Search results for: adaptive virtual reality training

Authors: Neerav Kumar, Naumaan Nayyar, Sharath Kashyap

Abstract:

Most companies see an increase in the adoption of machine learning (ML) applications across internal and external-facing use cases. ML applications vend output either in batch or real-time patterns. A complete batch ML pipeline architecture comprises data sourcing, feature engineering, model training, model deployment, model output vending into a data store for downstream application. Due to unclear role expectations, we have observed that scientists specializing in building and optimizing models are investing significant efforts into building the other components of the architecture, which we do not believe is the best use of scientists’ bandwidth. We propose a system architecture created using AWS services that bring industry best practices to managing the workflow and simplifies the process of model deployment and end-to-end data integration for an ML application. This narrows down the scope of scientists’ work to model building and refinement while specialized data engineers take over the deployment, pipeline orchestration, data quality, data permission system, etc. The pipeline infrastructure is built and deployed as code (using terraform, cdk, cloudformation, etc.) which makes it easy to replicate and/or extend the architecture to other models that are used in an organization.

Keywords: data pipeline, machine learning, AWS, architecture, batch machine learning

Procedia PDF Downloads 47

Authors: Saroj Pandey

Abstract:

COVID-19, declared by the World Health Organization a pandemic (WHO,2020), has created an unprecedented crisis world over endangering the human survival itself. Corona induced lockdowns forced approximately 140 million students of 190 countries at various levels of education from preprimary to higher education to remain confined to their homes. In India, approximately 360 million students were affected by the forced shut down of schools due to the countrywide lockdown in March 2020 and resultant disruption of education. After the initial shock and anxiety the Indian polity and education system bounced back with a number of initiatives, and online education came as a major rescuer for the education system of the country. The distance and online mode of learning that was treated as the poor cousin of conventional mode and often criticized for its quality became the major crusader overnight changing the entire ecosystem of traditional teaching -leaning towards the virtual mode. Teachers who were averse to technology were forced to remodel their educational pedagogies and reorient themselves overnight to use various online platforms such as Zoom, Google meet, and other such platforms to reach the learners. This metamorphosis through ensured students was meaningfully engaged in their studies during the lockdown period but it has its own set of challenges. This paper deals with the government initiatives, and teachers' self-efforts to keep the channel of teaching learning on providing academic and socio emotional support to students during the most difficult period of their life as well as the digital divide between the rich and poor, rural and urban, and boys and girls in India and resultant challenges. It also provides an overview of few significant self-initiatives of teachers to reach their students during the crisis period, who did not have internet and smartphone facilities as well as the initiatives being taken at the government level to address the learning needs and mitigate the learning gaps of learners, bridge the digital divide, strategic planning and upskilling of teachers to overcome the effect of COVID-19 crisis.

Keywords: COVID-19, online education, initiatives, challenges

Procedia PDF Downloads 100

Authors: Yue Wang

Abstract:

Five years passed since the newest English curriculum reform policy was published in China, hand-wringing spread among teachers who accused that this is another 'Wearing New Shoes to Walk the Old Road' policy. This paper provides a thoroughly philosophical policy analysis of serious efforts that had been made to support this reform and reveals the hindrances that bridled the reform to yield the desired effect. Blame could be easily put on teachers for their insufficient pedagogical content knowledge, conservative resistance, and the handicaps of large class sizes and limited teaching times, and so on. However, the underlying causes for this implementation failure are the interrelated factors in the NCEE-centred education system, such as the reluctant from students, the lack of school and education bureau support, and insufficient teacher training. A further discussion of 2017 to 2020’s NCEE reform on English prompt new possibilities for the authentic pedagogical approach reform in secondary English classes. In all, the pedagogical approach reform at the secondary level is heading towards a brighter future with the initiation of new NCEE reform.

Keywords: English curriculum, failure, NCEE, new possibilities, pedagogical, policy analysis, reform

Procedia PDF Downloads 129

Authors: Ninlawat Phuangchoke, Waraporn Viyanon, Setta Sasananan

Abstract:

The most important process of the water treatment plant process is the coagulation using alum and poly aluminum chloride (PACL), and the value of usage per day is a hundred thousand baht. Therefore, determining the dosage of alum and PACL are the most important factors to be prescribed. Water production is economical and valuable. This research applies an artificial neural network (ANN), which uses the Levenberg–Marquardt algorithm to create a mathematical model (Soft Jar Test) for prediction chemical dose used to coagulation such as alum and PACL, which input data consists of turbidity, pH, alkalinity, conductivity, and, oxygen consumption (OC) of Bangkhen water treatment plant (BKWTP) Metropolitan Waterworks Authority. The data collected from 1 January 2019 to 31 December 2019 cover changing seasons of Thailand. The input data of ANN is divided into three groups training set, test set, and validation set, which the best model performance with a coefficient of determination and mean absolute error of alum are 0.73, 3.18, and PACL is 0.59, 3.21 respectively.

Keywords: soft jar test, jar test, water treatment plant process, artificial neural network

Procedia PDF Downloads 151

Authors: Nsikan Affiah, Kayode Osungbade, Williams Uzoma

Abstract:

Malaria remains one of the major killers of humans worldwide, threatening the lives of more than one-third of the world’s population. Some people refers it to; a disease of poverty because it contributes towards national poverty through its impact on foreign direct investment, tourism, labour productivity, and trade. At the micro level, it may cause poverty through spending on health care, income losses, and premature deaths. Unfortunately, malaria is a disease that affects both low-income household and its high-income counterpart, but low-income households are still at greater risk because significant part of the available monthly income is dedicated to various preventive and treatment measures. The objective of this study is to estimate direct and indirect cost of malaria treatment in households in a section of South-South Region (Akwa Ibom State) of Nigeria. A cross-sectional study of Six Hundred and Forty (640) heads of households or any adult representative of households in three local government areas of Akwa Ibom State, Nigeria from May 1-31, 2015 were ascertained through interviewer-administered questionnaire adapted from Nigerian Malaria Indicator Survey Report. The clustering technique was used to select 640 households with the help of Primary Health Care (PHC) house numbering system. Using exchange rate of 197 Naira/USD, result shows that direct cost of malaria treatment was 8,894.44 USD while the indirect cost of malaria treatment was 11,012.81 USD. Total cost of treatment made up of 44.7% direct cost and 55.3% indirect cost, with average direct cost of malaria treatment per household estimated at 20.6 USD and the average indirect cost of treatment per household estimated at 25.1 USD. Average total cost for each episode (888) of malaria was estimated at 22.4 USD. While at household level, the average total cost was estimated at 45.5 USD. From the average total cost, low-income households would spend 36% of monthly household income on treating malaria and the impact could be said to be catastrophic, compared to high-income households where only 1.2% of monthly household income is spent on malaria treatment. It could be concluded that the cost of malaria treatment is well beyond the means of households and given the reality of repeated bouts of malaria and its contribution to the impoverishment of households, there is a need for urgent action.

Keywords: direct cost, indirect cost, low income households, malaria

Procedia PDF Downloads 238

Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

Abstract:

Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

Procedia PDF Downloads 242

Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

Abstract:

Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

Procedia PDF Downloads 303

Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

Abstract:

The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

Procedia PDF Downloads 227

Authors: Dalya Markovich

Abstract:

In the last decade art-educators strive to integrate critical pedagogy within the art classroom. Critical pedagogy aims to deconstruct the oppressive social reality and the false consciousness in which learners from both privileged and underprivileged groups are caught. Understanding oppression as a product of socio-political conditions seeks to instigate processes of change anchored in the student's views. Yet, growing empirical evidence show that these efforts often has resulted in art projects in which art teachers play an active role in the process of critical teaching, while the students remain passive listeners. In this common scenario, the teachers/artists become authoritarian moral guides of critical thinking and acting, while the students are often found to be indifferent or play along to satisfy the teachers'/artists aspirations. These responses indicate that the message of critical pedagogy – transforming the students' way of thinking and acting – mostly do not fulfill its emancipation goals. The study analyses the critical praxis embedded in new art projects and their influence on the participants. This type of projects replaces the individual producer with a collaborative work; switch the finite work with an ongoing project; and transforms the passive learner to an engaged co-producer. The research delves into the pedagogical framework of two of these art projects by using qualitative methods. In-depth interviews were conducted with 4 of the projects' initiator and managers, in order to access understandings of the art projects goals and pedagogical methods. Field work included 4 participant observation (two in each project) during social encounters in the project's settings, focusing on how critical thinking is enacted (or not) by the participants. The analysis exposes how the new art projects avoid the prepackaged "critical" assumptions and praxis, thus turning the participants from passive carriers of critical thinking to agents that actively use criticism. Findings invite researchers to explore new avenues for understanding critical pedagogy and developing various ways to implement critical pedagogy during art education, in view of the growing need of critical thinking and acting in school/society.

Keywords: critical pedagogy, education through art, collaborative work, agency

Procedia PDF Downloads 131

Authors: Irena Maus, Katharina Gabriella Cibis, Andreas Bremges, Yvonne Stolze, Geizecler Tomazetto, Daniel Wibberg, Helmut König, Alfred Pühler, Andreas Schlüter

Abstract:

Within the agricultural sector, the production of biogas from organic substrates represents an economically attractive technology to generate bioenergy. Complex consortia of microorganisms are responsible for biomass decomposition and biogas production. Recently, species belonging to the phylum Thermotogae were detected in thermophilic biogas-production plants utilizing renewable primary products for biomethanation. To analyze adaptive genome features of representative Thermotogae strains, Defluviitoga tunisiensis L3 was isolated from a rural thermophilic biogas plant (54°C) and completely sequenced on an Illumina MiSeq system. Sequencing and assembly of the D. tunisiensis L3 genome yielded a circular chromosome with a size of 2,053,097 bp and a mean GC content of 31.38%. Functional annotation of the complete genome sequence revealed that the thermophilic strain L3 encodes several genes predicted to facilitate growth of this microorganism on arabinose, galactose, maltose, mannose, fructose, raffinose, ribose, cellobiose, lactose, xylose, xylan, lactate and mannitol. Acetate, hydrogen (H2) and carbon dioxide (CO2) are supposed to be end products of the fermentation process. The latter gene products are metabolites for methanogenic archaea, the key players in the final step of the anaerobic digestion process. To determine the degree of relatedness of dominant biogas community members within selected digester systems to D. tunisiensis L3, metagenome sequences from corresponding communities were mapped on the L3 genome. These fragment recruitments revealed that metagenome reads originating from a thermophilic biogas plant covered 95% of D. tunisiensis L3 genome sequence. In conclusion, availability of the D. tunisiensis L3 genome sequence and insights into its metabolic capabilities provide the basis for biotechnological exploitation of genome features involved in thermophilic fermentation processes utilizing renewable primary products.

Keywords: genome sequence, thermophilic biogas plant, Thermotogae, Defluviitoga tunisiensis

Procedia PDF Downloads 480

Authors: Kaoutar Ben Azzou, Hanaa Talei

Abstract:

Human resources is an important department in each organization as it manages the life cycle of employees from recruitment training to retirement or termination of contracts. The recruitment process starts with a job opening, followed by a selection of the best-fit candidates from all applicants. Matching the best profile for a job position requires a manual way of looking at many CVs, which requires hours of work that can sometimes lead to choosing not the best profile. The work presented in this paper aims at reducing the workload of HR personnel by automating the preliminary stages of the candidate screening process, thereby fostering a more streamlined recruitment workflow. This tool introduces an automated system designed to help with the recruitment process by scanning candidates' CVs, extracting pertinent features, and employing machine learning algorithms to decide the most fitting job profile for each candidate. Our work employs natural language processing (NLP) techniques to identify and extract key features from unstructured text extracted from a CV, such as education, work experience, and skills. Subsequently, the system utilizes these features to match candidates with job profiles, leveraging the power of classification algorithms.

Keywords: automated recruitment, candidate screening, machine learning, human resources management

Procedia PDF Downloads 37

Authors: Widyani Fatwa Dewi, Subroto Athor

Abstract:

In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

Procedia PDF Downloads 148

Authors: Meruert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova

Abstract:

The article highlights and describes the characteristic features of real-time face detection in images and videos using machine learning algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As a result, the advantages and disadvantages of Haar Cascade (Haar Cascade OpenCV), HoG SVM (Histogram of Oriented Gradients, Support Vector Machine), and MMOD CNN Dlib (Max-Margin Object Detection, convolutional neural network) detectors used for face detection were determined. Dlib is a general-purpose cross-platform software library written in the programming language C++. It includes detectors used for determining face detection. The Cascade OpenCV algorithm is efficient for fast face detection. The considered work forms the basis for the development of machine learning methods by future computer science teachers.

Keywords: algorithm, artificial intelligence, education, machine learning

Procedia PDF Downloads 60

Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

Abstract:

Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

Procedia PDF Downloads 486

Authors: Pornpimol Chaiwuttisak

Abstract:

The study aims to compare the performance of the logistics for Thailand’s wholesale and retail trade industries (except motor vehicles, motorcycle, and stalls) by using data (data envelopment analysis). Thailand Standard Industrial Classification in 2009 (TSIC - 2009) categories that industries into sub-group no. 45: wholesale and retail trade (except for the repair of motor vehicles and motorcycles), sub-group no. 46: wholesale trade (except motor vehicles and motorcycles), and sub-group no. 47: retail trade (except motor vehicles and motorcycles. Data used in the study is collected by the National Statistical Office, Thailand. The study consisted of four input factors include the number of companies, the number of personnel in logistics, the training cost in logistics, and outsourcing logistics management. Output factor includes the percentage of enterprises having inventory management. The results showed that the average relative efficiency of small-sized enterprises equals to 27.87 percent and 49.68 percent for the medium-sized enterprises.

Keywords: DEA, wholesales and retails, logistics, Thailand

Procedia PDF Downloads 403

Authors: P. C. Li, R. H. Feng, S. J. Chen, Y. J. Yu, Y. L. Chen, X. Y. Fan

Abstract:

The purpose of this study is to investigate the effect of mindfulness and wisdom comprehensive strategy intervention on addiction to the Internet of college students by engaging fourteen intensive full-day mindfulness-based wisdom retreat curriculum. Wisdom, one of the practice method from the threefold training. Internet addiction, a kind of impulse control disorder, which attract the attentions of society due to its high prevalence and harmfulness in the last decade. Therefore, the study of internet addiction intervention is urgent. Participants with internet addiction were Chinese college students and screened by internet addiction disorder diagnose questionnaire (IAD-DQ). A quasi-experimental pretest and posttest design was used as research design. The finding shows that the mindfulness-based wisdom intervention strategy appeared to be effective in reducing the Internet addiction. Moreover, semi-structure interview method was conducted and outcomes included five themes: the reduction of internet use, the increment of awareness on emotion, self-control, present concentration and better positive lifestyle, indicating that mindfulness could be an effective intervention for this group with internet addiction.

Keywords: mindfulness, internet addiction, wisdom comprehensive intervention, cognitive-behavior therapy

Procedia PDF Downloads 167

Authors: Reyhan Tekin Sitrava

Abstract:

Teachers should have robust and comprehensive content knowledge for effective mathematics teaching. It was explained that content knowledge includes knowing the facts, truths, and concepts; explaining the reasons behind these facts, truths and concepts, and making relationship between the concepts and other disciplines. By virtue of its importance, it will be significant to explore teachers and prospective teachers’ content knowledge related to variety of topics in mathematics. From this point of view, the purpose of this study was to investigate prospective mathematics teachers’ content knowledge. Particularly, it was aimed to reveal the prospective teachers’ knowledge regarding the definition of limit and derivate. To achieve the purpose and to get in-depth understanding, a qualitative case study method was used. The data was collected from 34 prospective mathematics teachers through a questionnaire containing 2 questions. The first question required the prospective teachers to define the limit and the second one required to define the derivative. The data was analyzed using content analysis method. Based on the analysis of the data, although half of the prospective teachers (50%) could write the definition of the limit, nine prospective teachers (26.5%) could not define limit. However, eight prospective teachers’ definition was regarded as partially correct. On the other hand, twenty-seven prospective teachers (79.5%) could define derivative, but seven of them (20.5%) defined it partially. According to the findings, most of the prospective teachers have robust content knowledge on limit and derivative. This result is important because definitions have a virtual role in learning and teaching of mathematics. More specifically, definition is starting point to understand the meaning of a concept. From this point of view, prospective teachers should know the definitions of the concepts to be able to teach them correctly to the students. In addition, they should have knowledge about the relationship between limit and derivative so that they can explain these concepts conceptually. Otherwise, students may memorize the rules of calculating the derivative and the limit. In conclusion, the present study showed that most of the prospective mathematics teachers had enough knowledge about the definition of derivative and limit. However, the rest of them should learn their definition conceptually. The examples of correct, partially correct, and incorrect definition of both concepts will be presented and discussed based on participants’ statements. This study has some implications for instructors. Instructors should be careful about whether students learn the definition of these concepts or not. In order to this, the instructors may give prospective teachers opportunities to discuss the definition of these concepts and the relationship between the concepts.

Keywords: content knowledge, derivative, limit, prospective mathematics teachers

Procedia PDF Downloads 209

Authors: Beth Taylor, Kojima Mituaki, Atsushi Senda, Koji Morishita, Yasuhiro Otomo

Abstract:

Intestinal ischaemia-reperfusion injury drives systemic inflammation and organ failure following trauma/haemorrhagic shock (T/HS), through the release of pro-inflammatory mediators into the mesenteric lymph (ML). However, changes in the biological function of ML are not fully understood, and therefore, a specific model of intestinal ischaemia-reperfusion injury is required to obtain ML for the study of its biological function upon inflammatory cells. ML obtained from a model of intestinal ischaemia-reperfusion injury was used to assess biological function upon inflammatory cells and investigate changes in the biological function of individual ML components. An additional model was used to determine the effect of vagal nerve stimulation (VNS) upon biological function. Rat ML was obtained by mesenteric lymphatic duct cannulation before and after occlusion of the superior mesenteric artery (SMAO). ML was incubated with human polymorphonuclear neutrophils (PMNs), monocytes and lymphocytes, and the biological function of these cells was assessed. ML was then separated into supernatant, exosome and micro-vesicle components, and biological activity was compared in monocytes. A model with an additional VNS phase was developed, in which the right cervical vagal nerve was exposed and stimulated, and ML collected for comparison of biological function with the conventional model. The biological function of ML was altered by intestinal ischaemia-reperfusion injury, increasing PMN activation, monocyte activation, and lymphocyte apoptosis. Increased monocyte activation was only induced by the exosome component of ML, with no significant changes induced by the supernatant or micro-vesicle components. VNS partially attenuated monocyte activation, but no attenuation of PMN activation was observed. Intestinal ischaemia-reperfusion injury induces changes in the biological function of ML upon both innate and adaptive inflammatory cells, supporting the role of intestinal ischaemia-reperfusion injury in driving systemic inflammation following T/HS. The exosome component of ML appears to be critical to the transport of pro-inflammatory mediators in ML. VNS partially attenuates changes in innate inflammatory cell biological activity observed, presenting possibilities for future novel treatment development in multiple organ failure patients.

Keywords: exosomes, inflammation, intestinal ischaemia, mesenteric lymph, vagal stimulation

Procedia PDF Downloads 118

Authors: Jaqueline Bohn Donada

Abstract:

The significance of Euripides’ Medea for contemporary literature is noticeable. Even if the bulk of Classical Reception studies does not tend to look carefully and consistently to the literature produced outside the Anglophone world, Brazilian literature offers abundant materials for such studies. Indeed, a certain Classical background can be observed in Brazilian literature at least since 1975 when Gota d’Água [The Final Straw, in English], a play that recreates the story of Medea and sets it in a favela in Rio de Janeiro. Also worthy of notice is Ivo Bender’s Trilogia Perversa [Perverse Trilogy, in English], a series of three historical plays set in Southern Brazil and based on Aeschylus’ Oresteia and on Euripides’ Iphigenia in Aulis published in the 1980s. Since then, a number of works directly inspired by the plays of Aeschylus, Sophocles and Euripides have been published, not to mention several adaptations of Homer’s two epic poems. This paper proposes a comparative analysis of two such works: Grace Passô’s 2017 play Mata teu Pai [Kill your father, in English] and Marcia Lima’s 2019 play Medeia Negra [Black Medea, in English] from the perspective of Classical Reception Studies in an intersection with feminist literary criticism. The paper intends to look at the endurance of Euripides’ character in contemporary Brazilian literature with a focus on how the character seems to have acquired special relevance to the treatment of pressing issues of the twenty-first century. Whereas Grace Passô’s play sets Medea at the center of a group of immigrant women, Marcia Limma has the character enact the dilemmas of incarcerated women in Brazil. The hypothesis that this research aims at testing is that both artists preserve the pathos of Euripides’s original character at the same time that they recreate his Medea in concrete circumstances of Brazilian contemporary social reality. At the end, the research aims at stating the significance of the Medea theme to contemporary Brazilian literature.

Keywords: Euripides, Medea, Grace Passô, Marcia Limma, Brazilian literature

Procedia PDF Downloads 111

Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin

Abstract:

We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.

Keywords: human action recognition, pose estimation, D-CNN, deep learning

Procedia PDF Downloads 129

Authors: Mamadou Diop, Florence Crick, Momadou Sow, Kate Elizabeth Gannon

Abstract:

Understanding SME leaders’ responses to climate is essential to cope with ongoing changes in temperature and rainfall. This study analyzes the response of SME leaders to the adverse effects of climate change in semi-arid lands (SAL) in Senegal. Based on surveys administrated to 161 SME leaders, this research shows that 91% of economic units are affected by climatic conditions, although 70% do not have a plan to deal with climate risks. Economic actors have striven to take measures to adapt. However, their efforts are limited by various obstacles accentuated by a lack of support from public authorities. In doing so, substantial political, institutional and financial efforts at national and local levels are needed to promote an enabling environment for economic actors to adapt. This will focus on information and training about the threats and opportunities related to global warming, the creation of an adaptation support fund to support local initiatives and the improvement of the institutional, regulatory and political framework.

Keywords: small and medium-sized enterprises, climate change, adaptation, semi-arid lands

Procedia PDF Downloads 192

Authors: Jasper C. Dioco

Abstract:

The study was done to investigate the relationship of taxi company safety climate and drivers’ safety motivation and knowledge on taxi drivers’ safety performance. Data were collected from three Taxi Companies with taxi drivers as participants (N = 84). The Hiligaynon translated version of Transportation Companies’ Climate Scale (TCCS), Safety Motivation and Knowledge Scale, Occupational Safety Motivation Questionnaire and Global Safety Climate Scale were used to study the relationships among four parameters: (a) Taxi company safety climate; (b) Safety motivation; (c) Safety knowledge; and (d) Safety performance. Correlational analyses found that there is no relation between safety climate and safety performance. A Hierarchical regression demonstrated that safety motivation predicts the most variance in safety performance. The results will greatly impact how taxi company can increase safe performance through the confirmation of the proximity of variables to organizational outcome. A strong positive safety climate, in which employees perceive safety to be a priority and that managers are committed to their safety, is likely to increase motivation to be safety. Hence, to improve outcomes, providing knowledge based training and health promotion programs within the organization must be implemented. Policy change might include overtime rules and fatigue driving awareness programs.

Keywords: safety climate, safety knowledge, safety motivation, safety performance, taxi drivers

Procedia PDF Downloads 168

Authors: Warnakulasuriya Arachichige Malisha Ann Rosary Fernando, Udalamatta Gamage Omila Chalanka Jinadasa, Bihini Pabasara Amandi Amarasinghe, Manul Thisuraka Mandalawatta, Uthpala Samarakoon, Manori Gamage

Abstract:

The surge in sexually transmitted diseases (STDs) has become a critical public health crisis demanding urgent attention and action. Like many other nations, Sri Lanka is grappling with a significant increase in STDs due to a lack of education and awareness regarding their dangers. Presently, the available applications for tracking and managing STDs cover only a limited number of easily detectable infections, resulting in a significant gap in effectively controlling their spread. To address this gap and combat the rising STD rates, it is essential to leverage technology and data. Employing technology to enhance the tracking and management of STDs is vital to prevent their further propagation and to enable early intervention and treatment. This requires adopting a comprehensive approach that involves raising public awareness about the perils of STDs, improving access to affordable healthcare services for early detection and treatment, and utilizing advanced technology and data analysis. The proposed mobile application aims to cater to a broad range of users, including STD patients, recovered individuals, and those unaware of their STD status. By harnessing cutting-edge technologies like image detection, symptom-based identification, prevention methods, doctor and clinic recommendations, and virtual counselor chat, the application offers a holistic approach to STD management. In conclusion, the escalating STD rates in Sri Lanka and across the globe require immediate action. The integration of technology-driven solutions, along with comprehensive education and healthcare accessibility, is the key to curbing the spread of STDs and promoting better overall public health.

Keywords: STD, machine learning, NLP, artificial intelligence

Procedia PDF Downloads 64

Authors: Jane O`Halloran

Abstract:

This research aims to identify the issues and challenges of teaching English as a Foreign Language to Japanese university students who have special learning needs. This study sought to investigate factors influencing the academic performance of students with special or additional needs in an inclusive education context. This study will focus on a consideration of the methods available to support those with hearing impairments. While the study population is limited, it is important to give classes to be inclusive places where all students receive equal access to content. Hearing impairments provide an obvious challenge to language learning and, therefore, second-language learning. However, strategies and technologies exist to support the instructor without specialist training. This paper aims to identify these and present them to other teachers of English as a second language who wish to provide the best possible learning experience for every student. Two case studies will be introduced to compare and contrast the experience of in-class teaching and the online option and to share the positives and negatives of the two approaches. While the study focuses on the situation in a university in Japan, the lessons learned by the author may have universal value to any classroom with a student with a hearing disability.

Keywords: inclusive learning, special needs, hearing impairments, teaching strategies

Procedia PDF Downloads 112

Authors: Natalie Cutler, Lorna Moxham, Moira Stephens

Abstract:

Safety is a priority in mental health services, and no more so than in the acute inpatient setting. Mental health service policies and accreditation frameworks commonly approach safety from a risk reduction or elimination perspective leading to service approaches that are arguably more focused on risk than on safety. An exploration what safety means for people who have experienced admission to an acute mental health inpatient unit is currently under way in Sydney, Australia. Using a phenomenographic research approach, this study is seeking to understand the meaning of safety from the perspective of people who use, rather than those who deliver mental health services. Preliminary findings suggest that the meanings of safety for users of mental health services vary from the meanings inherent in the policies and frameworks that inform how mental health services and mental health practice are delivered. This variance has implications for the physical and environmental design of acute mental health inpatient facilities, the policies and practices, and the education and training of mental health staff in particular nurses, who comprise the majority of the mental health workforce. These variances will be presented, along with their implications for the way quality and safety in mental health services are evaluated.

Keywords: acute inpatient, mental health, nursing, phenomenography, recovery, safety

Procedia PDF Downloads 214

Authors: Murat Can Pehlivanoglu

Abstract:

Historically, gender equality has been mainly defended in the legal arenas of constitutional law and employment law. However, social and economic progress has required corporate law to provide gender equality on corporate boards. Recently, following the trend in Europe, the State of California (United States) enacted a law requiring that every publicly traded corporation based in California should have women on its board of directors. Still, the legal, social and economic implications of this law are yet to be discovered. The contractarian view of corporate law is predominant in the U.S. jurisprudence. However, gender quota law may not be justified through contractarian theory grounds. Therefore, the conformity of gender quota law with the general principles of U.S. corporate law remains questionable, and the immunity of close corporations from the scope of gender quota legislation provides support for the discrepancy. The methodology employed in this paper in the discussion of the rule’s conformity with corporate law is doctrinal, and American case law and legal scholarship are the basis for this discussion. This paper uses the aforementioned California law as sample legislation to evaluate the gender quota laws’ conformity with the contractarian theory of corporate law. It chooses California law as the sample due to its newness and the presence of pending shareholder lawsuits against it. Also, since California is home to global companies, the effect of such law is expected to be wider. As alternative theories laid down by corporate law may already be activated to provide gender equality on boards of publicly traded corporations, enacting a specific gender quota law would not be justified by an allegedly present statutory deficiency based on contractarian theory. However, this theoretical reality would not enable shareholders to succeed in their lawsuits against such law on corporate law grounds, and investors will have limited options against its results. This will eventually harm the integrity of the marketplace. Through the analysis of the contractarian theory of corporate law and California gender quota law, the major finding of this paper is that the contractarian theory of corporate law does not permit mandating board room equality through corporate law. In conclusion, it expresses that the issue should be dealt with through separate legislation with a different remedial structure, to preserve the traditional rationale of corporate law in U.S. law.

Keywords: board of directors, gender equality, gender quota, publicly traded corporations

Procedia PDF Downloads 112

Authors: Wilawan Kanhachon, Yodchai Boonprakob, Uraiwon Chatchawan, Junichiro Yamauchi

Abstract:

Introduction: Paslop is a traditional dance from Laos, which is popular in Laos and northeastern of Thailand. This unique type of Paslop dancing is to control body movement with the song. While dancing to the beat, dancers should contract their abdomen and back muscle all the time. Paslop may be a good alternative to improve strengthening, balance and flexibility. Objective: To investigate the effects of Paslop dance exercise on core strength, balance, and flexibility. Methods: Seven healthy participants (age, 20.57±1.13 yrs; height, 162.29±6.16 cm; body mass, 58.14±7.03 kg; mean± S.D.) were volunteered to perform the 45-minute Paslop dance exercise in three times a week for 8 weeks. Before, during and after the exercise period, core strength, balance and flexibility were measured with the pressure biofeedback unit (PBU), one-leg stance test (OLST), and sit and reach test (SAR), respectively. Result: PBU score for core strength increased from 2.12 mmHg in baseline to 6.34 mmHg at the 4th week and 10.10 mmHg at the 8th week after the Paslop dance training, while OLST and SAR did not change. Conclusion: The study demonstrates that 8-week Paslop dancing exercise can improve the core strength.

Keywords: balance, core strength, flexibility, Paslop

Procedia PDF Downloads 369

Authors: Ahmed M. Ashteyat

Abstract:

Artificial Neural Network (ANN) models have been widely used in material modeling, inter-correlations, as well as behavior and trend predictions when the nonlinear relationship between system parameters cannot be quantified explicitly and mathematically. In this paper, ANN was used to predict the residual modulus of elasticity (RME) of self compacted concrete (SCC) damaged by heat. The ANN model was built, trained, tested and validated using a total of 112 experimental data sets, gathered from available literature. The data used in model development included temperature, relative humidity conditions, mix proportions, filler types, and fiber type. The result of ANN training, testing, and validation indicated that the RME of SCC, exposed to different temperature and relative humidity levels, could be predicted accurately with ANN techniques. The reliability between the predicated outputs and the actual experimental data was 99%. This show that ANN has strong potential as a feasible tool for predicting residual elastic modulus of SCC damaged by heat within the range of input parameter. The ANN model could be used to estimate the RME of SCC, as a rapid inexpensive substitute for the much more complicated and time consuming direct measurement of the RME of SCC.

Keywords: residual modulus of elasticity, artificial neural networks, self compacted-concrete, material modeling

Procedia PDF Downloads 515

Authors: Natarajan Tirupattur Srinivasan

Abstract:

Quantum mechanics( QM) and (special) relativity (SR) have indeed revolutionized the very thinking of physicists, and the spectacular successes achieved over a century due to these two theories are mind-boggling. However, there is still a strong disquiet among some physicists. While the mathematical structure of these two theories has been established beyond any doubt, their physical interpretations are still being contested by many. Even after a hundred years of their existence, we cannot answer a very simple question, “What is an electron”? Physicists are struggling even now to come to grips with the different interpretations of quantum mechanics with all their ramifications. However, it is indeed strange that the (special) relativity theory of Einstein enjoys many orders of magnitude of “acceptance”, though both theories have their own stocks of weirdness in the results, like time dilation, mass increase with velocity, the collapse of the wave function, quantum jump, tunnelling, etc. Here, in this paper, it would be shown that by postulating an intrinsic internal motion to these enigmatic electrons, one can build a fairly consistent picture of reality, revealing a very simple picture of nature. This is also evidenced by Schrodinger’s ‘Zitterbewegung’ motion, about which so much has been written. This leads to a helical trajectory of electrons when they move in a laboratory frame. It will be shown that the helix is a three-dimensional wave having all the characteristics of our familiar 2D wave. Again, the helix, being a geodesic on an imaginary cylinder, supports ‘quantization’, and its representation is just the complex exponentials matching with the wave function of quantum mechanics. By postulating the instantaneous velocity of the electrons to be always ‘c’, the velocity of light, the entire relativity comes alive, and we can interpret the ‘time dilation’, ‘mass increase with velocity’, etc., in a very simple way. Thus, this model unifies both QM and SR without the need for a counterintuitive postulate of Einstein about the constancy of the velocity of light for all inertial observers. After all, if the motion of an inertial frame cannot affect the velocity of light, the converse that this constant also cannot affect the events in the frame must be true. But entire relativity is about how ‘c’ affects time, length, mass, etc., in different frames.

Keywords: quantum reconstruction, special theory of relativity, quantum mechanics, zitterbewegung, complex wave function, helix, geodesic, Schrodinger’s wave equations

Procedia PDF Downloads 52

Authors: M. Bartal

Abstract:

A large empirical evidence points to sizable lifetime earnings losses associated with the displacement of tenured workers. The causes of these losses are still not well-understood. Existing explanations are heavily based on human capital depreciation during non-employment spells. In this paper, a new avenue is explored. Evidence on the role of household liquidity constraints in accounting for the persistence of post-displacement earning losses is provided based on SIPP data. Then, a directed search and matching model with endogenous human capital and wealth accumulation is introduced. The model is computationally tractable thanks to its block-recursive structure and highlights a non-trivial, yet intuitive, interaction between wealth and human capital. Constrained workers tend to accept jobs with low firm-sponsored training because the latter are (endogenously) easier to find. This new channel provides a plausible explanation for why young (highly constrained) workers suffer persistent scars after displacement. Finally, the model is calibrated on US data to show that the interplay between wealth and human capital is crucial to replicate the observed lifecycle pattern of earning losses. JEL— E21, E24, J24, J63.

Keywords: directed search, human capital accumulation, job displacement, wealth accumulation

Procedia PDF Downloads 190