Search results for: feedback timing

Authors: Karl Baker-Green, Ian Woolsey

Abstract:

Historically, a variety of methods have been used to access the student voice within higher education, including module evaluations and informal classroom feedback. However, currently, the views articulated in student-staff-committee meetings bear the most weight and can therefore have the most significant impact on departmental policy. Arguably, these forums are exclusionary as several students, including those who experience severe anxiety, might feel unable to participate in this face-to-face (large) group activities. Similarly, students who declare a disability, but are not in possession of a learning contract, are more likely to withdraw from their studies than those whose additional needs have been formally recognised. It is also worth noting that whilst the number of disabled students in Higher Education has increased in recent years, the percentage of those who have been issued a learning contract has decreased. These issues foreground the need to explore the educational experiences of students with or without a learning contract in order to identify their respective aspirations and needs and therefore help shape education policy. This is in keeping with the ‘Nothing about us without us’, agenda, which recognises that disabled individuals are best placed to understand their own requirements and the most effective strategies to meet these.

Keywords: education, student voice, student experience, student retention

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Authors: Hestiasari Rante, Leonardo De Araújo, Heidi Schelhowe

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Usability testing with children is similar in many aspects to usability testing with adults. However, there are a few differences that one needs to be aware of in order to get the most out of the sessions, and to ensure that children are comfortable and enjoying the process. This paper presents the need to acquire methodological knowledge for involving children as test users in usability testing, with consideration on Piaget’s theory of cognitive growth. As a case study, we use BatiKids, an application developed to evoke children’s enthusiasm to be involved in culture heritage preservation. The usability test was applied to 24 children from 9 to 10 years old. The children were divided into two groups; one interacted with the application through a graphic tablet with pen, and the other through touch screen. Both of the groups had to accomplish the same amount of tasks. In the end, children were asked to give feedback. The results suggested that children who interacted using the graphic tablet with pen had more difficulties rather than children who interacted through touch screen. However, the difficulty brought by the graphic tablet with pen is an important learning objective in order to understand the difficulties of using canting, which is an important part of batik.

Keywords: batikids, children, child-computer interaction, usability test

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Authors: Dechrit Maneetham

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The aim of this paper was to design a void and head controlled electric power wheelchair (EPW). A novel activate the control system for quadriplegics with voice, head and neck mobility. Head movement has been used as a control interface for people with motor impairments in a range of applications. Acquiring measurements from the module is simplified through a synchronous a motor. Axis measures the two directions namely x and y. At the same time, patients can control the motorized wheelchair using voice signals (forward, backward, turn left, turn right, and stop) given by it self. The model of a dc motor is considered as a speed control by selection of a PID parameters using genetic algorithm. An experimental set-up constructed, which consists of micro controller as controller, a DC motor driven EPW and feedback elements. This paper is tuning methods of parameter for a pulse width modulation (PWM) control system. A speed controller has been designed successfully for closed loop of the dc motor so that the motor runs very closed to the reference speed and angle. Intelligent wheelchair can be used to ensure the person’s voice and head are attending the direction of travel asserted by a conventional, direction and speed control.

Keywords: wheelchair, quadriplegia, rehabilitation , medical devices, speed control

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Authors: Shyam Mehraaj, Samanthi E. R. Siriwardana, Shehara A. K. G. H., Wanigasinghe N. T., Wandana R. A. K., Wedage C. V.

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A presentation is a critical tool for conveying not just spoken information but also a wide spectrum of human emotions. The single most effective thing to make the presentation successful is to practice it beforehand. Preparing for a presentation has been shown to be essential for improving emotional control, intonation and prosody, pronunciation, and vocabulary, as well as the quality of the presentation slides. As a result, practicing has become one of the most critical parts of giving a good presentation. In this research, the main focus is to analyze the audio, video, and slides of the presentation uploaded by the presenters. This proposed solution is based on the Natural Language Processing and Computer Vision techniques to cater to the requirement for the presenter to do a presentation beforehand using a mobile responsive web application. The proposed system will assist in practicing the presentation beforehand by identifying the presenters’ emotions, body language, tonality, prosody, pronunciations and vocabulary, and presentation slides quality. Overall, the system will give a rating and feedback to the presenter about the performance so that the presenters’ can improve their presentation skills.

Keywords: presentation, self-evaluation, natural learning processing, computer vision

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Authors: Kuei-Hao Li, Eden Huang

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Motivation suggests the willingness one person has towards taking action. Learners’ motivation has frequently been regarded as the most crucial factor in successful language acquisition. Without sufficient motivation, learners cannot achieve long-term learning goals despite remarkable abilities. Therefore, the study aims to investigate motivation of interaction tasks designed by the researchers for college EFL learners in Travel English class in virtual reality environment, integrating CUE model, Cognition, Usage and Expansion in the course. Thirty college learners were asked to join the virtual language learning website designed by the researchers. Data was collected via feedback questionnaire, interview, and learner interactions. The findings indicated that the course in the CUE model in language learning website of virtual reality environment was effective at motivating EFL learners and improving their oral communication and social interactions in the learning process. Some pedagogical implications are also provided in helping both language instructors and EFL learners in virtual reality environment.

Keywords: motivation, virtual reality, virtual language learning, second language acquisition

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Authors: Naveen Kumar Perumalla, Devadas Kuna, Mohammed Akhter Ali

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Satellite Navigation System such as the United States Global Positioning System (GPS) plays a significant role in determining the user position. Similar to that of GPS, Indian Regional Navigation Satellite System (IRNSS) is a Satellite Navigation System indigenously developed by Indian Space Research Organization (ISRO), India, to meet the country’s navigation applications. This system is also known as Navigation with Indian Constellation (NavIC). The NavIC system’s main objective, is to offer Positioning, Navigation and Timing (PNT) services to users in its two service areas i.e., covering the Indian landmass and the Indian Ocean. Six NavIC satellites are already deployed in the space and their receivers are in the performance evaluation stage. Four NavIC dual frequency receivers are installed in the ‘Advanced GNSS Research Laboratory’ (AGRL) in the Department of Electronics and Communication Engineering, University College of Engineering, Osmania University, India. The NavIC receivers can be operated in two positioning modes: Stand-alone IRNSS and Hybrid (IRNSS+GPS) modes. In this paper, analysis of various parameters such as Dilution of Precision (DoP), three Dimension (3D) Root Mean Square (RMS) Position Error and Horizontal Position Error with respect to Visibility of Satellites is being carried out using the real-time IRNSS data, obtained by operating the receiver in both positioning modes. Two typical days (6th July 2017 and 7th July 2017) are considered for Hyderabad (Latitude-17°24'28.07’N, Longitude-78°31'4.26’E) station are analyzed. It is found that with respect to the considered parameters, the Hybrid mode operation of NavIC receiver is giving better results than that of the standalone positioning mode. This work finds application in development of NavIC receivers for civilian navigation applications.

Keywords: DoP, GPS, IRNSS, GNSS, position error, satellite visibility

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Authors: Francis Alterado, Jennifer Enriquez

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Semantic Based Analysis in Complaint Management System with Analytics is an enhanced tool of providing complaints by the clients as well as a mechanism for Palawan Polytechnic College to gather, process, and monitor status of these complaints. The study has a mobile application that serves as a remote facility of communication between the students and the school management on the issues encountered by the student and the solution of every complaint received. In processing the complaints, text mining and clustering algorithms were utilized. Every module of the systems was tested and based on the results; these are 100% free from error before integration was done. A system testing was also done by checking the expected functionality of the system which was 100% functional. The system was tested by 10 students by forwarding complaints to 10 departments. Based on results, the students were able to submit complaints, the system was able to process accordingly by identifying to which department the complaints are intended, and the concerned department was able to give feedback on the complaint received to the student. With this, the system gained 4.7 rating which means Excellent.

Keywords: technology adoption, emerging technology, issues challenges, algorithm, text mining, mobile technology

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Authors: Juliane Toce, Steven Holochwost

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A substantial and growing literature has demonstrated that arts education benefits young children’s socioemotional and cognitive development. Less is known about the capacity of arts-integrated instruction to yield benefits to similar domains, particularly among demographically and socioeconomically diverse groups of young children. However, the small literature on this topic suggests that arts-integrated instruction may foster young children’s socioemotional and cognitive development by presenting opportunities to 1) engage in instructional content in diverse ways, 2) experience and regulate strong emotions, 3) experience growth-oriented feedback, and 4) engage in collaborative work with peers. Start with the Art is a new program of arts-integrated instruction currently being implemented in four schools in a school district that serves students from a diverse range of backgrounds. The program employs a co-teaching model in which teaching artists and classroom teachers engage in collaborative lesson planning and instruction over the course of the academic year and is currently the focus of an impact study featuring a randomized-control design, as well as an implementation study, both of which are funded through an Educational Innovation and Research grant from the United States Department of Education. The paper will present the early results from the Start with the Art implementation study. These results will provide an overview of the extent to which the program was implemented in accordance with design, with a particular emphasis on the degree to which the four opportunities enumerated above (e.g., opportunities to engage in instructional content in diverse ways) were presented to students. There will be a review key factors that may influence the fidelity of implementation, including classroom teachers’ reception of the program and the extent to which extant conditions in the classroom (e.g., the overall level of classroom organization) may have impacted implementation fidelity. With the explicit purpose of creating a program that values and meets the needs of the teachers and students, Start with the Art incorporates the feedback from individuals participating in the intervention. Tracing its trajectory from inception to ongoing development and examining the adaptive changes made in response to teachers' transformative experiences in the post-pandemic classroom, Start with the Art continues to solicit input from experts in integrating artistic content into core curricula within educational settings catering to students from under-represented backgrounds in the arts. Leveraging the input from this rich consortium of experts has allowed for a comprehensive evaluation of the program’s implementation. The early findings derived from the implementation study emphasize the potential of arts-integrated instruction to incorporate restorative practices. Such practices serve as a crucial support system for both students and educators, providing avenues for children to express themselves, heal emotionally, and foster social development, while empowering teachers to create more empathetic, inclusive, and supportive learning environments. This all-encompassing analysis spotlights Start with the Art’s adaptability to any learning environment through the program’s effectiveness, resilience, and its capacity to transform - through art - the classroom experience within the ever-evolving landscape of education.

Keywords: arts-integration, social emotional learning, diverse learners, co-teaching, teaching artists, post-pandemic teaching

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Authors: Irbulat Utepbergenov, Lyazzat Issabekova, Shara Toybayeva

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This article discusses quality management using the bread baking process as an example. The baking process must be strictly controlled and repeatable. Automation and monitoring of quality management systems can help. After baking bread, quality control of the finished product should be carried out. This may include an evaluation of appearance, weight, texture, and flavor. It is important to continuously work to improve processes and products based on data and feedback from the quality management system. A method and model of automated quality management and an intelligent automated management system based on intelligent technologies are proposed, which allow to automate the processes of QMS implementation and support and improve the validity, efficiency, and effectiveness of management decisions by automating a number of functions of decision makers and staff. This project is supported by the grant of the Ministry of Education and Science of the Republic of Kazakhstan (Zhas Galym project No. AR 13268939 Research and development of digital technologies to ensure consistency of the carriers of normative documents of the quality management system).

Keywords: automated control system, quality management, efficiency evaluation, bakery oven, intelligent system

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Authors: Anthony J. Kondracki, Bonzo Reddick, Jennifer L. Barkin

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Background: Many pregnancies in the United States are affected each year with the most common sexually transmitted infections (STIs), including Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and Treponema pallidum (TP, syphilis), and the rate of congenital syphilis has reached a 20-year high. We sought to estimate the prevalence of CT, NG, and TP in pregnancy and the risk of preterm birth (PTB) (<37 weeks gestation) and low birthweight (LBW) (<2500g) deliveries according to utilization of prenatal care (PNC) during the COVID-19 pandemic. Methods: This study was based on the 2020 National Center for Health Statistics (NCHS) Natality File restricted to singleton births (N=3,512,858). We estimated the prevalence of CT, NG, TP, PTBand LBW across timing and the number of prenatal care (PNC) visits attended. In multivariable logistic regression models, adjusted odds ratios of PTB and LBW were assessed according to STIs and PNC status. E-values, based on effect size estimates and the lower bound of the 95% confidence intervals (CIs) of the association, examined the potential impact of unmeasured confounding. Results: CT (1.8%) was most prevalent in pregnancy, followed by NG (0.3%) and TP (0.1%). The strongest predictors of PTB and LBW were maternal NG (12.2% and 12.1%, respectively), late initiation/no PNC (8.5% and 7.6%, respectively), and ≤10 prenatal visits (13.1% and 10.3%, respectively). The odds of PTB and LBW were 2.5- to 3-fold greater for each STI in women who received ≤10 compared to >10 prenatal visits. E-values demonstrated the minimum strength of potential unmeasured confounding necessary to explain away observed associations. Conclusions: Timely initiation and receipt of recommended number of prenatal visits benefits screening and treatment of all women for STIs, including NG to substantially reduce infant morbidity and mortality related to PTB and LBW among infants born during the COVID-19 pandemic.

Keywords: COVID-19 pandemic, sexually transmitted infections, preterm birth, low birthweight, prenatal care

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Authors: Oral S. Saulters, Shanon D. Goldberg, Wendy A. Staples, Ellena I. Martinez, Lorie M. Sanchez, Diego E. Archuleta, Deborah L. Williams, Scot D. Johnson

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To address an important set of unverified field conditions, the Los Alamos National Laboratory Waste Compliance and Tracking System (WCATS) Wall-to-Wall Team performed an unprecedented and advanced inventory. This reconciliation involved confirmation analysis for approximately 5850 hazardous, low-level, mixed low-level, and transuranic waste containers located in more than 200 staging and storage areas across 33 technical areas. The interdisciplinary team scoped, planned, and developed the multidimensional assessments. Through coordination with cross-functional site hosts, they were able to verify and validate data while resolving discrepancies identified in WCATS. The results were extraordinary with an updated inventory, tailored outreach, more cohesive communications, and timely closed-loop feedback.

Keywords: circular economy, environmental performance data, social-ecological-technological systems, waste management

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Authors: Ludivine Saint-Mard, Masato Nakajima, Gloria Senfaute

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In areas with low to moderate seismicity, the probabilistic seismic hazard analysis frequently uses a Poisson approach, which assumes independence in time and space of events to determine the annual probability of earthquake occurrence. Nevertheless, in countries with high seismic rate, such as Japan, it is frequently use non-poissonian model which assumes that next earthquake occurrence depends on the date of previous one. The objective of this paper is to apply a non-poissonian models in a region of low to moderate seismicity to get a feedback on the following questions: can we overcome the lack of data to determine some key parameters?, and can we deal with uncertainties to apply largely this methodology on an industrial context?. The Brownian-Passage-Time model was applied to a fault located in France and conclude that even if the lack of data can be overcome with some calculations, the amount of uncertainties and number of scenarios leads to a numerous branches in PSHA, making this method difficult to apply on a large scale of low to moderate seismicity areas and in an industrial context.

Keywords: probabilistic seismic hazard, non-poissonian model, earthquake occurrence, low seismicity

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Authors: Nooralhuda Aljlas

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In the ever-growing field of non-governmental organizations, the enhancement of performance management and measurement systems has been increasingly acknowledged by political, economic, social, legal, technological and environmental factors. Within Bahrain Athletics Association, such enhancement results from the key factors leading performance management including collaboration, feedback, human resource management, leadership and participative management. The exploratory, qualitative research conducted reviewed performance management theory. As reviewed, the key factors leading performance management were identified. Drawing on a non-governmental organization case study, the key factors leading Bahrain Athletics Association’s performance management were explored. By exploring the key factors leading Bahrain Athletics Association’s performance management, the research study proposed a theoretical framework of the key factors leading performance management in non-governmental organizations in general. The research study recommended further investigation of the role of the two key factors of command and control and leadership, combining military and civilian approaches to enhancing non-governmental organizations’ performance management.

Keywords: Bahrain athletics association, exploratory, key factor, performance management

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Authors: Brahim Brahmi, Mohammed Hamza Laraki, Mohammad Habibur Rahman, Islam M. Rasedul, M. Assad Uz-Zaman

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The humanoid robot, specifically the NAO robot must be able to provide a highly dynamic performance on the soccer field. Maintaining the balance of the humanoid robot during the required motion is considered as one of a challenging problems especially when the robot is subject to external disturbances, as contact with other robots. In this paper, a dynamic controller is proposed in order to ensure a robust walking (stabilization) and to improve the dynamic balance of the robot during its contact with the environment (external disturbances). The generation of the trajectory of the center of mass (CoM) is done by a model predictive controller (MPC) conjoined with zero moment point (ZMP) technique. Taking into account the properties of the rotational dynamics of the whole-body system, a modified previous control mixed with feedback control is employed to manage the angular momentum and the CoM’s acceleration, respectively. This latter is dedicated to provide a robust gait of the robot in the presence of the external disturbances. Simulation results are presented to show the feasibility of the proposed strategy.

Keywords: preview control, Nao robot, model predictive control

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Authors: Lin Chih-Ken

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This paper explores the A Li Shan hotel of Taiwan during the Japanese occupation period, after over a hundred years of time, it has been handed over to the hotel managing enterprise to retain the historic building and the culture. Applying the innovative marketing strategies, coordinate the local government traveling policy then combined local tea agriculture and forestry specialty integrated marketing, to create the special hotel located in the Alishan National Scenic Area with the characteristics of landscape, innovative marketing and history, to attract domestic tourism and visitors around the world. This study interview the hotel owner, managers, employees and guests, in addition to collected message feedback from reservation website, to apply Ambidexterity Marketing Theory and Resource Base Theory to analyze the main impact factors. The conclusion showed that the integration of several key factors and make good use of resource strength generate heterogeneous product characteristics to attracting wider range of visitors.

Keywords: heritage tourism, historic hotel, marketing ambidexterity, resource base theory

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Authors: Shweta Pinjarkar, Shrutika Yawale, Mayuri Patil, Reshma Adagale

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Face recognition is the technique which can be applied to the wide variety of problems like image and film processing, human computer interaction, criminal identification etc. This has motivated researchers to develop computational models to identify the faces, which are easy and simple to implement. In this, demonstrates the face recognition system in android device using eigenface. The system can be used as the base for the development of the recognition of human identity. Test images and training images are taken directly with the camera in android device.The test results showed that the system produces high accuracy. The goal is to implement model for particular face and distinguish it with large number of stored faces. face recognition system detects the faces in picture taken by web camera or digital camera and these images then checked with training images dataset based on descriptive features. Further this algorithm can be extended to recognize the facial expressions of a person.recognition could be carried out under widely varying conditions like frontal view,scaled frontal view subjects with spectacles. The algorithm models the real time varying lightning conditions. The implemented system is able to perform real-time face detection, face recognition and can give feedback giving a window with the subject's info from database and sending an e-mail notification to interested institutions using android application. Face recognition is the technique which can be applied to the wide variety of problems like image and film processing, human computer interaction, criminal identification etc. This has motivated researchers to develop computational models to identify the faces, which are easy and simple to implement. In this , demonstrates the face recognition system in android device using eigenface. The system can be used as the base for the development of the recognition of human identity. Test images and training images are taken directly with the camera in android device.The test results showed that the system produces high accuracy. The goal is to implement model for particular face and distinguish it with large number of stored faces. face recognition system detects the faces in picture taken by web camera or digital camera and these images then checked with training images dataset based on descriptive features. Further this algorithm can be extended to recognize the facial expressions of a person.recognition could be carried out under widely varying conditions like frontal view,scaled frontal view subjects with spectacles. The algorithm models the real time varying lightning conditions. The implemented system is able to perform real-time face detection, face recognition and can give feedback giving a window with the subject's info from database and sending an e-mail notification to interested institutions using android application.

Keywords: face detection, face recognition, eigen faces, algorithm

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Authors: Filomena Lopes, Sandra Fernandes

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Active learning strategies are at the center of any change process aimed to improve the development of student skills. This paper aims to analyse the impact of teaching strategies, including problem-based learning (PBL), in the curricular unit of information system for management, based on students’ perceptions of how they contribute to develop the desired learning outcomes of the curricular unit. This course is part of the 1st semester and 3rd year of the graduate degree program in management at a private higher education institution in Portugal. The methodology included an online questionnaire to students (n=40). Findings from students reveal a positive impact of the teaching strategies used. In general, 35% considered that the strategies implemented in the course contributed to the development of courses’ learning objectives. Students considered PBL as the learning strategy that better contributed to enhance the courses’ learning outcomes. This conclusion brings forward the need for further reflection and discussion on the impact of student feedback on teaching and learning processes.

Keywords: higher education, active learning strategies, skills development, student assessment

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Authors: Aurelio Palacardo, Giulio Mangano, Alberto De Marco

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Nowadays, the global economic framework is extremely competitive, and it consequently requires an efficient deployment of the resources provided by EU. In this context, Project management practices are intended to be one of the levers for increasing such an efficiency. The objective of this work is to explore the usage of Project Management methodologies and good practices in the European-wide research program “Horizon2020” and establish whether their maturity might impact the project's success. This allows to identify strengths in terms of application of PM methodologies and good practices and, in turn, to provide feedback and opportunities for improvements to be implemented in future programs. In order to achieve this objective, the present research makes use of a survey-based data retrieval and correlation analysis to investigate the level of perceived PM maturity in H2020 projects and the correlation of maturity with project success. The results show the Project Managers involved in H2020 to hold a high level of PM maturity, confirming PM standards, which are imposed by the EU commission as a binding process, are effectively enforced.

Keywords: project management, project management maturity, maturity models, project success

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Authors: Richard Ren

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Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: R. Litt, A. Kana, K. House

Abstract:

The primary aim of this patient-related experience questionnaire was to gain a better understanding of our patients' experience as inpatients when they undergo orthognathic surgery. The secondary aim of this study was to identify ways in which we can improve the orthognathic inpatient experience and to share this with other units. All patients who received orthognathic surgery at an NHS hospital - Bristol Royal Infirmary, England, over the course of 6 months were asked to complete a questionnaire regarding their care. This data was then analysed and compared to the same questionnaire given to patients treated in a private hospital where orthognathic surgery was completed. All treatment was completed by the same surgeon. The design of the questions took into account NICE (National Institute for Health and Care Excellence) guidance on improving the experience of patient care. Particularly taking into account patients' essential requirements of care, for example, assessing and managing pain, ensuring adequate and appropriate nutrition, and ensuring the patients' personal needs are regularly reviewed and addressed. Overall the patient-related experience after orthognathic surgery was comparable in both the NHS and private hospitals. However, the questionnaire highlighted aspects of inpatient care after orthognathic surgery that can easily be improved in order to provide our patients with the best possible care.

Keywords: orthognathic surgery, patient feedback, jaw surgery, inpatient experience

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Authors: Jagmeet S. Kanwal, Julia F. Langley

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Music can heal the body, but a mechanistic understanding of this phenomenon is lacking. This study explores the effects of music presentation on neurologic and physiologic responses leading to metabolic changes in the human body. The mind and body co-exist in a corporeal entity and within this framework, sickness ensues when the mind-body balance goes awry. It is further hypothesized that music has the capacity to directly reset this balance. Two lines of inquiry taken together can provide a mechanistic understanding of this phenomenon 1) Empirical evidence for a sound-sensitive pressure sensor system in the body, and 2) The notion of a “healing center” within the brain that is activated by specific patterns of sounds. From an acoustics perspective, music is spatially distributed as pressure waves ranging from a few cm to several meters in wavelength. These waves interact and propagate in three-dimensions in unique ways, depending on the wavelength. Furthermore, music creates dynamically changing wave-fronts. Frequencies between 200 Hz and 1 kHz generate wavelengths that range from 5'6" to 1 foot. These dimensions are in the range of the body size of most people making it plausible that these pressure waves can geometrically interact with the body surface and create distinct patterns of pressure stimulation across the skin surface. For humans, short wavelength, high frequency (> 200 Hz) sounds are best received via cochlear receptors. For low frequency (< 200 Hz), long wavelength sound vibrations, however, the whole body may act as an ideal receiver. A vast array of highly sensitive pressure receptors (Pacinian corpuscles) is present just beneath the skin surface, as well as in the tendons, bones, several organs in the abdomen, and the sexual organs. Per the available empirical evidence, these receptors contribute to music perception by allowing the whole body to function as a sound receiver, and knowledge of how they function is essential to fully understanding the therapeutic effect of music. Neuroscientific studies have established that music stimulates the limbic system that can trigger states of anxiety, arousal, fear, and other emotions. These emotional states of brain activity play a crucial role in filtering top-down feedback from thoughts and bottom-up sensory inputs to the autonomic system, which automatically regulates bodily functions. Music likely exerts its pleasurable and healing effects by enhancing functional and effective connectivity and feedback mechanisms between brain regions that mediate reward, autonomic, and cognitive processing. Stimulation of pressure receptors under the skin by low-frequency music-induced sensations can activate multiple centers in the brain, including the amygdala, the cingulate cortex, and nucleus accumbens. Melodies in music in the low (< 600 Hz) frequency range may augment auditory inputs after convergence of the pressure-sensitive inputs from the vagus nerve onto emotive processing regions within the limbic system. The integration of music-generated auditory and somato-visceral inputs may lead to a synergistic input to the brain that promotes healing. Thus, music can literally heal humans through “touch” as it energizes the brain’s autonomic system for restoring homeostasis.

Keywords: acoustics, brain, music healing, pressure receptors

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Authors: Brendan Mooney

Abstract:

We often do not consider the quality of the way we express ourselves as being fundamental to well-being. Society focuses predominantly on what we do, not the way we do it, to our great detriment. For example, those who have experienced domestic violence often comment that it was not what was said that hurt the most but the way it was said. In other words, the quality in the way the words were used communicated far more than the actual words themselves. This is an important area of focus for practitioners who may be inclined to emphasize who said what but not bring equal, if not more, focus to the quality of one’s expression. The aim of this study is to highlight how and why the way we express ourselves is more important than what we express, which includes words and all behaviors. Given we are a sensitive species it matters to pay attention to the communication that is not said. For example, we have the ability to recognize that a person is upset or angry by the way they walk into a room, even if they do not say anything or look at anyone. Our sensitivity allows us to detect even the slightest change in another’s emotional state, irrespective of what their exterior behaviors may be exhibiting. This study will focus on the importance of recognizing the quality in the way we express as being fundamental to wellbeing, as it allows us to easily and simply navigate life and relationships without needing to experience the usual pitfalls that otherwise prevail. This research utilizes clinical experience, client observations and client feedback, and several case studies were utilized to illustrate real-life examples of the above. This study is not so much a model of life but a way of life that confirms our deepest nature, that we are incredibly sensitive and far more so than we appreciate or utilize in everyday practical human life.

Keywords: communication, integrity, quality, sensitivity, wellbeing

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Authors: Hilal Keklicek, Baris Cetin, Yeliz Salci, Ayla Fil, Umut Altinkaynak, Kadriye Armutlu

Abstract:

Objectives and Goals: Spasticity is a common symptom characterized with a velocity dependent increase in tonic stretch reflexes (muscle tone) in patient with multiple sclerosis (MS). Hypertonic muscles affect the normal plantigrade contact by disturbing accommodation of foot to the ground while walking. It is important to know the differences between healthy and neurologic foot features for management of spasticity related deformities and/or determination of rehabilitation purposes and contents. This study was planned with the aim of investigating the dynamic plantar pressure distribution in individuals with MS and determining the differences between healthy individuals (HI). Methods: Fifty-five individuals with MS (108 foot with spasticity according to Modified Ashworth Scale) and 20 HI (40 foot) were the participants of the study. The dynamic pedobarograph was utilized for evaluation of dynamic loading parameters. Participants were informed to walk at their self-selected speed for seven times to eliminate learning effect. The parameters were divided into 2 categories including; maximum loading pressure (N/cm2) and time of maximum pressure (ms) were collected from heal medial, heal lateral, mid foot, heads of first, second, third, fourth and fifth metatarsal bones. Results: There were differences between the groups in maximum loading pressure of heal medial (p < .001), heal lateral (p < .001), midfoot (p=.041) and 5th metatarsal areas (p=.036). Also, there were differences between the groups the time of maximum pressure of all metatarsal areas, midfoot, heal medial and heal lateral (p < .001) in favor of HI. Conclusions: The study provided basic data about foot pressure distribution in individuals with MS. Results of the study primarily showed that spasticity of lower extremity muscle disrupted the posteromedial foot loading. Secondarily, according to the study result, spasticity lead to inappropriate timing during load transfer from hind foot to forefoot.

Keywords: multiple sclerosis, plantar pressure distribution, gait, norm values

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Authors: A. Hol

Abstract:

21^st century brought waves of business and industry eTransformations. The impact of change is also being seen in education. To identify the extent of this, scenario analysis methodology was utilised with the aim to assess business transformations across industry sectors ranging from craftsmanship, medicine, finance and manufacture to innovations and adoptions of new technologies and business models. Firstly, scenarios were drafted based on the current eTransformation models and its dimensions. Following this, eTransformation framework was utilised with the aim to derive the key eTransformation parameters, the essential characteristics that have enabled eTransformations across the sectors. Following this, identified key parameters were mapped to the transforming domain-education. The mapping assisted in deriving a cognitive eTransformation framework for education sector. The framework highlights the importance of context and the notion that education today needs not only to deliver content to students but it also needs to be able to meet the dynamically changing demands of specific student and industry groups. Furthermore, it pinpoints that for such processes to be supported, specific technology is required, so that instant, on demand and periodic feedback as well as flexible, dynamically expanding study content can be sought and received via multiple education mediums.

Keywords: education sector, business transformation, eTransformation model, cognitive model, cognitive systems, eTransformation

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Authors: Anita Kushwaha

Abstract:

We propose a new evolutionary computational model called Artificial Reproduction System which is based on the complex process of meiotic reproduction occurring between male and female cells of the living organisms. Artificial Reproduction System is an attempt towards a new computational intelligence approach inspired by the theoretical reproduction mechanism, observed reproduction functions, principles and mechanisms. A reproductive organism is programmed by genes and can be viewed as an automaton, mapping and reducing so as to create copies of those genes in its off springs. In Artificial Reproduction System, the binding mechanism between male and female cells is studied, parameters are chosen and a network is constructed also a feedback system for self regularization is established. The model then applies Mendel’s law of inheritance, allele-allele associations and can be used to perform data analysis of imbalanced data, multivariate, multiclass and big data. In the experimental study Artificial Reproduction System is compared with other state of the art classifiers like SVM, Radial Basis Function, neural networks, K-Nearest Neighbor for some benchmark datasets and comparison results indicates a good performance.

Keywords: bio-inspired computation, nature- inspired computation, natural computing, data mining

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Authors: Marlene Kuhn, Franziska Schäfer, Heiner Otten

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Today´s production processes experience a constant increase in complexity paving new ways for progressive forms of leadership. In the customized production, individual customer requirements drive companies to adapt their manufacturing processes constantly while the pressure for smaller lot sizes, lower costs and faster lead times grows simultaneously. When production processes are becoming more dynamic and complex, the conventional quality management approaches show certain limitations. This paper gives an introduction to complexity science from a quality management perspective. By analyzing and evaluating different characteristics of complexity, the critical complexity parameters are identified and assessed. We found that the quality of leadership plays a crucial role when dealing with increasing complexity. Therefore, we developed a concept for qualitative leadership customized for the management within complex processes based on a maturity model. The maturity model was then applied in the industry to assess the leadership quality of several shop floor managers with a positive evaluation feedback. In result, the maturity model proved to be a sustainable approach to leverage the rising complexity in production processes more effectively.

Keywords: maturity model, process complexity, quality of leadership, quality management

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Authors: Ali Amin

Abstract:

This paper describes a flipped learning initiative which was trialed in the delivery of the course: structural analysis and modelling. A short series of interactive videos were developed, which introduced the key concepts of each topic. The purpose of the videos was to introduce concepts and give the students more time to develop their thoughts prior to the lecture. This allowed more time for face to face engagement during the lecture. As part of the initial study, videos were developed for half the topics covered. The videos included a short summary of the key concepts ( < 10 mins each) as well as fully worked-out examples (~30mins each). Qualitative feedback was attained from the students. On a scale from strongly disagree to strongly agree, students were rate statements such as 'The pre-class videos assisted your learning experience', 'I felt I could appreciate the content of the lecture more by watching the videos prior to class'. As a result of the pre-class engagement, the students formed more specific and targeted questions during class, and this generated greater comprehension of the material. The students also scored, on average, higher marks in questions pertaining to topics which had videos assigned to them.

Keywords: flipped learning, structural analysis, pre-class videos, engineering education

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Authors: Vasudeva Murthy Challakere Ramaswamy

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Background: Conventionally forensic pathology is learnt through autopsy demonstrations which carry various limitations such as unavailability of cases in the mortuary, medico-legal implication and infection. Over the years forensic pathology and science has undergone significant evolution in this digital world. Forensic imaging is a technology which can be effectively utilized for overcoming the current limitations in the undergraduate learning of forensic curriculum. Materials and methods: demonstration of forensic imaging was done using a novel technology of autopsy which has been recently introduced across the globe. Three sessions were conducted in international medical university for a total of 196 medical students. The innovative educational tool was evacuated by using quantitative questionnaire with the scoring scales between 1 to 10. Results: The mean score for acceptance of new tool was 82% and about 74% of the students recommended incorporation of the forensic imaging in the regular curriculum. 82% of students were keen on collaborative research and taking further training courses in forensic imaging. Conclusion: forensic imaging can be an effective tool and also a suitable alternative for teaching undergraduate students. This feedback also supports the fact that students favour the use of contemporary technologies in learning medicine.

Keywords: forensic imaging, forensic pathology, medical students, learning tool

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Authors: Sonja Giese, Tess N. Peacock

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South African based Innovation Edge is experimenting with technology to drive positive behavior change, enable data-driven decision making, and scale quality early years services. This paper uses five case studies to illustrate how technology can be used in resource-constrained environments to first, encourage parenting practices that build early language development (using a stage-based mobile messaging pilot, ChildConnect), secondly, to improve the quality of ECD programs (using a mobile application, CareUp), thirdly, how to affordably scale services for the early detection of visual and hearing impairments (using a mobile tool, HearX), fourthly, how to build a transparent and accountable system for the registration and funding of ECD (using a blockchain enabled platform, Amply), and finally enable rapid data collection and feedback to facilitate quality enhancement of programs at scale (the Early Learning Outcomes Measure). ChildConnect and CareUp were both developed using a design based iterative research approach. The usage and uptake of ChildConnect and CareUp was evaluated with qualitative and quantitative methods. Actual child outcomes were not measured in the initial pilots. Although parents who used and engaged on either platform felt more supported and informed, parent engagement and usage remains a challenge. This is contrast to ECD practitioners whose usage and knowledge with CareUp showed both sustained engagement and knowledge improvement. HearX is an easy-to-use tool to identify hearing loss and visual impairment. The tool was tested with 10000 children in an informal settlement. The feasibility of cost-effectively decentralising screening services was demonstrated. Practical and financial barriers remain with respect to parental consent and for successful referrals. Amply uses mobile and blockchain technology to increase impact and accountability of public services. In the pilot project, Amply is being used to replace an existing paper-based system to register children for a government-funded pre-school subsidy in South Africa. Early Learning Outcomes Measure defines what it means for a child to be developmentally ‘on track’ at aged 50-69 months. ELOM administration is enabled via a tablet which allows for easy and accurate data collection, transfer, analysis, and feedback. ELOM is being used extensively to drive quality enhancement of ECD programs across multiple modalities. The nature of ECD services in South Africa is that they are in large part provided by disconnected private individuals or Non-Governmental Organizations (in contrast to basic education which is publicly provided by the government). It is a disparate sector which means that scaling successful interventions is that much harder. All five interventions show the potential of technology to support and enhance a range of ECD services, but pathways to scale are still being tested.

Keywords: assessment, behavior change, communication, data, disabilities, mobile, scale, technology, quality

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Authors: Nan Ding, Motoki Shino, Nobuyasu Tomokuni, Genki Murata

Abstract:

The wheelchair is the major means of transport for physically disabled people. However, it cannot overcome architectural barriers such as curbs and stairs. In this paper, the authors proposed a method to avoid falling down of a wheeled inverted pendulum type robotic wheelchair for climbing stairs. The problem of this system is that the feedback gain of the wheels cannot be set high due to modeling errors and gear backlash, which results in the movement of wheels. Therefore, the wheels slide down the stairs or collide with the side of the stairs, and finally the wheelchair falls down. To avoid falling down, the authors proposed a slider control strategy based on skyhook model in order to decrease the movement of wheels, and a rotary link control strategy based on the staircase dimensions in order to avoid collision or slide down. The effectiveness of the proposed fall avoidance control strategy was validated by ODE simulations and the prototype wheelchair.

Keywords: EPW, fall avoidance control, skyhook, wheeled inverted pendulum

Procedia PDF Downloads 328