduced to less than 20. A correlation factor >0.8 was assumed for all correlations.

Keywords: long-term SHM, condition monitoring, machine learning, correlation analysis, component prediction, wear prediction, regressions analysis

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Authors: H. Nejadriahi

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Traditional architecture is a valuable source of inspiration, which needs to be studied and integrated in the contemporary designs for achieving an identifiable contemporary architecture. Traditional architecture of Iran is among the distinguished examples of being contextually responsive, not only by considering the environmental conditions of a region, but also in terms of respecting the socio-cultural values of its context. In order to apply these valuable features to the current designs, they need to be adapted to today's condition, needs and desires. In this paper, the main features of the traditional architecture of Iran are explained to interrogate them in the formation of a contemporary house in Tehran, Iran. Also a table is provided to compare the utilization of the traditional design concepts in the traditional houses and the contemporary example of it. It is believed that such study would increase the awareness of contemporary designers by providing them some clues on maintaining the traditional values in the current design layouts particularly in the residential sector that would ultimately improve the quality of space in the contemporary architecture.

Keywords: contemporary housing design, Iran, Tehran, traditional architecture

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Authors: Jaturong Som-ard

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The heavy rainfall from 3rd to 22th January 2017 had swamped much area of Ranot district in southern Thailand. Due to heavy rainfall, the district was flooded which had a lot of effects on economy and social loss. The major objective of this study is to detect flooding extent using Sentinel-1A data and identify a number of damaged buildings over there. The data were collected in two stages as pre-flooding and during flood event. Calibration, speckle filtering, geometric correction, and histogram thresholding were performed with the data, based on intensity spectral values to classify thematic maps. The maps were used to identify flooding extent using change detection, along with the buildings digitized and collected on JOSM desktop. The numbers of damaged buildings were counted within the flooding extent with respect to building data. The total flooded areas were observed as 181.45 sq.km. These areas were mostly occurred at Ban khao, Ranot, Takhria, and Phang Yang sub-districts, respectively. The Ban khao sub-district had more occurrence than the others because this area is located at lower altitude and close to Thale Noi and Thale Luang lakes than others. The numbers of damaged buildings were high in Khlong Daen (726 features), Tha Bon (645 features), and Ranot sub-district (604 features), respectively. The final flood extent map might be very useful for the plan, prevention and management of flood occurrence area. The map of building damage can be used for the quick response, recovery and mitigation to the affected areas for different concern organization.

Keywords: flooding extent, Sentinel-1A data, JOSM desktop, damaged buildings

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Authors: Saleh Abu Hashanah, Abirami Radhakrishnan, Dessa David

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Sustainable practices in the supply chain have been an area of focus that require consideration of environmental, economic, and social sustainability practices. This paper aims to examine the use of blockchain as a disruptive technology to promote sustainable supply chains. Content analysis was used to analyze the uses of blockchain technology in sustainable supply chains. The results showed that blockchain technology features such as traceability, transparency, smart contracts, accountability, trust, immutability, anti-fraud, and decentralization promote sustainable supply chains. It is found that these features have impacted organizational efficiency in operations, transportation, and production, minimizing costs and reducing carbon emissions. In addition, blockchain technology has been found to elicit customer trust in the products.

Keywords: blockchain technology, sustainability, supply chains, economic sustainability, environmental sustainability, social sustainability

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Authors: Mircan Kaya

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A multi-disciplinary approach is required in any intervention project for historical monuments. Due to the complexity of their geometry, the variable and unpredictable characteristics of original materials used in their creation, heritage structures are peculiar. Their histories are often complex, and they require correct diagnoses to decide on the techniques of intervention. This approach should not only combine technical aspects but also historical research that may help discover phenomena involving structural issues, and acquire a knowledge of the structure on its concept, method of construction, previous interventions, process of damage, and its current state. In addition to the traditional techniques like bed joint reinforcement, the repairing, strengthening and restoration of historical buildings may require several other modern methods which may be described as innovative techniques like pre-stressing and post-tensioning, use of shape memory alloy devices and shock transmission units, shoring, drilling, and the use of stainless steel or titanium. Regardless of the method to be incorporated in the strengthening process, which can be traditional or innovative, it is crucial to recognize that structural strengthening is the process of upgrading the structural system of the existing building with the aim of improving its performance under existing and additional loads like seismic loads. This process is much more complex than dealing with a new construction, owing to the fact that there are several unknown factors associated with the structural system. Material properties, load paths, previous interventions, existing reinforcement are especially important matters to be considered. There are several examples of seismic strengthening with traditional and innovative techniques around the world, which will be discussed in this paper in detail, including their pros and cons. Ultimately, however, the main idea underlying the philosophy of a successful intervention with the most appropriate techniques of strengthening a historic monument should be decided by a proper assessment of the specific needs of the building.

Keywords: bed joint reinforcement, historical monuments, post-tensioning, pre-stressing, seismic strengthening, shape memory alloy devices, shock transmitters, tie rods

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Authors: Shaolan Zou, Kullapa Soratana

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Construction industry faces tremendous challenges in sustainability issue in recent years. Building materials, generally, are non-recyclable with short service life time, leading to economic loss. Building sites also cause social issues, e.g. noise, hazardous substances, and particulate matters. Sustainable supply chain management (SSCM) has been recognized as an appropriate method to balance three pillars of sustainability: environment, economy, and society. However, most of construction companies cannot successfully adopt SSCM due to numerous challenges. In this study, a list of challenges in implementing SSCM was collected from peer-reviewed literature on sustainable implementation. A building materials company in Thailand, which has successfully adopted SSCM for almost two decades and established the sustainable development committee since 1995, was used as a case study. Management-level representatives in sustainability department of the company were interviewed, mainly, to examine which challenges on the list complies with the company’s condition when adopting SSCM. The interview result was analyzed by interpretive structural model (ISM) with sustainability experts’ opinions to identify top 5 influential challenges. The results could assist a building construction company in assigning appropriate strategies to overcome most influential barriers, as well as in using as a reference or guidance for other construction companies adopting SSCM.

Keywords: sustainable supply chain management, challenges, construction industry, interpretive structural model

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Authors: Liana Melissa E. de la Rosa, Jayson P. Ada

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This exploratory study was conducted to determine the sociology of the Facebook. Specifically, it aimed to know the socio-demographic profile of the respondents in terms of age, sex, year level and monthly allowance; find out the common usage of Facebook to the respondents; identify the features of Facebook that are commonly used by the respondents; understand the benefits and risks of using the Facebook; determine how frequent the respondents use the Facebook; and find out if there is a significant relationship between socio-demographic profile of the respondents and their Facebook usage. This study used the exploratory research design and correlational design employing research survey questionnaire as its main data gathering instrument. Students of the University of Eastern Philippines were selected as the respondents of this study through quota sampling. Ten (10) students were randomly selected from each college of the university. Based on the findings of this study, the following conclusion were drawn: The majority of the respondents are aged 18 and 21 old, female, are third year students, and have monthly allowance of P 2,000 above. On the respondents’ usage of Facebook, the majority of use the Facebook on a daily basis for one to two (1-2) hours everyday. And most users used Facebook by renting a computer in an internet cafe. On the use of Facebook, most users have created their profiles mainly to connect with people and gain new friends. The most commonly used features of Facebook, are: photos application, like button, wall, notification, friend, chat, network, groups and “like” pages status updates, messages and inbox and events. While the other Facebook features that are seldom used by the respondents are games, news feed, user name, video sharing and notes. And the least used Facebook features are questions, poke feature, credits and the market place. The respondents stated that the major benefit that the Facebook has given to its users is its ability to keep in touch with family members or friends while the main risk identified is that the users can become addicted to the Internet. On the tests of relationships between the respondents’ use of Facebook and the four (4) socio-demographic profile variables: age, sex, year level, and month allowance, were found to be not significantly related to the respondents’ use of the Facebook. While the variable found to be significantly related was gender.

Keywords: Facebook, sociology, social networking, exploratory study

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Authors: Majse Lind, Carla Sharp, Salome Vanwoerden

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Narrative identity is the dynamic and evolving story individuals create about their personal pasts, presents, and presumed futures. This storied sense of self develops in adolescence and is crucial for fostering a sense of self-unity and purpose in life. A growing body of work has shown that several characteristics of narrative identity are disturbed in adults suffering from borderline personality disorder (BPD). Very little research, however, has explored the stories told by adolescents with BPD features. Investigating narrative identity early in the lifespan and in relation to personality pathology is crucial; BPD is a developmental disorder with early signs appearing already in adolescence. In the current study, we examine narrative identity (focusing on themes of agency and communion) coded from self-defining memories derived from the child attachment interview in 174 inpatient adolescents (M = 15.12, SD = 1.52) at the time of admission. The adolescents’ social cognition was further assessed on the basis of their reactions to movie scenes (i.e., the MASC movie task). They also completed a trauma checklist and self-reported BPD features at three different time points (i.e., at admission, at discharge, and 6 months after admission). Preliminary results show that adolescents who told stories containing themes of agency and communion evinced better social cognition, and lower emotional abuse on the trauma checklist. In addition, adolescents who disclosed stories containing lower levels of agency and communion demonstrated more BPD symptoms at all three time points, even when controlling for the occurrence of traumatic life events. Surprisingly, social cognitive abilities were not significantly associated with BPD features. These preliminary results underscore the importance of narrative identity as an indicator, and potential cause, of incipient personality pathology. Thus, focusing on diminished themes of narrative-based agency and communion in early adolescence could be crucial in preventing the development of personality pathology over time.

Keywords: borderline personality disorder, inpatient adolescents, narrative identity, follow-ups

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Authors: M. Taha, Hala H. Zayed, T. Nazmy, M. Khalifa

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Recently, traffic monitoring has attracted the attention of computer vision researchers. Many algorithms have been developed to detect and track moving vehicles. In fact, vehicle tracking in daytime and in nighttime cannot be approached with the same techniques, due to the extreme different illumination conditions. Consequently, traffic-monitoring systems are in need of having a component to differentiate between daytime and nighttime scenes. In this paper, a HSV-based day/night detector is proposed for traffic monitoring scenes. The detector employs the hue-histogram and the value-histogram on the top half of the image frame. Experimental results show that the extraction of the brightness features along with the color features within the top region of the image is effective for classifying traffic scenes. In addition, the detector achieves high precision and recall rates along with it is feasible for real time applications.

Keywords: day/night detector, daytime/nighttime classification, image classification, vehicle tracking, traffic monitoring

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Authors: H. Katkhuda

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A time domain approach is used in this paper to identify unknown dynamic forces applied on two dimensional frames using the measured dynamic structural responses for a sub-structure in the two dimensional frame. In this paper a sub-structure finite element model with short length of measurement from only three or four accelerometers is required, and an iterative least-square algorithm is used to identify the unknown dynamic force applied on the structure. Validity of the method is demonstrated with numerical examples using noise-free and noise-contaminated structural responses. Both harmonic and impulsive forces are studied. The results show that the proposed approach can identify unknown dynamic forces within very limited iterations with high accuracy and shows its robustness even noise- polluted dynamic response measurements are utilized.

Keywords: dynamic force identification, dynamic responses, sub-structure, time domain

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Authors: Sandip Suman

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Mechanical defects (cracks, voids, irregularities) in rocket motor propellant are not new and it is induced due to various reasons, which could be an improper manufacturing process, lot-to-lot variation in chemicals or just the natural aging of the products. These defects are normally identified during the examination of radiographic films by quality inspectors. However, a lot of times, these defects are under or over-classified by human inspectors, which leads to unpredictable performance during lot acceptance tests and significant economic loss. The human eye can only visualize larger cracks and defects in the radiographs, and it is almost impossible to visualize every small defect through the human eye. A different artificial intelligence-based machine vision methodology has been proposed in this work to identify and classify the structural defects in the radiographic films of rocket motors with solid propellant. The proposed methodology can extract the features of defects, characterize them, and make intelligent decisions for acceptance or rejection as per the customer requirements. This will automatize the defect detection process during manufacturing with human-like intelligence. It will also significantly reduce production downtime and help to restore processes in the least possible time. The proposed methodology is highly scalable and can easily be transferred to various products and processes.

Keywords: artificial intelligence, machine vision, defect detection, rocket motor propellant grains

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Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

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Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

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Authors: Mehdi Brahimi, Kamal Medjaher, Noureddine Zerhouni, Mohammed Leouatni

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The Prognostics and Health Management is a system engineering discipline which provides solutions and models to the implantation of a predictive maintenance. The approach is based on extracting useful information from monitoring data to assess the “health” state of an industrial equipment or an asset. In this paper, we examine multiple extracted features from Pantograph-Catenary contact force in order to select the most relevant ones to achieve a diagnostics function. The feature extraction methodology is based on simulation data generated thanks to a Pantograph-Catenary simulation software called INPAC and measurement data. The feature extraction method is based on both statistical and signal processing analyses. The feature selection method is based on statistical criteria.

Keywords: catenary/pantograph interaction, diagnostics, Prognostics and Health Management (PHM), quality of current collection

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Authors: Ben D. Atkins

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This study will examine the impact of September 11, 2001, terrorist attacks on the organizational development of American federal law enforcement through focusing on the Federal Bureau of Investigation. Content analysis of discourse in a federal law enforcement practitioner publication along with official FBI statements will be used to gain a better understanding of FBI organizational changes that have taken place since the events of September 11, 2001. Analysis of content trends in the FBI Law Enforcement Bulletin and public discourse of FBI officials from 1999 to 2005 indicate that, in addition to structural changes, the bureau has also undergone a variety of cultural changes. The results offer some support for the institutional theoretical perspective, suggesting that post-9/11 organizational changes such as new mission priorities and the establishment of new branches were partially initiated due to a variety external pressures, which lends support for coercive isomorphism. Furthermore, structural changes are discussed in relation to the attainment and maintenance of organizational legitimacy.

Keywords: institutional theory, organizational theory, law enforcement, public administration

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Mohd Khairezan Rahmat

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Since the impetus of becoming a develop country by the year 2020, the Malaysian government have been proactive in strengthening the integration of ICT into the national educational system. Teacher-education programs have the responsibility to prepare the nation future teachers by instilling in them the desire, confidence, and ability to fully utilized the potential of ICT into their instruction process. In an effort to fulfill this responsibility, teacher-education program are beginning to create alternatives means for preparing cutting-edge teachers. One of the alternatives is the student’s learning portal. In line with this mission, this study investigates the Faculty of Education, University Teknologi MARA (UiTM) pre-service teachers’ perception of usefulness, attitude, and ability toward the usage of the university learning portal, known as iLearn. The study also aimed to predict factors that might hinder the pre-service teachers’ decision to used iLearn as their platform in learning. The Structural Equation Modeling (SEM), was employed in analyzed the survey data. The suggested findings informed that pre-service teacher’s successful integration of the iLearn was highly influenced by their perception of usefulness of the system. The findings also suggested that the more familiar the pre-service teacher with the iLearn, the more possibility they will use the system. In light of similar study, the present findings hope to highlight the important to understand the user’s perception toward any proposed technology.

Keywords: e-learning, prediction factors, pre-service teacher, structural equation modeling (SEM)

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Authors: Beema Akbar, Varun P. Gopi, V. Suresh Babu

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Colon cancer causes the deaths of about half a million people every year. The common method of its detection is histopathological tissue analysis, it leads to tiredness and workload to the pathologist. A novel method is proposed that combines both structural and statistical pattern recognition used for the detection of colon cancer. This paper presents a comparison among the different classifiers such as Multilayer Perception (MLP), Sequential Minimal Optimization (SMO), Bayesian Logistic Regression (BLR) and k-star by using classification accuracy and error rate based on the percentage split method. The result shows that the best algorithm in WEKA is MLP classifier with an accuracy of 83.333% and kappa statistics is 0.625. The MLP classifier which has a lower error rate, will be preferred as more powerful classification capability.

Keywords: colon cancer, histopathological image, structural and statistical pattern recognition, multilayer perception

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Authors: Wenhao Li, Shijun Guo

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Carbon fibre reinforced composites are progressively replacing metal structures in modern civil aircraft. This is because composite materials have large potential of weight saving compared with metal. However, the achievement to date of weight saving in composite structure is far less than the theoretical potential due to many uncertainties in structural integrity and safety concern. Unlike the conventional metallic structure, composite components are bonded together along the joints where structural integrity is a major concern. To ensure the safety, metal fasteners are used to reinforce the composite bonded joints. One of the solutions for a significant weight saving of composite structure is to develop an effective technology of on-board Structural Health Monitoring (SHM) System. By monitoring the real-life stress status of composite structures during service, the safety margin set in the structure design can be reduced with confidence. It provides a means of safeguard to minimize the need for programmed inspections and allow for maintenance to be need-driven, rather than usage-driven. The aim of this paper is to develop smart composite joint. The key technology is a multifunctional thermoplastic composite fastener (MTCF). The MTCF will replace some of the existing metallic fasteners in the most concerned locations distributed over the aircraft composite structures to reinforce the joints and form an on-board SHM network system. Each of the MTCFs will work as a unit of the AU and AE technology. The proposed MTCF technology has been patented and developed by Prof. Guo in Cranfield University, UK in the past a few years. The manufactured MTCF has been successfully employed in the composite SLJ (Single-Lap Joint). In terms of the structure integrity, the hybrid SLJ reinforced by MTCF achieves 19.1% improvement in the ultimate failure strength in comparison to the bonded SLJ. By increasing the diameter or rearranging the lay-up sequence of MTCF, the hybrid SLJ reinforced by MTCF is able to achieve the equivalent ultimate strength as that reinforced by titanium fastener. The predicted ultimate strength in simulation is in good agreement with the test results. In terms of the structural health monitoring, a signal from the MTCF was measured well before the load of mechanical failure. This signal provides a warning of initial crack in the joint which could not be detected by the strain gauge until the final failure.

Keywords: composite single-lap joint, crack propagation, multifunctional composite fastener, structural health monitoring

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Authors: Issam Derkaoui, Mohammed Khenfouch, Bakang M. Mothudi, Malik Maaza, Izeddine Zorkani, Anouar Jorio

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Transition metal oxides nanoparticles with different morphologies have attracted a lot of attention recently owning to their distinctive geometries, and demonstrated promising electrical properties for various applications. In this paper, we discuss the time and annealing effects on the structural and electrical properties of vanadium oxides nanoparticles (VO-NPs) prepared by microwave method. In this sense, transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman Spectroscopy, Ultraviolet-visible absorbance spectra (Uv-Vis) and electrical conductivity were investigated. Hence, the annealing state and the time are two crucial parameters for the improvement of the optoelectronic properties. The use of these nanostructures is promising way for the development of technological applications especially for energy storage devices.

Keywords: Vanadium oxide, Microwave, Electrical conductivity, Optoelectronic properties

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Authors: Niladri Roy, M. Longshithung Patton

Abstract:

This paper is aimed at studying the structural response of circular hollow steel tubular (HST), concrete filled steel tubular (CFST), and reinforced cement concrete (RCC) stub columns when subjected to only axial compressive forces and also examining their comparative nature using finite element (FE) models. These results are further compared with the respective experimental results. FE software package ABAQUS 6.14 has been used for further parametric studies where a total of 108 FE models were modelled. The diameters of the HST, CFST, and RCC stub columns are kept as 100, 140, 180, and 220, with length to diameter ratio fixed at 3 to avoid end effects and flexural failure. To keep the same percentage of steel (by volume), the thicknesses of steel tubes in HST and CFST columns were varied in response to the change in diameter of the main reinforcement bar in RCC columns. M25 grade of concrete was used throughout. The objective is to compare the structural behaviour of HST, CFST, and RCC stub columns on the basis of their axial compressive load carrying capacity and failure modes. The studies show that filling the circular HST columns with concrete increases the Pu of the CCFST columns by 2.97 times. It was also observed that the Pu (HST) is about 0.72 times Pu (RCC) on average, and the Pu (CFST) is about 2.08 times Pu (RCC) on average. After the analysis and comparison, it has been proved that CFST has much more load carrying capacity than HST and RCC and also provides the same strength at a very less sectional size.

Keywords: HST columns, stub columns, CFST columns, RCC columns, finite element modeling, ABAQUS

Procedia PDF Downloads 99

Authors: Hanna Poikonen, Petri Toiviainen, Mari Tervaniemi

Abstract:

Event-related potentials (ERPs) evoked by simple tones in the brain have been extensively studied. However, in reality the music surrounding us is spectrally and temporally complex and dynamic. Thus, the research using natural sounds is crucial in understanding the operation of the brain in its natural environment. Music is an excellent example of natural stimulation, which, in various forms, has always been an essential part of different cultures. In addition to sensory responses, music elicits vast cognitive and emotional processes in the brain. When compared to laymen, professional musicians have stronger ERP responses in processing individual musical features in simple tone sequences, such as changes in pitch, timbre and harmony. Here we show that the ERP responses evoked by rapid changes in individual musical features are more intense in musicians than in laymen, also while listening to long excerpts of the composition Carmen. Interestingly, for professional dancers, the amplitudes of the cognitive P300 response are weaker than for musicians but still stronger than for laymen. Also, the cognitive P300 latencies of musicians are significantly shorter whereas the latencies of laymen are significantly longer. In contrast, sensory N100 do not differ in amplitude or latency between musicians and laymen. These results, acquired from a novel ERP methodology for natural music, suggest that we can take the leap of studying the brain with long pieces of natural music also with the ERP method of electroencephalography (EEG), as has already been made with functional magnetic resonance (fMRI), as these two brain imaging devices complement each other.

Keywords: electroencephalography, expertise, musical features, real-life music

Procedia PDF Downloads 481

Authors: Eric Chabrol, Sidonia B.G. Eckle, Renate de Boer, James McCluskey, Jamie Rossjohn, Mirjam H.M. Heemskerk, Stephanie Gras

Abstract:

αβ and γδ T-cells are disparate T-cell lineages that, via their use of either αβ or γδ T-cell antigen receptors (TCRs) respectively, can respond to distinct antigens. Here we characterise a new population of human T-cells, term δβ T-cells, that express TCRs comprising a TCR-δ variable gene fused to a Joining-α/Constant-α domain, paired with an array of TCR-β chains. We characterised the cellular, functional, biophysical and structural characteristic feature of this new T-cells population that reveal some new insight into TCR diversity. We provide molecular bases of how δβ T-cells can recognise viral peptide presented by Human Leukocyte Antigen (HLA) molecule. Our findings highlight how components from αβ and γδTCR gene loci can recombine to confer antigen specificity thus expanding our understanding of T-cell biology and TCR diversity.

Keywords: new delta-beta TCR, HLA, viral peptide, structural immunology

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Authors: Brahim Bahloul, Khatir Babesse, Azzedine Dkhira, Yacine Bahloul, Dalila Hammoutene

Abstract:

In this paper, we investigate with ab initio calculations some structural and optoelectronic properties of Li₂S compound. The structural and electronic properties of the Li₂S antifluorite structure have been studied by first-principles calculations within the density functional theory (DFT), whereas the optical properties have been obtained using empirical relationships such as the modified Moss relation. Our calculated lattice parameters are in good agreement with the experimental data and other theoretical calculations. The electronic band structures and density of states were obtained. The anti-fluorite Li₂S present an indirect band gap of 3.388 eV at equilibrium. The top of the valence bands reflects the p electronic character for both structures. The calculated energy gaps and optical constants are in good agreement with experimental measurements.

Keywords: Ab initio calculations, antifluorite, electronic properties, optical properties

Procedia PDF Downloads 288

Authors: Preecha Phongpeng

Abstract:

The objectives of this research are to assess the prevalence of travel businesses websites in Thailand, investigate and evaluate the quality of travel business websites in Thailand. The sample size includes 323 websites from the population of 1,458 websites. The study covers 4 types of travel business websites including: 78 general travel agents, 30 online reservation travel agents, 205 hotels, 7 airlines, and 3 car-rental companies with nation-wide operation. The findings indicated that e-tourism in Thailand is at its growth stage, with only 13% of travel businesses having websites, 28% of them providing e-mail and the quality of travel business websites in Thailand was at the average level. Seven common problems were found in websites: lack of travel essential information, insufficient transportation information, lack of navigation tools, lack of link pages to other organizations, lack of safety features, unclear online booking functions, and lack of special features also as well.

Keywords: traveling business, website evaluation, e-commerce, e-tourism

Procedia PDF Downloads 300