Search results for: Learning materials

Authors: Hernán G. Cortés-Mora, José I. Péna-Reyes, Alfonso Herrera-Jiménez

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This article describes the implementation experience of Project-Based Learning (PBL) in an engineering course of the Universidad Nacional de Colombia, with the aim of strengthening student skills necessary for the exercise of their profession under a sustainability framework. Firstly, we present a literature review on the education for sustainability field, emphasizing the skills and knowledge areas required for its development, as well as the commitment of the Faculty of Engineering of the Universidad Nacional de Colombia, and other engineering faculties of the country, regarding education for sustainability. This article covers the general aspects of the course, describes how students team were formed, and how their experience was during the first semester of 2017. During this period two groups of students decided to develop their course project aiming to solve a problem regarding a Non-Governmental Organization (NGO) that works with head-of-household mothers in a low-income neighborhood in Bogota (Colombia). Subsequently, we show how sustainability is involved in the course, how tools are provided to students, and how activities are developed as to strengthen their abilities, which allows them to incorporate sustainability in their projects while also working on the methodology used to develop said projects. Finally, we introduce the results obtained by the students who sent the prototypes of their projects to the community they were working on and the conclusions reached by them regarding the course experience.

Keywords: sustainability, project-based learning, engineering education, higher education for sustainability

Procedia PDF Downloads 338

Authors: Kyungsuk Cho, H. Y. Kim, S. U. Chae, J. H. Choi

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Technological development has led to the construction of super-tall buildings and insulators are increasingly used as exterior finishing materials to save energy. However, insulators are usually combustible and vulnerable to fire. Fires like that at Wooshin Golden Suite Building in Busan, Korea in 2010 and that at CCTV Building in Beijing, China are the major examples of fire spread accelerated by combustible insulators. The exterior finishing materials of a high-rise building are not made of insulators only, but they are integrated with the building’s external cladding system. There is a limit in evaluating the fire safety of a cladding system with a single small-unit material such as a cone calorimeter. Therefore, countries provide codes to evaluate the fire safety of exterior finishing materials using full-scale tests. This study compares and to examine the applicability of the methods to Korea. Standard analysis showed differences in the type and size of fire sources and duration and exterior finishing materials also differed in size. In order to confirm the differences, fire tests were conducted on identical external cladding systems to compare fire safety. Although the exterior finishing materials were identical, varying degrees of fire spread were observed, which could be considered as differences in the type and size of the fire sources and duration. Therefore, it is deduced that extended studies should be conducted before the evaluation methods and standards are employed in Korea. The two standards for evaluating fire safety provided different results. Peak heat release rate was 5.5MW in ISO method and 3.0±0.5MW in BS method. Peak heat release rate in ISO method continued for 15 minutes. Fire ignition, growth, full development and decay evolved for 30 minutes in BS method where wood cribs were used as fire sources. Therefore, follow-up studies should be conducted to determine which of the two standards provides fire sources that approximate the size of flames coming out from the openings or those spreading to the outside when a fire occurs at a high-rise building.

Keywords: external cladding systems, fire safety evaluation, ISO 13785-2, BS 8414

Procedia PDF Downloads 229

Authors: Rahul Paul, Peter Mctaggart, Luke Skinner

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Maintaining public safety and network reliability are the core objectives of all electricity distributors globally. For many electricity distributors, managing vegetation clearances from their above ground assets (poles and conductors) is the most important and costly risk mitigation control employed to meet these objectives. Light Detection And Ranging (LiDAR) is widely used by utilities as a cost-effective method to inspect their spatially-distributed assets at scale, often captured using high powered LiDAR scanners attached to fixed wing or rotary aircraft. The resulting 3D point cloud model is used by these utilities to perform engineering grade measurements that guide the prioritisation of vegetation cutting programs. Advances in computer vision and machine-learning approaches are increasingly applied to increase automation and reduce inspection costs and time; however, real-world LiDAR capture variables (e.g., aircraft speed and height) create complexity, noise, and missing data, reducing the effectiveness of these approaches. This paper proposes a method for identifying each conductor from LiDAR data via clustering methods that can precisely reconstruct conductors in complex real-world configurations in the presence of high levels of noise. It proposes 3D catenary models for individual clusters fitted to the captured LiDAR data points using a least square method. An iterative learning process is used to identify potential conductor models between pole pairs. The proposed method identifies the optimum parameters of the catenary function and then fits the LiDAR points to reconstruct the conductors.

Keywords: point cloud, LİDAR data, machine learning, computer vision, catenary curve, vegetation management, utility industry

Procedia PDF Downloads 84

Authors: Daphne Alroy-Thiberge

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Trauma-impacted individuals present unique treatment challenges that include high reactivity, hyper-and hypo-arousal, poor adherence to therapy, as well as powerful transference and counter-transference experiences in therapy. This work provides an overview of the clinical tenets most often encountered in trauma-impacted individuals. Further, it provides readily applicable clinical techniques to optimize therapeutic rapport and facilitate accelerated positive mental health outcomes. Finally, integrated neuroscience and clinical evidence-based data are discussed to shed new light on crisis states in trauma-impacted individuals. This knowledge is utilized to provide effective and concrete interventions towards rapid and successful de-escalation of the impacted individual. A highly interactive, adult-learning-principles-based modality is utilized to provide an organic learning experience for participants. The information and techniques learned aim to increase clinical effectiveness, reduce staff injuries and burnout, and significantly enhance positive mental health outcomes and self-determination for the trauma-impacted individuals treated.

Keywords: clinical competencies, crisis interventions, psychotherapy techniques, trauma informed care

Procedia PDF Downloads 81

Authors: Hiba S. Ahmed, Abbas A. Allawi, Riyadh A. Hindi

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Pultruded materials made of fiber-reinforced polymer (FRP) come in a broad range of shapes, such as bars, I-sections, C-sections, and other structural sections. These FRP materials are starting to compete with steel as structural materials because of their great resistance, low self-weight, and cheap maintenance costs-especially in corrosive conditions. This study aimed to evaluate the effectiveness of Glass Fiber Reinforced Polymer (GFRP) of the hybrid columns built by combining (GFRP) profiles with concrete columns because of their low cost and high structural efficiency. To achieve the aims of this study, nine circular columns with a diameter of (150 mm) and a height of (1000mm) were cast using normal concrete with compression strength equal to (35 MPa). The research involved three different types of reinforcement: hybrid circular columns type (IG) with GFRP I-section and 1% of the reinforcement ratio of steel bars, hybrid circular columns type (IS) with steel I-section and 1% of the reinforcement ratio of steel bars, (where the cross-section area of I-section for GFRP and steel was the same), compared with reference column (R) without I-section. To investigate the ultimate capacity, axial and lateral deformation, strain in longitudinal and transverse reinforcement, and failure mode of the circular column under different loading conditions (concentric and eccentric) with eccentricities of 25 mm and 50 mm, respectively. In the second part, an analytical finite element model will be performed using ABAQUS software to validate the experimental results.

Keywords: composite, columns, reinforced concrete, GFRP, axial load

Procedia PDF Downloads 37

Authors: Sungho Kim, Jaechoul Shin, Yujin Baek, Nakseok Kim, Kyungnam Kim, Shinhaeng Jo

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The climate change and increase in heavy traffic have been accelerating damages that cause the problems such as pothole on asphalt pavement. Pothole causes traffic accidents, vehicle damages, road casualties and traffic congestion. A quick and efficient maintenance method is needed because pothole is caused by stripping and accelerates pavement distress. In this study, we propose a rapid and systematic pothole management by developing a pothole automated repairing equipment including a volume measurement system of pothole. Three kinds of cold mix asphalt mixture were investigated to select repair materials. The materials were evaluated for satisfaction with quality standard and applicability to automated equipment. The volume measurement system of potholes was composed of multi-sensor that are combined with laser sensor and ultrasonic sensor and installed in front and side of the automated repair equipment. An algorithm was proposed to calculate the amount of repair material according to the measured pothole volume, and the system for releasing the correct amount of material was developed. Field test results showed that the loss of repair material amount could be reduced from approximately 20% to 6% per one point of pothole. Pothole rapid automated repair equipment will contribute to improvement on quality and efficient and economical maintenance by not only reducing materials and resources but also calculating appropriate materials. Through field application, it is possible to improve the accuracy of pothole volume measurement, to correct the calculation of material amount, and to manage the pothole data of roads, thereby enabling more efficient pavement maintenance management. Acknowledgment: The author would like to thank the MOLIT(Ministry of Land, Infrastructure, and Transport). This work was carried out through the project funded by the MOLIT. The project name is 'development of 20mm grade for road surface detecting roadway condition and rapid detection automation system for removal of pothole'.

Keywords: automated equipment, management, multi-beam sensor, pothole

Procedia PDF Downloads 214

Authors: Stefan Nicolae, Cristina Cirtoaje, Emil Petrescu, Florin-Razvan Duca

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In the context of the accelerated expansion of urban agglomerations and the exponential development of industry, a huge amount of water is used, and a crisis of drinking water may occur any time. Classic wastewater treatment removes most pollutants but, for some chemical residues, special methods are needed. Carbon nanotubes and other carbon materials might be used in many cases [1-2], especially for heavy metals removal but also on pharmaceutical products such as paracetamol [3]. Our research has confirmed the better efficiency of nanotubes compared to graphene on paracetamol removal from water, but even better results were obtained on single-walled nanotubes (SWCNTs) and graphene nanoplatelets. This can be due to their better dispersion in water which leads to an increased contact surface, so we propose a filtration system of membranes and carbon materials that can be used for paracetamol removal from wastewater but also for other drugs that affect the aquatic life as well as terrestrial animals and people who use this contaminated water.

Keywords: applied physics, wastewater, nanomaterials, enviromental science

Procedia PDF Downloads 175

Authors: M. Z. Abdul Rofiq Badril Rizal

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This research is aimed to know the improvement of students’ English writing skill by using brainstorming technique. The technique used in writing is able to help the students’ difficulties in generating ideas and to lead the students to arrange the ideas well as well as to focus on the topic developed in writing. The research method used is classroom action research. The data sources of the research are an English teacher who acts as an observer and the students of class X.MIA5 consist of 35 students. The test result and observation are collected as the data in this research. Based on the research result in cycle one, the percentage of students who reach minimum accomplishment criteria (MAC) is 76.31%. It shows that the cycle must be continued to cycle two because the aim of the research has not accomplished, all of the students’ scores have not reached MAC yet. After continuing the research to cycle two and the weaknesses are improved, the process of teaching and learning runs better. At the test which is conducted in the end of learning process in cycle two, all of the students reach the minimum score and above 76 based on the minimum accomplishment criteria. It means the research has been successful and the percentage of students who reach minimum accomplishment criteria is 100%. Therefore, the writer concludes that brainstorming technique is able to improve the students’ English writing skill at the tenth grade of SMAN 2 Jember.

Keywords: brainstorming technique, improving, writing skill, knowledge and innovation engineering

Procedia PDF Downloads 351

Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Maria L. Ulloa, Ruth M. Crawford, Stephanie Kelly, Joey Domdom

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In recent decades health and social service delivery have become more collaborative and interdisciplinary. Emerging trends suggest the need for an integrative and interprofessional approach to meet the challenges faced by professionals navigating the complexities of health and social service practice environments. Terms such as multidisciplinary practice, interprofessional collaboration, interprofessional education and transprofessional practice have become the common language used across a range of social services and health providers in western democratic systems. In Aotearoa New Zealand, one example of an interprofessional collaborative approach to curriculum design and delivery in health and social service is the development of an innovative Masters of Professional Practice programme. This qualification is the result of a strategic partnership between two tertiary institutions – Whitireia New Zealand (NZ) and the Wellington Institute of Technology (Weltec) in Wellington. The Master of Professional Practice programme was designed and delivered from the perspective of a collaborative, interprofessional and relational approach. Teachers and students in the programme come from a diverse range of cultural, professional and personal backgrounds and are engaged in courses using a blended learning approach that incorporates the values and pedagogies of interprofessional education. Students are actively engaged in professional practice while undertaking the programme. This presentation describes the themes of exploratory qualitative formative observations of engagement in class and online, student assessments, student research projects, as well as qualitative interviews with the programme teaching staff. These formative findings reveal the development of critical practice skills around the common themes of the programme: research and evidence based practice, education, leadership, working with diversity and advancing critical reflection of professional identities and interprofessional practice. This presentation will provide evidence of enhanced learning experiences in higher education and learning in multi-disciplinary contexts.

Keywords: diversity, exploratory research, interprofessional education, professional identity

Procedia PDF Downloads 286

Authors: R. Ramesh, K. K. Shivaraman

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The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

Procedia PDF Downloads 289

Authors: Z. Mahmood, F. U. Babar, S. Naz, H. U. Rehman

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Cadmium Sulphide (CdS) was deposited on a Tec 15 glass substrate with the help of CBD (chemical bath deposition process) and then cadmium telluride CdTe was deposited on CdS with the help of CSS (closed spaced sublimation technique) for the construction of a solar cell. The thicknesses of all the deposited materials were measured with the help of Ellipsometry. The IV graphs were drawn in order to observe the current voltage output. The efficiency of the cell was graphed with the fill factor as well (graphs not given here). The efficiency came out to be approximately 16.5 % and the CIGS (copper-indium–gallium-selenide) maximum efficiency is 20 %. The efficiency of a solar cell can further be enhanced by adapting quality materials, good experimental devices and proper procedures. The grain size was analyzed with the help of scanning electron microscope using RBS (Rutherford backscattering spectroscopy).

Keywords: Chemical Bath Deposition Technique (CBD), cadmium sulphide (CdS), CdTe, CSS (Closed Space Sublimation)

Procedia PDF Downloads 347

Authors: Ertuğrul Yılmaz

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One of the most important occupational groups in the food chain from farm to fork is a veterinary medicine. This occupational group, which has important duties in the prevention of many zoonotic diseases and in public health, takes place in many critical control points from soil to our kitchen. It has important duties from mycotoxins transmitted from the soil to slaughterhouses or milk processing facilities. Starting from the soil, which constitutes 70% of mycotoxin contamination, up to the TMR made from raw materials obtained from the soil, there are all critical control points from feeding to slaughterhouses and milk production enterprises. We can take the precaution of mycotoxins such as Aflatoxin B1, Ochratoxin, Zearalenone, and Fumonisin, which we encounter on farms while in the field. It has been reported that aflatoxin B1 is a casenerogen and passes into milk in studies. It is likely that many mycotoxins pose significant threats to public health and will turn out to be even more dangerous over time. Even raw material storage and TMR preparation are very important for public health. The danger of fumonisin accumulating in the liver will be understood over time. Zoonotic diseases are also explained with examples. In this study, how important veterinarians are in terms of public health is explained with examples. In the two-year mycotoxin screenings, fumonisin mycotoxin was found to be very high in corn and corn by-products, and it was determined that it accumulated in the liver for a long time and remained cornic in animals. It has been determined that mycotoxins are present in all livestock feeds, poultry feeds, and raw materials, not alone, but in double-triple form. Starting from the end, mycotoxin scans should be carried out from feed to raw materials and from raw materials to soil. In this way, we prevent the transmission of mycotoxins to animals and from animals to humans. Liver protectors such as toxin binders, beta-glucan, mannan oligosaccharides, activated carbon, prebiotics, and silymarin were used in certain proportions in the total mixed ratio, and positive results were obtained. Humidity and temperature controls of raw material silos were made at certain intervals. Necropsy was performed on animals that died as a result of mycotoxicosis, and macroscopic photographs were taken of the organs. We have determined that the mycotoxin screening in experimental animals and the feeds made without detecting the presence and amount of bacterial factors affect the results of the project to be made. For this, a series of precautionary plans have been created, starting from the production processes.

Keywords: mycotoxins, feed safety, processes, public health

Procedia PDF Downloads 65

Authors: Qi Wang

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Chinese has become a powerful worldwide language and millions of learners are studying it all over the words. Chinese is a tone language with unique meaningful characters, which makes foreign learners master it with more difficulties. On the other hand, as each foreign language, the learners of Chinese first will learn the basic Chinese Sound Structure (the initials and finals, tones, Neutral Tone and Tone Sandhi). It’s quite common that in the following studies, teachers made a lot of efforts on drilling and error correcting, in order to help students to pronounce correctly, but ignored the training of suprasegmental features (e.g. stress, intonation). This paper analysed the oral data based on our graduation students (two-year program) from 2006-2013, presents the intonation pattern of our graduates to speak Chinese as second language -high and plain with heavy accents, without lexical stress, appropriate stop endings and intonation, which led to the misunderstanding in different real contexts of communications and the international official Chinese test, e.g. HSK (Chinese Proficiency Test), HSKK (HSK Speaking Test). This paper also demonstrated how the Chinese to use the suprasegmental features strategically in different functions and moods (declarative, interrogative, imperative, exclamatory and rhetorical intonations) in order to train the learners to achieve better Communicative Performance.

Keywords: second language learning, suprasegmental, communication, HSK (Chinese Proficiency Test)

Procedia PDF Downloads 424

Authors: Haikal Achmad Thaha, Pujo Laksono, Dhamma Nibbana Putra

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Customer service is one of the core interest in a service sector of a company, whether as the core business or as service part of the operation. Most of the time, the people and the previous research in service industry is focused on finding the best business model solution for the service sector, usually to decide between total in house customer service, outsourcing, or something in between. Conventionally, to take this decision is some important part of the management job, and this is a process that usually takes some time and staff effort, meanwhile market condition and overall company needs may change and cause loss of income and temporary disturbance in the companies operation . However, in this paper we have offer a new concept model to assist decision making process in service industry. This model will featured information platform as central tool to integrate service industry operation. The result is service information model which would ideally increase response time and effectivity of the decision making. it will also help service industry in switching the service solution system quickly through machine learning when the companies growth and the service solution needed are changing.

Keywords: service industry, customer service, machine learning, decision making, information platform

Procedia PDF Downloads 605

Authors: Ayu Nabila Kusuma Pradana, Aprinaldi Jasa Mantau, Tomohiko Akahoshi

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The incidence of Disseminated intravascular coagulation (DIC) following gastrointestinal surgery has a poor prognosis. Therefore, it is important to determine the factors that can predict the prognosis of DIC. This study will investigate the factors that may influence the outcome of DIC in patients after gastrointestinal surgery. Eighty-one patients were admitted to the intensive care unit after gastrointestinal surgery in Kyushu University Hospital from 2003 to 2021. Acute DIC scores were estimated using the new Japanese Association for Acute Medicine (JAAM) score from before and after surgery from day 1, day 3, and day 7. Acute DIC scores will be compared with The Sequential Organ Failure Assessment (SOFA) score, platelet count, lactate level, and a variety of biochemical parameters. This study applied machine learning algorithms to predict the prognosis of DIC after gastrointestinal surgery. The results of this study are expected to be used as an indicator for evaluating patient prognosis so that it can increase life expectancy and reduce mortality from cases of DIC patients after gastrointestinal surgery.

Keywords: the survival rate, gastrointestinal surgery, JAAM score, neural network, machine learning, disseminated intravascular coagulation (DIC)

Procedia PDF Downloads 238

Authors: Mehrnoush Tajnia, Simin Sadeghi-Saeb

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Students and teachers have different perceptions of effectiveness of instruction. Comparing students’ and teachers’ beliefs and finding the mismatches between them can increase L2 students’ satisfaction. Few studies have taken into account the beliefs of both students and teachers on different aspects of pedagogy and the effect of learners’ level of education and contexts on effective foreign language teacher practices. Therefore, the present study was conducted to compare students’ and teachers’ perceptions on effective foreign language teaching. A sample of 303 learners and 54 instructors from different private language institutes and universities participated in the study. A questionnaire was developed to elicit participants’ beliefs on effective foreign language teaching and learning. The analysis of the results revealed that: a) there is significant difference between the students’ beliefs about effective teacher practices and teachers’ belief, b) Class level influences students’ perception of effective foreign language teacher, d) There is a significant difference of opinion between those learners who study foreign languages at university and those who study foreign language in private institutes with respect to effective teacher practices. The present paper concludes that finding the gap between students’ and teachers’ beliefs would help both of the groups to enhance their learning and teaching.

Keywords: effective teacher, effective teaching, students’ beliefs, teachers’ beliefs

Procedia PDF Downloads 302

Authors: Talita Veldsman, Elzette Fritz

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Many children attending special education present with sensory integration difficulties that hamper their learning and behaviour. These learners can benefit from therapeutic interventions as part of their classroom curriculum that can address sensory development and allow for holistic development to take place. A research study was conducted by utilizing socio-drama as a therapeutic intervention in the classroom in order to develop sensory integration skills. The use of socio-drama as therapeutic intervention proved to be a successful multi-disciplinary approach where education and psychology could build a bridge of growth and integration. The paper describes how socio-drama was used in the classroom and how these sessions were designed. The research followed a qualitative approach and involved six Afrikaans-speaking children attending special secondary school in the age group 12-14 years. Data collection included observations during the session, reflective art journals, semi-structured interviews with the teacher and informal interviews with the adolescents. The analysis found improved self-confidence, better social relationships, sensory awareness and self-regulation in the participants after a period of a year.

Keywords: education, sensory integration, sociodrama, classroom intervention, psychology

Procedia PDF Downloads 563

Authors: Wojciech J. Nowak, Natalia Maciaszek, Marcin Drajewicz

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A constant requirement to reduce greenhouse gas emissions in combination with the desire to increase gas turbine efficiency results in a continuous trend to increase the operating temperature of gas turbines. An increase in operating temperature will result in lower fuel consumption, and a higher combustion temperature will result in lower pollution release. Moreover, there is a strong trend for hydrogen to be used as an alternative and clean fuel. However, using hydrogen or hydrogen-rich fuel results in a higher combustion temperature, as well as an increase in the water vapor content in the exhaust gases. Commonly used Ni-base alloys have their limits. Moreover, the presence of water vapor worsens the oxidation behavior of Ni-based alloys at a high temperature. Therefore, a new brand of materials is demanded to be used in gas turbines operated with hydrogen-rich fuel. High-entropy alloys (HEAs) seem to be very promising materials to replace commonly used Ni-based alloys. HEAs are the group of materials consisting of at least five main equiatomic elements. These alloys can be doped by other elements in amounts less than 5 at. % in total. Thus, in the present study, NiCoCrAlFe-X alloys are studied in terms of oxidation behavior during exposure to dry and wet atmospheres up to 1000 h. NiCoCrAlFe-X alloys are doped with minor alloying elements in amounts ranging from 1-5 at.%. The effect of the chemical composition on oxidation resistance in dry and wet atmospheres will be shown and discussed.

Keywords: high entropy alloys, oxidation resistance, hydrogen fuel, water vapor

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

Procedia PDF Downloads 158

Authors: Ting Lu, Ya-Ru Diao, Fei Fan, Ye Xue, Lei Shi, Xian-e Tang, Meng-jun Zhan, Zhen-hua Deng

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Accurate adult age estimation (AAE) is a significant and challenging task in forensic and archeology fields. Attempts have been made to explore optimal adult age metrics, and the rib is considered a potential age marker. The traditional way is to extract age-related features designed by experts from macroscopic or radiological images followed by classification or regression analysis. Those results still have not met the high-level requirements for practice, and the limitation of using feature design and manual extraction methods is loss of information since the features are likely not designed explicitly for extracting information relevant to age. Deep learning (DL) has recently garnered much interest in imaging learning and computer vision. It enables learning features that are important without a prior bias or hypothesis and could be supportive of AAE. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. Chest CT data were reconstructed using volume rendering (VR). Retrospective data of 2500 patients aged 20.00-69.99 years were obtained between December 2019 and September 2021. Five-fold cross-validation was performed, and datasets were randomly split into training and validation sets in a 4:1 ratio for each fold. Before feeding the inputs into networks, all images were augmented with random rotation and vertical flip, normalized, and resized to 224×224 pixels. ResNeXt was chosen as the DL baseline due to its advantages of higher efficiency and accuracy in image classification. Mean absolute error (MAE) was the primary parameter. Independent data from 100 patients acquired between March and April 2022 were used as a test set. The manual method completely followed the prior study, which reported the lowest MAEs (5.31 in males and 6.72 in females) among similar studies. CT data and VR images were used. The radiation density of the first costal cartilage was recorded using CT data on the workstation. The osseous and calcified projections of the 1 to 7 costal cartilages were scored based on VR images using an eight-stage staging technique. According to the results of the prior study, the optimal models were the decision tree regression model in males and the stepwise multiple linear regression equation in females. Predicted ages of the test set were calculated separately using different models by sex. A total of 2600 patients (training and validation sets, mean age=45.19 years±14.20 [SD]; test set, mean age=46.57±9.66) were evaluated in this study. Of ResNeXt model training, MAEs were obtained with 3.95 in males and 3.65 in females. Based on the test set, DL achieved MAEs of 4.05 in males and 4.54 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. Those results showed that the DL of the ResNeXt model outperformed the manual method in AAE based on CT reconstruction of the costal cartilage and the developed system may be a supportive tool for AAE.

Keywords: forensic anthropology, age determination by the skeleton, costal cartilage, CT, deep learning

Procedia PDF Downloads 58

Authors: Muhammad Maisam Abbas, Salman Tariq, Usama Riaz, Muhammad Tanveer, Humaira Abdul Ghafoor

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Autism Spectrum Disorder (ASD) refers to a developmental disorder that impairs an individual's communication and interaction ability. Individuals feel difficult to read facial expressions while communicating or interacting. Facial Expression Recognition (FER) is a unique method of classifying basic human expressions, i.e., happiness, fear, surprise, sadness, disgust, neutral, and anger through static and dynamic sources. This paper conducts a comprehensive comparison and proposed optimal method for a continued research project—a system that can assist people who have Autism Spectrum Disorder (ASD) in recognizing facial expressions. Comparison has been conducted on three supervised learning algorithms EigenFace, FisherFace, and LBPH. The JAFFE, CK+, and TFEID (I&II) datasets have been used to train and test the algorithms. The results were then evaluated based on variance, standard deviation, and accuracy. The experiments showed that FisherFace has the highest accuracy for all datasets and is considered the best algorithm to be implemented in our system.

Keywords: autism spectrum disorder, ASD, EigenFace, facial expression recognition, FisherFace, local binary pattern histogram, LBPH

Procedia PDF Downloads 157

Authors: Teja Koduru

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Untreated Lyme disease can lead to neurological, cardiac, and dermatological complications. Rapid diagnosis of the erythema migrans (EM) rash, a characteristic symptom of Lyme disease is therefore crucial to early diagnosis and treatment. In this study, we aim to utilize deep learning frameworks including Tensorflow and Keras to create deep convolutional neural networks (DCNN) to detect images of acute Lyme Disease from images of erythema migrans. This study uses a custom database of erythema migrans images of varying quality to train a DCNN capable of classifying images of EM rashes vs. non-EM rashes. Images from publicly available sources were mined to create an initial database. Machine-based removal of duplicate images was then performed, followed by a thorough examination of all images by a clinician. The resulting database was combined with images of confounding rashes and regular skin, resulting in a total of 683 images. This database was then used to create a DCNN with an accuracy of 93% when classifying images of rashes as EM vs. non EM. Finally, this model was converted into a web and mobile application to allow for rapid diagnosis of EM rashes by both patients and clinicians. This tool could be used for patient prescreening prior to treatment and lead to a lower mortality rate from Lyme disease.

Keywords: Lyme, untreated Lyme, erythema migrans rash, EM rash

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Authors: A. M. Jungudo, M. A. Lasan

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Affordable housing has long been one of the basic necessities of life to man. The ever rising prices of building materials are one of the major causes of housing shortage in many developing countries. Breaching the gap of housing needs in developing countries like Nigeria is an awaiting task longing for attention. This is due to lack of research in the development of local materials that will suit the troubled economies of these countries. The use of earth material to meet the housing needs is a sustainable option and its material is freely available universally. However, people are doubtful of using the earth material due to its modest outlook and uncertain durability. This research aims at enhancing the durability of Compressed Earth Bricks (CEBs) using stone dust as a stabilizer. The result indicates that partial replacement of lateritic soil with stone dust at 30% improves its compressive strength along with abrasive resistance.

Keywords: earth construction, durability, stone dust, sustainable

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

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Authors: Paweł Strzępek, Małgorzata Zasadzińska, Szymon Kordaszewski, Wojciech Ściężor

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The constant tendency toward the materials properties improvement nowadays creates opportunities for the scientists, and furthermore the manufacturers all over the world to design, form and produce new alloys almost every day. Considering the fact that companies all over the world look for alloys with the highest values of mechanical properties coexisting with a reasonable electrical conductivity made it necessary to develop new materials based on copper, such as copper magnesium alloys with over 2 wt. % of Mg. Though, before such new material may be mass produced it must undergo a series of tests in order to determine the production technology and its parameters. The presented study is based on the numerical simulations calculated with the use of finite element method analysis, where the geometry of the cooling system, the material used to produce the cooling system and the surface quality of the graphite crystallizer at the place of contact with the cooling system and its influence on the temperatures throughout the continuous casting process is being investigated. The calculated simulations made it possible to propose the optimal set of equipment necessary for the continuous casting process to be carried out in laboratory conditions with various casting parameters and to determine basic materials properties of the obtained alloys such as hardness, electrical conductivity and homogeneity of the chemical composition. The authors are grateful for the financial support provided by The National Centre for Research and Development – Research Project No. LIDER/33/0121/L-11/19/NCBR/2020.

Keywords: CuMg alloys, continuous casting, temperature analysis, finite element method

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Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

Procedia PDF Downloads 421

Authors: S. Kudlacik-Kramarczyk, M. Kedzierska, B. Tyliszczak

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Recently, the continuous development of medicine and related sciences has been observed. Particular emphasis is directed on the development of biomaterials, i.e., non-toxic, biocompatible and biodegradable materials that may improve the effectiveness of treatment as well as the comfort of patients. This is particularly important in the case of cancer treatment. Currently, there are many methods of cancer treatment based primarily on chemotherapy and the surgical removal of the tumor, but it is worth noting that these therapies also cause many side effects. Among women, the most common cancer is breast cancer. It may be completely cured, but the consequence of treatment is partial or complete breast mastectomy and radiation therapy, which results in severe skin burns. The skin of the patient after radiation therapy is very burned, and therefore requires intensive care and high frequency of dressing changes. The traditional dressing adheres to the burn wounds and does not absorb adequate amount of exudate from injuries and the patient is forced to change the dressing every 2 hours. Therefore, the main purpose was to develop an innovative combination of dressing material with drug carriers that may be used in anti-cancer therapy. The innovation of this solution is the combination of these two products into one system, i.e., a transdermal system with the possibility of a controlled release of the drug- cytostatic. Besides, the possibility of modifying the hydrogel matrix with aloe vera juice provides this material with new features favorable from the point of view of healing processes of burn wounds resulting from the radiation therapy. In this study, hydrogel materials containing protein spheres with the active substance have been obtained as a result of photopolymerization process. The reaction mixture consisting of the protein (albumin) spheres incorporated with cytostatic, chitosan, adequate crosslinking agent and photoinitiator has been subjected to the UV radiation for 2 minutes. Prepared materials have been subjected to the numerous studies including the analysis of cytotoxicity using murine fibroblasts L929. Analysis was conducted based on the mitochondrial activity test (MTT reduction assay) which involves the determining the number of cells characterized by proper metabolism. Hydrogel materials obtained using different amount of crosslinking agents have been subjected to the cytotoxicity analysis. According to the standards, tested material is defined as cytotoxic when the viability of cells after 24 h incubation with this material is lower than 70%. In the research, hydrogel polymer materials containing protein spheres incorporated with the active substance, i.e. a cytostatic, have been developed. Such a dressing may support the treatment of cancer due to the content of the anti-cancer drug - cytostatic, and may also provide a soothing effect on the healing of the burn wounds resulted from the radiation therapy due to the content of aloe vera juice in the hydrogel matrix. Based on the conducted cytotoxicity studies, it may be concluded that the obtained materials do not adversely affect the tested cell lines, therefore they can be subjected to more advanced analyzes.

Keywords: hydrogel polymers, cytostatics, drug carriers, cytotoxicity

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Authors: Christopher J. Johnston

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This paper presents the finding of a research study in which novice (first and second year) elementary teachers (grades Kindergarten – six) evaluated various mathematics Virtual Manipulatives, websites, and Applets (tools) for use in mathematics instruction. Participants identified the criteria they used for evaluating these types of resources and provided recommendations for or against five pre-selected tools. During the study, participants participated in three data collection activities: (1) A brief Likert-scale survey which gathered information about their attitudes toward technology use; (2) An identification of criteria for evaluating technology tools; and (3) A review of five pre-selected technology tools in light of their self-identified criteria. Data were analyzed qualitatively using four theoretical categories (codes): Software Features (41%), Mathematics (26%), Learning (22%), and Motivation (11%). These four theoretical categories were then grouped into two broad categories: Content and Instruction (Mathematics and Learning), and Surface Features (Software Features and Motivation). These combined, broad categories suggest novice teachers place roughly the same weight on pedagogical features as they do technological features. Implications for mathematics teacher educators are discussed, and suggestions for future research are provided.

Keywords: mathematics education, novice teachers, technology, virtual manipulatives

Procedia PDF Downloads 117