Search results for: distributed training

Authors: Natascha C. D'Amico, Enzo Grossi, Giovanni Valbusa, Ala Malasevschi, Gianpiero Cardone, Sergio Papa

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Purpose: To characterize, through a radiomic approach, the nature of areas classified PI-RADS (Prostate Imaging Reporting and Data System) 3/5, recognized in multiparametric prostate magnetic resonance with T2-weighted (T2w), diffusion and perfusion sequences with paramagnetic contrast. Methods and Materials: 24 cases undergoing multiparametric prostate MR and biopsy were admitted to this pilot study. Clinical outcome of the PI-RADS 3/5 was found through biopsy, finding 8 malignant tumours. The analysed images were acquired with a Philips achieva 1.5T machine with a CE- T2-weighted sequence in the axial plane. Semi-automatic tumour segmentation was carried out on MR images using 3DSlicer image analysis software. 45 shape-based, intensity-based and texture-based features were extracted and represented the input for preprocessing. An evolutionary algorithm (a TWIST system based on KNN algorithm) was used to subdivide the dataset into training and testing set and select features yielding the maximal amount of information. After this pre-processing 20 input variables were selected and different machine learning systems were used to develop a predictive model based on a training testing crossover procedure. Results: The best machine learning system (three-layers feed-forward neural network) obtained a global accuracy of 90% ( 80 % sensitivity and 100% specificity ) with a ROC of 0.82. Conclusion: Machine learning systems coupled with radiomics show a promising potential in distinguishing benign from malign tumours in PI-RADS 3/5 areas.

Keywords: machine learning, MR prostate, PI-Rads 3, radiomics

Procedia PDF Downloads 175

Authors: M. E. Joret, F. Germeys, P. Van de Craen

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Background: Previous studies have shown that ‘full’ bilingualism seems to enhance the executive functions in children, young adults and elderly people. Executive functions refer to a complex cognitive system responsible for self-controlled and planned behavior and seem to predict academic achievement. The present study aimed at investigating whether similar effects could be found in children learning their second language at school in immersion education programs. Methods: In this study, 44 children involved in immersion education for 4 to 5 years were compared to 48 children in traditional schools. All children were between 9 and 11 years old. To assess executive functions, the Simon task was used, a neuropsychological measure assessing executive functions with reaction times and accuracy on congruent and incongruent trials. To control for background measures, all children underwent the Raven’s coloured progressive matrices, to measure non-verbal intelligence and the Echelle de Vocabulaire en Images Peabody (EVIP), assessing verbal intelligence. In addition, a questionnaire was given to the parents to control for other confounding variables, such as socio-economic status (SES), home language, developmental disorders, etc. Results: There were no differences between groups concerning non-verbal intelligence and verbal intelligence. Furthermore, the immersion learners showed overall faster reaction times on both congruent and incongruent trials compared to the traditional learners, but only after 5 years of training, not before. Conclusion: These results show that the cognitive benefits found in ‘full’ bilinguals also appear in children involved in immersion education, but only after a sufficient exposure to the second language. Our results suggest that the amount of second language training needs to be sufficient before these cognitive effects may emerge.

Keywords: bilingualism, executive functions, immersion education, Simon task

Procedia PDF Downloads 422

Authors: Yasin Ibrahim, Hisham Almohandes, Chia Hsu, Regina Baronia, Jesse Worsham, Sara Abdelgawad, Mansour Shawky, Mohammed Abdelfattah, Nesif Alhemiary

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Background: The matrix model (MM) is an evidence-based program for treating substance use disorders. Since first translated into Arabic in 2010, the MM has been gaining popularity in Arabic countries. However, there is no published data as pertains to its efficacy and feasibility in Arabic communities. Here we aimed at exploring providers’ perspectives on its feasibility and efficacy. Methods: Eight addiction treatment centers from four Arabic countries, namely Egypt, Kingdom of Saudi Arabia, the United Arab Emirates, and Iraq, were contacted via email. They were asked to fill in a 21-item questionnaire. Results: Matrix model continues to be utilized in 6 out of the 8 contacted programs. One center in Egypt has discontinued the MM as the providers felt it was not suitable for substance disorders other than stimulants, which are not common in Egypt. Baghdad University Medical Center has substituted MM with Colombo Program as there have been more training opportunities available for it. Data showed wide variability in regards to number of clients treated with the MM (from 300 to 2500). The Arabic version was utilized for training providers in 5 out of the 8 centers while the providers of the other 3 have been trained in the United States. All providers reported that MM made their job significantly easier, and seven providers believed that MM has favorably affected the relapse rate. In all of the six centers, MM is being utilized for many substance use disorders in addition to stimulant use disorders. Reported challenges included the acceptability of patients and their families, difficulty understanding some concepts, and high drop rates in some centers. Conclusion: Matrix model seems to be a valuable modality for the treatment of substance use disorders in Arabic countries. It has its own challenges and limitations that call for more culturally adapted versions.

Keywords: addiction, Arabic countries, developing countries, matrix model

Procedia PDF Downloads 141

Authors: Adesuyi Ayodeji Steve, Zahn Munch

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This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

Procedia PDF Downloads 384

Authors: Estephanie S. López Contreras, Vicente Aranda Palacios, Daniela Flores Silva, Felipe Oliveros Olivares, Romina Riquelme Escobedo, Iñaki Westerhout Usabiaga

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This research explored whether the context of online instruction has influenced the development of first-year English-teaching students' communication skills, being these speaking and listening. The theoretical basis finds its niche in the need to bridge the gap in knowledge about the Chilean online educational context and the development of English communicative skills. An interpretative paradigm and a phenomenological design were implemented in this study. Twenty- two first-year students and two teachers from an English teaching training program participated in the study. The students' ages ranged from 18 to 26 years of age, and the teachers' years of experience ranged from 5 to 13 years in the program. For data collection purposes, semi- structured interviews were applied to both students and teachers. Interview questions were based on the initial conceptualization of the central phenomenon. Observations, field notes, and focus groups with the students are also part of the data collection process. Data analysis considered two-cycle methods. The first included descriptive coding for field notes, initial coding for interviews, and creating a codebook. The second cycle included axial coding for both field notes and interviews. After data analysis, the findings show that students perceived online classes as instances in which active communication cannot always occur. In addition, changes made to the curricula as a consequence of the COVID-19 pandemic have affected students' speaking and listening skills.

Keywords: attitudes, communicative skills, EFL teaching training program, online instruction, and perceptions

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Authors: Diana F. Lopes, Jorge Simoes, José Martins, Eduardo Castro

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Health professionals are the keystone of any health system, by delivering health services to the population. Given the time and cost involved in training new health professionals, the planning process of the health workforce is particularly important as it ensures a proper balance between the supply and demand of these professionals and it plays a central role on the Health 2020 policy. In the past 40 years, the planning of the health workforce in Portugal has been conducted in a reactive way lacking a prospective vision based on an integrated, comprehensive and valid analysis. This situation may compromise not only the productivity and the overall socio-economic development but the quality of the healthcare services delivered to patients. This is even more critical given the expected shortage of the health workforce in the future. Furthermore, Portugal is facing an aging context of some professional classes (physicians and nurses). In 2015, 54% of physicians in Portugal were over 50 years old, and 30% of all members were over 60 years old. This phenomenon associated to an increasing emigration of young health professionals and a change in the citizens’ illness profiles and expectations must be considered when planning resources in healthcare. The perspective of sudden retirement of large groups of professionals in a short time is also a major problem to address. Another challenge to embrace is the health workforce imbalances, in which Portugal has one of the lowest nurse to physician ratio, 1.5, below the European Region and the OECD averages (2.2 and 2.8, respectively). Within the scope of the HEALTH 2040 project – which aims to estimate the ‘Future needs of human health resources in Portugal till 2040’ – the present study intends to get a comprehensive dynamic approach of the problem, by (i) estimating the needs of physicians and nurses in Portugal, by specialties and by quinquenium till 2040; (ii) identifying the training needs of physicians and nurses, in medium and long term, till 2040, and (iii) estimating the number of students that must be admitted into medicine and nursing training systems, each year, considering the different categories of specialties. The development of such approach is significantly more critical in the context of limited budget resources and changing health care needs. In this context, this study presents the drivers of the healthcare needs’ evolution (such as the demographic and technological evolution, the future expectations of the users of the health systems) and it proposes a Bayesian methodology, combining the best available data with experts opinion, to model such evolution. Preliminary results considering different plausible scenarios are presented. The proposed methodology will be integrated in a user-friendly decision support system so it can be used by politicians, with the potential to measure the impact of health policies, both at the regional and the national level.

Keywords: bayesian estimation, health economics, health workforce planning, human health resources planning

Procedia PDF Downloads 239

Authors: Birsel Canan Demirbag, Kıymet Yesilcicek Calik, Hacer Kobya Bulut

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Objective: This study was conducted to evaluate the problems of women over 65 with incontinence diagnosis. Methods: This descriptive study was conducted with women over 65 with incontinence diagnosis in four Family Health Centers in a city in Eastern Black Sea region between November 1, and December 20, 2015. 203, 107, 178, 180 women over 65 were registered in these centers and 262 had incontinence diagnosis at least once and had an ongoing complaint. 177 women were volunteers for the study. During home visits and using face-to-face survey methodology, participants were given socio-demographic characteristics survey, Sandvik severity scale, Incontinence Quality of Life Scale, Urogenital Distress Inventory and a questionnaire including challenges experienced due to incontinence developed by the researcher. Data were analyzed with SPSS program using percentages, numbers, Chi-square, Man-Whitney U and t test with 95% confidence interval and a significance level p <0.05. Findings: 67 ± 1.4 was the mean age, 2.05 ± 0.04 was parity, 44.5 ± 2.12 was menopause age, 66.3% were primary school graduates, 45.7% had deceased spouse, 44.4% lived in a large family, 67.2% had their own room, 77.8% had income, 89.2% could meet self- care, 73.2% had a diagnosis of mixed incontinence, 87.5% suffered for 6-20 years % 78.2 had diuretics, antidepressants and heart medicines, 20.5% had urinary fecal cases, 80.5% had bladder training at least once, 90.1% didn’t have bladder diary calendar/control training programs, 31.1% had hysterectomy for prolapse, 97.1'i% was treated with lower urinary tract infection at least once, 66.3% saw a doctor to get drug in the last three months, 76.2 could not go out alone, 99.2 % had at least one chronic disease, 87.6 % had constipation complain, 2.9% had chronic cough., 45.1% fell due to a sudden rise for toilet. Incontinence Impact Questionnaire Average score was (QOL) 54.3 ± 21.1, Sandvik score was 12.1 ± 2.5, Urogenital Distress Inventory was 47.7 ± 9.2. Difficulties experienced due to incontinence were 99.5% feeling of unhappiness, 67.1% constant feeling of urine smell due to failing to change briefs frequently, % 87.2 move away from social life, 89.7 unable to use pad, 99.2% feeling of disturbing households / other individuals, 87.5% feel dizziness/fall due to sudden rise, 87.4% feeling of others’ imperceptions about the situation, % 94.3 insomnia, 78.2 lack of assistance, 84.7% couldn’t afford urine protection briefs. Results: With this study, it was found out that there were a lot of unsolved issues at individual and community level affecting the life quality of women with incontinence. In accordance with this common problem in women, to facilitate daily life it is obvious that regular home care training programs at institutional level in our country will be effective.

Keywords: health problems, incontinence, incontinence quality of life questionnaire, old age, urinary urogenital distress inventory, Sandviken severity, women

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Authors: Ruiyuan Wang, Jing Han, Bruno Di Biase, Mark Antoniou

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The purpose of this study is to explore the relationship between working memory (WM) capacity and Chinese-English consecutive interpreting (CI) performance in interpreting learners with different study abroad experience (SAE). Such relationship is not well understood. This study also examines whether Chinese interpreting learners with SAE in English-speaking countries, demonstrate a better performance in inflectional morphology and agreement, notoriously unstable in Chinese speakers of English L2, in their interpreting output than learners without SAE. Fifty Chinese university students, majoring in Chinese-English Interpreting, were recruited in Australia (n=25) and China (n=25). The two groups matched in age, language proficiency, and interpreting training period. Study abroad (SA) group has been studying in an English-speaking country (Australia) for over 12 months, and none of the students recruited in China (the no study abroad = NSA group) had ever studied or lived in an English-speaking country. Data on language proficiency and training background were collected via a questionnaire. Lexical retrieval performance and working memory (WM) capacity data were collected experimentally, and finally, interpreting data was elicited via a direct CI task. Main results of the study show that WM significantly correlated with participants' CI performance independently of learning context. Moreover, SA outperformed NSA learners in terms of subject-verb number agreement. Apart from that, WM capacity was also found to correlate significantly with their morphosyntactic accuracy. This paper sheds some light on the relationship between study abroad, WM capacity, and CI performance. Exploring the effect of study abroad on interpreting trainees and how various important factors correlate may help interpreting educators bring forward more targeted teaching paradigms for participants with different learning experiences.

Keywords: study abroad experience, consecutive interpreting, working memory, inflectional agreement

Procedia PDF Downloads 87

Authors: Yasmin Hussein Embaby

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Introduction: Athletic Training has its essentials, rules, and methods that help individual in reaching the maximum possible athletic level during the exercised physical activity, therefore; it is important for those working in athletic field to recognize and understand what is going on inside our bodies. This will show the close relationship between physiology and athletic training as the science that explains the various changes that happen to respond to the practice of physical activities. Swimming is one of the water sports that play a major role in influencing the full compatibility of body parts and its systems during the practice of different swimming methods, which uses aqueous to move. It is the initial nucleus in swimming learning and through which the beginner gain a sense of security, safety and the ability to move in aqueous by learning basic skills. Research Methodology: The researcher used the experimental methodology by using pre and post measurement on two equal groups (experimental – control) because it is appropriate for the research. Conclusions: Through the results and information found by the researcher, and in light of the related studies, theoretical readings and the statistical treatments of data; the researcher reached the following conclusions: 1. Muscle relaxation exercises have a positive effect on performance level in crawl swimming and on endorphin hormone as it helps in increasing its normal rater in body, the improvement percentage for experimental group in the relaxation ability, level of endorphin hormone exceeds those of control group. 2. The validity of muscle relaxation exercises proposed for the application, which achieved its objectives, namely increasing the level of endorphin hormone in the body; where research results showed a statistically significant difference in the level of endorphin hormone in favor of the experimental sample.

Keywords: beginners, endorphin hormone, relaxing exercises, swimming

Procedia PDF Downloads 191

Authors: Vilma Deltuvaitė

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Comparisons of financial development across countries are central to answering many of the questions on factors leading to economic development. For this reason this study analyzes the implications of financial system’s development on country’s economic development. The aim of the article: to analyze the impact of financial system’s development on economic development. The following research methods were used: systemic, logical and comparative analysis of scientific literature, analysis of statistical data, time series model (Autoregressive Distributed Lag (ARDL) Model). The empirical results suggest about positive short and long term effect of stock market development on GDP per capita.

Keywords: banking sector, economic development, financial system’s development, stock market, private bond market

Procedia PDF Downloads 365

Authors: M. Olmedilla, M. R. Martinez-Torres, S. L. Toral

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Electronic Word-Of-Mouth (eWOM) communities represent today an important source of information in which more and more customers base their purchasing decisions. They include thousands of reviews concerning very different products and services posted by many individuals geographically distributed all over the world. Due to their massive audience, eWOM communities can help users to find the product they are looking for even if they are less popular or rare. This is known as the long tail effect, which leads to a larger number of lower-selling niche products. This paper analyzes the long tail effect in a well-known eWOM community and defines a tool for finding niche products unavailable through conventional channels.

Keywords: eWOM, online user reviews, long tail theory, product categorization, social network analysis

Procedia PDF Downloads 404

Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

Procedia PDF Downloads 100

Authors: Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir

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Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community.

Keywords: biological ontology, linked data, semantic data integration, semantic web

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Authors: Taonga Temwani Chibaka

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This qualitative research study was conducted to identify the common risk factors that affect the construction industry in Malawi in the building and infrastructure (civil works) projects. The study then evaluates the possible risk responses that are done to mitigate the various risk factors that were identified. I addition the research also established the barriers to risk management implementation with lastly mapping out as where the identified risk factors fall on which stage of the project and then also map out the knowledge areas that need to be worked on the cases on Malawian construction industry in order to mitigate most of the identified risk factors. The study involved the interviewing the professionals from the construction industry in Malawi where insights and ideas were collected, analysed and interpreted. The key study findings show that risks related to clients group are perceived as most critical followed by the contractor related, consultant related and then external group related factors respectively where preventive measures are the most applied risk response technique where the aim to avoid most of the risk factors from happening. Most of the risk factors identified were internal risks and in managerial category which suggested that risk planning was to be emphasized at pre-contract stage to minimize these risks since a bigger percentage of the risk factors were mapped out at implementation stage. Furthermore, barriers to risk management were identified and the key barriers were lack of awareness; lack of knowledge; lack of formal policies in place; regarded as costly and limited time which resulted in proposing that regulating authorities to purposefully introduce intense training on risk management to make known of this new knowledge area. The study then recommends that organisation should formally implement risk management where policies should be introduced to enforce all parties to undertake this. Risk planning was regarded as paramount and this to be done from pre-contract phase so as to mitigate 80% of the risk factors. Finally, training should be done on all project management knowledge areas.

Keywords: risk management, risk factors, risks, malawi

Procedia PDF Downloads 285

Authors: Masayuki Higashino, Toshiya Kawato, Takao Kawamura

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A monolithic service is based on the N-tier architecture in many cases. In order to divide a monolithic service into microservices, it is necessary to redefine a model as a new microservice by extracting and merging existing models across layers. Refactoring a monolithic service into microservices requires advanced technical capabilities, and it is a difficult way. This paper proposes a design and concept to ease the migration of a monolithic service to microservices using the mobile agent technology. Our proposed approach, mobile agents-based design and concept, enables to ease dividing and merging services.

Keywords: mobile agent, microservice, web service, distributed system

Procedia PDF Downloads 148

Authors: Kazuhisa Takagi

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This paper is about movies and dynamic objects for mobile phones. Dynamic objects are the software programmed by JavaScript. They consist of geometric figures and work on HTML5-compliant browsers. Mobile phones are very popular among teenagers. They like watching movies and playing games on them. So, mathematics movies and dynamic objects would enhance teaching and learning processes. In the movies, manga characters speak with artificially synchronized voices. They teach trigonometry together with dynamic mathematical objects. Many movies are created. They are Windows Media files or MP4 movies. These movies and dynamic objects are not only used in the classroom but also distributed to students. By watching movies, students can study trigonometry before or after class.

Keywords: dynamic mathematical object, javascript, google drive, transfer jet

Procedia PDF Downloads 247

Authors: Hsin-Yi Huang, Ming-Sheng Liu, Jiun-Yan Shiau

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Planning the order picking lists of warehouses to achieve when the costs associated with logistics on the operational performance is a significant challenge. In e-commerce era, this task is especially important productive processes are high. Nowadays, many order planning techniques employ supervised machine learning algorithms. However, the definition of which features should be processed by such algorithms is not a simple task, being crucial to the proposed technique’s success. Against this background, we consider whether unsupervised algorithms can enhance the planning of order-picking lists. A Zone2 picking approach, which is based on using clustering algorithms twice, is developed. A simplified example is given to demonstrate the merit of our approach.

Keywords: order picking, warehouse, clustering, unsupervised learning

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Authors: Mera-Martínez Daniela

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Dendropsophus minutus, is a species distributed in South America including the slopes of the Andes, the Amazon basin, forests of southeastern Brazil and in Guyana where tropical forests are characteristic. The relationship of amphibians found in this locality is evidenced by molecular markers, with the objective of analyzing if the geographic distance is influencing the structure of the populations of D. minutus in Guyana; we analyzed 65 sequences from the 3 localities of Guyana where haplotype networks, Mantel Test and phylogeny were realized to know the influence. It was evidenced that there is a haplotypic difference in the locality of Guyana compared to Suriname and French Guyana, but this does not have a correlation with the geographic distance, but this one can be influenced by the conditions of the places.

Keywords: phylogeography, Dendropsophus, geographic distance, molecular markers

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Authors: Shreyansh Adesara, Sneha Pandiya

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The mobile ad-hoc network (MANET) is an important and open area research for the examination and determination of the performance evolution. Temporary ordered routing algorithm (TORA) is adaptable and distributed MANET routing algorithm which is totally dependent on internet MANET Encapsulation protocol (IMEP) for the detection of the link and sensing of the link. If IMEP detect the wrong link failure then the network suffer from congestion and unnecessary route maintenance. Thus, the improvement in link detection method of TORA is introduced by various methods on IMEP by different perspective from different person. There are also different reactive routing protocols like AODV, TORA and DSR has been compared for the knowledge of the routing scenario for different parameter and using different model.

Keywords: IMEP, mobile ad-hoc network, protocol, TORA

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Authors: A. Clementking, C. Jothi Venkateswaran

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Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources.

Keywords: master-slave back propagation neural network model(MSBPNNM), water quality analysis, multivariate analysis, environmental mining

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Authors: Yulan Wu

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With the rapid development of social media, the issue of fake news has gained considerable prominence, drawing the attention of both the public and governments. The widespread dissemination of false information poses a tangible threat across multiple domains of society, including politics, economy, and health. However, much research has concentrated on supervised training models within specific domains, their effectiveness diminishes when applied to identify fake news across multiple domains. To solve this problem, some approaches based on domain labels have been proposed. By segmenting news to their specific area in advance, judges in the corresponding field may be more accurate on fake news. However, these approaches disregard the fact that news records can pertain to multiple domains, resulting in a significant loss of valuable information. In addition, the datasets used for training must all be domain-labeled, which creates unnecessary complexity. To solve these problems, an unsupervised domain knowledge discovery framework for fake news detection is proposed. Firstly, to effectively retain the multidomain knowledge of the text, a low-dimensional vector for each news text to capture domain embeddings is generated. Subsequently, a feature extraction module utilizing the unsupervisedly discovered domain embeddings is used to extract the comprehensive features of news. Finally, a classifier is employed to determine the authenticity of the news. To verify the proposed framework, a test is conducted on the existing widely used datasets, and the experimental results demonstrate that this method is able to improve the detection performance for fake news across multiple domains. Moreover, even in datasets that lack domain labels, this method can still effectively transfer domain knowledge, which can educe the time consumed by tagging without sacrificing the detection accuracy.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Jamerson Felipe Pereira Lima, Jeane Cecília Bezerra de Melo

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The difficulty and cost related to obtaining of protein tertiary structure information through experimental methods, such as X-ray crystallography or NMR spectroscopy, helped raising the development of computational methods to do so. An approach used in these last is prediction of tridimensional structure based in the residue chain, however, this has been proved an NP-hard problem, due to the complexity of this process, explained by the Levinthal paradox. An alternative solution is the prediction of intermediary structures, such as the secondary structure of the protein. Artificial Intelligence methods, such as Bayesian statistics, artificial neural networks (ANN), support vector machines (SVM), among others, were used to predict protein secondary structure. Due to its good results, artificial neural networks have been used as a standard method to predict protein secondary structure. Recent published methods that use this technique, in general, achieved a Q3 accuracy between 75% and 83%, whereas the theoretical accuracy limit for protein prediction is 88%. Alternatively, to achieve better results, support vector machines prediction methods have been developed. The statistical evaluation of methods that use different AI techniques, such as ANNs and SVMs, for example, is not a trivial problem, since different training sets, validation techniques, as well as other variables can influence the behavior of a prediction method. In this study, we propose a prediction method based on artificial neural networks, which is then compared with a selected SVM method. The chosen SVM protein secondary structure prediction method is the one proposed by Huang in his work Extracting Physico chemical Features to Predict Protein Secondary Structure (2013). The developed ANN method has the same training and testing process that was used by Huang to validate his method, which comprises the use of the CB513 protein data set and three-fold cross-validation, so that the comparative analysis of the results can be made comparing directly the statistical results of each method.

Keywords: artificial neural networks, protein secondary structure, protein structure prediction, support vector machines

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Authors: Samir Bentata, Fatima Bendahma

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We have numerically investigated the effect of Aluminium concentration on the the laser wavelength of random trimer barrier AlxGa1-xAs superlattices (RTBSL). Such systems consist of two different structures randomly distributed along the growth direction, with the additional constraint that the barriers of one kind appear in triply. An explicit formula is given for evaluating the transmission coefficient of superlattices (SL's) with intentional correlated disorder. The method is based on Airy function formalism and the transfer-matrix technique. We discuss the impact of the Aluminium concentration associate to the structure profile on the laser wavelengths.

Keywords: superlattices, correlated disorder, transmission coefficient, laser wavelength

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Authors: Mohanad Moustafa, Julia Sieberer, Rhys Davies

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Background: Orbital compartment syndrome (OCS) is a sight-threatening Ophthalmologic emergency caused by rapidly increasing intraorbital pressure. It is usually caused by a retrobulbar hemorrhage as a result of trauma. If not treated in a timely manner, permanent vision loss can occur. Lateral canthotomy and cantholysis are minor procedures that can be performed bedside with equipment available in the emergency department. The aim of the procedure is to release the attachments between the suspensory ligaments of the eye and the bony orbital wall, leading to a decrease in intraorbital pressure and preventing irreversible loss of vision. As most Ophthalmologists across the UK provide non-resident on-call service, this may lead to a delay in the treatment of OCS and stresses the need for Emergency medical staff to be able to provide this sight-saving procedure independently. Aim: To survey current training, experience, and confidence levels among Emergency Medicine doctors in performing emergency lateral canthotomy and to establish whether these variables change the following teaching from experienced ophthalmologists. RESULTS: Most EM registrars had little to no experience in performing lateral canthotomy and cantholysis. The majority of them showed a significant increase in their confidence to perform the procedure following ophthalmic-led teaching. The survey also showed that the registrars felt such training should be added to/part of the EM curriculum. Conclusion: The involvement of Ophthalmologists in the teaching of EM doctors to recognise and treat OCS independently may prevent delays in treatment and reduce the risk of permanent sight loss. This project showed potential in improving patient care and will lead to a National Survey of EM doctors across the UK.

Keywords: lateral canthotomy, retrobulbar hemorrhage, Ophthalmology, orbital compartment syndrome, sight loss, blindness

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Authors: Yehjune Heo

Abstract:

As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: anti-spoofing, CNN, fingerprint recognition, loss function, optimizer

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Authors: Shaista Meghani, Rozina Karmaliani, Khairulnissa Ajani, Shireen Shahzad, Nadeem Ullah Khan

Abstract:

Background: The concept of Family Presence During Resuscitation (FPDR) is gradually gaining recognition in western countries, however, it is rarely considered in South Asian countries including Pakistan. Over time, patients’ and families’ rights have gained recognition and healthcare has progressed to become more patient-family centered. Objectives: The objective of this study was to evaluate the impact of an educational program on the Knowledge, Attitude, and Practices (KAP) of healthcare professionals (HCPs) towards FPDR in Emergency Department (ED), at a tertiary care setting, in Karachi, Pakistan. Methods: This was a Pre-test and Post-test study design. A convenient universal sampling was done, and all ED nurses and physicians with more than one year of experience were eligible. The intervention included one-hour training sessions for physicians (three sessions) and nurses (eight sessions), The KAP of nurses and physicians were assessed immediately after (post-test I), and two weeks(post-test II) after the intervention using a pretested questionnaire. Results: The findings of the study revealed that the mean scores of knowledge and attitude of HCPs at both time points were statistically significant (p-value=<0.001), however, an insignificant difference was found on practice of FPDR (p-value=>0.05). Conclusion: The study findings recommend that the educational program on FPDR for HCPs needs to be offered on an ongoing basis. Moreover, training modules need to be developed for the staff, and formal guidelines need to be proposed for FPDR, through a multidisciplinary team approach.

Keywords: family presence, cardiopulmonary resuscitation, attitude, education, practices, health care professionals

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Authors: Natalia Bustos Sierra, Katrien Tersago

Abstract:

It is currently proven that both extremes of temperature are associated with increased mortality and that air pollution is associated with temperature. This relationship is complex, and in countries with important seasonal variations in weather such as Belgium, some effects can appear as non-significant when the analysis is done over the entire year. We, therefore, analyzed the effect of short-term outdoor air pollution exposure on cardiovascular mortality during the warmer and colder months separately. We used daily cardiovascular deaths from acute cardiovascular diagnostics according to the International Classification of Diseases, 10th Revision (ICD-10: I20-I24, I44-I49, I50, I60-I66) during the period 2008-2013. The environmental data were population-weighted concentrations of particulates with an aerodynamic diameter less than 10 µm (PM₁₀) and less than 2.5 µm (PM₂.₅) (daily average), nitrogen dioxide (NO₂) (daily maximum of the hourly average) and ozone (O₃) (daily maximum of the 8-hour running mean). A Generalized linear model was applied adjusting for the confounding effect of season, temperature, dew point temperature, the day of the week, public holidays and the incidence of influenza-like illness (ILI) per 100,000 inhabitants. The relative risks (RR) were calculated for an increase of one interquartile range (IQR) of the air pollutant (μg/m³). These were presented for the four hottest months (June, July, August, September) and coldest months (November, December, January, February) in Belgium. We applied both individual lag model and unconstrained distributed lag model methods. The cumulative effect of a four-day exposure (day of exposure and three consecutive days) was calculated from the unconstrained distributed lag model. The IQR for PM₁₀, PM₂.₅, NO₂, and O₃ were respectively 8.2, 6.9, 12.9 and 25.5 µg/m³ during warm months and 18.8, 17.6, 18.4 and 27.8 µg/m³ during cold months. The association with CV mortality was statistically significant for the four pollutants during warm months and only for NO₂ during cold months. During the warm months, the cumulative effect of an IQR increase of ozone for the age groups 25-64, 65-84 and 85+ was 1.066 (95%CI: 1.002-1.135), 1.041 (1.008-1.075) and 1.036 (1.013-1.058) respectively. The cumulative effect of an IQR increase of NO₂ for the age group 65-84 was 1.066 (1.020-1.114) during warm months and 1.096 (1.030-1.166) during cold months. The cumulative effect of an IQR increase of PM₁₀ during warm months reached 1.046 (1.011-1.082) and 1.038 (1.015-1.063) for the age groups 65-84 and 85+ respectively. Similar results were observed for PM₂.₅. The short-term effect of air pollution on cardiovascular mortality is greater during warm months for lower pollutant concentrations compared to cold months. Spending more time outside during warm months increases population exposure to air pollution and can, therefore, be a confounding factor for this association. Age can also affect the length of time spent outdoors and the type of physical activity exercised. This study supports the deleterious effect of air pollution on cardiovascular mortality (CV) which varies according to season and age groups in Belgium. Public health measures should, therefore, be adapted to seasonality.

Keywords: air pollution, cardiovascular, mortality, season

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Authors: R. S. Al Abri, M. H. Albadi, M. H. Al Abri, U. K. Al Rasbi, M. H. Al Hasni, S. M. Al Shidi

Abstract:

This paper presents an analysis study on the impacts of the changes of the capacitor banks, the loss of a transformer, and the installation of distributed generation on the voltage total harmonic distortion and harmonic resonance. The study is applied in a real system in Oman, Sohar Industrial Port–C Substation Network. Frequency scan method and Fourier series analysis method are used with the help of EDSA software. Moreover, the results are compared with limits specified by national Oman distribution code.

Keywords: power quality, capacitor bank, voltage total harmonics distortion, harmonic resonance, frequency scan

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Authors: Boularas El-Alia, Arbi Raja, Bachir Bouiadjra Sara, Rezk-Kallah Haciba, Rezkkallah Baghdad

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Objectives : To determine the handling practices of cytotoxic drugs and to describe clinical manifestations expressed by hospital personnel of Sidi Bel Abbes during the year 2014. Methods: Sectional descriptive study conducted in 3 center university hospital units (Hematology, Oncology and Urology) and Gynecology of EHS Sidi Bel Abbes. A questionnaire was administered to hospital workers regulary exposed to cytotoxic drugs. A work-place visit was performed to have an overview about working conditions. The Cytotoxic Contact Index (CCI) was calculated for each nurse on a period of 15 working days. Treatment of the results was done using SPSS software. Results: The survey reveals that 22 men and 58 women are exposed to cytotoxic drugs for an average of 7 years. Many symptoms such as ocular irritation (38,75%), throat irritation (56,25%), headache (68,75%), dizziness (43,75%), nausea (37,5%), metallic taste (30%), were reported with high frequency. Are noted in the offspring, 3 congenital anomalies,2 diaphragmatic hernia and a cleft palate. The Cytotoxic Contact Index (CCI) was higher than 3 among Oncology nurses and higher than 1 for most of the nurses of Hematology and Gynecology service. The wearing of personal protective clothing was not respected by all workers: (22/23) wear gloves and (20/23) wear a mask,(5/23) wear a cap, (2/23) wear glasses. Only 3 nurses have benefited from continuous training on handling cytotoxic drugs. Conclusion: This study shows a high occupational exposure risk to cytotoxic drugs among persons handling these drugs and the necessity to apply rigorously all measures related to personal protection awareness and training of personnel to minimize these exposure.

Keywords: cytotoxic drugs, handling, clinical manifestations, hospital staff

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Authors: Marc Sebastián-Romagosa, Woosang Cho, Rupert Ortner, Christy Li, Christoph Guger

Abstract:

Neurorehabilitation based on Brain-Computer Interfaces (BCIs) shows important rehabilitation effects for patients after stroke. Previous studies have shown improvements for patients that are in a chronic stage and/or have severe hemiparesis and are particularly challenging for conventional rehabilitation techniques. For this publication, seven stroke patients in the chronic phase with hemiparesis in the lower extremity were recruited. All of them participated in 25 BCI sessions about 3 times a week. The BCI system was based on the Motor Imagery (MI) of the paretic ankle dorsiflexion and healthy wrist dorsiflexion with Functional Electrical Stimulation (FES) and avatar feedback. Assessments were conducted to assess the changes in motor improvement before, after and during the rehabilitation training. Our primary measures used for the assessment were the 10-meters walking test (10MWT), Range of Motion (ROM) of the ankle dorsiflexion and Timed Up and Go (TUG). Results show a significant increase in the gait speed in the primary measure 10MWT fast velocity of 0.18 m/s IQR = [0.12 to 0.2], P = 0.016. The speed in the TUG was also significantly increased by 0.1 m/s IQR = [0.09 to 0.11], P = 0.031. The active ROM assessment increased 4.65º, and IQR = [ 1.67 - 7.4], after rehabilitation training, P = 0.029. These functional improvements persisted at least one month after the end of the therapy. These outcomes show the feasibility of this BCI approach for chronic stroke patients and further support the growing consensus that these types of tools might develop into a new paradigm for rehabilitation tools for stroke patients. However, the results are from only seven chronic stroke patients, so the authors believe that this approach should be further validated in broader randomized controlled studies involving more patients. MI and FES-based non-invasive BCIs are showing improvement in the gait rehabilitation of patients in the chronic stage after stroke. This could have an impact on the rehabilitation techniques used for these patients, especially when they are severely impaired and their mobility is limited.

Keywords: neuroscience, brain computer interfaces, rehabilitat, stroke

Procedia PDF Downloads 80