Search results for: learning center

Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

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Authors: M. R. Vaezi, R. Ghasemi, A. Akramizadeh

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Frequency stability of microgrids under islanded operation attracts particular attention recently. A new cooperative frequency control strategy based on centralized multi-agent system (CMAS) is proposed in this study. On this strategy, agents sent data and furthermore each component has its own to center operating decisions (MGCC). After deciding on the information, they are returned. Frequency control strategies include primary and secondary frequency control and disposal of multi-stage load in which this study will also provide a method and algorithm for load shedding. This could also be a big problem for the performance of micro-grid in times of disaster. The simulation results show the promising performance of the proposed structure of the controller based on multi agent systems.

Keywords: frequency control, islanded microgrid, multi-agent system, load shedding

Procedia PDF Downloads 459

Authors: Mouli Banga, Chaya Ravindra

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Wound classification is a crucial step in wound diagnosis. An effective classifier can aid wound specialists in identifying wound types with reduced financial and time investments, facilitating the determination of optimal treatment procedures. This study presents a deep neural network-based classifier that leverages wound images and their corresponding locations to categorize wounds into various classes, such as diabetic, pressure, surgical, and venous ulcers. By incorporating a developed body map, the process of tagging wound locations is significantly enhanced, providing healthcare specialists with a more efficient tool for wound analysis. We conducted a comparative analysis between two prominent convolutional neural network models, ResNet50 and MobileNetV2, utilizing a dataset of 730 images. Our findings reveal that the RestNet50 outperforms MovileNetV2, achieving an accuracy of approximately 90%, compared to MobileNetV2’s 83%. This disparity highlights the superior capability of ResNet50 in the context of this dataset. The results underscore the potential of integrating deep learning with spatial data to improve the precision and efficiency of wound diagnosis, ultimately contributing to better patient outcomes and reducing healthcare costs.

Keywords: wound classification, MobileNetV2, ResNet50, multimodel

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Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

Procedia PDF Downloads 85

Authors: Talal Alsulaiman, Khaldoun Khashanah

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In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent's attributes. Also, the influence of social networks in the developing of agents’ interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.

Keywords: artificial stock markets, market dynamics, bounded rationality, agent based simulation, learning, interaction, social networks

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Authors: Enikő Bene

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Our sowing date experiment took place in the Demonstration Garden of Institution of Plant Sciences, Agricultural Center of University of Debrecen, in 2012-2014. The thesis contains data of test year 2014. Our purpose, besides several other examinations, was to observe how sowing date influences leaf area index and activity of photosynthesis of maize hybrids, and how those factors affect fruiting. In the experiment we monitored the change of the leaf area index and the photosynthesis of hybrids with four different growing seasons. The results obtained confirm that not only the environmental and agricultural factors in the growing season have effect on the yield, but also other factors like the leaf area index and the photosynthesis are determinative parameters, and all those factors together, modifying effects of each other, develop average yields

Keywords: sowing date, hybrid, leaf area index, photosynthetic capacity

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Authors: Hanbhin Ryu, Ilkwon Yun

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Automotive industry targets to provide an improvement in safety and convenience through realizing fully autonomous vehicle. For partially realizing fully automated driving, Current vehicles already feature varieties of advanced driver assistance system (ADAS) for safety and infotainment systems for the driver’s convenience. This paper presents Cloud Connected Vehicle (CCV) which connected vehicles with cloud data center via the access network to control the vehicle for achieving next autonomous driving form and describes its features. This paper also describes the shortcoming of the existing In-Vehicle Network (IVN) to be a next generation IVN of CCV and organize the 802.3 Ethernet, the next generation of IVN, related research issue to verify the feasibility of using Ethernet. At last, this paper refers to additional considerations to adopting Ethernet-based IVN for CCV.

Keywords: autonomous vehicle, cloud connected vehicle, ethernet, in-vehicle network

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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 349

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

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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

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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

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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: Fatima Ezzahra Siragi, Omar Benaini

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For many years, Generation Y has been at the center of controversies. This topic made the buzz in the Media as well as in scientific literature. Previous research led to contradictory results; some scholars considered this population a wealth for companies, while the others believe it constitutes a young danger in need of proper control. Existing literature has almost studied Generation Y in developed countries; very rare studies were conducted in developing countries. To our knowledge, no published articles have treated Generation Y in Morocco. The purpose of this research is to examine the distinctive characteristics of Generation Y in Morocco as well as their behavior at work. Using quantitative method, the study was conducted on a sample of 250 Moroccan employees that have a high educational level and who belong to Generation Y. Our results have shown high resemblance between Moroccan and Occidental Yers (France, USA, Canada …)

Keywords: Behavior in Organizations, Generation Y, Key Characteristics, Moroccan Yers, Motivation

Procedia PDF Downloads 276

Authors: Arjun, A. D. Singh

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Concrete the basic material using in construction across the global these days. The purpose of this special concrete is to provide extra strength and stability for irregular structure where the center of gravity is disturbed. In this paper an effort has been made to use different type of material aggregates has been discussed. We named As "STAR Aggregates" which has qualities to resist Shear, tension and compression forces. We have been divided into coarse aggregates and fine aggregates according to their sizes. Star Aggregates has interlocking behavior and cutting edge technology. Star aggregates had been draft and deign in Auto CAD and then analysis in ANSYS software. by using special aggregates we deign concrete grade of M40 for mega structures and irregular structure. This special concrete with STAR aggregates use in construction for irregular structure like Bridges, Skyscrapers or in deigned buildings.

Keywords: star aggregates, high performance concrete, material aggregates, interlocking

Procedia PDF Downloads 562

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 298

Authors: Siham Soulaimi

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Europe has become a difficult destination due to strict migration policies and border controls, making Morocco an immigration country. Morocco is currently at the center of the international migration debate because it not only hosts regular migrants but also must deal with the problem of irregular migrants entering its territory. Sub-Saharan irregular migration is full of challenges that might cause a delay for the migrants, announcing a death sentence for many others. The journey's hurdles are likely to cause a crisis in expectations, resulting in serious consequences on the migrants' mental health. Our research study emphasizes that sub-Saharan migrants begin irregular journeys with high hopes, only to be disappointed by how unexpectedly cruel it turns out to be. We also pointed to specific physical and, more crucially, mental health problems that they end up with after survival, resulting in somatic disorders.

Keywords: irregular migration, Sub-Saharan migrants, challenges, experiences crisis, mental health, somatoform disorder

Procedia PDF Downloads 130

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

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Authors: H. A. Erdogan

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Historical masonry arch bridges built centuries ago have fulfilled their function until recent decades. However, from the beginning of 20th century, these bridges have remained inadequate as a result of increasing speed, size and capacity of the means of transport. Although new bridges have been built in many places, masonry bridges located within the city limits still need to be used. When the size and transportation loads of modern vehicles are taken into account, it is apparent that historical masonry arch bridges would be exposed to greater loads than their initial design loads. Because of that, many precautions taken either remain insufficient or damage these bridges. In this study, the history of Debbaglar Bridge, one of the historic bridges located in the city center of Aksaray/Turkey is presented and its existing condition is evaluated. Structural analysis of the bridge under present conditions and loads is explained. Moreover, the retrofit and restoration application prepared considering the analysis data is described.

Keywords: adaptive re-use, Aksaray debbaglar bridge, masonry bridge, reconstruction

Procedia PDF Downloads 308

Authors: Bongsu Choi, Tae-Ho Song

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Vacuum insulation panel (VIP) is a promising thermal insulator for buildings, refrigerator, LNG carrier and so on. In general, it has the thermal conductivity of 2~4 mW/m•K. However, this thermal conductivity is that measured at the center of VIP. The total effective thermal conductivity of VIP is larger than this value due to the edge conduction through the envelope. In this paper, the edge conduction of VIP is examined theoretically, numerically and experimentally. To confirm the existence of the edge conduction, numerical analysis is performed for simple two-dimensional VIP model and a theoretical model is proposed to calculate the edge conductivity. Also, the edge conductivity is measured using the vacuum guarded hot plate and the experiment is validated against numerical analysis. The results show that the edge conductivity is dependent on the width of panel and thickness of Al-foil. To reduce the edge conduction, it is recommended that the VIP should be made as big as possible or made of thin Al film envelope.

Keywords: envelope, edge conduction, thermal conductivity, vacuum insulation panel

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Authors: ShuYing Chung, Minchuan Huang, Iping Chen

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Taking the medical staff in an emergency ward of a medical center in Central Taiwan as the research object, the questionnaire data were collected by anonymous and voluntary reporting methods with structured questionnaire to explore the actual situation, willingness and attitude of organ donation. Only 80 valid questionnaires were collected. Among the 8 questions, the average correct rate was 5.9 + 1.2, and the correct rate was 73.13%. The willingness of organ donation that 7.5% of the people are not willing; 92.5% of the people are willing, of which 62.5% have considered but have not yet decided; 21.3% are willing but have not signed the consent of organ donation; They have signed the consent of organ donation 8.7%. The average total score (standard deviation) of attitude towards organ donation was 36.2. There is no significant difference between the demographic variables and the awareness and willingness of organ donation, but there is a significant correlation between the marital status and the attitude of organ donation.

Keywords: clinical psychology, organ donation, doctors affecting psychological disorders, commitment

Procedia PDF Downloads 134

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 432

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

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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 251

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

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Authors: Marzieh Ahmadi, Ruhullah Alikhan Gorgani

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The usual methods of measuring regional inequalities can not reflect the internal changes of the country in terms of their displacement in different development groups, and the indicators of inequalities are not effective in demonstrating the dynamics of the distribution of inequality. For this purpose, this paper examines the dynamics of the urban inertial transport in the country during the period of 2006-2016 using the CIRD multidimensional index and stochastic kernel density method. it firstly selects 25 indicators in five dimensions including macroeconomic conditions, science and innovation, environmental sustainability, human capital and public facilities, and two-stage Principal Component Analysis methodology are developed to create a composite index of inequality. Then, in the second stage, using a nonparametric analytical approach to internal distribution dynamics and a stochastic kernel density method, the convergence hypothesis of the CIRD index of the Iranian provinces center is tested, and then, based on the ergodic density, long-run equilibrium is shown. Also, at this stage, for the purpose of adopting accurate regional policies, the distribution dynamics and process of convergence or divergence of the Iranian provinces for each of the five. According to the results of the first Stage, in 2006 & 2016, the highest level of development is related to Tehran and zahedan is at the lowest level of development. The results show that the central cities of the country are at the highest level of development due to the effects of Tehran's knowledge spillover and the country's lower cities are at the lowest level of development. The main reason for this may be the lack of access to markets in the border provinces. Based on the results of the second stage, which examines the dynamics of regional inequality transmission in the country during 2006-2016, the first year (2006) is not multifaceted and according to the kernel density graph, the CIRD index of about 70% of the cities. The value is between -1.1 and -0.1. The rest of the sequence on the right is distributed at a level higher than -0.1. In the kernel distribution, a convergence process is observed and the graph points to a single peak. Tends to be a small peak at about 3 but the main peak at about-0.6. According to the chart in the final year (2016), the multidimensional pattern remains and there is no mobility in the lower level groups, but at the higher level, the CIRD index accounts for about 45% of the provinces at about -0.4 Take it. That this year clearly faces the twin density pattern, which indicates that the cities tend to be closely related to each other in terms of development, so that the cities are low in terms of development. Also, according to the distribution dynamics results, the provinces of Iran follow the single-density density pattern in 2006 and the double-peak density pattern in 2016 at low and moderate inequality index levels and also in the development index. The country diverges during the years 2006 to 2016.

Keywords: Urban Disparity, CIRD Index, Convergence, Distribution Dynamics, Random Kernel Density

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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

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Authors: Chananchida Yuktirat, Nichanant Sermsri

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The objectives of this research were to design and develop foot massage plate from coconut shell. The research investigated on the satisfaction of the users on the developed foot massage plate on 4 aspects; usage, practical in use, safety, and materials & production process. The sample group included 64 people joining the service at Wat Paitan Health Center, Bangkok. The samples were randomly tried on the massage plate and evaluated according to the 4 aspects. The data were analyzed to find mean, percentage, and standard deviation. The result showed that the overall satisfaction was at good level (mean = 3.80). When considering in details, it was found that the subjects reported their highest satisfaction on the practical usage (mean = 4.16), followed by safety (mean = 3.82); then, materials and production process (mean = 3.78). The least satisfaction aspect was on function and usage (mean = 3.45) or moderate level.

Keywords: coconut shell, design, foot massage, foot massage plate

Procedia PDF Downloads 236

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

Abstract:

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

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Authors: Seokbeen Lim, KyeongSeok Sim, DaKyeong Shin, Gilwon Yoon

Abstract:

Event-related potential (ERP) is one of the useful tools for investigating cognitive reactions. In this study, the potential of ERP components detected after auditory and visual stimuli was examined. Subjects were asked to respond upon stimuli that were of three categories; Target, Non-Target and Standard stimuli. The ERP after stimulus was measured. In the experiment of visual evoked potentials (VEPs), the subjects were asked to gaze at a center point on the monitor screen where the stimuli were provided by the reversal pattern of the checkerboard. In consequence of the VEP experiments, we observed consistent reactions. Each peak voltage could be measured when the ensemble average was applied. Visual stimuli had smaller amplitude and a longer latency compared to that of auditory stimuli. The amplitude was the highest with Target and the smallest with Standard in both stimuli.

Keywords: auditory stimulus, EEG, event related potential, oddball task, visual stimulus

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

Abstract:

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

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