Search results for: smart training

Authors: Zeta Dooly, Aidan Duane

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The collaborative European funded research and development landscape provides prime environmental conditions for multi-disciplinary teams to learn and enhance their knowledge beyond the capability of training and learning within their own organisation cocoons. Whilst the emergence of the academic entrepreneur has changed the focus of educational institutions to that of quasi-businesses, the training and professional development of lecturers and academic staff are often not formalised to the same level as industry. This research focuses on industry and academic collaborative research funded by the European Commission. The impact of research is scalable if an optimum research network is created and managed effectively. This paper investigates network embeddedness, the nature of relationships, links, and nodes within a research network, and the enhancement of the network’s knowledge. The contribution of this paper extends our understanding of establishing and maintaining effective collaborative research networks. The effects of network embeddedness are recognized in the literature as pertinent to innovation and the economy. Network theory literature claims that networks are essential to innovative clusters such as Silicon valley and innovation in high tech industries. This research provides evidence to support the impact collaborative research has on the disparate individuals toward their innovative contributions to their organisations and their own professional development. This study adopts a qualitative approach and uncovers some of the challenges of multi-disciplinary research through case study insights. The contribution of this paper recommends the establishment of scaffolding to accommodate cooperation in research networks, role appointment, and addressing contextual complexities early to avoid problem cultivation. Furthermore, it suggests recommendations in relation to network formation, intra-network challenges in relation to open data, competition, friendships, and competency enhancement. The network capability is enhanced by the adoption of the relevant theories; network theory, open innovation, and social exchange, with the understanding that the network structure has an impact on innovation and social exchange in research networks. The research concludes that there is an opportunity to deepen our understanding of the impact of network reuse and network hoping that provides scaffolding for the network members to enhance and build upon their knowledge using a progressive approach.

Keywords: research networks, competency building, network theory, case study

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Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri

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Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.

Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing

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Authors: Ahmad Alwosheel, Ahmed Alqaraawi

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This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process.

Keywords: Quran, speaker recognition, reciter recognition, Gaussian Mixture Model

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we want to determine whether the accurate prediction of nicotine concentration can be obtained by using a network of smart objects and an e-cigarette. The approach consists of, first, the recognition of factors influencing smoking cessation such as physical activity recognition and participant’s behaviors (using both smartphone and smartwatch), then the prediction of the configuration of the e-cigarette (in terms of nicotine concentration, power, and resistance of e-cigarette). The study uses a network of commonly connected objects; a smartwatch, a smartphone, and an e-cigarette transported by the participants during an uncontrolled experiment. The data obtained from sensors carried in the three devices were trained by a Long short-term memory algorithm (LSTM). Results show that our LSTM-based model allows predicting the configuration of the e-cigarette in terms of nicotine concentration, power, and resistance with a root mean square error percentage of 12.9%, 9.15%, and 11.84%, respectively. This study can help to better control consumption of nicotine and offer an intelligent configuration of the e-cigarette to users.

Keywords: Iot, activity recognition, automatic classification, unconstrained environment

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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Authors: Ayman M. Mansour

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In this paper, we propose an intelligent system that is used for monitoring the health conditions of Patients. Monitoring the health condition of Patients is a complex problem that involves different medical units and requires continuous monitoring especially in rural areas because of inadequate number of available specialized physicians. The proposed system will Improve patient care and drive costs down comparing to the existing system in Jordan. The proposed system will be the start point to Faster and improve the communication between different units in the health system in Jordan. Connecting patients and their physicians beyond hospital doors regarding their geographical area is an important issue in developing the health system in Jordan. The propose system will provide an intelligent system that will generate initial diagnosing to the patient case. This will assist and advice clinicians at the point of care. The decision is based on demographic data and laboratory test results of patient data. Using such system with the ability of making medical decisions, the quality of medical care in Jordan and specifically in Tafial is expected to be improved. This will provide more accurate, effective, and reliable diagnoses and treatments especially if the physicians have insufficient knowledge.

Keywords: GSM, SMS, patient, monitoring system, fuzzy logic, multi-agent system

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Authors: Shihab-Uz-Zaman Shah, Sanjeeb Roy, Habiba Akter

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Keeping the high impact of the Ready-Made Garments (RMG) on the country’s economic growth in mind, this research paves a way for the implementation of Industry 4.0 in the garments industry of Bangladesh. At present, Industry 4.0 is a common buzzword representing the adoption of digital technologies in the production process to transform the existing industries into smart factories and create a great change in the global value chain. The RMG industry is the largest industrial sector of Bangladesh which provides 12.26% to its National GDP (Gross Domestic Product). The work starts with identifying possible enablers of Industry 4.0. To evaluate the enablers, a Multiple-Criteria Decision-Making (MCDM) procedure named Fuzzy Analytical Hierarchy Process (FAHP) was used. A questionnaire was developed as a part of a survey for collecting and analyzing expert opinions from relevant academicians and industrialists. The responses were eventually used as the input for the FAHP which helped to assign weight matrices to the enablers. This weight matrix indicated the level of importance of these enablers. The full paper will discuss the way of a successful evaluation of the enablers and implementation of Industry 4.0 by using these enablers.

Keywords: enablers, fuzzy AHP, industry 4.0, RMG sector

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Authors: Rama Bhattacharyya, S. N. Ojha, Umesh K. Singh

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Anthropogenic control on groundwater chemistry has emerged as a critical concern now-a-days, especially in the industrial areas. In view of this, a comprehensive study on the distribution of the heavy metal in the groundwater was conducted to investigate the impact of urbanization in the aquatic media. Water samples either from well or borehole from Fourty different sites in and around, Durgapur, West Bengal were collected for this purpose. The samples were analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for Calcium (Ca), Cobalt (Co), Chromium (Cr), Copper (Cu), Iron (Fe), Potassium (K), Magnesium (Mg), Manganese (Mn), Sodium (Na), Nickel (Ni), Lead (Pb), Zinc (Zn) content and the levels were compared with WHO specified maximum contaminant level as well as permissible limits given by the Bureau of Indian Standards (BIS). The result obtained from the present study indicates a significant risk to the population of this important emerging ‘smart city’ of eastern India. Because of the toxicity of these metals and the fact that for many tube-wells, dug-wells and bore-wells are the only sources of the water supply for a major fraction of the population in this environment. In this study, an attempt has been made to develop metal contamination risk map.

Keywords: heavy metals, ground water, maximum contamination level, ICP-MS

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Authors: Christos Spandonidis, Stefanos Tsantilas, Elias Sedikos, Nektarios Galiatsatos, Fotios Giannopoulos, Panagiotis Papadopoulos, Nikolaos Demagos, Dimitrios Reppas, Christos Giordamlis

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In the present paper, a low cost, compact and modular Internet of Things (IoT) platform for air quality monitoring in urban areas is presented. This platform comprises of dedicated low cost, low power hardware and the associated embedded software that enable measurement of particles (PM2.5 and PM10), NO, CO, CO₂ and O₃ concentration in the air, along with relative temperature and humidity. This integrated platform acts as part of a greater air pollution data collecting wireless network that is able to monitor the air quality in various regions and neighborhoods of an urban area, by providing sensor measurements at a high rate that reaches up to one sample per second. It is therefore suitable for Big Data analysis applications such as air quality forecasts, weather forecasts and traffic prediction. The first real world test for the developed platform took place in Thessaloniki, Greece, where 16 devices were installed in various buildings in the city. In the near future, many more of these devices are going to be installed in the greater Thessaloniki area, giving a detailed air quality map of the city.

Keywords: distributed sensor system, environmental monitoring, Internet of Things, smart cities

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Authors: Ruchika Goyal, Ashwani Kumar, Sandeep Jain

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The experimental data for anticancer potential of curcumin nanoparticles was calculated by means of eclectic data. The optimal descriptors were examined using Monte Carlo method based CORAL SEA software. The statistical quality of the model is following: n = 14, R² = 0.6809, Q² = 0.5943, s = 0.175, MAE = 0.114, F = 26 (sub-training set), n =5, R²= 0.9529, Q² = 0.7982, s = 0.086, MAE = 0.068, F = 61, Av Rm² = 0.7601, ∆R²m = 0.0840, k = 0.9856 and kk = 1.0146 (test set) and n = 5, R² = 0.6075 (validation set). This data can be used to build predictive QSAR models for anticancer activity.

Keywords: anticancer potential, curcumin, model, nanoparticles, optimal descriptors, QSAR

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Authors: Esha Banerjee, Atul Kumar Ojha, Girish Nath Jha

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This study aims to develop an intonation labeled database for Hindi. Although no single standard for prosody labeling exists in Hindi, researchers in the past have employed perceptual and statistical methods in literature to draw inferences about the behavior of prosody patterns in Hindi. Based on such existing research and largely agreed upon intonational theories in Hindi, this study attempts to develop a manually annotated prosodic corpus of Hindi speech data, which can be used for training speech models for natural-sounding speech in the future. 100 sentences ( 500 words) each for declarative and interrogative types have been labeled using Praat.

Keywords: speech dataset, Hindi, intonation, labeled corpus

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Authors: Sophia El Ouazzani, Amer M. Burhan, Mary Wickenden

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Background: Despite improvements in recent years, the Moroccan mental healthcare system still face disparity between available resources and the current population’sneeds. The societal stigma, and limited economic, political, and human resources are all factors in shaping the psychiatric system, exacerbating the discontinuity of services for users after discharged from the hospital. As a result, limited opportunities for social inclusion and meaningful community engagement undermines human rights and recovery potential for people with mental health problems, especially those with psychiatric disabilities from serious mental illness (SMI). Recovery-oriented practice, such as mental health rehabilitation, addresses the complex needs of patients with SMI and support their community inclusion. The cultural acceptability of recovery-oriented practice is an important notion to consider for a successful implementation. Exploring the extent to which recovery-oriented practices are used in Morocco is a necessary first step to assess the cultural relevance of such a practice model. Aims: This study aims to explore understanding and knowledge, perception, and perspective about core concepts in mental health rehabilitation, including psychiatric disability, recovery, and engagement in meaningful occupations for people with SMI in Morocco. Methods: A pilot qualitative study was undertaken. Data was collected via semi-structured interviews and focusgroup discussions with healthcare professional students. Questions were organised around the following themes: 1) students’ perceptions, understanding, and expectations around concepts such as SMI, mental health disability, and recovery, and 2) changes in their views and expectations after starting their professional training. Further analysis of students’ perspectives on the concept of ‘meaningful occupation’ and how is this viewed within the context of the research questions was done. The data was extracted using an inductive thematic analysis approach. This is a pilot stage of a doctoral project, further data will be collected and analysed until saturation is reached. Results: A total of eight students were included in this study which included occupational therapy and mental health nursing students receiving training in Morocco. The following themes emerged as influencing students’ perceptions and views around the main concepts: 1) Stigma and discrimination, 2) Fatalism and low expectations, 3) Gendered perceptions, 4) Religious causation, 5) Family involvement, 6) Professional background, 7) Inaccessibility of services and treatment. Discussion/Contribution: Preliminary analysis of the data suggests that students’ perceptions changed after gaining more clinical experiences and being exposed to people with psychiatric disabilities. Prior to their training, stigma shaped greatly how they viewed people with SMI. The fear, misunderstanding, and shame around SMI and their functional capacities may contribute to people with SMI being stigmatizedand marginalised from their family and their community. Religious causations associated to SMIsare understood as further deepening the social stigma around psychiatric disability. Perceptions are influenced by gender, with women being doubly discriminated against in relation to recovery opportunities. Therapeutic pessimism seems to persist amongst students and within the mental healthcare system in general and regarding the recovery potential and opportunities for people with SMI. The limited resources, fatalism, and stigma all contribute to the low expectations for recovery and community inclusion. Implications and future directions will be discussed.

Keywords: disability, mental health rehabilitation, recovery, serious mental illness, transcultural psychiatry

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Authors: Ahmed Amrani, Oussama Allali, Amira Ben Hamida, Felix Defrance, Stephanie Morland, Eva Pineau, Thomas Lacroix

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The energy planning process is a very complex task that involves several stakeholders and requires the consideration of several local and global factors and constraints. In order to optimize and simplify this process, we propose a tool-based iterative approach applied to district heating planning. We build our tool with the collaboration of a French territory using actual district data and implementing the European incentives. We set up an iterative process including data visualization and analysis, identification and extraction of information related to the area concerned by the operation, design of sustainable planning scenarios leveraging local renewable and recoverable energy sources, and finally, the evaluation of scenarios. The last step is performed by a dynamic digital twin replica of the city. Territory’s energy experts confirm that the tool provides them with valuable support towards sustainable energy planning.

Keywords: climate change, data management, decision support, digital twin, district heating, energy planning, renewables, smart city

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Authors: Ashraf Ibrahim Awad

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It is important to formulate suitable learning environments ca-pable to be customized according to value perceptions of the university. In this paper, light is shed on the concepts of integration between knowledge management (KM), and e-learning (EL) in the higher education sector of the economy in Abu Dhabi Emirate, United Arab Emirates (UAE). A discussion on and how KM and EL can be integrated and leveraged for effective education and training is presented. The results are derived from the literature and interviews with 16 of the academics in eight universities in the Emirate. The conclusion is that KM and EL have much to offer each other, but this is not yet reflected at the implementation level, and their boundaries are not always clear. Interviews have shown that both concepts perceived to be closely related and, responsibilities for these initiatives are practiced by different departments or units.

Keywords: knowledge management, e-learning, learning integration, universities, UAE

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Authors: Mustapha Majid

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Gone were the days when technicians/mechanics will have to spend too much time trying to identify a mechanical fault and rectify the problem. Now the emphasis is on the use of Automobile diagnosing Equipment through the use of computers and special software. An investigation conducted at Tamale Metropolis and Accra in the Northern and Greater Accra regions of Ghana, respectively. Methodology for data gathering were; questionnaires, physical observation, interviews, and newspaper. The study revealed that majority of mechanics lack computer skills which can enable them use diagnosis tools such as Exhaust Gas Analyzer, Scan Tools, Electronic Wheel Balancing machine, etc.

Keywords: diagnosing, local garages and modern garages, lack of knowledge of diagnosing posing an existential threat, training of local mechanics

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Authors: Chen Xi, Liu Xuebing, Lao Xuerui, Kuan Sinman, Jiang Yike, Wang Hanwei, Yang Xiaolang, Zhou Junjie, Xie Jinpeng

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Climate change is a global problem facing humanity and this is now the consensus of the mainstream scientific community. In accordance with the carbon peak and carbon neutral targets and visions set out in the United Nations Framework Convention on Climate Change, the Kyoto Protocol and the Paris Agreement, this project uses the City Intelligent Model (CIM) and Artificial Intelligence Machine Vision (ICR) as the core technologies to accurately quantify low carbon behaviour into green corn, which is a means of guiding ecologically sustainable living patterns. Using individual communities as management units and blockchain as a guarantee of fairness in the whole cycle of green currency circulation, the project will form a modern resident carbon track management system based on the principle of enhancing the ecological resilience of communities and the cohesiveness of community residents, ultimately forming an ecologically sustainable smart village that can be self-organised and managed.

Keywords: urban planning, urban governance, CIM, artificial Intelligence, sustainable development

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Authors: Mojtaba Ghodsi, Hamidreza Ziaifar, Morteza Mohammadzaheri, Payam Soltani

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This article presents an analytical model to estimate the harvested power from a Magnetostrictive cantilevered beam with tip excitation. Furthermore, the effects of internal and external damping on harvested power are investigated. The magnetostrictive material in this harvester is Galfenol. In comparison to other popular smart materials like Terfenol-D, Galfenol has higher strength and machinability. In this article, first, a mechanical model of the Euler-Bernoulli beam is employed to calculate the deflection of the harvester. Then, the magneto-mechanical equation of Galfenol is combined with Faraday's law to calculate the generated voltage of the Magnetostrictive cantilevered beam harvester. Finally, the beam model is incorporated in the aforementioned combination. The results show that a 30×8.5×1 mm Galfenol cantilever beam harvester with 80 turn pickup coil can generate up to 3.7 mV and 9 mW. Furthermore, sensitivity analysis made by Response Surface Method (RSM) shows that the harvested power is only sensitive to the internal damping coefficient.

Keywords: internal damping coefficient, external damping coefficient, euler-bernoulli, energy harvester, galfenol, magnetostrictive, response surface method

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Authors: Abas Mohsenzadeh, Tariq Bashir, Waseen Tahir, Ulf Stigh, Mikael Skrifvars, Kim Bolton

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The area of artificial muscles has received significant attention from many research domains including soft robotics, biomechanics and smart textiles in recent years. Poly(vinylidene fluoride) (PVDF) has been used to form artificial muscles since it contracts upon heating when under load. In this study, PVDF fibers were produced by melt spinning technique at different solid state draw ratios and then actuation mechanism for PVDF tie molecules within the semicrystalline region of PVDF polymer has been investigated using molecular dynamics simulations. Tie molecules are polymer chains that link two (or more) crystalline regions in semicrystalline polymers. The changes in fiber length upon heating have been investigated using a novel simulation technique. The results show that conformational changes of the tie molecules from the longer all-trans conformation at low temperature (β structure) to the shorter conformation (α structure) at higher temperature accrue by increasing the temperature. These results may be applied to understand the actuation observed for PVDF upon heating.

Keywords: poly(vinylidene fluoride), molecular dynamics, simulation, actuators, tie molecules, semicrystalline

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Authors: Ivy Jeralyn T. Andres, Eva B. Macugay

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Using a descriptive research design utilizing the Research and Development (R&D) methodology, this study focused on the development of Reproductive Health Education (RHE) Toolkit for Science Teachers that provides a guide in teaching reproductive health. Based on the findings, the teacher-respondents identified nine topics that can be included in the development of the RHE toolkit. The topics included are The Male Reproductive System, The Female Reproductive System, The Roles of Hormones in Male and Female Reproductive System, Menstrual Cycle, Fertilization, Pregnancy and Childbirth, Breastfeeding, Human Reproductive and Developmental Concerns and Reproductive Health Management and Diseases. The developed RHE Toolkit is remarked as very highly valid and very highly acceptable learning material. The validators and evaluators acknowledged the developed RHE toolkit as clear, creative, and academically useful supplemental material for educating reproductive health. Moreover, it follows the principles of SMART objectives, factual, timely, and relevant content for both learners and the community as a whole. Science teachers should employ the RHE Toolkit in teaching reproductive health education into their respective classes. It is also suggested that the developed RHE toolkit can be implemented to elementary pupils and the community, particularly in rural areas.

Keywords: reproductive health education, toolkit, science teachers, supplemental material

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Authors: Abdreheman Seid Abdella

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This study examined instructors willingness, self-efficacy beliefs, attitudes and knowledge about provisions of instructional accommodations for students with disabilities in universities. Major concepts used in this study operationally defined and some models of disability were reviewed. Questionnaires were distributed to a total of 181 instructors from four universities and quantitative data was generated. Then to analyze the data, appropriate methods of data analysis were employed. The result indicated that on average instructors had positive willingness, strong self-efficacy beliefs and positive attitudes towards providing instructional accommodations. In addition, the result showed that the majority of participants had moderate level of knowledge about provision of instructional accommodations. Concerning the relationship between instructors background variables and dependent variables, the result revealed that location of university and awareness raising training about Inclusive Education showed statistically significant relationship with all dependent variables (willingness, self-efficacy beliefs, attitudes and knowledge). On the other hand, gender and college/faculty did not show a statistically significant relationship. In addition, it was found that among the inter-correlation of dependent variables, the correlation between attitudes and willingness to provide accommodations was the strongest. Furthermore, using multiple linear regression analysis, this study also indicated that predictor variables like self-efficacy beliefs, attitudes, knowledge and teaching methodology training made statistically significant contribution to predicting the criterion willingness. Predictor variables like willingness and attitudes made statistically significant contribution to predicting self-efficacy beliefs. Predictor variables like willingness, Special Needs Education course and self-efficacy beliefs made statistically significant contribution to predict attitudes. Predictor variables like Special Needs Education courses, the location of university and willingness made statistically significant contribution to predicting knowledge. Finally, using exploratory factor analysis, this study showed that there were four components or factors each that represent the underlying constructs of willingness and self-efficacy beliefs to provide instructional accommodations items, five components for attitudes towards providing accommodations items and three components represent the underlying constructs for knowledge about provisions of instructional accommodations items. Based on the findings, recommendations were made for improving the situation of instructional accommodations in Ethiopian universities.

Keywords: willingness, self-efficacy belief, attitude, knowledge

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Authors: Seyed Hamidreza Hejazi Dehghani, Neda Khounsari

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Quick growth of electronic commerce in developed countries means that developing nations must change in their commerce strategies fundamentally. Most organizations are aware of the impact of the Internet and e-Commerce on the future of their firm, and thus, they have to focus on organizational factors that have an effect on the deployment of an e-Commerce strategy. In this situation, it is essential to identify organizational factors such as the organizational culture, human resources, size, structure and product/service that impact an e-commerce strategy. Accordingly, this research specifies the effects of organizational factors on applying an e-commerce strategy in the Namakin food industry. The statistical population of this research is 95 managers and employees. Cochran's formula is used for determination of the sample size that is 77 of the statistical population. Also, SPSS and Smart PLS software were utilized for analyzing the collected data. The results of hypothesis testing show that organizational factors have positive and significant effects of applying an e-Commerce strategy. On the other hand, sub-hypothesizes show that effectiveness of the organizational culture and size criteria were rejected and other sub-hypothesis were accepted.

Keywords: electronic commerce, organizational factors, attitude of managers, organizational readiness

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Authors: Nader Ghareeb, Rüdiger Schmidt

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Minimizing the weight in flexible structures means reducing material and costs as well. However, these structures could become prone to vibrations. Attenuating these vibrations has become a pivotal engineering problem that shifted the focus of many research endeavors. One technique to do that is to design and implement an active control system. This system is mainly composed of a vibrating structure, a sensor to perceive the vibrations, an actuator to counteract the influence of disturbances, and finally a controller to generate the appropriate control signals. In this work, two different techniques are explored to create two different mathematical models of an active control system. The first model is a finite element model with a reduced number of nodes and it is called a super-element. The second model is in the form of state-space representation, i.e. a set of partial differential equations. The damping coefficients are calculated and incorporated into both models. The effectiveness of these models is demonstrated when the system is excited by its first natural frequency and an active control strategy is developed and implemented to attenuate the resulting vibrations. Results from both modeling techniques are presented and compared.

Keywords: damping coefficients, finite element analysis, super-element, state-space model

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Authors: Stella Laletas, Andrea Reupert, Melinda Goodyear, Bradley Morgan

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Background: Many people with a mental illness have young children. Research has shown that early childhood is a particularly vulnerable time for children whose parents have a mental illness. Moreover, repeated research has demonstrated the effectiveness of a multiagency approach to family focused practice for improving parental functioning and preventing adverse outcomes in children whose parents have a mental illness, particularly in the early years of a child’s life. However, there is a paucity of professional development resources for professionals who work with families where a parent has a mental illness and has young children. Significance of the study: This study will make a contribution to addressing knowledge gaps around resource development and workforce needs for early childhood and mental health professionals working with young children where a parent has a mental illness. Objective: This presentation describes a newly developed resource, 'Pathways of Care', specifically designed for early childhood educators and mental health workers, alongside pilot evaluation data regarding its effectiveness. ‘Pathways of Care’ aims to promote collaborative practice and present early identification and referral processes for workers in this sector. The resource was developed by the Children of Parents with a Mental Illness (COPMI) National Initiative which is funded by the Australian Government. Method: Using a mixed method design, the effectiveness of the training resource is also presented. Fifteen workers completed the Family Focus Mental Health Practice Questionnaire pre and post using the resource, to measure confidence and practice change; semi-structured interviews were also conducted with eight of these same workers to further explore the utility of the resource. Findings: The findings indicated the resource was effective in increasing knowledge and confidence, particularly for new and/or inexperienced staff. Examples of how the resource was used in practice by various professions emerged from the interview data. Conclusions: Collaborative practice, early identification and intervention in early childhood can potentially play a key role in altering the life trajectory of children who are at risk. This information has important implications for workforce development and staff training in both the early childhood and mental health sectors. Implications for policy and future research are discussed.

Keywords: parents with mental ilnesses, early intervention, evaluation, preschool children

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Authors: Aaron Yan

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Tennis elbow has been a leading injury and problem among amateur and even professional players. Many factors contribute to tennis elbow. In this research, we apply state of the art sensor-less computer vision and AI technology to study the biomechanics of a player’s tennis movements during training and competition as they relate to the causes of tennis elbow. We provide a framework for the analysis of key biomechanical parameters and their correlations with specific tennis stroke and movements that can lead to tennis elbow or elbow injury. We also devise a method for using AI to automatically detect player’s forms that can lead to tennis elbow development for on-court injury prevention.

Keywords: Tennis Elbow, Computer Vision, AI, 3DAT

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Authors: Archit Yajnik

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Part of Speech Tagging has always been a challenging task in the era of Natural Language Processing. This article presents POS tagging for Nepali text using Hidden Markov Model and Viterbi algorithm. From the Nepali text, annotated corpus training and testing data set are randomly separated. Both methods are employed on the data sets. Viterbi algorithm is found to be computationally faster and accurate as compared to HMM. The accuracy of 95.43% is achieved using Viterbi algorithm. Error analysis where the mismatches took place is elaborately discussed.

Keywords: hidden markov model, natural language processing, POS tagging, viterbi algorithm

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Authors: Ali Babaei

Abstract:

There is a significant relationship between Soul Faculties and human relationships. Man has at least three levels of relationship according to three levels of his Faculties: individual (with himself), dual (with another) and collective (with others). Since all human actions are organized by the type of use of their internal faculties, their "hierarchy of relations" is related to the "hierarchy of their Faculties." In the final explanation based on the ontology of Islamic wisdom, one can consider the hierarchy of human Faculties in three levels: 1. senses, 2. intellect and heart, and 3. Soul. The best relationship, in the individual one is that every human being, with healthy senses, achieves both the intellectual growth and the perfection of the heart, which we call "Clear-headed" and "Good-hearted.” The result of human evolution in this two aspects will lead to the development of a powerful personality which can be interpreted as "spiritual prosperity"; having a great soul is the result of such evolution. A smart brain without a "Good-heart"ince can lead to criminality; and mere "Good-heart"ince" without "Clear-head"ince leads to "naivety". “clear-head”ince is achieved through thoughtfulness and study, and "Good-heart"ince through love and worship. So the best way to achieve perfection in a personal relationship is to have a dependable appearance, a coherent thinking

Keywords: Ontology , good-heartince, wisdom, relationship, clear-head”ince, criminality, naivety

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Authors: Yuichi Kida, Takuro Kida

Abstract:

In a new smart society supported by the recent development of 5G and 6G Communication systems, the im- portance of wireless power transmission is increasing. These systems contain discrete sampling systems in the middle of the transmission path and equivalent circuit representation of lossless or lossy power transmission through these systems is an important issue in circuit theory. In this paper, for the given weight function, we show that a lossless power transmission system with the given weight is expressed by an equivalent circuit representation of the Kida’s optimal signal prediction system followed by a reactance multi-port circuit behind it. Further, it is shown that, when the system is lossy, the system has an equivalent circuit in the form of connecting a multi-port positive-real circuit behind the Kida’s optimal signal prediction system. Also, for the convenience of the reader, in this paper, the equivalent circuit expression of the reactance multi-port circuit and the positive- real multi-port circuit by Cauer and Ohno, whose information is currently being lost even in the world of the Internet.

Keywords: signal prediction, pseudo inverse matrix, artificial intelligence, power transmission

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Authors: Hoda Rahmani, Gary Weckman

Abstract:

The body of research examining the integration of drones into various industries is expanding rapidly. Despite progress made in addressing the cybersecurity concerns for commercial drones, knowledge deficits remain in determining potential occupational hazards and risks of drone use to employees’ well-being and health in the workplace. This creates difficulty in identifying key approaches to risk mitigation strategies and thus reflects the need for raising awareness among employers, safety professionals, and policymakers about workplace drone-related accidents. The purpose of this study is to investigate the prevalence of and possible risk factors for drone-related mishaps by comparing the application of drones in construction with manufacturing industries. The chief reason for considering these specific sectors is to ascertain whether there exists any significant difference between indoor and outdoor flights since most construction sites use drones outside and vice versa. Therefore, the current research seeks to examine the causes and patterns of workplace drone-related mishaps and suggest possible ergonomic interventions through data collection. Potential ergonomic practices to mitigate hazards associated with flying drones could include providing operators with professional pieces of training, conducting a risk analysis, and promoting the use of personal protective equipment. For the purpose of data analysis, two data mining techniques, the random forest and association rule mining algorithms, will be performed to find meaningful associations and trends in data as well as influential features that have an impact on the occurrence of drone-related accidents in construction and manufacturing sectors. In addition, Spearman’s correlation and chi-square tests will be used to measure the possible correlation between different variables. Indeed, by recognizing risks and hazards, occupational safety stakeholders will be able to pursue data-driven and evidence-based policy change with the aim of reducing drone mishaps, increasing productivity, creating a safer work environment, and extending human performance in safe and fulfilling ways. This research study was supported by the National Institute for Occupational Safety and Health through the Pilot Research Project Training Program of the University of Cincinnati Education and Research Center Grant #T42OH008432.

Keywords: commercial drones, ergonomic interventions, occupational safety, pattern recognition

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Authors: Jiang Niu, Yue Jiang

Abstract:

With the ultimate goal to automatically generate translated texts with high quality, machine translation has made tremendous improvements. However, its translations of literary works are still plagued with problems in coherence, esp. the translation between distant language pairs. One of the causes of the problems is probably the lack of linguistic knowledge to be incorporated into the training of machine translation systems. In order to enable readers to better understand the problems of machine translation in coherence, to seek out the potential knowledge to be incorporated, and thus to improve the quality of machine translation products, this study applies Theme-Rheme structure to examine how a machine-translated literary text is organized and developed in terms of coherence. Theme-Rheme structure in Systemic Functional Linguistics is a useful tool for analysis of textual coherence. Theme is the departure point of a clause and Rheme is the rest of the clause. In a text, as Themes and Rhemes may be connected with each other in meaning, they form thematic and rhematic progressions throughout the text. Based on this structure, we can look into how a text is organized and developed in terms of coherence. Methodologically, we chose Chinese and English as the language pair to be studied. Specifically, we built a comparable corpus with two modes of English translations, viz. machine translation (MT) and human translation (HT) of one Chinese literary source text. The translated texts were annotated with Themes, Rhemes and their progressions throughout the texts. The annotated texts were analyzed from two respects, the different types of Themes functioning differently in achieving coherence, and the different types of thematic and rhematic progressions functioning differently in constructing texts. By analyzing and contrasting the two modes of translations, it is found that compared with the HT, 1) the MT features “pseudo-coherence”, with lots of ill-connected fragments of information using “and”; 2) the MT system produces a static and less interconnected text that reads like a list; these two points, in turn, lead to the less coherent organization and development of the MT than that of the HT; 3) novel to traditional and previous studies, Rhemes do contribute to textual connection and coherence though less than Themes do and thus are worthy of notice in further studies. Hence, the findings suggest that Theme-Rheme structure be applied to measuring and assessing the coherence of machine translation, to being incorporated into the training of the machine translation system, and Rheme be taken into account when studying the textual coherence of both MT and HT.

Keywords: coherence, corpus-based, literary translation, machine translation, Theme-Rheme structure

Procedia PDF Downloads 188