Search results for: learning management

Authors: Mohd Afif Md Nasir, Faizal Amin Nur Yunus, Jamaluddin Hashim, Abd Samad Hassan Basari, A. Halim Sahelan

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The aim of this study is to identify the suitability of Virtual Learning Environment (VLE) in welding simulator application towards Computer-Based Training (CBT) in developing skills upon new students at the Advanced Technology Training Center (ADTEC), Batu Pahat, Johor, Malaysia and GIATMARA, Batu Pahat, Johor, Malaysia. The purpose of the study is to create a computer-based skills development approach in welding technology among new students in ADTEC and GIATMARA, as well as cultivating the elements of general skills among them. This study is also important in elevating the number of individual knowledge workers (K-workers) working in manufacturing industry in order to achieve a national vision which is to be an industrial nation in the year of 2020. The design of the study is a survey type of research which uses questionnaires as the instruments and 136 students from ADTEC and GIATMARA were interviewed. Descriptive analysis is used to identify the frequency and mean values. The findings of the study shows that the welding technology skills have developed in the students as a result of the application of VLE simulator at a high level and the respondents agreed that the skills could be embedded through the application of the VLE simulator. In summary, the VLE simulator is suitable in welding skills development training in terms of exposing new students with the relevant characteristics of welding skills and at the same time spurring the students’ interest towards learning more about the skills.

Keywords: computer-based training (CBT), knowledge workers (K-workers), virtual learning environment, welding simulator, welding technology

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Authors: Mohammed Saif Al-Saidi, John P. T. Mo

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Modern engineering assets are complex and very high in value. They are expected to function for years to come, with ability to handle the change in technology and ageing modification. The aging of an engineering asset and continues increase of vendors and contractors numbers forces the asset operation management (or Owner) to design an asset system which can capture these changes. Furthermore, an accurate performance measurement and risk evaluation processes are highly needed. Therefore, this paper explores the nature of the asset management system performance evaluation for an engineering asset based on the System Support Engineering (SSE) principles. The research work explores the asset support system from a range of perspectives, interviewing managers from across a refinery organisation. The factors contributing to complexity of an asset management system are described in context which clusters them into several key areas. It is proposed that SSE framework may then be used as a tool for analysis and management of asset. The paper will conclude with discussion of potential application of the framework and opportunities for future research.

Keywords: asset management, performance, evaluation, modern engineering, System Support Engineering (SSE)

Procedia PDF Downloads 678

Authors: Albatool Al Abdulwahid, Bayan Shakally, Mariam Mohamed, Wed Almokri

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Artificial intelligence has infiltrated every part of our life and every field we can think of. With technical developments, artificial intelligence applications are becoming more prevalent. We chose ELSA speak because it is a magnificent example of Artificial intelligent applications, ELSA speak is a smartphone application that is free to download on both IOS and Android smartphones. ELSA speak utilizes artificial intelligence to help non-native English speakers pronounce words and phrases similar to a native speaker, as well as enhance their English skills. It employs speech-recognition technology that aids the application to excel the pronunciation of its users. This remarkable feature distinguishes ELSA from other voice recognition algorithms and increase the efficiency of the application. This study focused on evaluating ELSA speak application, by testing the degree of effectiveness based on survey questions. The results of the questionnaire were variable. The generality of the participants strongly agreed that ELSA has helped them enhance their pronunciation skills. However, a few participants were unconfident about the application’s ability to assist them in their learning journey.

Keywords: ELSA speak application, artificial intelligence, speech-recognition technology, language learning, english pronunciation

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Authors: Brandy Yee, Dianne Yee

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Through the exploration of the lived experiences, beliefs and values of instructional leaders, teachers and students in Finland, Germany and Canada, we investigated the factors which contribute to developmentally responsive, intellectually engaging middle-level learning environments for early adolescents. Student-centred leadership dimensions, effective instructional practices and student agency were examined through the lens of current policy and research on middle-level learning environments emerging from the Canadian province of Manitoba. Consideration of these three research perspectives in the context of early adolescent learning, placed against an international backdrop, provided a previously undocumented perspective on leading, teaching and learning in the middle years. Aligning with a social constructivist, qualitative research paradigm, the study incorporated collective case study methodology, along with constructivist grounded theory methods of data analysis. Data were collected through semi-structured individual and focus group interviews and document review, as well as direct and participant observation. Three case study narratives were developed to share the rich stories of study participants, who had been selected using maximum variation and intensity sampling techniques. Interview transcript data were coded using processes from constructivist grounded theory. A cross-case analysis yielded a conceptual framework highlighting key factors that were found to be significant in the establishment of developmentally responsive, intellectually engaging middle-level learning environments. Seven core categories emerged from the cross-case analysis as common to all three countries. Within the visual conceptual framework (which depicts the interconnected nature of leading, teaching and learning in middle-level learning environments), these seven core categories were grouped into Essential Factors (student agency, voice and choice), Contextual Factors (instructional practices; school culture; engaging families and the community), Synergistic Factors (instructional leadership) and Cornerstone Factors (education as a fundamental cultural value; preservice, in-service and ongoing teacher development). In addition, sub-factors emerged from recurring codes in the data and identified specific characteristics and actions found in developmentally responsive, intellectually engaging middle-level learning environments. Although this study focused on 12 schools in Finland, Germany and Canada, it informs the practice of educators working with early adolescent learners in middle-level learning environments internationally. The authentic voices of early adolescent learners are the most important resource educators have to gauge if they are creating effective learning environments for their students. Ongoing professional dialogue and learning is essential to ensure teachers are supported in their work and develop the pedagogical practices needed to meet the needs of early adolescent learners. It is critical to balance consistency, coherence and dependability in the school environment with the necessary flexibility in order to support the unique learning needs of early adolescents. Educators must intentionally create a school culture that unites teachers, students and their families in support of a common purpose, as well as nurture positive relationships between the school and its community. A large, urban school district in Canada has implemented a school cohort-based model to begin to bring developmentally responsive, intellectually engaging middle-level learning environments to scale.

Keywords: developmentally responsive learning environments, early adolescents, middle level learning, middle years, instructional leadership, instructional practices, intellectually engaging learning environments, leadership dimensions, student agency

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Authors: Amreth Chandrasehar

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Machine Learning (ML) Models are developed and run in production to solve various use cases that help organizations to be more efficient and help drive the business. But this comes at a massive development cost and lost business opportunities. According to the Gartner report, 85% of data science projects fail, and one of the factors impacting this is not paying attention to Model Observability. Model Observability helps the developers and operators to pinpoint the model performance issues data drift and help identify root cause of issues. This paper focuses on providing insights into incorporating model observability in model development and operationalizing it in production.

Keywords: model observability, monitoring, drift detection, ML observability platform

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Authors: Ashleigh Medina

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Chronic conditions account for the majority of total health care spending both in the United States and globally. Encouraging self-management to improve chronic conditions, which in turn could decrease the strain placed on hospitals, requires resources to address the patient’s social concerns in addition to their medical concerns. Transitional care has been identified as a possible bridge between acutely managing conditions at the hospital to chronically managing conditions in a community setting. The aim of this integrative review was to examine the impact of transitional care on self-management outcomes of chronic conditions in un/underinsured populations. Both transitional care, by assisting with resources such as funding sources for healthcare and medications or identifying a healthcare provider for continued care, and self-management, by increasing responsibility for one’s care through goal setting and taking action, can impact health outcomes while providing health care cost-savings.

Keywords: chronic conditions, self-management, transitional care, uninsured

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Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

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Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Mamoon Mousa Atout

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The industries of construction are in continuous growth not only in Middle East rejoin but almost all over the world. For the last fifteen years, big expansion and increase of different types of projects has been observed. Many infrastructural projects have been developed, high rise buildings, big shopping malls, power sub-stations, roads, bridges, schools, universities and developing many of new cities with full and complete facilities. The growth and enlargement of the mentioned developed projects has been accomplished through many international and local contracting organizations. Senior management of these organizations depend on their qualified and experienced team whom are aware of the implications of project management, construction management, engineering management and resource management during tendering till final completion of the project. This research aims to find out why reasons of principles of construction engineering and management are necessary for contracting firms and their managers. Principles of construction management help contracting organizations to accomplish and deliver projects without delay. This can be maintained by establishing guidelines’ details for updating the adopted system of construction management that they have through qualified and experienced project managers. The research focuses on benefits of other essential skills of projects planning, monitoring and control. Defining roles and responsibilities of contractor project managers during tendering and execution is a part of the investigated factors that will be analyzed. Other skills like optimizing and utilizing the obtainable project resources to deliver the project within time, cost and quality will be also investigated to find out how these factors are affecting the performance of contracting firms, projects managers and projects. The conclusion of the research will help senior management team and the contractors project managers about the benefits of implications and benefits construction management system and its effect upon the performance and knowledge of contract values that they have, and the optimal profit margin of the firm it.

Keywords: construction management, contracting firms, project managers, planning processes, roles and responsibilities

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Authors: William Li

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Lane-change path planning is a crucial and yet complex task in autonomous driving. The traditional path planning approach based on a system of carefully-crafted rules to cover various driving scenarios becomes unwieldy as more and more rules are added to deal with exceptions and corner cases. This paper proposes to divide the entire path planning to two stages. In the first stage the ego vehicle travels longitudinally in the source lane to reach a safe state. In the second stage the ego vehicle makes lateral lane-change maneuver to the target lane. The paper derives the safe state conditions based on lateral lane-change maneuver calculation to ensure collision free in the second stage. To determine the acceleration sequence that minimizes the time to reach a safe state in the first stage, the paper proposes three schemes, namely, kinetic model based optimization, deep reinforcement learning, and 5G vehicle-to-vehicle (V2V) communications. The paper investigates these schemes via simulation. The model-based optimization is sensitive to the model assumptions. The deep reinforcement learning is more flexible in handling scenarios beyond the model assumed by the optimization. The 5G V2V eliminates uncertainty in predicting future behaviors of surrounding vehicles by sharing driving intents and enabling cooperative driving.

Keywords: lane change, path planning, autonomous driving, deep reinforcement learning, 5G, V2V communications, connected vehicles

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Authors: Michael Lekan-Kehinde, Abimbola Asojo, Bonnie Sanborn

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Good acoustic performance has been identified as one of the critical Indoor Environmental Quality (IEQ) factors for student learning and development by the National Research Council. Childhood presents the opportunity for children to develop lifelong skills that will support them throughout their adult lives. Acoustic performance of a space has been identified as a factor that can impact language acquisition, concentration, information retention, and general comfort within the environment. Increasingly, students learn by communication between both teachers and fellow students, making speaking and listening crucial. Neurodiversity - while initially coined to describe individuals with autism spectrum disorder (ASD) - widely describes anyone with a different brain process. As the understanding from cognitive and neurosciences increases, the number of people identified as neurodiversity is nearly 30% of the population. This research looks at guidelines and standard for spaces with good acoustical quality and relates it with the experiences of students with autism spectrum disorder (ASD), their parents, teachers, and educators through a mixed methods approach, including selected case studies interviews, and mixed surveys. The information obtained from these sources is used to determine if selected materials, especially properties relating to sound absorption and reverberation reduction, are equally useful in small, medium sized, and large learning spaces and methodologically approaching. The results describe the potential impact of acoustics on Neurodiverse students, considering factors that determine the complexity of sound in relation to the auditory processing capabilities of ASD students. In conclusion, this research extends the knowledge of how materials selection influences the better development of acoustical environments for autism students.

Keywords: acoustics, autism spectrum disorder (ASD), children, education, learning, learning spaces, materials, neurodiversity, sound

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Authors: N. S. Kubanza, D. Simatele, D. K. Das

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The purpose of this paper is to understand the urban environmental problems in Kinshasa and the consequences of these for the poor. This paper particularly examines the concept of environmental injustice in solid waste management in Kinshasa, the capital of the Democratic Republic of Congo (DRC). The urban low-income communities in Kinshasa face multiple consequences of poor solid waste management associated with unhealthy living conditions. These situations stemmed from overcrowding, poor sanitary, accumulation of solid waste, resulting in the prevalence of water and air borne diseases. Using a mix of reviewed archival records, scholarly literature, a semi-structured interview conducted with the local community members and qualitative surveys among stakeholders; it was found that solid waste management challenge in Kinshasa is not only an environmental and health risk issues, but also, a problem that generates socio-spatial disparities in the distribution of the solid waste burden. It is argued in the paper that the urban poor areas in Kinshasa are often hardest affected by irregularities of waste collection. They lack sanitary storage capacities and have undermined organizational capacity for collective action within solid waste management. In view of these observations, this paper explores mechanisms and stakeholders’ engagement necessary to lessen environmental injustice in solid waste management (SWM) in Kinshasa.

Keywords: environmental justice, solid waste management, urban environmental problems, urban poor

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Authors: Thomas Trouton, Sebastian Tanner, Manvir Sandher

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The use of simulation in undergraduate and postgraduate medical curricula is ever-growing, is a useful addition to the traditional apprenticeship model of learning within medical education, and better prepares graduates for the team-based approach to healthcare seen in real-life clinical practice. As a learning tool, however, undergraduate medical students often have little understanding of the theory behind the use of medical simulation and have little experience in planning and delivering their own simulated teaching sessions. We designed and implemented a student-selected component (SSC) as part of the undergraduate medical curriculum at the University of Buckingham Medical School to introduce students to the concepts behind the use of medical simulation in education and allow them to plan and deliver their own simulated medical scenario to their peers. The SSC took place over a 2-week period in the 3rd year of the undergraduate course. There was a mix of lectures, seminars and interactive group work sessions, as well as hands-on experience in the simulation suite, to introduce key concepts related to medical simulation, including technical considerations in simulation, human factors, debriefing and troubleshooting scenarios. We evaluated the success of our SSC using “Net Promotor Scores” (NPS) to assess students’ confidence in planning and facilitating a simulation-based teaching session, as well as leading a debrief session. In all three domains, we showed an increase in the confidence of the students. We also showed an increase in confidence in the management of common medical emergencies as a result of the SSC. Overall, the students who chose our SSC had the opportunity to learn new skills in medical education, with a particular focus on the use of simulation-based teaching, and feedback highlighted that a number of students would take these skills forward in their own practice. We demonstrated an increase in confidence in several domains related to the use of medical simulation in education and have hopefully inspired a new generation of medical educators.

Keywords: simulation, SSC, teaching, medical students

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Authors: G. Infante Dennis, S. Balinas Elvira, C. Valencia Yolanda, Cunanan

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This paper examined the feed-backs, concerns, and insights of the teachers, supervisors, and teacher-trainers on the nature and qualities of the K-12 grade 8 design, content, approaches, and materials. Specifically, it sought to achieve the following objectives: 1) to describe the critical nature and qualities of the design, content, teaching-learning-and-evaluation approaches, and the materials to be utilized in the implementation of the grade 8 curriculum; 2) to extract the possible challenges relevant to the implementation of the design, content, teaching-learning-and-evaluation approaches, and the materials of the grade 8 curriculum in terms of the linguistic and technical competence of the teachers, readiness to implement, willingness to implement, and capability to make relevant adaptations; 3) to present essential demands on the successful and meaningful implementation of the grade 8 curriculum in terms of teacher-related factors, school-related factors, and student-related concerns.

Keywords: curriculum reforms, K-12, teacher-training, language teaching, learning

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Authors: Seyong Jeong, Jinmo Park, Jinhyoun Park, Boram Kim, Kyoungsoo Ahn

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Energy management in the maritime industry is being required by economics and in conformity with new legislative actions taken by the International Maritime Organization (IMO) and the European Union (EU). In response, the various performance monitoring methodologies and data collection practices have been examined by different stakeholders. While many assorted advancements in operation and technology are applicable, their adoption in the shipping industry stays small. This slow uptake can be considered due to many different barriers such as data analysis problems, misreported data, and feedback problems, etc. This study presents a conceptual design of an energy management system (EMS) and proposes the methodology to resolve the limitations (e.g., data normalization using naval architectural evaluation, management of misrepresented data, and feedback from shore to ship through management of performance analysis history). We expect this system to make even short-term charterers assess the ship performance properly and implement sustainable fleet control.

Keywords: data normalization, energy management system, naval architectural evaluation, ship performance analysis

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Authors: Mukhtar Maigari

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The landscape of Higher Education (HE) institutions, especially following the CVID-19 pandemic, necessitates advanced approaches to manage Indoor Environmental Quality (IEQ) which is crucial for the comfort, health, and productivity of students and staff. This study investigates the application of Building Information Modelling (BIM) as a multifaceted tool for monitoring, assessing, and controlling IEQ in HE buildings aiming to bridge the gap between traditional management practices and the innovative capabilities of BIM. Central to the study is a comprehensive literature review, which lays the foundation by examining current knowledge and technological advancements in both IEQ and BIM. This review sets the stage for a deeper investigation into the practical application of BIM in IEQ management. The methodology consists of Post-Occupancy Evaluation (POE) which encompasses physical monitoring, questionnaire surveys, and interviews under the umbrella of case studies. The physical data collection focuses on vital IEQ parameters such as temperature, humidity, CO2 levels etc, conducted by using different equipment including dataloggers to ensure accurate data. Complementing this, questionnaire surveys gather perceptions and satisfaction levels from students, providing valuable insights into the subjective aspects of IEQ. The interview component, targeting facilities management teams, offers an in-depth perspective on IEQ management challenges and strategies. The research delves deeper into the development of a conceptual BIM-based framework, informed by the insight findings from case studies and empirical data. This framework is designed to demonstrate the critical functions necessary for effective IEQ monitoring, assessment, control and automation with real time data handling capabilities. This BIM-based framework leads to the developing and testing a BIM-based prototype tool. This prototype leverages on software such as Autodesk Revit with its visual programming tool i.e., Dynamo and an Arduino-based sensor network thereby allowing for real-time flow of IEQ data for monitoring, control and even automation. By harnessing the capabilities of BIM technology, the study presents a forward-thinking approach that aligns with current sustainability and wellness goals, particularly vital in the post-COVID-19 era. The integration of BIM in IEQ management promises not only to enhance the health, comfort, and energy efficiency of educational environments but also to transform them into more conducive spaces for teaching and learning. Furthermore, this research could influence the future of HE buildings by prompting universities and government bodies to revaluate and improve teaching and learning environments. It demonstrates how the synergy between IEQ and BIM can empower stakeholders to monitor IEQ conditions more effectively and make informed decisions in real-time. Moreover, the developed framework has broader applications as well; it can serve as a tool for other sustainability assessments, like energy analysis in HE buildings, leveraging measured data synchronized with the BIM model. In conclusion, this study bridges the gap between theoretical research and real-world application by practicalizing how advanced technologies like BIM can be effectively integrated to enhance environmental quality in educational institutions. It portrays the potential of integrating advanced technologies like BIM in the pursuit of improved environmental conditions in educational institutions.

Keywords: BIM, POE, IEQ, HE-buildings

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Authors: Ibrahim Abubakar Mikugi

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Nigerian graduates could only be self-employed and marketable if they acquire relevant skills and knowledge for successful establishment in various occupation and gainful employment. Research has shown that entrepreneurship education will be successful through developing individual entrepreneurial attitudes, raising awareness of career options by integrating and inculcating a positive attitude in the mind of students through skill acquisition. This paper examined the student- led entrepreneurship education through skill acquisition with specific emphasis on analysis of David Kolb experiential learning cycle. This Model allows individual to review their experience through reflection and converting ideas into action by doing. The methodology used was theoretical approach through journal, internet and Textbooks. Challenges to entrepreneurship education through skill acquisition were outlined. The paper concludes that entrepreneurship education is recognised by both policy makers and academics; entrepreneurship is more than mere encouraging business start-ups. Recommendations were given which include the need for authorities to have a clear vision towards entrepreneurship education and skill acquisition. Authorities should also emphasise a periodic and appropriate evaluation of entrepreneurship and to also integrate into schools academic curriculum to encourage practical learning by doing.

Keywords: entrepreneurship, entrepreneurship education, active learning, Cefe methodology

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Authors: Jaya Mathew

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Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

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Authors: Venkata Venugopal Rao Gudlur

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The Proposed Policies referred to as “SOP”, on the Internet of Things (IoT) based Forensic Investigation into Process Management is the latest revolution to save time and quick solution for investigators. The forensic investigation process has been developed over many years from time to time it has been given the required information with no policies in investigation processes. This research reveals that the current IoT based forensic investigation into Process Management based is more connected to devices which is the latest revolution and policies. All future development in real-time information on gathering monitoring is evolved with smart sensor-based technologies connected directly to IoT. This paper present conceptual framework on process management. The smart devices are leading the way in terms of automated forensic models and frameworks established by different scholars. These models and frameworks were mostly focused on offering a roadmap for performing forensic operations with no policies in place. These initiatives would bring a tremendous benefit to process management and IoT forensic investigators proposing policies. The forensic investigation process may enhance more security and reduced data losses and vulnerabilities.

Keywords: Internet of Things, Process Management, Forensic Investigation, M2M Framework

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Authors: Mitat Uysal, Aynur Uysal

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Artificial Intelligence (AI) is fundamentally driven by mathematics, with many of its core algorithms rooted in mathematical principles such as linear algebra, probability theory, calculus, and optimization techniques. This paper explores the deep connection between AI and mathematics, highlighting the role of mathematical concepts in key AI techniques like machine learning, neural networks, and optimization. To demonstrate this connection, a case study involving the implementation of a neural network using Python is presented. This practical example illustrates the essential role that mathematics plays in training a model and solving real-world problems.

Keywords: AI, mathematics, machine learning, optimization techniques, image processing

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Authors: Muhammed Kök & Adem Kantar

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Learning a foreign language can be affected by various factors such as age, aptitude, motivation, L2 disposition, etc. Among these factors, masculine gender roles stress (MGRS) that male learners possess is the least touched area that has been examined so far.MGRS can be defined as the traditional male role stress when the male learners feel the masculinity threat against their traditionally adopted masculinity norms. Traditional masculine norms include toughness, accuracy, completeness, and faultlessness. From this perspective, these norms are diametrically opposed to the language learning process since learning a language, by its nature, involves stages such as making mistakes and errors, not recalling words, pronouncing sounds incorrectly, creating wrong sentences, etc. Considering the potential impact of MGRS on the language learning process, the main purpose of this study is to investigate the mediating role of MGRS on the relationship between the EFL learners’ self-disclosure and English class anxiety. Data were collected from Turkish EFL learners (N=282) who study different majors in various state universities across Turkey. Data were analyzed by means of the Bootstraping method using the SPSS Process Macro plugin. The findings show that the indirect effect of self-disclosure level on the English Class Anxiety via MGRS was significant. We conclude that one of the reasons why Turkish EFL learners have English class anxiety might be the pressure that they feel because of their traditional gender role stress.

Keywords: masculine, gender role stress, english class anxiety, self-disclosure, masculinity norms

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Authors: Bernardo Cortez, Florian Peter, Thomas Lausenhammer, Paulo Oliveira

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Designing missiles’ autopilot controllers have been a complex task, given the extensive flight envelope and the nonlinear flight dynamics. A solution that can excel both in nominal performance and in robustness to uncertainties is still to be found. While Control Theory often debouches into parameters’ scheduling procedures, Reinforcement Learning has presented interesting results in ever more complex tasks, going from videogames to robotic tasks with continuous action domains. However, it still lacks clearer insights on how to find adequate reward functions and exploration strategies. To the best of our knowledge, this work is a pioneer in proposing Reinforcement Learning as a framework for flight control. In fact, it aims at training a model-free agent that can control the longitudinal non-linear flight dynamics of a missile, achieving the target performance and robustness to uncertainties. To that end, under TRPO’s methodology, the collected experience is augmented according to HER, stored in a replay buffer and sampled according to its significance. Not only does this work enhance the concept of prioritized experience replay into BPER, but it also reformulates HER, activating them both only when the training progress converges to suboptimal policies, in what is proposed as the SER methodology. The results show that it is possible both to achieve the target performance and to improve the agent’s robustness to uncertainties (with low damage on nominal performance) by further training it in non-nominal environments, therefore validating the proposed approach and encouraging future research in this field.

Keywords: Reinforcement Learning, flight control, HER, missile autopilot, TRPO

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Authors: Mehrnoosh Askarizadeh

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In an ever changeable and globalized world with new economic and global competitors competing for the same customers and resources, is increasing the pressure on organizations' competitiveness. In addition, organizations faces additional challenges due to an ever-growing amount of data and the ever-bigger challenge of analyzing that data and keeping the data secure. Successful companies are characterized by exploiting their intellectual capital in an efficient manner. Thus, the most valuable asset an organization has today has become its employees' knowledge. To enable this, there is a tool that supports easier handling and optimizes the use of knowledge, which is knowledge management. Based on the theoretical framework and careful review as well as analysis of interviews and observations resulted in six essential areas: structure, management, compensation, communication, trust and motivation. The analysis showed that the scientific articles and literature have different perspectives, different definitions and are based on different theories but the essence is that they all finally seems to arrive at the same result and conclusion, although with different viewpoints and perspectives. This is regardless of whether the focus is on management style, rewards or communication they all focus on the individual. The conclusion is that organizational culture affects knowledge management and dissemination of information, because of its direct impact on the individual. The largest and most important underlying factor why we choose to participate in improvement work or share knowledge is our motivation. Motivation is the reason for and the reason behind our actions.

Keywords: lean, lean production, knowledge management, information management, motivation

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Authors: H. M. Nuralam, Z. Xiao-lan, B. K. Dubey, D. Wen-Chuan

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Healthcare waste (HCW) is one of the major concerns in environmental issues due to its infectious and hazardous nature that is requires specific treatment and systematic management prior to final disposal. This study aimed to assess HCW management system in Dhaka City (DC), Bangladesh, by investigating the present practices implemented by the city. In this study, five different healthcare establishments were selected in DC. Field visits and interviews with health personnel and staff who are concerned with the waste management were conducted. The information was gathered through questionnaire focus on the different aspect of HCW management like, waste segregation and collection, storage and transport, awareness as well. The results showed that a total of 7,215 kg/day (7.2 ton/day) of waste were generated, of which 79.36% (5.6 ton/day) was non-hazardous waste and 20.6% (1.5 ton/day) was hazardous waste. The rate of waste generation in these healthcare establishments (HCEs) was 2.6 kg/bed/day. There was no appropriate and systematic management of HCWs except at few private HCEs that segregate their hazardous waste. All the surveyed HCEs dumped their HCW together with the municipal waste, and some staff members were also found to be engaged in improper handling of the generated waste. Furthermore, the used sharp instruments, saline bags, blood bags and test tubes were collected for resale or reuse. Nevertheless, the lack of awareness, appropriate policy, regulation and willingness to act, were responsible for the improper management of HCW in DC. There was lack of practical training of concerned healthcare to handle the waste properly, while the nurses and staff were found to be aware of the health impacts of HCW.

Keywords: awareness, disposal, Dhaka city, healthcare waste management, waste generation

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Authors: Swarna Pundir, Prabuddha Hans

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As AI has already stepped into the daily life of human society, one cannot be ignorant about the data it collects and used it to provide a quality of services depending up on the individuals’ choices. It also helps in giving option for making decision Vs choice selection with a calculation based on the history of our search criteria. Over the past decade or so, the way Artificial Intelligence (AI) has impacted society is undoubtedly large.AI has changed the way we shop, the way we entertain and challenge ourselves, the way information is handled, and has automated some sections of our life. We have answered as to what AI is, but not why one may see it as useful. AI is useful because it is capable of learning and predicting outcomes, using Machine Learning (ML) and Deep Learning (DL) with the help of Artificial Neural Networks (ANN). AI can also be a system that can act like humans. One of the major impacts be Joblessness through automation via AI which is seen mostly in manufacturing sectors, especially in the routine manual and blue-collar occupations and those without a college degree. It raises some serious concerns about AI in regards of less employment, ethics in making moral decisions, Individuals privacy, human judgement’s, natural emotions, biased decisions, discrimination. So, the question is if an error occurs who will be responsible, or it will be just waved off as a “Machine Error”, with no one taking the responsibility of any wrongdoing, it is essential to form some rules for using the AI where both machines and humans are involved.

Keywords: AI, ML, DL, ANN

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Authors: N. S. Achour, M. Hamdaoui, S. Ben Nasrallah, A. Perwuelz

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The main idea of this work is to investigate the effect of knitted fabrics characteristics on moisture management properties. Wetting and transport properties of single jersey, Rib 1&1 and English Rib fabrics made out of cotton and blended Cotton/Polyester yarns were studied. The dynamic water sorption of fabrics was investigated under same isothermal and terrestrial conditions at 20±2°C-65±2% by using the Moisture Management Tester (MMT) which can be used to quantitatively measure liquid moisture transfer in one step in a fabric in multi directions: Absorption rate, moisture absorbing time of the fabric's inner and outer surfaces, one-way transportation capability, the spreading/drying rate, the speed of liquid moisture spreading on fabric's inner and outer surfaces are measured, recorded and discussed. The results show that fabric’s composition and knit’s structure have a significant influence on those phenomena.

Keywords: knitted fabrics characteristics, moisture management properties, multi directions, the moisture management tester

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Authors: Namubiru Zula, Janice Desire Busingue

Abstract:

Pro-active educational approaches have lately been adapted Globally in the Conservation of Natural Resources. This led to the introduction of the co-management system, which worked for some European Countries on the conservation of sharks and other Natural resources. However, this approach has drastically failed in the Fishery sector on Lake Victoria; and the punitive education approach has been re-instated. Literature is readily available about the punitive educational approaches and scanty with the pro-active one. This article analyses the pro-active approach adopted by the Department of Fisheries for the orientation of BMU leaders in a co-management system. The study is interpreted using the social constructivist lens for co-management of the fishery resource to ensure that fishers are also back to fishing sustainably. It highlights some of the education methods used, methodological challenges that included the power and skills gap of the facilitators and program designers, and some implications to practice.

Keywords: beach management units, fishers, education methods, proactive approach, punitive approach

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Authors: Abdullah Bal

Abstract:

This paper presents a one-class classification (OCC) technique based on Fukunaga-Koontz Transform (FKT) for binary classification problems. The FKT is originally a powerful tool to feature selection and ordering for two-class problems. To utilize the standard FKT for data domain description problem (i.e., one-class classification), in this paper, a set of non-class samples which exist outside of positive class (target class) describing boundary formed with limited training data has been constructed synthetically. The tunnel-like decision boundary around upper and lower border of target class samples has been designed using statistical properties of feature vectors belonging to the training data. To capture higher order of statistics of data and increase discrimination ability, the proposed method, termed one-class FKT (OC-FKT), has been extended to its nonlinear version via kernel machines and referred as OC-KFKT for short. Multiple kernel learning (MKL) is a favorable family of machine learning such that tries to find an optimal combination of a set of sub-kernels to achieve a better result. However, the discriminative ability of some of the base kernels may be low and the OC-KFKT designed by this type of kernels leads to unsatisfactory classification performance. To address this problem, the quality of sub-kernels should be evaluated, and the weak kernels must be discarded before the final decision making process. MKL/OC-FKT and selective MKL/OC-FKT frameworks have been designed stimulated by ensemble learning (EL) to weight and then select the sub-classifiers using the discriminability and diversities measured by eigenvalue ratios. The eigenvalue ratios have been assessed based on their regions on the FKT subspaces. The comparative experiments, performed on various low and high dimensional data, against state-of-the-art algorithms confirm the effectiveness of our techniques, especially in case of small sample size (SSS) conditions.

Keywords: ensemble methods, fukunaga-koontz transform, kernel-based methods, multiple kernel learning, one-class classification

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Elin Cahyaningsih, Dana Indra Sensuse, Wahyu Catur Wibowo, Sofiyanti Indriasari

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Bureaucracy reform program drives Indonesian government to change their management and supporting unit in order to enhance their organization performance. Information technology as one of supporting unit became one of strategic plan that organization tried to improve, because IT can automate and speed up process, reduce business process life cycle become more effective and efficient. Knowledge management system is a technology application for supporting knowledge management implementation in government which is requirement based on problem and potential functionality of each knowledge management process. Define knowledge management that suitable for each organization it is difficult, that why we should make the knowledge management system strategy as an alignment of knowledge management process in the organization. Knowledge management system is one of information system development in people perspective, because this system has high dependency in human interaction and participation. Strategic plan for developing knowledge management system can be determine using some of information system strategic methods. This research conducted to define type of strategic method of information system, stage of activity each method, the strategic method strength and weakness. The author use literature review methods for identify and classify strategic methods of information system for differentiate method type, categorize common activities, strength and weakness. Result of this research are determine and compare six strategic information system methods, there are Balanced Scorecard, Five Force Porter, SWOT analysis, Value Chain Analysis, Risk Analysis and Gap Analysis. Balanced Scorecard and Risk Analysis believe as common strategic method that usually used and have the highest excellence strength.

Keywords: knowledge management system, balanced scorecard, five force, risk analysis, gap analysis, value chain analysis, SWOT analysis

Procedia PDF Downloads 478