Search results for: active learning

Authors: Omar Elharrouss, Youssef Hmamouche, Assia Kamal Idrissi, Btissam El Khamlichi, Amal El Fallah-Seghrouchni

Abstract:

Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.

Keywords: edge detection, convolutional neural networks, deep learning, scale-representation, backbone

Procedia PDF Downloads 97

Authors: Armine Vahanyan

Abstract:

Humanity has been still coping with the new COVID-19 pandemic. Healthcare providers, economists, psychologists, and other specialists speak about the impact of the virus on different spheres of our life. In the list of similar discussions, the impact of pandemics on global education is of utmost importance. Ideally, providing quality education services should be crucial, and the ways education programs are being adapted will determine the success or failure of the service providers. The paper aims to summarize the research touching upon the current situation of higher education in Armenia. The research includes data from official reports, surveys among education leads, faculty, and students, as well as personal observations and consideration. Through descriptive analysis, the findings of the research are being presented from various aspects. Interim results of the research unveiled two major issues in the sector of higher education in Armenia. On the one hand, the entire compulsory digitization of instruction, assessment, and grading has evoked serious gaps related to the lack of technical competencies. There is an urgent need for professional development programs that will address most of the concerns due to the shift to the online instruction mode. On the other hand, online teaching and learning require revision and adaptation of the existing curricula. Given that the content of certain programs may not be compromised, the teaching methods, the assignments, and evaluation require profound transformation, which will still be in line with course learning outcomes and student learning outcomes. The given paper focuses on the ways the mentioned issues are being addressed in Armenia. The extent of commitment for changes and adaptability to the new situation varies from the government-funded and private universities. In particular, the paper compares and contrasts activities and measures taken at the Armenian State Pedagogical University and the American University of Armenia. Thus, the Pedagogical University focused on the use of Google Classroom as the only means for teaching and learning as well as adopted the compulsory synchronous instruction mode. The American University, on the contrary, kept practicing the academic freedom, enabling both synchronous and asynchronous instruction modes, ensuring alignment of the course learning outcomes and student learning outcomes. The State University utilized the assignments and assessment, which would work for the on-campus instruction mode, while the American university employed a variety of assignments applicable for online teaching mode. The latter has suggested the utilization of multiple apps, internet sources, and online library access for a better online instant. Discussions with faculty through online forums and/or professional development workshops also facilitate restructuring and adaptation of the courses. Finally, the paper will synthesize the results of the undertaken research and will outline the e-learning perspectives and opportunities boosted by the known devastating healthcare issue.

Keywords: assessment, compulsory digitization of education services, online teaching, instruction mode, program restructuring

Procedia PDF Downloads 120

Authors: Andrea Fiorista

Abstract:

The learning of prepositions is a quite problematic aspect in foreign language instruction, and Italian is certainly not an exception. In their prototypical function, prepositions express schematic relations of two entities in a highly abstract, typically image-schematic way. In other terms, prepositions assume concepts such as directionality, collocation of objects in space and time and, in Cognitive Linguistics’ terms, the position of a trajector with respect to a landmark. Learners of different native languages may conceptualize them differently, implying that they are supposed to operate a recategorization (or create new categories) fitting with the target language. However, most current Italian Foreign/Second Language handbooks and didactic grammars do not facilitate learners in carrying out the task, as they tend to provide partial and idiosyncratic descriptions, with the consequent learner’s effort to memorize them, most of the time without success. In their prototypical meaning, prepositions are used to specify precise topographical positions in the physical environment which become less and less accurate as they radiate out from what might be termed a concrete prototype. According to that, the present study aims to elaborate a cognitive and conceptually well-grounded analysis of some extensive uses of the Italian preposition a, in order to propose effective pedagogical solutions in the Teaching/Learning process. Image schemas, cognitive metaphors and embodiment represent efficient cognitive tools in a task like this. Actually, while learning the merely spatial use of the preposition a (e.g. Sono a Roma = I am in Rome; vado a Roma = I am going to Rome,…) is quite straightforward, it is more complex when a appears in constructions such as verbs of motion +a + infinitive (e.g. Vado a studiare = I am going to study), inchoative periphrasis (e.g. Tra poco mi metto a leggere = In a moment I will read), causative construction (e.g. Lui mi ha mandato a lavorare = He sent me to work). The study reports data from a teaching intervention of Focus on Form, in which a basic cognitive schema is used to facilitate both teachers and students to respectively explain/understand the extensive uses of a. The educational material employed translates Cognitive Linguistics’ theoretical assumptions, such as image schemas and cognitive metaphors, into simple images or proto-scenes easily comprehensible for learners. Illustrative material, indeed, is supposed to make metalinguistic contents more accessible. Moreover, the concept of embodiment is pedagogically applied through activities including motion and learners’ bodily involvement. It is expected that replacing rote learning with a methodology that gives grammatical elements a proper meaning, makes learning process more effective both in the short and long term.

Keywords: cognitive approaches to language teaching, image schemas, embodiment, Italian as FL/SL

Procedia PDF Downloads 84

Authors: Imen Blibech, Mohiedine Ksantini, Mohamed Bouaziz

Abstract:

Prays oleae is a major insect of olive in the Mediterranean Region. In an effort to find effective and affordable ways of controlling this pest, larvicidal activity of essential oils from Tunisian Thymus capitatus were analyzed in comparison to Azadirachtin, a biologically active compound insecticide. The essential oils were extracted by hydrodistillation, and their chemical composition was determined by gas liquid-chromatography coupled with mass spectroscopy. The main components of chemical components were oxygenated monoterpenes (60.24%). The most abundant oxygenated monoterpenes were carvacrol (54.11%). Monoterpenes hydrocarbons were much more abundant and dominated by the o-cymene (16.68%). Both active compounds of Azadirachtin and Thymus capitatus oil extracts exhibited significant larvicidal activity against P. oleae with LC50 values 81.30 ppm and 52.49 ppm respectively. Dose-response relationships were established with almost 100% mortality when using the highest dose 100 ppm of T. capitatus oil extracts and 80 ppm of Azadirachtin. At the lowest dose (10 ppm), T. capitatus oil extracts and Azadirachtin caused 60% and 76% larval mortality in 48 hours respectively. The larval mortality rate greatly decreased with increases of the dilution of both oil extract compounds. Larval development duration appeared to be prolonged to about 12 days for larvae feeding on control diet. The maximum antifeedant activity was shown by both T. capitatus oil extract and Azadirachtin at LC90 values (47.5 and 50.1 ppm respectively). Tunisian T. capitatus oil extract used at low concentrations could be considered as eco-friendly promising insecticide similar to Azadirachtin that has significant potential for the biological control of P. oleae.

Keywords: Thymus capitatus, chemical composition, azadirachtin, larvicidal effects, antifeedant activity, Prays oleae

Procedia PDF Downloads 194

Authors: Florin Leon, Silvia Curteanu

Abstract:

Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a process without any knowledge about its particular physical and chemical laws. Therefore, they are useful for modeling complex processes, such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. The goal is to generate accurate predictions of monomer conversion, numerical average molecular weight and gravimetrical average molecular weight. This process is associated with non-linear gel and glass effects. For this purpose, an adaptive sampling technique is presented, which can select more samples around the regions where the values have a higher variation. Several machine learning methods are used for the modeling and their performance is compared: support vector machines, k-nearest neighbor, k-nearest neighbor and random forest, as well as an original algorithm, large margin nearest neighbor regression. The suggested method provides very good results compared to the other well-known regression algorithms.

Keywords: batch bulk methyl methacrylate polymerization, adaptive sampling, machine learning, large margin nearest neighbor regression

Procedia PDF Downloads 299

Authors: Maatouk Mohamed, Nouira Mariem

Abstract:

Background and aims: Healthcare-associated infections (HAI) represent a major public health problem worldwide. They represent one of the most serious adverse events in health care. The objectives of our study were to estimate the prevalence of HAI at the Charles Nicolle Hospital (CNH) and to identify the main associated factors as well as to estimate the frequency of antibiotic use. Methods: It was a cross sectional study at the CNH with a unique passage per department (OctoberDecember 2018). All patients present at the wards for more than 48 hours were included. All patients from outpatient consultations, emergency and dialysis departments were not included. The site definitions of infections proposed by the Centers for Disease Control and Prevention (CDC) were used. Only clinically and/or microbiologically confirmed active HAIs were included. Results: A total of 318 patients were included with a mean age of 52 years and a sex ratio (Female/Male) of 1.05. A total of 41 patients had one or more active HAIs, corresponding to a prevalence of 13.1% (95% CI: 9.3%-16.9%). The most frequent sites infections were urinary tract infections and pneumonia. Multivariate analysis among adult patients (>=18 years) (n=261), revealed that infection on admission (p=0.01), alcoholism (p=0.01), high blood pressure (p=0.008), having at least one invasive device inserted (p=0.004), and history of recent surgery (p=0.03), increased significantly the risk of HAIs. More than 1 of 3 patients (35.4%) were under antibiotics on the day of the survey, of which more than half (57.4%) were under 2 or more types of antibiotics. Conclusion: The prevalence of HAIs and antibiotic prescriptions at the CNH were considerably high. An infection prevention and control committee, as well as the development of an Antibiotic stewardship program with continuous monitoring using repeated prevalence surveys must be implemented to limit the frequency of these infections effectively.

Keywords: tumors, oesophagectomy, esophagogastric junction, systematic review

Procedia PDF Downloads 78

Authors: Shofiayuningtyas Luftiani

Abstract:

Some private schools in Indonesia started integrating the online program Gizmos in the teaching-learning process. Gizmos was developed to supplement the existing curriculum by integrating it into the instructional programs. The program has some features using an inquiry-based simulation, in which students conduct exploration by using a worksheet while teachers use the teacher guidelines to direct and assess students’ performance In this study, the discussion about Gizmos highlights its features as the assessment media of mathematics learning for secondary school students. The discussion is based on the case study and literature review from the Indonesian context. The purpose of applying Gizmos as an assessment media refers to the diagnostic assessment. As a part of the diagnostic assessment, the teachers review the student exploration sheet, analyze particularly in the students’ difficulties and consider findings in planning future learning process. This assessment becomes important since the teacher needs the data about students’ persistent weaknesses. Additionally, this program also helps to build student’ understanding by its interactive simulation. Currently, the assessment over-emphasizes the students’ answers in the worksheet based on the provided answer keys while students perform their skill in translating the question, doing the simulation and answering the question. Whereas, the assessment should involve the multiple perspectives and sources of students’ performance since teacher should adjust the instructional programs with the complexity of students’ learning needs and styles. Consequently, the approach to improving the assessment components is selected to challenge the current assessment. The purpose of this challenge is to involve not only the cognitive diagnosis but also the analysis of skills and error. Concerning the selected setting for this diagnostic assessment that develops the combination of cognitive diagnosis, skills analysis and error analysis, the teachers should create an assessment rubric. The rubric plays the important role as the guide to provide a set of criteria for the assessment. Without the precise rubric, the teacher potentially ineffectively documents and follows up the data about students at risk of failure. Furthermore, the teachers who employ the program of Gizmos as the diagnostic assessment might encounter some obstacles. Based on the condition of assessment in the selected setting, the obstacles involve the time constrain, the reluctance of higher teaching burden and the students’ behavior. Consequently, the teacher who chooses the Gizmos with those approaches has to plan, implement and evaluate the assessment. The main point of this assessment is not in the result of students’ worksheet. However, the diagnostic assessment has the two-stage process; the process to prompt and effectively follow-up both individual weaknesses and those of the learning process. Ultimately, the discussion of Gizmos as the media of the diagnostic assessment refers to the effort to improve the mathematical learning process.

Keywords: diagnostic assessment, error analysis, Gizmos online program, skills analysis

Procedia PDF Downloads 177

Authors: Elisabeth Unterfrauner, Christian Voigt

Abstract:

Since the foundation of the first Fab Lab by the Massachusetts Institute of Technology about 17 years ago, the Maker movement has spread globally with the foundation of maker spaces and Fab Labs worldwide. In these workshops, citizens have access to digital fabrication technologies such as 3D printers and laser cutters to develop and test their own ideas and prototypes, which makes it an attractive place for start-up companies. Know-How is shared not only in the physical space but also online in diverse communities. According to the Horizon report, the Maker movement, however, will also have an impact on educational settings in the following years. The European project ‘DOIT - Entrepreneurial skills for young social innovators in an open digital world’ has incorporated key elements of making to develop an early entrepreneurial education program for children between the age of six and 16. The Maker pedagogy builds on constructive learning approaches, learning by doing principles, learning in collaborative and interdisciplinary teams and learning through trial and error where mistakes are acknowledged as learning opportunities. The DOIT program consists of seven consecutive elements. It starts with a motivation phase where students get motivated by envisioning the scope of their possibilities. The second step is about Co-design: Students are asked to collect and select potential ideas for innovations. In the Co-creation phase students gather in teams and develop first prototypes of their ideas. In the iteration phase, the prototype is continuously improved and in the next step, in the reflection phase, feedback on the prototypes is exchanged between the teams. In the last two steps, scaling and reaching out, the robustness of the prototype is tested with a bigger group of users outside of the educational setting and finally students will share their projects with a wider public. The DOIT program involves 1,000 children in two pilot phases at 11 pilot sites in ten different European countries. The comprehensive evaluation design is based on a mixed method approach with a theoretical backbone on Lackeus’ model of entrepreneurship education, which distinguishes between entrepreneurial attitudes, entrepreneurial skills and entrepreneurial knowledge. A pre-post-test with quantitative measures as well as qualitative data from interviews with facilitators, students and workshop protocols will reveal the effectiveness of the program. The evaluation results will be presented at the conference.

Keywords: early entrepreneurial education, Fab Lab, maker education, Maker movement

Procedia PDF Downloads 119

Authors: Kourosh Modarresi

Abstract:

The abundance of media channels and devices has given users a variety of options to extract, discover, and explore information in the digital world. Since, often, there is a long and complicated path that a typical user may venture before taking any (significant) action (such as purchasing goods and services), it is critical to know how each node (media channel) in the path of user has contributed to the final action. In this work, the significance of each media channel is computed using statistical analysis and machine learning techniques. More specifically, “Regularized Singular Value Decomposition”, and “Sparse Principal Component” has been used to compute the significance of each channel toward the final action. The results of this work are a considerable improvement compared to the present approaches.

Keywords: multimedia attribution, sparse principal component, regularization, singular value decomposition, feature significance, machine learning, linear systems, variable shrinkage

Procedia PDF Downloads 306

Authors: Mohammed Alhammad

Abstract:

The movement towards the inclusion of students with special educational needs (SEN) in mainstream schools has become widely accepted practice in many countries. However in Saudi Arabia, this is not happening. Instead the practice for students with learning difficulties (LD) is to study in special classrooms in mainstream schools and they are not included with their peers, except at break times and morning assembly, and on school trips. There are a number of barriers that face implementing inclusion for students with LD in mainstream classrooms: one such barrier is the training of teachers. The training, either pre- or in-service, that teachers receive is seen as playing an important role in leading to the successful implementation of inclusion. The aim of this presentation is to explore how pre-service training and in-service training are acting as barriers for implementing inclusion of students with LD in mainstream primary schools in Saudi Arabia from the perspective of teachers. The qualitative research approach was used to explore this barrier. Twenty-four teachers (general education teachers, special education teachers) were interviewed using semi-structured interview and a number of documents were used as method of data collection. The result showed teachers felt that not much attention was paid to inclusion in pre-services training for general education teachers and special education teachers in Saudi Arabia. In addition, pre-service training for general education teachers does not normally including modules on special education. Regarding the in-service training, no courses at all about inclusion are provided for teachers. Furthermore, training courses in special education are few. As result, the knowledge and skills required to implemented inclusion successfully.

Keywords: inclusion, learning difficulties, Saudi Arabia, training

Procedia PDF Downloads 373

Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

Abstract:

Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

Procedia PDF Downloads 338

Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary

Abstract:

Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM

Procedia PDF Downloads 186

Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

Abstract:

Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

Procedia PDF Downloads 104

Authors: Yongde Xia, Laicong Deng, Zhuxian Yang

Abstract:

Developing cost-effective electrocatalysts for oxygen reduction reaction (ORR), oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) is vital in energy conversion and storage applications. Herein, we report a simple method for the synthesis of graphene-reinforced cobalt sulfide/carbon nanocomposites and the evaluation of their electrocatalytic performance for typical electrocatalytic reactions. Nanocomposites of cobalt sulfide embedded in N, S co-doped porous carbon and graphene (CoS@C/Graphene) were generated via simultaneous sulfurization and carbonization of one-pot synthesized graphite oxide-ZIF-67 precursors. The obtained CoS@C/Graphene nanocomposite was characterized by X-ray diffraction, Raman spectroscopy, Thermogravimetric analysis-Mass spectroscopy, Scanning electronic microscopy, Transmission electronic microscopy, X-ray photoelectron spectroscopy and gas sorption. It was found that cobalt sulfide nanoparticles were homogenously dispersed in the in-situ formed N, S co-doped porous carbon/Graphene matrix. The CoS@C/10Graphene composite not only shows excellent electrocatalytic activity toward ORR with high onset potential of 0.89 V, four-electron pathway and superior durability of maintaining 98% current after continuously running for around 5 hours, but also exhibits good performance for OER and HER, due to the improved electrical conductivity, increased catalytic active sites and connectivity between the electrocatalytic active cobalt sulfide and the carbon matrix. This work offers a new approach for the development of novel multifunctional nanocomposites for the next generation of energy conversion and storage applications.

Keywords: MOF derivative, graphene, electrocatalyst, oxygen reduction reaction, oxygen evolution reaction, hydrogen evolution reaction

Procedia PDF Downloads 46

Authors: Cristina Costa-Lobo, Sandra Fernandes, Miguel Magalhães, José Dinis-Carvalho, Alfredo Regueiro, Ana Carvalho

Abstract:

This paper is a report on the findings of the construction and the validation of a questionnaire monetized in a portuguese higher education context with undergraduate students. The Questionnaire for the Evaluation of Entrepreneurship Project Psychopedagogical Practices consists of six scales: Critical appraisal of the project, Developed Learning and Skills, Teamwork, Teacher and Tutor Roles, Evaluation of Student Performance, and Project Effectiveness as a Teaching-Learning Methodology. The proceedings of its construction are analyzed, and the validity and internal consistency analysis are described. Findings indicate good indicators of validity, good fidelity and an interpretable factorial structure.

Keywords: entrepreneurship project, higher education, psychopedagogical practices, teacher and tutor roles

Procedia PDF Downloads 375

Authors: Marta Fonseca, Pedro Marvão, Beatriz Oliveira, Bruno Heleno, Pedro Carreiro-Martins, Nuno Neuparth, António Rendas

Abstract:

Background: Concept maps (CMs) visually represent hierarchical connections among related ideas. They foster logical organization and clarify idea relationships, potentially aiding medical students in critical thinking (to think clearly and rationally about what to do or what to believe). However, there are inconsistent claims about the use of CMs in undergraduate medical education. Our three research questions are: 1) What studies have been published on concept mapping in undergraduate medical education? 2) What was the impact of CMs on students’ critical thinking? 3) How and why have these interventions had an educational impact? Methods: Eight databases were systematically searched (plus a manual and an additional search were conducted). After eliminating duplicate entries, titles, and abstracts, and full-texts were independently screened by two authors. Data extraction and quality assessment of the studies were independently performed by two authors. Qualitative and quantitative data were integrated using mixed-methods. The results were reported using the structured approach to the reporting in healthcare education of evidence synthesis statement and BEME guidance. Results: Thirty-nine studies were included from 26 journals (19 quantitative, 8 qualitative and 12 mixed-methods studies). CMs were considered as a tool to promote critical thinking, both in the perception of students and tutors, as well as in assessing students’ knowledge and/or skills. In addition to their role as facilitators of knowledge integration and critical thinking, CMs were considered both teaching and learning methods. Conclusions: CMs are teaching and learning tools which seem to help medical students develop critical thinking. This is due to the flexibility of the tool as a facilitator of knowledge integration, as a learning and teaching method. The wide range of contexts, purposes, and variations in how CMs and instruments to assess critical thinking are used increase our confidence that the positive effects are consistent.

Keywords: concept map, medical education, undergraduate, critical thinking, meaningful learning

Procedia PDF Downloads 113

Authors: George Zhou, Yunchan Chen, Candace Chien

Abstract:

Kidney replacement therapy is the current standard of care for end-stage renal diseases. In-center or home hemodialysis remains an integral component of the therapeutic regimen. Arteriovenous fistulas (AVF) make up the vascular circuit through which blood is filtered and returned. Naturally, AVF patency determines whether adequate clearance and filtration can be achieved and directly influences clinical outcomes. Our aim was to build a deep learning model for automated AVF stenosis screening based on the sound of blood flow through the AVF. A total of 311 patients with AVF were enrolled in this study. Blood flow sounds were collected using a digital stethoscope. For each patient, blood flow sounds were collected at 6 different locations along the patient’s AVF. The 6 locations are artery, anastomosis, distal vein, middle vein, proximal vein, and venous arch. A total of 1866 sounds were collected. The blood flow sounds are labeled as “patent” (normal) or “stenotic” (abnormal). The labels are validated from concurrent ultrasound. Our dataset included 1527 “patent” and 339 “stenotic” sounds. We show that blood flow sounds vary significantly along the AVF. For example, the blood flow sound is loudest at the anastomosis site and softest at the cephalic arch. Contextualizing the sound with location metadata significantly improves classification performance. How to encode and incorporate categorical metadata is an active area of research1. Herein, we study ordinal (i.e., integer) encoding schemes. The numerical representation is concatenated to the flattened feature vector. We train a vision transformer (ViT) on spectrogram image representations of the sound and demonstrate that using scalar multiples of our integer encodings improves classification performance. Models are evaluated using a 10-fold cross-validation procedure. The baseline performance of our ViT without any location metadata achieves an AuROC and AuPRC of 0.68 ± 0.05 and 0.28 ± 0.09, respectively. Using the following encodings of Artery:0; Arch: 1; Proximal: 2; Middle: 3; Distal 4: Anastomosis: 5, the ViT achieves an AuROC and AuPRC of 0.69 ± 0.06 and 0.30 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 10; Proximal: 20; Middle: 30; Distal 40: Anastomosis: 50, the ViT achieves an AuROC and AuPRC of 0.74 ± 0.06 and 0.38 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 100; Proximal: 200; Middle: 300; Distal 400: Anastomosis: 500, the ViT achieves an AuROC and AuPRC of 0.78 ± 0.06 and 0.43 ± 0.11. respectively. Interestingly, we see that using increasing scalar multiples of our integer encoding scheme (i.e., encoding “venous arch” as 1,10,100) results in progressively improved performance. In theory, the integer values do not matter since we are optimizing the same loss function; the model can learn to increase or decrease the weights associated with location encodings and converge on the same solution. However, in the setting of limited data and computation resources, increasing the importance at initialization either leads to faster convergence or helps the model escape a local minimum.

Keywords: arteriovenous fistula, blood flow sounds, metadata encoding, deep learning

Procedia PDF Downloads 80

Authors: L. Sasikala, Bhaarathi Dhurai

Abstract:

Increase in antibiotic resistance bacteria leads to the development of active wound dressings, which absorb any bodily fluid, evaporation of moisture at a certain rate and can be easily removed after healing. Natural materials like chitosan, herbs, and honey have number of active materials present in them to accelerate wound healing and to arrest wound in infections. Hence with the advantages of biomaterials, a film was prepared using chitosan and honey. There are a lot of practical considerations with respect to honey. Honey exerts many beneficial actions on the wound surface only when it remains. The attempts to hold honey on the surface of the wound remain a question because honey becomes a very runny liquid when it comes to body temperature. Hence, this research was focused on development of a new form of wound dressing, by holding honey on the wound surface in different form and also which has a combined effect of manuka (Leptospermum scoparium) honey and chitosan. Chitosan-honey film was prepared using casting technique. Films were prepared in different variations; with acetic acid and with lactic acid; with and without honey. In summary, the film produced from 2% chitosan- 1% lactic acid as a solvent, with 10% honey shows optimum inclined values in all the tests, like thickness, folding endurance, weight, water vapor transmission, tensile strength, swelling ratio and antimicrobial activity, with specific reference to wound dressings. The film has water vapor transmission of 1680 g/m²/day, water absorption of 225%, tensile strength of 39.1N/mm² and elongation of 50.3%. There is a notable inhibition zone of 29 mm against S. aureus and 24 mm against E. coli in the case of chitosan-lactic acid-honey film. The film also arrests, microbes transmitting from the outside environment to wound bed, which can be used as an effective wound dressing material.

Keywords: casting technique, chitosan, honey, film, wound dressings

Procedia PDF Downloads 238

Authors: Kate Benedicta Amenador, Dianjian Wang, Bright Nkrumah

Abstract:

This extensive bibliometric study explores the dynamic influence of artificial intelligence in the field of English as a Foreign Language (EFL) between 2012 and 2024. The study, which examined 4,500 articles from Google Scholar, Modern Language Association Linguistics Abstracts, Web of Science, Scopus, Researchgate, and library genesis databases, indicates that AI integration in EFL is on the rise. This notable increase is ascribed to a variety of transformative events, including increased academic funding for higher education and the COVID-19 epidemic. The results of the study identify leading contributors, prominent authors, publishers and sources, with the United States, China and the United Kingdom emerging as key contributors. The co-occurrence analysis of key terms reveals five clusters highlighting patterns in AI-enhanced language instruction and learning, including evaluation strategies, educational technology, learning motivation, EFL teaching aspects, and learner feedback. The study also discusses the impact of various AIs in enhancing EFL writing skills with software such as Grammarly, Quilbot, and Chatgpt. The current study recognizes limitations in database selection and linguistic constraints. Nevertheless, the results provide useful insights for educators, researchers and policymakers, inspiring and guiding a cross-disciplinary collaboration and creative pedagogical techniques and approaches to teaching and learning in the future.

Keywords: artificial intelligence, bibliometrics study, VOSviewer visualization, English as a foreign language

Procedia PDF Downloads 17

Authors: Aaditya U. Jhamb

Abstract:

Due to the current energy crisis that many countries are battling, energy-efficient buildings are the subject of extensive research in the modern technological era because of growing worries about energy consumption and its effects on the environment. The paper explores 8 factors that help determine energy efficiency for a building: (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution), with Tsanas and Xifara providing a dataset. The data set employed 768 different residential building models to anticipate heating and cooling loads with a low mean squared error. By optimizing these characteristics, machine learning algorithms may assess and properly forecast a building's heating and cooling loads, lowering energy usage while increasing the quality of people's lives. As a result, the paper studied the magnitude of the correlation between these input factors and the two output variables using various statistical methods of analysis after determining which input variable was most closely associated with the output loads. The most conclusive model was the Decision Tree Regressor, which had a mean squared error of 0.258, whilst the least definitive model was the Isotonic Regressor, which had a mean squared error of 21.68. This paper also investigated the KNN Regressor and the Linear Regression, which had to mean squared errors of 3.349 and 18.141, respectively. In conclusion, the model, given the 8 input variables, was able to predict the heating and cooling loads of a residential building accurately and precisely.

Keywords: energy efficient buildings, heating load, cooling load, machine learning models

Procedia PDF Downloads 90

Authors: Afsaneh Jasmine Majidi

Abstract:

Language testing is an important part of language teaching experience and language learning process as it presents assessment strategies for teachers to evaluate the efficiency of teaching and for learners to examine their outcomes. However, language testing is demanding and challenging because it should provide the opportunity for proper and objective decision. In addition to all the efforts test designers put to design valid and reliable tests, there are some other determining factors which are even more complex and complicated. These factors affect the educational system, individuals, and society, and the impact of the tests vary according to the scope of the test. Seemingly, the impact of a simple classroom assessment is not the same as that of high stake tests such as International English Language Testing System (IELTS). As the importance of the test increases, it affects wider domain. Accordingly, the impacts of high stake tests are reflected not only in teaching, learning strategies but also in society. Testing experts use the term ‘washback’ or ‘impact’ to define the different effects of a test on teaching, learning, and community. This paper first looks at the theoretical background of ‘washback’ and ‘impact’ in language testing by reviewing of relevant literature in the field and then investigates washback effects of IELTS test of on Iranian IELTS teachers and students. The study found significant relationship between the washback effect of IELTS test and teaching strategies of Iranian IELTS teachers as well as performance of Iranian IELTS candidates and their community.

Keywords: high stake tests, IELTS, Iranian Candidates, language testing, test impact, washback

Procedia PDF Downloads 323

Authors: Lina Vyas

Abstract:

Gender gap, unfortunately, is still prevalent in the workplace around the world. In most countries, women are less likely than men to participate in the workplace. They earn considerably less than men for doing the same work and are generally expected to prioritize family obligations over work responsibilities. Women often face more conflicts while balancing the increasingly normalized roles of both worker and mother. True gender equality in the workplace is still a long way off. In Hong Kong, no less is this true. Despite the fact that female students are outnumbered by males at universities, only 55% of women are active participants in the labour market, and for those in the workforce, the gender pay gap is 22%. This structural inequality also exacerbates the issues of confronting biases at work for choosing to be employed as a mother, as well as reinforces the societal expectation of women to be the primary caregiver at home. These pressures are likely to add up for women and contribute to increased levels of work-life conflict, which may be a further barrier for the inclusion of women into the workplace. Family-friendly policies have long been thought to be an alleviator of work-life conflict through helping employees balance the demands in both work and family. Particularly, for women, this could be a facilitator of their integration into the workplace. However, little research has looked at how family-friendly policies may also have a gender differential in effect, as opposed to traditional notions of having universal efficacy. This study investigates both how and how much the gender dimension impacts work-family conflict. In addition to disentangling the reasons for gender gaps existing in work-life conflict for women, this study highlights what can be done at an organizational level to alleviate these conflicts. Most importantly, the policies recommendations derived from this study serve as an avenue for more active participation for women in the workplace and can be considered as a pathway for promoting greater gender egalitarianism and fairness in a traditionally gender-segregated society.

Keywords: family-friendly policies, Hong Kong, work-family conflict, workplace

Procedia PDF Downloads 168

Authors: W. Zekri

Abstract:

This paper explores Zohra Drif’s memoir, Inside the Battle of Algiers, which narrates her desires as a student to become a revolutionary activist. She exemplified, in her narrative, the different roles, she and her fellows performed as combatants in the Casbah during the Algerian Revolution 1954-1962. This book review aims to evaluate the concept of women’s agency through education and language learning, and its impact on empowering women’s desires. Close-reading method and thematic analysis are used to explore the text. The analysis identified themes that refine the meaning of agency which are social and cultural supports, education, and language proficiency. These themes aim to contribute to the representation in Inside the Battle of Algiers of a woman guerrilla who engaged herself to perform national acts of resistance.

Keywords: agency, education, learning, women

Procedia PDF Downloads 173

Authors: Indu Gamage

Abstract:

Though commonly utilized as a language improvement technique, reading has not been fully employed by both language teachers and learners to develop reading comprehension skills in English as a second language. In a Sri Lankan context, this area has to be delved deep into as the learners’ show more propensity to analyze. Reading comprehension is an area that most language teachers and learners struggle with though it appears easy. Most ESL learners engage in reading tasks without being properly aware of the objective of doing reading comprehension. It is observed that when doing reading tasks, the language learners’ concern is more on the meanings of individual words than on the overall comprehension of the given text. The passiveness with which the ESL learners engage themselves in reading comprehension makes reading a tedious task for the learner thereby giving the learner a sense of disappointment at the end. Certain reading tasks take the form of translations. The active cognitive participation of the learner in the mode of using productive strategies for predicting, employing schemata and using contextual clues seems quite less. It was hypothesized that the learners’ lack of knowledge of the productive strategies of reading was the major obstacle that makes reading comprehension a tedious task for them. This study is based on a group of 30 tertiary students who read English only as a fundamental requirement for their degree. They belonged to the Faculty of Humanities and Social Sciences of the University of Ruhuna, Sri Lanka. Almost all learners hailed from areas where English was hardly utilized in their day to day conversations. The study is carried out in the mode of a questionnaire to check their opinions on reading and a test to check whether the learners are using productive strategies of reading when doing reading comprehension tasks. The test comprised reading questions covering major productive strategies for reading. Then the results were analyzed to see the degree of their active engagement in comprehending the text. The findings depicted the validity of the hypothesis as grounds behind the difficulties related to reading comprehension.

Keywords: reading, comprehension, skills, reading strategies

Procedia PDF Downloads 169

Authors: Ahmed I. Alutabi, Naghmeh Dezhabad, Sudhakar Ganti

Abstract:

Data centers have been growing in size and dema nd continuously in the last two decades. Planning for the deployment of resources has been shallow and always resorted to over-provisioning. Data center operators try to maximize the availability of their services by allocating multiple of the needed resources. One resource that has been wasted, with little thought, has been energy. In recent years, programmable resource allocation has paved the way to allow for more efficient and robust data centers. In this work, we examine the predictability of resource usage in a data center environment. We use a number of models that cover a wide spectrum of machine learning categories. Then we establish a framework to guarantee the client service level agreement (SLA). Our results show that using prediction can cut energy loss by up to 55%.

Keywords: machine learning, artificial intelligence, prediction, data center, resource allocation, green computing

Procedia PDF Downloads 103

Authors: Todd Zhou, Mikhail Yurochkin

Abstract:

Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

Procedia PDF Downloads 119

Authors: Emmanuel Kamal Aziz Saba, Hussein Al-Moghazy Sultan

Abstract:

The aim of this study was to evaluate clinically and electro physiologically the autonomic nervous system changes associated with rheumatoid arthritis (RA). The present study included 25 patients with RA [22 women (88%)] and 30 apparently healthy control subjects [27 women (90%)]. A thorough clinical examination was carried out. Disease activity and functional disability were assessed. Tests for assessment of autonomic functions include active and passive orthostatic stress tests, and sympathetic skin response (SSR). The presence of abnormality in 2 tests or more was a clue for the presence of autonomic neuropathy (AN). Sural sensory nerve conduction study and posterior tibial motor nerve conduction study were done. There was a statistically significant decrease in standing systolic and diastolic blood pressure (BP) components of the active orthostatic stress test and SSR amplitude as well as statistically significant prolongation of SSR latency of RA patients when compared to control. Three patients (12%) had clinical symptoms suggestive of AN; increased to 14 patients (56 %) when orthostatic stress tests and SSR were utilized. There were no statistically significant differences between patients with different disease activity score 28 with 4 variables grades of RA activity and SSR latency and amplitude. There were no statistically significant differences between patients with different Stanford Health Assessment Questionnaire Disability Index grades of RA functional disability and SSR latency and amplitude. In conclusion, autonomic neuropathy is a common extra-articular manifestation of RA affecting sympathetic and parasympathetic fibers.

Keywords: autonomic neuropathy, orthostatic stress test, rheumatoid arthritis, sympathetic skin response

Procedia PDF Downloads 351

Authors: M. Isabel Santos, Ana Breda, Ana Margarida Almeida

Abstract:

Research on academic abilities of individuals with autism spectrum disorder (ASD) underlines the importance of mathematics interventions. Yet the proposal of digital applications for children and youth with ASD continues to attract little attention, namely, regarding the development of mathematical reasoning, being the use of the digital technologies an area of great interest for individuals with this disorder and its use is certainly a facilitative strategy in the development of their mathematical abilities. The use of digital technologies can be an effective way to create innovative learning opportunities to these students and to develop creative, personalized and constructive environments, where they can develop differentiated abilities. The children with ASD often respond well to learning activities involving information presented visually. In this context, we present the digital Learning Environment on Mathematics for Autistic children (LEMA) that was a research project conducive to a PhD in Multimedia in Education and was developed by the Thematic Line Geometrix, located in the Department of Mathematics, in a collaboration effort with DigiMedia Research Center, of the Department of Communication and Art (University of Aveiro, Portugal). LEMA is a digital mathematical learning environment which activities are dynamically adapted to the user’s profile, towards the development of mathematical abilities of children aged 6–12 years diagnosed with ASD. LEMA has already been evaluated with end-users (both students and teacher’s experts) and based on the analysis of the collected data readjustments were made, enabling the continuous improvement of the prototype, namely considering the integration of universal design for learning (UDL) approaches, which are of most importance in ASD, due to its heterogeneity. The learning strategies incorporated in LEMA are: (i) provide options to custom choice of math activities, according to user’s profile; (ii) integrates simple interfaces with few elements, presenting only the features and content needed for the ongoing task; (iii) uses a simple visual and textual language; (iv) uses of different types of feedbacks (auditory, visual, positive/negative reinforcement, hints with helpful instructions including math concept definitions, solved math activities using split and easier tasks and, finally, the use of videos/animations that show a solution to the proposed activity); (v) provides information in multiple representation, such as text, video, audio and image for better content and vocabulary understanding in order to stimulate, motivate and engage users to mathematical learning, also helping users to focus on content; (vi) avoids using elements that distract or interfere with focus and attention; (vii) provides clear instructions and orientation about tasks to ease the user understanding of the content and the content language, in order to stimulate, motivate and engage the user; and (viii) uses buttons, familiarly icons and contrast between font and background. Since these children may experience little sensory tolerance and may have an impaired motor skill, besides the user to have the possibility to interact with LEMA through the mouse (point and click with a single button), the user has the possibility to interact with LEMA through Kinect device (using simple gesture moves).

Keywords: autism spectrum disorder, digital technologies, inclusion, mathematical abilities, mathematical learning activities

Procedia PDF Downloads 109

Authors: Daniel Todorov, Anton Hinkov, Petya Angelova, Kalina Shishkova, Venelin Tsvetkov, Stoyan Shishkov

Abstract:

Human herpes viruses (HHV) are ubiquitous pathogens with a pandemic spread across the globe. HHV type 1 is the main causative agent of cold sores and fever blisters around the mouth and on the face, whereas HHV type 2 is generally responsible for genital herpes outbreaks. The treatment of both viruses is more or less successful with antivirals from the nucleoside analogues group. Their wide application increasingly leads to the emergence of resistant mutants In the past, medicinal plants have been used to treat a number of infectious and non-infectious diseases. Their diversity and ability to produce the vast variety of secondary metabolites according to the characteristics of the environment give them the potential to help us in our warfare with viral infections. The variable chemical characteristics and complex composition is an advantage in the treatment of herpes since the emergence of resistant mutants is significantly complicated. The screening process is difficult due to the lack of standardization. That is why it is especially important to follow the mechanism of antiviral action of plants. On the one hand, it may be expected to interact with its compounds, resulting in enhanced antiviral effects, and the most appropriate environmental conditions can be chosen to maximize the amount of active secondary metabolites. During our study, we followed the activity of various plant extracts on the viral replication cycle as well as their effect on the extracellular virion. We obtained our results following the logical sequence of the experimental settings - determining the cytotoxicity of the extracts, evaluating the overall effect on viral replication and extracellular virion.During our research, we have screened a variety of plant extracts for their antiviral activity against both virus replication and the virion itself. We investigated the effect of the extracts on the individual stages of the viral replication cycle - viral adsorption, penetration and the effect on replication depending on the time of addition. If there are positive results in the later experiments, we had studied the activity over viral adsorption, penetration and the effect of replication according to the time of addition. Our results indicate that some of the extracts from the Lamium album have several targets. The first stages of the viral life cycle are most affected. Several of our active antiviral agents have shown an effect on extracellular virion and adsorption and penetration processes. Our research over the last decade has shown several curative antiviral plants - some of which are from the Lamiacea family. The rich set of active ingredients of the plants in this family makes them a good source of antiviral preparation.

Keywords: human herpes virus, antiviral activity, Lamium album, Nepeta nuda

Procedia PDF Downloads 153

Authors: Vesile Evrim, Aliyu Awwal

Abstract:

Today as in the other domains, there are an enormous number of political transcripts available in the Web which is waiting to be mined and used for various purposes such as statistics and recommendations. Therefore, automatically determining the political orientation on these transcripts becomes crucial. The methodologies used by machine learning algorithms to do the automatic classification are based on different features such as Linguistic. Considering the ideology differences between Liberals and Conservatives, in this paper, the effect of Personality Traits on political orientation classification is studied. This is done by considering the correlation between LIWC features and the BIG Five Personality Traits. Several experiments are conducted on Convote U.S. Congressional-Speech dataset with seven benchmark classification algorithms. The different methodologies are applied on selecting different feature sets that constituted by 8 to 64 varying number of features. While Neuroticism is obtained to be the most differentiating personality trait on classification of political polarity, when its top 10 representative features are combined with several classification algorithms, it outperformed the results presented in previous research.

Keywords: politics, personality traits, LIWC, machine learning

Procedia PDF Downloads 490