Search results for: young children with learning disabilities

Authors: Mehmet Akif Bütüner, İlhan Koşalay

Abstract:

Hydroelectric power plants (HEPPs) are renewable energy power plants with the highest installed power in the world. While the control systems operating in these power plants ensure that the system operates at the desired operating point, it is also responsible for stopping the relevant unit safely in case of any malfunction. While these control systems are expected not to miss signals that require stopping, on the other hand, it is desired not to cause unnecessary stops. In traditional control systems including modern systems with SCADA infrastructure, alarm conditions to create warnings or trip conditions to put relevant unit out of service automatically are usually generated with predefined limits regardless of different operating conditions. This approach results in alarm/trip conditions to be less likely to detect minimal changes which may result in serious malfunction scenarios in near future. With the methods proposed in this research, routine behavior of the oil circulation of hydraulic governor of a HEPP will be modeled with machine learning methods using historical data obtained from SCADA system. Using the created model and recently gathered data from control system, oil pressure of hydraulic accumulators will be estimated. Comparison of this estimation with the measurements made and recorded instantly by the SCADA system will help to foresee failure before becoming worse and determine remaining useful life. By using model outputs, maintenance works will be made more planned, so that undesired stops are prevented, and in case of any malfunction, the system will be stopped or several alarms are triggered before the problem grows.

Keywords: hydroelectric, governor, anomaly detection, machine learning, regression

Procedia PDF Downloads 81

Authors: Brian Fleischli, Shani Robins

Abstract:

In recent years, the implementation of wisdom skills, including emotional intelligence, mindfulness, empathy, compassion, gratitude, realism (Cognitive-Behavioral Therapy), and humility, within K-12 educational settings has demonstrated significant benefits in reducing stress, anxiety, anger, and conflict among students. This study summarizes the findings of research conducted over several years, showcasing the positive outcomes associated with teaching these skills to elementary and high school students. Additionally, this overview includes an updated synthesis of current literature concerning the application and effectiveness of training these skill sets in K-12 schools. The research outcomes highlight substantial improvements in student well-being and behavior. Demonstrated with treatment group students exhibiting notable reductions in anger, anxiety, depression, and disruptive behaviors compared to control groups. For instance, fourth-grade students showed enhanced empathy, responsibility, and attention, particularly benefiting those with lower initial scores on these measures. Specific interaction effects suggest that older students and males particularly benefit from these interventions, showcasing the nuanced impact of wisdom skill training across different demographics. Furthermore, this presentation emphasizes the critical role of Social and Emotional Learning (SEL) programs in addressing the multifaceted challenges faced by children and adolescents, including mental health issues, academic performance, and social behaviors. The integration of wisdom skills into school curricula not only fosters individual growth and emotional regulation but also enhances overall school climate and academic achievement. In conclusion, the findings contribute to the growing body of empirical evidence supporting the efficacy of teaching wisdom skills in educational settings. The success of these interventions underscores the potential for widespread implementation of evidence-based programs to promote emotional well-being and academic success among students nationwide.

Keywords: wisdom skills, CBT, cognitive behavioral training, mindfulness, empathy, anxiety

Procedia PDF Downloads 35

Authors: P. Prasannakumar, L. Myoung Young, G. Seung Kye, P. Sang Hun, S. Chul Gyu

Abstract:

In this paper, we discussed the coupling medium in the optoacoustic imaging. The coupling medium is placed between the scanned object and the ultrasound transducers. Water with varying temperature was used as the coupling medium. The water temperature is gradually varied between 25 to 40 degrees. This heating process is taken with care in order to avoid the bubble formation. Rise in the photoacoustic signal is noted through an unfocused transducer with frequency of 2.25 MHz as the temperature increases. The temperature rise is monitored using a NTC thermistor and the values in degrees are calculated using an embedded evaluation kit. Also the temperature is transmitted to PC through a serial communication. All these processes are synchronized using a trigger signal from the laser source.

Keywords: embedded, optoacoustic, ultrasound , unfocused transducer

Procedia PDF Downloads 337

Authors: P. Wlodarczak, J. Soar, M. Ally

Abstract:

The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: social media, text mining, knowledge discovery, predictive analysis, machine learning

Procedia PDF Downloads 417

Authors: Evelyn Fabian

Abstract:

The focus of this discussion centres on the emerging issues in Early Childhood Care and development in Nigeria. Early childhood care is the bedrock of Nigeria’s educational system. However, there are critical issues that had not been addressed and it is frustrating the entire educational process. Thus, this paper will show the inter-connectedness between these issues such as poor funding, trained skillful teachers that would supervise the learning process of the kids, unconducive learning environment and lack of relevant facilities. For a clear grasp of these issues, the researcher visited 36 early childhood centres distributed across the 36 spates of Nigeria. The findings which were expressed in simple percentages revealed a near total absence or government neglect of these critical areas. The findings equally showed a misplaced priority in the government allocation of funds to early child care education and development. The study concludes that this mismatch in the training of these categories of pupils, government should expedite action in addressing these emerging issues in early childhood care and development in Nigeria.

Keywords: early childhood, ECCE, education, emerging issues

Procedia PDF Downloads 516

Authors: Jarek Krajewski, Andre Wittenborn, Martin Sauerland

Abstract:

This abstract describes a promising approach for estimating post-traumatic stress disorder (PTSD) based on prosodic speech characteristics. It illustrates the validity of this method by briefly discussing results from an Arabic refugee sample (N= 47, 32 m, 15 f). A well-established standardized self-report scale “Reaction of Adolescents to Traumatic Stress” (RATS) was used to determine the ground truth level of PTSD. The speech material was prompted by telling about autobiographical related sadness inducing experiences (sampling rate 16 kHz, 8 bit resolution). In order to investigate PTSD-induced speech changes, a self-developed set of 136 prosodic speech features was extracted from the .wav files. This set was adapted to capture traumatization related speech phenomena. An artificial neural network (ANN) machine learning model was applied to determine the PTSD level and reached a correlation of r = .37. These results indicate that our classifiers can achieve similar results to those seen in speech-based stress research.

Keywords: speech prosody, PTSD, machine learning, feature extraction

Procedia PDF Downloads 84

Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

Abstract:

In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

Procedia PDF Downloads 371

Authors: L. S. Chathurika

Abstract:

Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

Procedia PDF Downloads 111

Authors: S. Chee Choy, Pauline Swee Choo Goh, Yow Lin Liew

Abstract:

The purpose of this study is to evaluate the English version and a Malay translation of the 21-item Learner Awareness Questionnaire for its application to assess student learning in higher education. The Learner Awareness Questionnaire, originally written in English, is a quantitative measure of how and why students learn. The questionnaire gives an indication of the process and motives to learn using four scales: survival, establishing stability, approval, and loving to learn. Data in the present study came from 680 university students enrolled in various programs in Malaysia. The Malay version of the questionnaire supported a similar four-factor structure and internal consistency to the English version. The four factors of the Malay version also showed moderate to strong correlations with those of the English versions. The results suggest that the Malay version of the questionnaire is similar to the English version. However, further refinement for the questions is needed to strengthen the correlations between the two questionnaires.

Keywords: student learning, learner awareness, questionnaire development, instrument validation

Procedia PDF Downloads 419

Authors: Taksina Bunbut

Abstract:

Personality and grooming are vital for creating professionalism and safety image for all staffs in the airline industry. Airline Business Program also has an aim to educate students through the subject Personality Development and Grooming in order to elevate the quality of students to meet standard requirements of the airline industry. However, students agree that there are many difficulties that cause unsuccessful learning experience in this subject. The research is to study problems that can afflict students from getting good results in the classroom. Furthermore, exploring possible solutions to overcome challenges are also included in this study. The research sample consists of 140 students who attended the class of Personality Development and Grooming. The employed research instrument is a questionnaire. Statistic for data analysis is t-test and Multiple Regression Analysis. The result found that although students are satisfied with teaching and learning of this subject, they considered that teaching in English and teaching topics in social etiquette in different cultures are difficult for them to understand.

Keywords: personality development, grooming, Airline Business Program, soft skill

Procedia PDF Downloads 231

Authors: Subhajit Das, Nirjhar Dhang

Abstract:

Structural damage detection is a challenging work in the field of structural health monitoring (SHM). The damage detection methods mainly focused on the determination of the location and severity of the damage. Model updating is a well known method to locate and quantify the damage. In this method, an error function is defined in terms of difference between the signal measured from ‘experiment’ and signal obtained from undamaged finite element model. This error function is minimised with a proper algorithm, and the finite element model is updated accordingly to match the measured response. Thus, the damage location and severity can be identified from the updated model. In this paper, an error function is defined in terms of modal data viz. frequencies and modal assurance criteria (MAC). MAC is derived from Eigen vectors. This error function is minimized by teaching-learning-based optimization (TLBO) algorithm, and the finite element model is updated accordingly to locate and quantify the damage. Damage is introduced in the model by reduction of stiffness of the structural member. The ‘experimental’ data is simulated by the finite element modelling. The error due to experimental measurement is introduced in the synthetic ‘experimental’ data by adding random noise, which follows Gaussian distribution. The efficiency and robustness of this method are explained through three examples e.g., one truss, one beam and one frame problem. The result shows that TLBO algorithm is efficient to detect the damage location as well as the severity of damage using modal data.

Keywords: damage detection, finite element model updating, modal assurance criteria, structural health monitoring, teaching learning based optimization

Procedia PDF Downloads 206

Authors: Justin D. Olmanson, Fitsum Abebe, Valerie Jones, Eric Kyle, Xianquan Liu, Katherine Robbins, Guieswende Rouamba

Abstract:

The history of education technology--and designing, adapting, and adopting technologies for use in educational spaces--is nuanced, complex, and dynamic. Yet, despite a range of continually emerging technologies, the design and development process often yields results that appear quite similar in terms of affordances and interactions. Through this study we (1) verify the extent to which designs have been constrained, (2) consider what might account for it, and (3) offer a way forward in terms of how we might identify and strategically sidestep these influences--thereby increasing the diversity of our designs with a given technology or within a particular learning domain. We begin our inquiry from the perspective that a host of co-influencing elements, fields, and meta narratives converge on the education technology design process to exert a tangible, often homogenizing effect on the resultant designs. We identify several elements that influence design in often implicit or unquestioned ways (e.g. curriculum, learning theory, economics, learning context, pedagogy), we describe our methodology for identifying the elemental positionality embedded in a design, we direct our analysis to a particular subset of technologies in the field of literacy, and unpack our findings. Our early analysis suggests that the majority of education technologies designed for use/used in US public schools are heavily influenced by a handful of mainstream theories and meta narratives. These findings have implications for how we approach the education technology design process--which we use to suggest alternative methods for designing/ developing with emerging technologies. Our analytical process and re conceptualized design process hold the potential to diversify the ways emerging and established technologies get incorporated into our designs.

Keywords: curriculum, design, innovation, meta narratives

Procedia PDF Downloads 502

Authors: Sofia Horjales, Florencia Palma

Abstract:

Our society is currently experiencing a profound transformation, demanding a proactive response from governmental bodies and higher education institutions to empower the next generation as catalysts for change. Environmental sustainability is rooted in the critical need to maintain the equilibrium and integrity of natural ecosystems, ensuring the preservation of precious natural resources and biodiversity for the benefit of both present and future generations. It is an essential cornerstone of sustainable development, complementing social and economic sustainability. In this evolving landscape, active methodologies take a central role, aligning perfectly with the principles of the 2030 Agenda for Sustainable Development and emerging as a pivotal element of teacher education. The emphasis on active learning methods has been driven by the urgent need to nurture sustainability and instill social responsibility in our future leaders. The Universidad Tecnológica of Uruguay (UTEC) is a public, technologically-oriented institution established in 2012. UTEC is dedicated to decentralization, expanding access to higher education throughout Uruguay, and promoting inclusive social development. Operating through Regional Technological Institutes (ITRs) and associated centers spread across the country, UTEC faces the challenge of remote student populations. To address this, UTEC utilizes e-learning for equal opportunities, self-regulated learning, and digital skills development, enhancing communication among students, teachers, and peers through virtual classrooms. The Interdisciplinary Continuing Education Program is part of the Innovation and Entrepreneurship Department of UTEC. The main goal is to strengthen innovation skills through a transversal and multidisciplinary approach. Within this Program, we have developed a Case of Study and Project-Based Learning Virtual Course designed for university students and open to the broader UTEC community. The primary aim of this course is to establish a strong foundation for comprehending and addressing environmental sustainability issues from an interdisciplinary perspective. Upon completing the course, we expect students not only to understand the intricate interactions between social and ecosystem environments but also to utilize their knowledge and innovation skills to develop projects that offer enhancements or solutions to real-world challenges. Our course design centers on innovative learning experiences, rooted in active methodologies. We explore the intersection of these methods with sustainability and social responsibility in the education of university students. A paramount focus lies in gathering student feedback, empowering them to autonomously generate ideas with guidance from instructors, and even defining their own project topics. This approach underscores that when students are genuinely engaged in subjects of their choice, they not only acquire the necessary knowledge and skills but also develop essential attributes like effective communication, critical thinking, and problem-solving abilities. These qualities will benefit them throughout their lifelong learning journey. We are convinced that education serves as the conduit to merge knowledge and cultivate interdisciplinary collaboration, igniting awareness and instigating action for environmental sustainability. While systemic changes are undoubtedly essential for society and the economy, we are making significant progress by shaping perspectives and sparking small, everyday actions within the UTEC community. This approach empowers our students to become engaged global citizens, actively contributing to the creation of a more sustainable future.

Keywords: active learning, environmental education, project-based learning, soft skills development

Procedia PDF Downloads 58

Authors: Michael Brazley

Abstract:

Virtual Reality (VR) and 3D videos are the most current generation of learning technology today. Virtual Reality and 3D videos are being used in professional offices and Schools now for marketing and education. Technology in the field of design has progress from two dimensional drawings to 3D models, using computers and sophisticated software. Virtual Reality is being used as collaborative means to allow designers and others to meet and communicate inside models or VR platforms using avatars. This research proposes to teach students from different backgrounds how to take a digital model into a 3D video, then into VR, and finally VR with multiple avatars communicating with each other in real time. The next step would be to develop the model where people from three or more different locations can meet as avatars in real time, in the same model and talk to each other. This research is longitudinal, studying the use of 3D videos in graduate design and Virtual Reality in XR (Extended Reality) courses. The research methodology is a combination of quantitative and qualitative methods. The qualitative methods begin with the literature review and case studies. The quantitative methods come by way of student’s 3D videos, survey, and Extended Reality (XR) course work. The end product is to develop a VR platform with multiple avatars being able to communicate in real time. This research is important because it will allow multiple users to remotely enter your model or VR platform from any location in the world and effectively communicate in real time. This research will lead to improved learning and training using Virtual Reality and Avatars; and is generalizable because most Colleges, Universities, and many citizens own VR equipment and computer labs. This research did produce a VR platform with multiple avatars having the ability to move and speak to each other in real time. Major implications of the research include but not limited to improved: learning, teaching, communication, marketing, designing, planning, etc. Both hardware and software played a major role in project success.

Keywords: virtual reality, avatars, education, XR

Procedia PDF Downloads 90

Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

Abstract:

Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

Procedia PDF Downloads 86

Authors: Sangeeta Dhamdhere, G. V. P. Rao

Abstract:

Adolescent students need more energy, proteins, vitamins, and minerals because they grow to maturity in this age. Balanced diet plays important role in their wellbeing and health. The study conducted showed 48% students are not normal in their height and weight. 26% students found underweight, 18% overweight and 4% students found obese. The annual income group of underweight students was below 7 Lac and more than 90% students were staying at their home. The researcher has analysed the eating pattern of these students and concluded that there is need of awareness among the parents and students about balance diet and nutrition. The present research will help students improve their dietary habits and health, increase the number of attendees, and achieve academic excellence.

Keywords: balanced diet, nutrition, malnutrition, obesity, health education

Procedia PDF Downloads 60

Authors: Sakshi V. Tantak, Vishap K. Malik, Neelanjney Pilarisetty

Abstract:

A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon.

Keywords: abstractive summarization, deep learning, natural language Processing, patent document

Procedia PDF Downloads 113

Authors: Narendra Babu Bommenahalli Veerabhadrappa

Abstract:

Teaching sustainability concepts along with profit maximizing philosophy of business in management education is a challenge. This paper explores and evaluates various learning models to inculcate sustainability concepts in management education. The paper explains about a new pedagogy that was tested in a business management school (Indus Business Academy, Bangalore, India) to teach sustainability. The pedagogy was designed by intertwining concepts related to sustainability with digital marketing concepts. As part of this experimental method, students (in groups) were assigned with various topics of sustainability and were asked to work with concepts of digital marketing and thus market the concepts of sustainability. The paper explains as a case study as to how sustainability was integrated with digital marketing tools and how learning towards sustainability was facilitated. It also explains the outcomes of this pedagogical method, in terms of inculcating sustainability concepts amongst management students as well as marketing and proliferation of sustainability concepts to bring about the behavioral changes amongst target audience towards sustainability.

Keywords: management-education, pedagogy, sustainability, behavior

Procedia PDF Downloads 237

Authors: Tali Neta

Abstract:

Humans’ impact on the environment has been incredibly brutal, with enormous plastic- and other pollutants (e.g., cigarette buds, paper cups, tires) worldwide. On land, litter costs taxpayers a fortune. Most of the litter pollution comes from the land, yet it is one of the greatest hazards to marine environments. Due to spatial and temporal limitations, previous litter data covered very small areas. Currently, smartphones can be used to obtain information on various pollutants (through citizen science), and they can greatly assist in acknowledging and mitigating the environmental impact of litter. Litter app data, such as the Litterati, are available for study through a global map only; these data are not available for download, and it is not clear whether irrelevant hashtags have been eliminated. Instagram and Twitter open-source geospatial data are available for download; however, these are considered inaccurate, computationally challenging, and impossible to quantify. Therefore, the resulting data are of poor quality. Other downloadable geospatial data (e.g., Marine Debris Tracker8 and Clean Swell10) are focused on marine- rather than terrestrial litter. Therefore, accurate terrestrial geospatial documentation of litter distribution is needed to improve environmental awareness. The current research employed citizen science to examine litter distribution in Lethbridge, Alberta, Canada, using the OpenLitterMap (OLM) app. The OLM app is an application used to track litter worldwide, and it can mark litter locations through photo georeferencing, which can be presented through GIS-designed maps. The OLM app provides open-source data that can be downloaded. It also offers information on various litter types and “hot-spots” areas where litter accumulates. In this study, Lethbridge College students collected litter data with the OLM app. The students produced GIS Story Maps (interactive web GIS illustrations) and presented these to school children to improve awareness of litter's impact on environmental health. Preliminary results indicate that towards the Lethbridge Coulees’ (valleys) East edges, the amount of litter significantly increased due to shrubs’ presence, that acted as litter catches. As wind generally travels from west to east in Lethbridge, litter in West-Lethbridge often finds its way down in the east part of the coulees. The students’ documented various litter types, while the majority (75%) included plastic and paper food packaging. The students also found metal wires, broken glass, plastic bottles, golf balls, and tires. Presentations of the Story Maps to school children had a significant impact, as the children voluntarily collected litter during school recess, and they were looking into solutions to reduce litter. Further litter distribution documentation through Citizen Science is needed to improve public awareness. Additionally, future research will be focused on Drone imagery of highly concentrated litter areas. Finally, a time series analysis of litter distribution will help us determine whether public education through Citizen Science and Story Maps can assist in reducing litter and reaching a cleaner and healthier environment.

Keywords: citizen science, litter pollution, Open Litter Map, GIS Story Map

Procedia PDF Downloads 68

Authors: Genziana Lay

Abstract:

A wide majority of older children and adolescents who enter the juvenile court system present with an array of problematic symptoms and behaviors including anxiety, depression, aggressive acting out, detachment, and substance abuse. Attachment theory offers a framework for understanding normative and pathological functioning, which during development is influenced by emotional, social and cognitive elements. There is clear evidence that children and adolescents with the highest risk of developing adaptation problems present an insecure attachment profile. Most offending minors have experienced dysfunctional family relationships as well as social and/or economic deprivation. Their maladaptive attachment develops not only through their relationship with caregivers but with the environment at large. Activation of their faulty attachment system leads them to feel emotionally overwhelmed and engage in destructive behaviors and decision-making. A psycho-social intervention with this population conceptualized as attachment therapy is a multi-faceted, practical approach that has shown excellent results in terms of increased psychological well-being and drastically reduced rates of re-offense/ destructive behavior. Through several; components including psychotherapy, monitoring, volunteering, meditation and socialization, the program focuses on seven dimensions: self-efficacy, responsibility, empathy/reparation, autonomy/security, containment/structure, insight building, and relational health. This paper presents the program and illustrates how the framework of attachment theory practically applied to psycho-social intervention has great therapeutic and social reparation potential. Preliminary evidence drawn from the Sassari Juvenile Court is very promising; this paper will illustrate these results and propose an even more comprehensive, applicable approach to psycho-social reparative intervention that leads to greater psychological health and reduced recidivism in the child and adolescent population.

Keywords: attachment, child, adolescent, crime, juvenile, psychosocial

Procedia PDF Downloads 167

Authors: Jacopo Baboni Schilingi

Abstract:

We here describe the theoretical and philosophical understanding of a long term use and development of algorithmic computer-based tools applied to music composition. The findings of our research lead us to interrogate some specific processes and systems of communication engaged in the discovery of specific cultural artworks: artistic creation in the sono-musical domain. Our hypothesis is that the patterns of auditory learning cannot be only understood in terms of social transmission but would gain to be questioned in the way they rely on various ranges of acoustic stimuli modes of consciousness and how the different types of memories engaged in the percept-action expressive systems of our cultural communities also relies on these shadowy conscious entities we named “Reduced Descriptive Structures”.

Keywords: algorithmic sonic computation, corrected and self-correcting learning patterns in acoustic perception, morphological derivations in sensorial patterns, social unconscious modes of communication

Procedia PDF Downloads 146

Authors: Oumaima Lahmar

Abstract:

This paper aims to define a structured topography for finance researchers seeking to navigate the body of knowledge in their extrapolation of finance phenomena. To make sense of the body of knowledge in finance, a probabilistic topic modeling approach is applied on 6000 abstracts of academic articles published in three top journals in finance between 1976 and 2020. This approach combines both machine learning techniques and natural language processing to statistically identify the conjunctions between research articles and their shared topics described each by relevant keywords. The topic modeling analysis reveals 35 coherent topics that can well depict finance literature and provide a comprehensive structure for the ongoing research themes. Comparing the extracted topics to the Journal of Economic Literature (JEL) classification system, a significant similarity was highlighted between the characterizing keywords. On the other hand, we identify other topics that do not match the JEL classification despite being relevant in the finance literature.

Keywords: finance literature, textual analysis, topic modeling, perplexity

Procedia PDF Downloads 159

Authors: Yuanchao Sang, Zhihao Gong, Longsheng Chen, Long Chen

Abstract:

Clothing fashion represents a human’s aesthetic appreciation towards everyday outfits and appetite for fashion, and it reflects the development of status in society, humanity, and economics. However, modelling fashion by machine is extremely challenging because fashion is too abstract to be efficiently described by machines. Even human beings can hardly reach a consensus about fashion. In this paper, we are dedicated to answering a fundamental fashion-related problem: what image feature best describes clothing fashion? To address this issue, we have designed and evaluated various image features, ranging from traditional low-level hand-crafted features to mid-level style awareness features to various current popular deep neural network-based features, which have shown state-of-the-art performance in various vision tasks. In summary, we tested the following 9 feature representations: color, texture, shape, style, convolutional neural networks (CNNs), CNNs with distance metric learning (CNNs&DML), AutoEncoder, CNNs with multiple layer combination (CNNs&MLC) and CNNs with dynamic feature clustering (CNNs&DFC). Finally, we validated the performance of these features on two publicly available datasets. Quantitative and qualitative experimental results on both intra-domain and inter-domain fashion clothing image retrieval showed that deep learning based feature representations far outweigh traditional hand-crafted feature representation. Additionally, among all deep learning based methods, CNNs with explicit feature clustering performs best, which shows feature clustering is essential for discriminative fashion feature representation.

Keywords: convolutional neural network, feature representation, image processing, machine modelling

Procedia PDF Downloads 131

Authors: Meenakshi

Abstract:

Background: The complications during pregnancy are the major cause of morbidity and deaths among women in the reproductive age group. Childbearing is the most important phase in women’s lives that occur mainly in the adolescent and adult years. Maternal health, thus is an important issue as this as this is important phase is also productive time for women as they strive fulfill their capabilities as an individual, mothers, family members and also as a citizen. The objective of the study is to document the coverage of ANC and its determinants among domestic workers. Method: A survey of 300 domestic workers were carried in Delhi. Only respondents in the age group (15-49) and whose recent birth was of 5 years preceding the survey were included. Socio-demographic data and information on maternal health was collected from these respondents Information on ANC was collected from total 300 respondents. Standard of living index were composed based on households assists and similarly autonomy index was computed based on women decision making power in the households taking certain key variables. Cross tabulations were performed to obtain frequency and percentages. Potential socio-economic determinants of utilization of ANC among domestic workers were examined using binary logistic regressions. Results: Out of 300 domestic workers survey, only 70.7 per cent per cent received ANC. Domestic workers who married at age 18 years and above are 4 times more likely to utilize antenatal services during their last birth (***p< 0.01). Comparison to domestic workers with number of living children two or less, domestic workers with number of living children more than two are less likely to utilize antenatal care services (**p< 0.05). Domestic workers belonging to Other Backward Castes are more likely to utilize antenatal care services than domestic workers belonging to scheduled tribes ((**p< 0.05). Conclusion: The level of utilization of maternal health services are less among domestic workers is less, as they spend most of their time at the employers household. Though demonstration effect do have impact on their life styles but utilization of maternal health services is poor. Strategies and action are needed to improve the utilization of maternal health services among this section of workers as they are vulnerable because of no proper labour legislations.

Keywords: antenatal care, domestic workers, health services, maternal health, women’s health

Procedia PDF Downloads 193

Authors: Bharatendra Rai

Abstract:

The sequence of words in text data has long-term dependencies and is known to suffer from vanishing gradient problems when developing deep learning models. Although recurrent networks such as long short-term memory networks help to overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine the advantages of long short-term memory networks and convolutional neural networks can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning.

Keywords: long short-term memory networks, convolutional recurrent networks, text classification, hyperparameter tuning, Tukey honest significant differences

Procedia PDF Downloads 109

Authors: S. Odabasi Cimer, A. Cimer

Abstract:

Recently, the poor quality of education, low achieving students, low international exam performances and little or no effect of the education reforms on the teaching in the classrooms are the main problems of education discussed in Turkey. Research showed that the quality of an education system can not exceed the quality of its teachers and teaching. Therefore, in-service training (INSET) courses are important to improve teacher quality, thereby, the quality of education. However, according to the research conducted on the evaluation of the INSET courses in Turkey, they are not effective in improving the quality of teaching in the classroom. The main reason for this result is because INSET courses are conducted and delivered in limited time and presented theoretically, which does not meet the needs of teachers and as a result, the knowledge and skills taught are not used in the classrooms. Recently, developed countries have been using Critical Friends Groups (CFGs) successfully for the purpose of school-based training of teachers. CFGs are the learning groups which contain 6-10 teachers aimed at fostering their capacities to undertake instructional and personal improvement and schoolwide reform. CFGs have been recognized as a critical feature in school reform, improving teaching practice and improving student achievement. In addition, in the USA, teachers have named CFGs one of the most powerful professional development activities in which they have ever participated. Whereas, in Turkey, the concept is new. This study aimed to investigate the implications of application, evaluation, and promotion of CFGs which has the potential to contribute to teacher development and student learning in schools in Turkey. For this purpose, the study employed a qualitative approach and case study methodology to implement the model in high schools. The research was conducted in two schools and 13 teachers working in these schools participated. The study lasted two years and the data were collected through various data collection tools including interviews, meeting transcripts, questionnaires, portfolios, and diaries. The results of the study showed that CFGs contributed professional development of teachers and their students’ learning. It also contributed to a culture of collaborative work in schools. A number of barriers and challenges which prevent effective implementation were also determined.

Keywords: critical friends group, education reform, science learning, teacher education

Procedia PDF Downloads 118

Authors: Zzibu, Mark, Brian

Abstract:

As software systems become increasingly complex, ensuring code security is a growing concern. Traditional vulnerability detection methods often rely on manual code reviews or static analysis tools, which can be time-consuming and prone to errors. This paper presents a distinct approach to enhancing code security by leveraging artificial intelligence (AI) and machine learning (ML) techniques. Our proposed system utilizes a combination of natural language processing (NLP) and deep learning algorithms to identify and classify vulnerabilities in real-world codebases. By analyzing vast amounts of open-source code data, our AI-powered tool learns to recognize patterns and anomalies indicative of security weaknesses. We evaluated our system on a dataset of over 10,000 open-source projects, achieving an accuracy rate of 92% in detecting known vulnerabilities. Furthermore, our tool identified previously unknown vulnerabilities in popular libraries and frameworks, demonstrating its potential for improving software security.

Keywords: AI, machine language, cord security, machine leaning

Procedia PDF Downloads 6

Authors: Nouzha Yasmina Soulimane-Benhabib

Abstract:

Language is a medium of communication and a means of expression that is why today the learning of foreign languages especially the English language has become a basic necessity for every student who is ambitious. It is known that culture and language are inseparable and complementary, however, in the process of teaching a foreign language, teachers used to focus mainly on preparing adequate syllabi for ESP students, yet, some parameters should be considered. For instance; the culture of the target language may play an important role since students attitudes towards a foreign language enhance their learning or vice versa. The aim of this study is to analyse how culture could influence the teaching of a foreign language, we have taken the example of the English language as it is considered as the second foreign language in Algeria after French. The study is conducted at the Preparatory School of Sciences and Techniques, Tlemcen where twenty-five students participated in this research. The reasons behind learning the English language are various, and since English is the most widely-spoken language in the world, it is the language of research and education and it is used in many other fields, we have to take into consideration one important factor which is the social distance between the culture of the Algerian learner and the culture of the target language, this gap may lead to a culture shock. Two steps are followed in this research: The first one is to collect data from those students who are studying at the Preparatory School under the form of questionnaire and an interview is submitted to six of them in order to reinforce our research and get effective and precise results, and the second step is to analyse these data taking into consideration the diversity of the learners within this institution. The results obtained show that learners’ attitudes towards the English community and culture are mixed and it may influence their curiosity and attention to learn. Despite of big variance between Algerian and European cultures, some of the students focused mainly on the benefits of the English language since they need it in their studies, research and a future carrier, however, the others manifest their reluctance towards this language and this is mainly due to the profound impact of the English culture which is different from the Algerian one.

Keywords: Algeria, culture, English, impact

Procedia PDF Downloads 378

Authors: Giancarlo La Marca, Engy Shokry, Fabio Villanelli

Abstract:

Epilepsy is one of the most common neurological disorders, with an estimated prevalence of 50 million people worldwide. Twenty five percent of the epilepsy population is represented in children under the age of 15 years. For antiepileptic drugs (AED), there is a poor correlation between plasma concentration and dose especially in children. This was attributed to greater pharmacokinetic variability than adults. Hence, therapeutic drug monitoring (TDM) is recommended in controlling toxicity while drug exposure is maintained. Carbamazepine (CBZ) is a first-line AED and the drug of first choice in trigeminal neuralgia. CBZ is metabolised in the liver into carbamazepine-10,11-epoxide (CBZE), its major metabolite which is equipotent. This develops the need for an assay able to monitor the levels of both CBZ and CBZE. The aim of the present study was to develop and validate a LC-MS/MS method for simultaneous quantification of CBZ and CBZE in dried blood spots (DBS). DBS technique overcomes many logistical problems, ethical issues and technical challenges faced by classical plasma sampling. LC-MS/MS has been regarded as superior technique over immunoassays and HPLC/UV methods owing to its better specificity and sensitivity, lack of interference or matrix effects. Our method combines advantages of DBS technique and LC-MS/MS in clinical practice. The extraction process was done using methanol-water-formic acid (80:20:0.1, v/v/v). The chromatographic elution was achieved by using a linear gradient with a mobile phase consisting of acetonitrile-water-0.1% formic acid at a flow rate of 0.50 mL/min. The method was linear over the range 1-40 mg/L and 0.25-20 mg/L for CBZ and CBZE respectively. The limit of quantification was 1.00 mg/L and 0.25 mg/L for CBZ and CBZE, respectively. Intra-day and inter-day assay precisions were found to be less than 6.5% and 11.8%. An evaluation of DBS technique was performed, including effect of extraction solvent, spot homogeneity and stability in DBS. Results from a comparison with the plasma assay are also presented. The novelty of the present work lies in being the first to quantify CBZ and its metabolite from only one 3.2 mm DBS disc finger-prick sample (3.3-3.4 µl blood) by LC-MS/MS in a 10 min. chromatographic run.

Keywords: carbamazepine, carbamazepine-10, 11-epoxide, dried blood spots, LC-MS/MS, therapeutic drug monitoring

Procedia PDF Downloads 406

Authors: Matthias Haenisch, Katharina Hermann, Marc Godau, Verena Weidner

Abstract:

Over the past two decades, international music education has been characterized by a growing interest in informal learning for formal contexts and a "compositional turn" that has moved from closed to open forms of composing. This change occurs under social and technological conditions that permeate 21st-century musical practices. This forms the background of Musical Communities in the (Post)Digital Age (MusCoDA), a four-year joint research project of the University of Erfurt (UE) and the University of Education Karlsruhe (PHK), funded by the German Federal Ministry of Education and Research (BMBF). Both explore songwriting processes as an example of collective creativity in (post)digital communities, one in formal and the other in informal learning contexts. Collective songwriting will be studied from a network perspective, that will allow us to view boundaries between both online and offline as well as formal and informal or hybrid contexts as permeable and to reconstruct musical learning practices. By comparing these songwriting processes, possibilities for a pedagogical-didactic interweaving of different educational worlds are highlighted. Therefore, the subproject of the University of Erfurt investigates school music lessons with the help of interviews, videography, and network maps by analyzing new digital pedagogical and didactic possibilities. In the first step, the international literature on songwriting in the music classroom was examined for design development. The analysis focused on the question of which methods and practices are circulating in the current literature. Results from this stage of the project form the basis for the first instructional design that will help teachers in planning regular music classes and subsequently reconstruct musical learning practices under these conditions. In analyzing the literature, we noticed certain structural methods and concepts that recur, such as the Building Blocks method and the pre-structuring of the songwriting process. From these findings, we developed a deck of cards that both captures the current state of research and serves as a method for design development. With this deck of cards, both teachers and students themselves can plan their individual songwriting lessons by independently selecting and arranging topic, structure, and action cards. In terms of science communication, music educators' interactions with the card game provide us with essential insights for developing the first design. The overall goal of MusCoDA is to develop an empirical model of collective musical creativity and learning and an instructional design for teaching music in the postdigital age.

Keywords: card game, collective songwriting, community of practice, network, postdigital

Procedia PDF Downloads 54