Search results for: short learning programme

Authors: Aisling Gough, Ruth F. Hunter, Jianjun Tang, Sarah F. Brennan, Oliver Smith, Mark A. Tully, Chris Patterson, Alberto Longo, George Hutchinson, Lindsay Prior, David French, Jean Adams, Emma McIntosh, Frank Kee

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Background: As a large proportion of the UK workforce is employed in sedentary occupations, worksite interventions have the potential to contribute significantly to the health of the population. The UK Government is currently encouraging the use of financial incentives to promote healthier lifestyles but there is a dearth of evidence regarding the effectiveness and sustainability of incentive schemes to promote physical activity in the workplace. Methods: A large cluster RCT is currently underway, incorporating nested behavioural economic field experiments and process evaluation, to evaluate the effectiveness of a Physical Activity Loyalty Scheme. Office-based employees were recruited from large public sector organisations in Lisburn and Belfast (Northern Ireland) and randomised to an Intervention or Control group. Participants in the Intervention Group were encouraged to take part in 150 minutes of physical activity per week through provision of financial incentives (retailer vouchers) to those who met physical activity targets throughout the course of the 6 month intervention. Minutes of physical activity were monitored when participants passed by sensors (holding a keyfob) placed along main walking routes, parks and public transport stops nearby their workplace. Participants in the Control Group will complete the same outcome assessments (waiting-list control). The primary outcome is steps per day measured via pedometers (7 days). Secondary outcomes include health and wellbeing (Short Form-8, EuroQol-5D-5L, Warwick Edinburgh Mental Well Being Scale), and work absenteeism and presenteeism. Data will be collected at baseline, 6, 12 and 18 months. Information on PAL card & website usage, voucher downloads and redemption of vouchers will also be collected as part of a comprehensive process evaluation. Results: In total, 853 participants have been recruited from 9 workplaces in Lisburn, 12 buildings within the Stormont Estate, Queen’s University Belfast and Belfast City Hospital. Participants have been randomised to intervention and control groups. Baseline and 6-month data for the Physical Activity Loyalty Scheme has been collected. Findings regarding the effectiveness of the intervention from the 6-month follow-up data will be presented. Discussion: This study will address the gap in knowledge regarding the effectiveness and cost-effectiveness of a workplace-based financial incentive scheme to promote a healthier lifestyle. As the UK workforce is increasingly sedentary, workplace-based physical activity interventions have significant potential in terms of encouraging employees to partake in physical activity during the working day which could lead to substantial improvements in physical activity levels overall. Implications: If a workplace based physical activity intervention such as this proves to be both effective and cost-effective, there is great potential to contribute significantly to the health and wellbeing of the workforce in the future. Workplace-based physical activity interventions have the potential to improve the physical and mental health of employees which may in turn lead to economic benefits for the employer, such as reduction in rates of absenteeism and increased productivity.

Keywords: behaviour change, cluster randomised controlled trial, loyalty scheme, physical activity

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Authors: Kareem Abdelwahed, Ahmed Salama, Ahmed Rabie, Ahmed Hamdy, Waleed Abdelfattah, Ahmed Abd El-Rahman

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Conventional vapor-compression refrigeration systems rely on typical refrigerants, such as CFC, HCFC and ammonia. Despite of their suitable thermodynamic properties and their stability in the atmosphere, their corresponding global warming potential and ozone depletion potential raise concerns about their usage. Thus, the need for new refrigeration systems, which are environment-friendly, inexpensive and simple in construction, has strongly motivated the development of thermoacoustic energy conversion systems. A thermoacoustic refrigerator (TAR) is a device that is mainly consisting of a resonator, a stack and two heat exchangers. Typically, the resonator is a long circular tube, made of copper or steel and filled with Helium as a the working gas, while the stack has short and relatively low thermal conductivity ceramic parallel plates aligned with the direction of the prevailing resonant wave. Typically, the resonator of a standing-wave refrigerator has one end closed and is bounded by the acoustic driver at the other end enabling the propagation of half-wavelength acoustic excitation. The hot and cold heat exchangers are made of copper to allow for efficient heat transfer between the working gas and the external heat source and sink respectively. TARs are interesting because they have no moving parts, unlike conventional refrigerators, and almost no environmental impact exists as they rely on the conversion of acoustic and heat energies. Their fabrication process is rather simpler and sizes span wide variety of length scales. The viscous and thermal interactions between the stack plates, heat exchangers' plates and the working gas significantly affect the flow field within the plates' channels, and the energy flux density at the plates' surfaces, respectively. Here, the design, the manufacture and the testing of a compact refrigeration system that is based on the thermoacoustic energy-conversion technology is reported. A 1-D linear acoustic model is carefully and specifically developed, which is followed by building the hardware and testing procedures. The system consists of two harmonically-oscillating pistons driven by a simple 1-HP rotary drive mechanism operating at a frequency of 42Hz -hereby, replacing typical expensive linear motors and loudspeakers-, and a thermoacoustic stack within which the energy conversion of sound into heat is taken place. Air at ambient conditions is used as the working gas while the amplitude of the driver's displacement reaches 19 mm. The 30-cm-long stack is a simple porous ceramic material having 100 square channels per square inch. During operation, both oscillating-gas pressure and solid-stack temperature are recorded for further analysis. Measurements show a maximum temperature difference of about 27 degrees between the stack hot and cold ends with a Carnot coefficient of performance of 11 and estimated cooling capacity of five Watts, when operating at ambient conditions. A dynamic pressure of 7-kPa-amplitude is recorded, yielding a drive ratio of 7% approximately, and found in a good agreement with theoretical prediction. The system behavior is clearly non-linear and significant non-linear loss mechanisms are evident. This work helps understanding the operation principles of thermoacoustic refrigerators and presents a keystone towards developing commercial thermoacoustic refrigerator units.

Keywords: refrigeration system, rotary drive mechanism, standing-wave, thermoacoustic refrigerator

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Authors: Abbas Ali Husseini, Ali Mohammad Yazdani, Fatemeh Ghadiri, Alper Şişman

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We developed a microchip device that uses surface acoustic waves for rapid lysis of low level of cell samples. The device incorporates sharp-edge glass microparticles for improved performance. We optimized the lysis conditions for high efficiency and evaluated the device's feasibility for point-of-care applications. The microchip contains a 13-finger pair interdigital transducer with a 30-degree focused angle. It generates high-intensity acoustic beams that converge 6 mm away. The microchip operates at a frequency of 16 MHz, exciting Rayleigh waves with a 250 µm wavelength on the LiNbO3 substrate. Cell lysis occurs when Candida albicans cells and glass particles are placed within the focal area. The high-intensity surface acoustic waves induce centrifugal forces on the cells and glass particles, resulting in cell lysis through lateral forces from the sharp-edge glass particles. We conducted 42 pilot cell lysis experiments to optimize the surface acoustic wave-induced streaming. We varied electrical power, droplet volume, glass particle size, concentration, and lysis time. A regression machine-learning model determined the impact of each parameter on lysis efficiency. Based on these findings, we predicted optimal conditions: electrical signal of 2.5 W, sample volume of 20 µl, glass particle size below 10 µm, concentration of 0.2 µg, and a 5-minute lysis period. Downstream analysis successfully amplified a DNA target fragment directly from the lysate. The study presents an efficient microchip-based cell lysis method employing acoustic streaming and microparticle collisions within microdroplets. Integration of a surface acoustic wave-based lysis chip with an isothermal amplification method enables swift point-of-care applications.

Keywords: cell lysis, surface acoustic wave, micro-glass particle, droplet

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Authors: Elizabeth Perry, Jylana Sheats

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Scholars posit that there is an overlap between the fields of Behavioral Economics (BE) and Behavior Science (BSci)—and that consideration of concepts from both may facilitate a greater understanding of health decision-making processes. For example, the ‘intention-action gap’ is one BE concept to explain sup-optimal decision-making. It is described as having knowledge that does not translate into behavior. Complementary best BSci practices may provide insights into behavioral determinants and relevant behavior change techniques (BCT). Within the context of BSci, this exploratory study aimed to apply a BE concept with demonstrated effectiveness in financial decision-making to a health behavior: influenza (flu) vaccine uptake. Adults aged >18 years were recruited on Amazon’s Mechanical Turk, a digital labor market where anonymous users perform simple tasks at low cost. Eligible participants were randomized into 2 groups, reviewed a scenario, and then completed a survey on the likelihood of receiving a flu shot. The ‘usual care’ group’s scenario included standard CDC guidance that supported the behavior. The ‘intervention’ group’s scenario included messaging about people who did not receive the flu shot. The framing was such that participants could learn from others’ (strangers) mistakes and the subsequent health consequences: ‘Last year, other people who didn’t get the vaccine were about twice as likely to get the flu, and a number of them were hospitalized or even died. Don’t risk it.’ Descriptive statistics and chi-square analyses were performed on the sample. There were 648 participants (usual care, n=326; int., n=322). Among racial/ethnic minorities (n=169; 57% aged < 40), the intervention group was 22% more likely to report that they were ‘extremely’ or ‘moderately’ likely to get the flu vaccine (p = 0.11). While not statistically significant, findings suggest that framing messages from the perspective of learning from the mistakes of unknown others coupled with the BCT ‘knowledge about the health consequences’ may help influence flu vaccine uptake among the study population. With the widely documented disparities in vaccine uptake, exploration of the complementary application of these concepts and strategies may be critical.

Keywords: public health, decision-making, vaccination, behavioral science

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Authors: Neuma Chaveiro, Juliana Guimarães Faria

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Introduction: The thematic that guides this study is teacher training for the teaching of sign language in a perspective of bilingual education – specifically aimed at Brazilian public schools that offer inclusive education, and that have, among its students, deaf children who use Brazilian Sign Language as a means of communication and expression. In the Teacher Training Course for Letters/Libras at the Universidade Federal de Goiás/UFG, we developed a bilingual education project for the deaf, linked to PIBID (Institutional Scholarship for Teaching Initiation Program), funded by the Brazilian Federal Government through CAPES (Coordination for the Improvement of Higher Education Personnel). Goals: to provide the education of higher education teachers to work in public schools in basic education and to insert students from the UFG’s Letters/Libras course in the school’s daily life, giving them the opportunity for the creation and participation in methodological experiences and of teaching practices in order to overcome the problems identified in the teaching-learning process of deaf students, in a bilingual perspective, associating Libras (Brazilian Sign Language) and Portuguese. Methodology: qualitative approach and research-action, prioritizing action – reflection – action of the people involved. The Letters-Libras PIBID of the College of Letters/UFG, in this qualitative context, is guided by the assumptions of investigation-action to contribute to the education of the Libras teacher. Results: production of studies and researches in the area of education, professionalization and teaching practice for the degree holder in Letters: Libras; b) studies, research and training in bilingual education; c) clarification and discussion of the myths that permeate the reality of users of sign languages; d) involving students in the development of didactic materials for bilingual education. Conclusion: the PIBID Project Letters/Libras allows, both to the basic education school and to the teachers in training for the teaching of Libras, an integrated and collective work partnership, with discussions and changes in relation to bilingual education for the deaf and the teaching of Libras.

Keywords: deaf, sign language, teacher training, educacion

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Authors: Anne Schattel, Mitja Echim, Christof Büskens

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Trajectory planning for deep space missions has become a recent topic of great interest. Flying to space objects like asteroids provides two main challenges. One is to find rare earth elements, the other to gain scientific knowledge of the origin of the world. Due to the enormous spatial distances such explorer missions have to be performed unmanned and autonomously. The mathematical field of optimization and optimal control can be used to realize autonomous missions while protecting recourses and making them safer. The resulting algorithms may be applied to other, earth-bound applications like e.g. deep sea navigation and autonomous driving as well. The project KaNaRiA ('Kognitionsbasierte, autonome Navigation am Beispiel des Ressourcenabbaus im All') investigates the possibilities of cognitive autonomous navigation on the example of an asteroid mining mission, including the cruise phase and approach as well as the asteroid rendezvous, landing and surface exploration. To verify and test all methods an interactive, real-time capable simulation using virtual reality is developed under KaNaRiA. This paper focuses on the specific challenge of the guidance during the cruise phase of the spacecraft, i.e. trajectory optimization and optimal control, including first solutions and results. In principle there exist two ways to solve optimal control problems (OCPs), the so called indirect and direct methods. The indirect methods are being studied since several decades and their usage needs advanced skills regarding optimal control theory. The main idea of direct approaches, also known as transcription techniques, is to transform the infinite-dimensional OCP into a finite-dimensional non-linear optimization problem (NLP) via discretization of states and controls. These direct methods are applied in this paper. The resulting high dimensional NLP with constraints can be solved efficiently by special NLP methods, e.g. sequential quadratic programming (SQP) or interior point methods (IP). The movement of the spacecraft due to gravitational influences of the sun and other planets, as well as the thrust commands, is described through ordinary differential equations (ODEs). The competitive mission aims like short flight times and low energy consumption are considered by using a multi-criteria objective function. The resulting non-linear high-dimensional optimization problems are solved by using the software package WORHP ('We Optimize Really Huge Problems'), a software routine combining SQP at an outer level and IP to solve underlying quadratic subproblems. An application-adapted model of impulsive thrusting, as well as a model of an electrically powered spacecraft propulsion system, is introduced. Different priorities and possibilities of a space mission regarding energy cost and flight time duration are investigated by choosing different weighting factors for the multi-criteria objective function. Varying mission trajectories are analyzed and compared, both aiming at different destination asteroids and using different propulsion systems. For the transcription, the robust method of full discretization is used. The results strengthen the need for trajectory optimization as a foundation for autonomous decision making during deep space missions. Simultaneously they show the enormous increase in possibilities for flight maneuvers by being able to consider different and opposite mission objectives.

Keywords: deep space navigation, guidance, multi-objective, non-linear optimization, optimal control, trajectory planning.

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Authors: Nathaniel Deboever, Malcolm Breeze, Adrian Sheen

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Basic surgical and suturing techniques are a fundamental requirement for all doctors. In order to gain confidence and competence, doctors in training need to obtain sufficient teaching and just as importantly: practice. Young doctors with an apt level of expertise on these simple surgical skills, which are often used in the Emergency Department, can help alleviate some pressure during a busy evening. Unfortunately, learning these skills can be quite difficult during medical school or even during junior doctor years. The aim of this project was to adequately train medical students attending University of Sydney’s Nepean Clinical School through a series of workshops highlighting practical skills, with hopes to further extend this program to junior doctors in the hospital. The sessions instructed basic skills via tutorials, demonstrations, and lastly, the sessions cemented these proficiencies with practical sessions. During such an endeavor, it is fundamental to employ models that appropriately resemble what students will encounter in the clinical setting. The sustainability of workshops is similarly important to the continuity of such a program. To address both these challenges, the authors have developed models including suturing platforms, knot tying, and vessel ligation stations, as well as a shave and punch biopsy models and ophthalmologic foreign body device. The unique aspect of this work is that we utilized hands-on teaching sessions, to address a gap in doctors-in-training and junior doctor curriculum. Presented to you through this poster are our approaches to creating models that do not employ animal products and therefore do not necessitate particular facilities or discarding requirements. Covering numerous skills that would be beneficial to all young doctors, these models are easily replicable and affordable. This exciting work allows for countless sessions at low cost, providing enough practice for students to perform these skills confidently as it has been shown through attendee questionnaires.

Keywords: medical education, surgical models, surgical simulation, surgical skills education

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Authors: Myriam Rousseau, Suzie McKinnon, Mathieu Mireault, Anaïs V. Berthiaume, Marie-Hélène Poulin, Jacinthe Bourassa, Louis-Simon Maltais

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The increasing presence of young people with autism is forcing schools to adapt to this new situation and to offer services that meet the needs of this clientele. However, school staff often feels unqualified to support these students, lacking the preparation, skills and training to meet their needs. Continuing education for these staff is therefore essential to ensure that they can meet the needs of these students. As a result, the Government of Quebec has developed a bilingual (French and English) online training on autism specific to the needs of school staff. Therefore, adequate training for all school staff is likely to provide quality learning opportunities for these students. The research project focuses on the participants' appreciation, contributions, and social validity of the training. More specifically, it aims to: 1) evaluate the knowledge and self-efficacy of the participants, 2) evaluate the social validity and 3) document the evaluation of the ergonomics of the platform hosting the training. The evaluation carried out as part of this descriptive study uses a quantitative method. Data are collected using questionnaires completed online. The analysis of preliminary data reveals that participants' knowledge of autism and their sense of self-efficacy increased significantly. They value the training positively and consider it to be acceptable, appropriate, and suitable. The participants find it important for school staff to take this training. Almost all the items measuring the ergonomics of the platform have averages above 4.57/5. In general, the study shows that the training allows participating of the trainee school staff to improve their knowledge of autism and their sense of self-efficacy with young people with autism. In addition, participants recognize that the training has good social validity and appreciate the online modality. However, these results should be interpreted with caution given the limited number of participants who completed the research project. It is therefore important to continue the research with a larger number of participants to allow an adequate and general representativeness of the social validity, the feeling of competence and the appreciation of the platform.

Keywords: autism, online training, school staff, social validity

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Authors: Mireya Mateo Gomez

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The purpose of this study was to examine the perceptions first-generation Mexican origin mothers living in San Diego have on postpartum depression (PPD), with a special focus on social influences and cultural beliefs towards those meanings. This study also aimed to examine possible PPD help-seeking behaviors that first-generation Mexican origin mothers can perform. The Health Belief Model (HBM) and Social Ecological Model (SEM) were the guiding theoretical frameworks for this study. Data for this study were collected from three focus groups, four in-depth interviews, and the distribution of an acculturation survey (ARSMA II). There were a total of 15 participants, in which participant’s mean age was 45, and the mean age migrated to the United States being 22. Most participants identified as being married, born in Southern or Western Mexico, and with a strong Mexican identity in relation to the ARSMA survey. Participants identified four salient PPD perceptions corresponding to the interpersonal level of SEM. These four main perceptions were: 1) PPD affecting the identity of motherhood; 2) PPD being a natural part of a mother’s experience but mitigated by networks; 3) PPD being a U.S. phenomenon due to family and community breakdown; and 4) natural remedies as a preferred PPD treatment. In regard to themes relating to help seeking behaviors, participants identified seven being: 1) seeking help from immediate family members; 2) practicing home remedies; 3) seeking help from a medical professional; 4) obtaining help from a clinic or organization; 5) seeking help from God; 6) participating in PPD support groups; and 7) talking to a friend. It was evident in this study that postpartum depression is not a well discussed topic within the Mexican immigrant population. In relation to the role culture and social influences have on PPD perceptions, most participants shared hearing or learning about PPD from their family members or friends. Participants also stated seeking help from family members if diagnosed with PPD and seeking out home remedies. This study as well provides suggestions to increase the awareness of PPD among the Mexican immigrant community.

Keywords: cultural beliefs, health belief model, Mexican origin mothers, perceptions, postpartum depression social ecological model

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Authors: Patsy Tan, Dana Baltutis

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This presentation demonstrates the effectiveness of music therapy in co-treatment with speech pathology and occupational therapy as an innovative way when working with children and adolescents with complex individual differences to facilitate communication, emotional, motor and social skills development. Each child with special needs and their carer has an individual profile which encompasses their visual-spatial, auditory, language, learning, mental health, family dynamic, sensory-motor, motor planning and sequencing profiles. The most common issues among children with special needs, especially those diagnosed with Autism Spectrum Disorder, are in the areas of regulation, communication, and social-emotional development. The ability of children living with challenges to communicate and use language and understand verbal and non-verbal information, as well as move their bodies to explore and interact with their environments in social situations, depends on the children being regulated both internally and externally and trusting their communication partners and understanding what is happening in the moment. For carers, it is about understanding the tempo, rhythm, pacing, and timing of their own individual profile, as well as the profile of the child they are interacting with, and how these can sync together. In this study, music therapy is used in co-treatment sessions with a speech pathologist and/or an occupational therapist using the DIRFloortime approach to facilitate the regulation, attention, engagement, reciprocity and social-emotional capacities of children presenting with complex individual differences. Documented changes in 10 domains of children’s development over a 12-month period using the Individual Music Therapy Assessment Profile (IMTAP) were observed. Children were assessed biannually, and results show significant improvements in the social-emotional, musicality and receptive language domains indicating that co-treatment with a music therapist using the DIRFloortime framework is highly effective. This presentation will highlight strategies that facilitate regulation, social-emotional and communication development for children and adolescents with complex individual profiles.

Keywords: communication, shared attention, regulation, social emotional

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Authors: Dacheng Li, Bo Huang, Qinjin Han, Ming Li

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Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions.

Keywords: spatiotemporal fusion, sparse representation, K-SVD algorithm, dictionary learning

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Authors: Sousan Arafeh

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Educational systems and schools worldwide are increasingly reliant on information and communication technology (ICT). Unfortunately, most educational leadership development programs do not offer formal curricular and/or field experiences that prepare students for managing ICT resources, personnel, and processes. The result is a steep learning curve for the leader and his/her staff and dissipated organizational energy that compromises desired outcomes. To address this gap in education leaders’ development, Arafeh’s Integrated Technology Leadership Model (AITLM) was created. It is a conceptual model and tool that educational leadership students can use to better understand the ICT ecology that exists within their schools. The AITL Model consists of six 'infrastructure types' where ICT activity takes place: technical infrastructure, communications infrastructure, core business infrastructure, context infrastructure, resources infrastructure, and human infrastructure. These six infrastructures are further divided into 16 key areas that need management attention. The AITL Model was created by critically analyzing existing technology/ICT leadership models and working to make something more authentic and comprehensive regarding school leaders’ purview and experience. The AITL Model then served as a tool when it was distributed to over 150 educational leadership students who were asked to review it and qualitatively share their reactions. Students said the model presented crucial areas of consideration that they had not been exposed to before and that the exercise of reviewing and discussing the AITL Model as a group was useful for identifying areas of growth that they could pursue in the leadership development program and in their professional settings. While development in all infrastructures and key areas was important for students’ understanding of ICT, they noted that they were least aware of the importance of the intangible area of the resources infrastructure. The AITL Model will be presented and session participants will have an opportunity to review and reflect on its impact and utility. Ultimately, the AITL Model is one that could have significant policy and practice implications. At the very least, it might help shape ICT content in educational leadership development programs through curricular and pedagogical updates.

Keywords: education leadership, information and communications technology, ICT, leadership capacity building, leadership development

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Authors: F. Nasser-Abu Alhija

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In many countries, much efforts and resources are devoted to empowering and integrating minorities within the mainstream population. Major ventures in this route are crafted in higher education institutions where different outreach programs and methods such as lenient entry requirements, monitory incentives, learning skills workshops, tutoring and mentoring, are utilized. Although there is some information regarding these programs, their effectiveness still needs to be thoroughly examined. The Ethiopian community In Israel is one of the minority groups that has been targeted by sponsoring foundations and higher education institutions with the aim to ease the access, persistence and success of its young people in higher education and later in the job market. The evaluation study we propose to present focuses on the implementation of a program designed for this purpose. This program offers relevant candidates for study at a prestigious university a variety of generous incentives that include tuitions, livening allowance, tutoring, mentoring, skills and empowerment workshops and cultural meetings. Ten students were selected for the program and they started their studies in different subject areas before three and half years. A longitudinal evaluation has been conducted since the implementation of the program. Data were collected from different sources: participating students, program coordinator, mentors, tutors, program documents and university records. Questionnaires and interviews were used for collecting data on the different components of the program and on participants' perception of their effectiveness. Participants indicate that the lenient entry requirements and the monitory incentives are critical for starting their studies. During the first year, skills and empowering workshops, torturing and mentoring were evaluated as very important for persistence and success in studies. Tutoring was perceived as very important also at the second year but less importance is attributed to mentoring. Mixed results regarding integration in the Israeli culture emerged. The results are discussed with reference to findings from different settings around the world.

Keywords: access to higher education, minority groups, monitory incentives, torturing, mentoring

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Authors: Tomiwa, Akinyemi Clement

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Tropical regions experience diverse and complex precipitation patterns, posing significant challenges for accurate rainfall estimation and forecasting. This study addresses the problem of effectively classifying tropical rain types and refining the Z-R (Reflectivity-Rain Rate) relationship to enhance rainfall estimation accuracy. Through a combination of remote sensing, meteorological analysis, and machine learning, the research aims to develop an advanced classification framework capable of distinguishing between different types of tropical rain based on their unique characteristics. This involves utilizing high-resolution satellite imagery, radar data, and atmospheric parameters to categorize precipitation events into distinct classes, providing a comprehensive understanding of tropical rain systems. Additionally, the study seeks to improve the Z-R relationship, a crucial aspect of rainfall estimation. One year of rainfall data was analyzed using a Micro Rain Radar (MRR) located at The Federal University of Technology Akure, Nigeria, measuring rainfall parameters from ground level to a height of 4.8 km with a vertical resolution of 0.16 km. Rain rates were classified into low (stratiform) and high (convective) based on various microstructural attributes such as rain rates, liquid water content, Drop Size Distribution (DSD), average fall speed of the drops, and radar reflectivity. By integrating diverse datasets and employing advanced statistical techniques, the study aims to enhance the precision of Z-R models, offering a more reliable means of estimating rainfall rates from radar reflectivity data. This refined Z-R relationship holds significant potential for improving our understanding of tropical rain systems and enhancing forecasting accuracy in regions prone to heavy precipitation.

Keywords: remote sensing, precipitation, drop size distribution, micro rain radar

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Authors: Annika Stechemesser, Rachel Pounds, Emma Lucas, Chris Dawson, Julia Lipecki, Pavle Vrljicak, Jan Brosens, Sean Kehoe, Jason Yap, Lawrence Young, Sascha Ott

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Advances in technology make it now possible to retrieve the genetic information of thousands of single cancerous cells. One of the key challenges in single cell analysis of cancerous tissue is to determine the number of different cell types and their characteristic genes within the sample to better understand the tumors and their reaction to different treatments. For this analysis to be possible, it is crucial to filter out background noise as it can severely blur the downstream analysis and give misleading results. In-depth analysis of the state-of-the-art filtering methods for single cell data showed that they do, in some cases, not separate noisy and normal cells sufficiently. We introduced an algorithm that filters and clusters single cell data simultaneously without relying on certain genes or thresholds chosen by eye. It detects communities in a Shared Nearest Neighbor similarity network, which captures the similarities and dissimilarities of the cells by optimizing the modularity and then identifies and removes vertices with a weak clustering belonging. This strategy is based on the fact that noisy data instances are very likely to be similar to true cell types but do not match any of these wells. Once the clustering is complete, we apply a set of evaluation metrics on the cluster level and accept or reject clusters based on the outcome. The performance of our algorithm was tested on three datasets and led to convincing results. We were able to replicate the results on a Peripheral Blood Mononuclear Cells dataset. Furthermore, we applied the algorithm to two samples of ovarian cancer from the same patient before and after chemotherapy. Comparing the standard approach to our algorithm, we found a hidden cell type in the ovarian postchemotherapy data with interesting marker genes that are potentially relevant for medical research.

Keywords: cancer research, graph theory, machine learning, single cell analysis

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Authors: Lisa Fox, Jill Aylott

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There is a global need to identify ways to engage doctors in non-clinical matters such as medical leadership, service improvement and health system transformation. Using the core principles of Realistic Evaluation (RE), this study examined what works, for doctors of different grades, specialities and experience in an acute NHS Hospital Trust in the UK. Realistic Evaluation is an alternative to more traditional cause and effect evaluation models and seeks to understand the interdependencies of Context, Mechanism and Outcome proposing that Context (C) + Mechanism (M) = Outcome (O). In this study, the context, mechanism and outcome were examined from within individual medical leaders to determine what enables levels of medical engagement in a specific improvement project to reduce hospital inpatient mortality. Five qualitative case studies were undertaken with consultants who had regularly completed mortality reviews over a six month period. The case studies involved semi-structured interviews to test the theory behind the drivers for medical engagement. The interviews were analysed using a theory-driven thematic analysis to identify CMO configurations to explain what works, for whom and in what circumstances. The findings showed that consultants with a longer length of service became more engaged if there were opportunities to be involved in the beginning of an improvement project, with more opportunities to affect the design. Those that are new to a consultant role were more engaged if they felt able to apply any learning directly into their own settings or if they could use it as an opportunity to understand more about the organisation they are working in. This study concludes that RE is a useful methodology for better understanding the complexities of motivation and consultant engagement in a trust wide service improvement project. The study showed that there should be differentiated and bespoke training programmes to maximise each individual doctor’s propensity for medical engagement. The RE identified that there are different ways to ensure that doctors have the right skills to feel confident in service improvement projects.

Keywords: realistic evaluation, medical leadership, medical engagement, patient safety, service improvement

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Authors: Vishal Raj, Noorhan Abbas

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Ambiguity in NLP (Natural language processing) refers to the ability of a word, phrase, sentence, or text to have multiple meanings. This results in various kinds of ambiguities such as lexical, syntactic, semantic, anaphoric and referential am-biguities. This study is focused mainly on solving the issue of Lexical ambiguity. Word Sense Disambiguation (WSD) is an NLP technique that aims to resolve lexical ambiguity by determining the correct meaning of a word within a given context. Most WSD solutions rely on words for training and testing, but we have used lemma and Part of Speech (POS) tokens of words for training and testing. Lemma adds generality and POS adds properties of word into token. We have designed a novel method to create an affinity matrix to calculate the affinity be-tween any pair of lemma_POS (a token where lemma and POS of word are joined by underscore) of given training set. Additionally, we have devised an al-gorithm to create the sense clusters of tokens using affinity matrix under hierar-chy of POS of lemma. Furthermore, three different mechanisms to predict the sense of target word using the affinity/similarity value are devised. Each contex-tual token contributes to the sense of target word with some value and whichever sense gets higher value becomes the sense of target word. So, contextual tokens play a key role in creating sense clusters and predicting the sense of target word, hence, the model is named Contextual SenSe Model (CSM). CSM exhibits a noteworthy simplicity and explication lucidity in contrast to contemporary deep learning models characterized by intricacy, time-intensive processes, and chal-lenging explication. CSM is trained on SemCor training data and evaluated on SemEval test dataset. The results indicate that despite the naivety of the method, it achieves promising results when compared to the Most Frequent Sense (MFS) model.

Keywords: word sense disambiguation (wsd), contextual sense model (csm), most frequent sense (mfs), part of speech (pos), natural language processing (nlp), oov (out of vocabulary), lemma_pos (a token where lemma and pos of word are joined by underscore), information retrieval (ir), machine translation (mt)

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Authors: Xuan Li

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The English language occupies a crucial position in the Chinese educational system and is officially introduced in the school curriculum from the third year of primary school onward. However, it is worth noting that along with the movement to remove primary-oriented education from preschools, the teaching of English is banned in preschools. Considering the worldwide trend of learning English at a young age, whether this ban can be implemented successfully is doubtful. With an initial focus on the interaction of language-in-education planning and policy (LEPP) at the macro level and actual practice at the micro level, this research selected three private preschools and two public preschools to explore what is taking place in terms of English education. All data collected is qualitative and is gained from documentary analysis, school observation, interviews, and focus groups. The findings show that: (1) although the English ban in preschool education aims to regulate all types of preschools and all adult Chinese participants are aware of this ban, there are very different scenarios according to type of preschool, such that no English classes are found in public schools while private preschools commonly provide some kind of English education; (2) even public schools do not have an English-free environment and parents’ demand for English education is high; (3) there is an obvious top-down hierarchy in both public and private schools, in which administrators make the decisions while others have little power to influence the school curriculum; (4) there is a clear gap in the perception of English teaching between children and adults, in which adults prefer foreign English teachers and think English teaching is just playing, while children do not have a clear preference regarding teachers and do not think English class is just for fun; (5) without macro support, there are many challenges involved in preschool English education, including the shortage of qualified teachers and teaching resources, ineffective personnel management and few opportunities for speaking English in daily life. Hopefully, this research will not only highlight the interaction of LEPP at different levels and the importance of individual agency but also raise the awareness of how to provide qualified and equal education for all children.

Keywords: individual agency, language-in-education planning and policy, micro context, preschool English education

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Authors: Afshin Gharib

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Often, instructors prefer to bring into class a guest lecturer who can provide an “experiential” perspective on a particular topic. The assumption is that the personal experience brought into the classroom makes the material resonate more with students and that students would have a preference for material being taught from an experiential perspective. The question we asked in the present study was whether a guest lecture from an “experiential” expert with a disability (e.g. a guest suffering from cone-rod dystrophy lecturing on vision, or a dyslexic lecturing on the psychology of reading) would be more effective than the course instructor in capturing students attention and conveying information in an Introduction to Psychology class. Students in two sections of Introduction to Psychology (N = 25 in each section) listened to guest lecturers with disabilities lecturing on a topic related to their disability, one in the area of Sensation and Perception (the guest lecturer is vision impaired) and one in the area of Language Development (the guest lecturer is dyslexic). The Guest lecturers lectured on the same topic in both sections, however, each lecturer used their own experiences to highlight the topics they cover in one section but not the other (counterbalanced between sections), providing students in one section with experiential testimony. Following each of the 4 lectures (two experiential, two non-experiential) students rated the lecture on several dimensions including overall quality, level of engagement, and performance. In addition, students in both sections were tested on the same test items from the lecture material to ascertain degree of learning, and given identical “pop” quizzes two weeks after the exam to measure retention. It was hypothesized that students would find the experiential lectures from lecturers talking about their disabilities more engaging, learn more from them, and retain the material for longer. We found that students in fact preferred the course instructor to the guests, regardless of whether the guests included a discussion of their own disability in their lectures. Performance on the exam questions and the pop quiz items were not different between “experiential” and “non-experiential” lectures, suggesting that guest lecturers who discuss their own disabilities in lecture are not more effective in conveying material and students are not more likely to retain material delivered by “experiential” guests. In future research we hope to explore the reasons for students preference for their regular instructor over guest lecturers.

Keywords: guest lecturer, student perception, retention, experiential

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Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae

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Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.

Keywords: canny pruning, hand recognition, machine learning, skin tracking

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Authors: Ahn Christine Myunghee, Sim Wonjin, Cho Kristina, Ahn Mira, Hong Yeju, Kwok Jihae, Lim Sooyeon, Park Hansol

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With a sharp increase in the prevalence rate for mental health issues in Korea, there is a need for specific and effective intervention strategies in counseling and psychotherapy for use with Korean clients. With the cultural emphasis on restraining emotional expression and not disclosing personal and familial problems to outsiders, clients often find it difficult to discuss their emotional issues even to therapists. Exploring a client’s internal psychological processes bypassing this culture-specific mode of therapeutic communication often becomes a challenge in the therapeutic setting. Given this socio-cultural context, the purpose of the current study was to investigate the effectiveness of using dream work to individuals in Korea. The current study conducted one 60-90 minute dream session and analyzed the dream content of 39 Korean young adults to evaluate the effectiveness of the Hill dream model in accessing the intra-psychic materials, determining essential emotional themes, and learning how the individuals interpreted the contents of their dreams. The transcribed data, which included a total of 39 sessions from 39 volunteer university students, were analyzed by the Consensus Qualitative Research (CQR) approach in terms of domains and core ideas. Self-report measures on Dream Salience, Gains from Dream Interpretations and the Session Evaluation Scale were administered before and after each of their dream sessions. The results indicated that dream work appears to be an effective way to understand unconscious motivations, thoughts, and feelings related to a person’s sense of self, and also how these people relate to other people. Current findings need to be replicated with clients referred for counseling and psychotherapy to determine if the dream work is an appropriate and useful intervention in counseling settings. Limitations of the current study and suggestions for future follow-ups are included in the discussion.

Keywords: dream work, dream interpretation, Korean, young adults, CQR

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Authors: Elizabeth Howarth, Dawn Green, Sean Connolly, Geena Vabulas, Sara Smolley

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Through the EU Horizon 2020 Nuvoic Project, the project team recruited 70 individuals in the UK and Ireland to test the Voiceitt speech recognition app and provide user feedback to developers. The app is designed for people with dysarthric speech, to support communication with unfamiliar people and access to speech-driven technologies such as smart home equipment and smart assistants. Participants with atypical speech, due to a range of conditions such as cerebral palsy, acquired brain injury, Down syndrome, stroke and hearing impairment, were recruited, primarily through organisations supporting disabled people. Most had physical or learning disabilities in addition to dysarthric speech. The project team worked with individuals, their families and local support teams, to provide access to the app, including through additional assistive technologies where needed. Testing was user-led, with participants asked to identify and test use cases most relevant to their daily lives over a period of three months or more. Ongoing technical support and training were provided remotely and in-person throughout the testing period. Structured interviews were used to collect feedback on users' experiences, with delivery adapted to individuals' needs and preferences. Informal feedback was collected through ongoing contact between participants, their families and support teams and the project team. Focus groups were held to collect feedback on specific design proposals. User feedback shared with developers has led to improvements to the user interface and functionality, including faster voice training, simplified navigation, the introduction of gamification elements and of switch access as an alternative to touchscreen access, with other feature requests from users still in development. This work offers a case-study in successful and inclusive co-design with the disabled community.

Keywords: co-design, assistive technology, dysarthria, inclusive speech recognition

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Authors: Silvia Benvenuti, Alessandra Cardinali

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In recent years, for instance, in relation to the Covid 19 pandemic and the evidence of climate change, it is becoming quite clear that the development of a young kid into an adult citizen requires a solid scientific background. Citizens are required to exert logical thinking and know the methods of science in order to adapt, understand, and develop as persons. Mathematics sits at the core of these required skills: learning the axiomatic method is fundamental to understand how hard sciences work and helps in consolidating logical thinking, which will be useful for the entire life of a student. At the same time, research shows that the axiomatic study of geometry is a problematic topic for students, even for those with interest in mathematics. With this in mind, the main goals of the research work we will describe are: (1) to show whether non-Euclidean geometries can be a tool to allow students to consolidate the knowledge of Euclidean geometries by developing it in a critical way; (2) to promote the understanding of the modern axiomatic method in geometry; (3) to give students a new perspective on mathematics so that they can see it as a creative activity and a widely discussed topic with a historical background. One of the main issues related to the state-of-the-art in this topic is the shortage of experimental studies with students. For this reason, our aim is to show further experimental evidence of the potential benefits of teaching non-Euclidean geometries at high school, based on data collected from a study started in 2005 in the frame of the Italian National Piano Lauree Scientifiche, continued by a teacher training organized in September 2018, perfected in a pilot study that involved 77 high school students during the school years 2018-2019 and 2019-2020. and finally implemented through an experimental study conducted in 2020-21 with 87 high school students. Our study shows that there is potential for further research to challenge current conceptions of the school mathematics curriculum and of the capabilities of high school mathematics students.

Keywords: Non-Euclidean geometries, beliefs about mathematics, questionnaires, modern axiomatic method

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Authors: Annika J. Meyer, Tom Piechotta

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Unmanned Aerial Vehicles (UAVs) have increasingly become vital tools in various applications, including surveillance, search and rescue, and environmental monitoring. One common approach to ensure redundant communication systems when flying beyond visual line of sight is for UAVs to employ multiple mobile data modems by different providers. Although widely adopted, this approach suffers from several drawbacks, such as high costs, added weight and potential increases in signal interference. In light of these challenges, this paper proposes a communication framework intermeshing mobile networks and LoRa (Long Range) technology—a low-power, long-range communication protocol. LoRaWAN (Long Range Wide Area Network) is commonly used in Internet of Things applications, relying on stationary gateways and Internet connectivity. This paper, however, utilizes the underlying LoRa protocol, taking advantage of the protocol’s low power and long-range capabilities while ensuring efficiency and reliability. Conducted in collaboration with the Potsdam Fire Department, the implementation of mobile network technology in combination with the LoRa protocol in small UAVs (take-off weight < 0.4 kg), specifically designed for search and rescue and area monitoring missions, is explored. This research aims to test the viability of LoRa as an additional redundant communication system during UAV flights as well as its intermeshing with the primary, mobile network-based controller. The methodology focuses on direct UAV-to-UAV and UAV-to-ground communications, employing different spreading factors optimized for specific operational scenarios—short-range for UAV-to-UAV interactions and long-range for UAV-to-ground commands. This explored use case also dramatically reduces one of the major drawbacks of LoRa communication systems, as a line of sight between the modules is necessary for reliable data transfer. Something that UAVs are uniquely suited to provide, especially when deployed as a swarm. Additionally, swarm deployment may enable UAVs that have lost contact with their primary network to reestablish their connection through another, better-situated UAV. The experimental setup involves multiple phases of testing, starting with controlled environments to assess basic communication capabilities and gradually advancing to complex scenarios involving multiple UAVs. Such a staged approach allows for meticulous adjustment of parameters and optimization of the communication protocols to ensure reliability and effectiveness. Furthermore, due to the close partnership with the Fire Department, the real-world applicability of the communication system is assured. The expected outcomes of this paper include a detailed analysis of LoRa's performance as a communication tool for UAVs, focusing on aspects such as signal integrity, range, and reliability under different environmental conditions. Additionally, the paper seeks to demonstrate the cost-effectiveness and operational efficiency of using a single type of communication technology that reduces UAV payload and power consumption. By shifting from traditional cellular network communications to a more robust and versatile cellular and LoRa-based system, this research has the potential to significantly enhance UAV capabilities, especially in critical applications where reliability is paramount. The success of this paper could pave the way for broader adoption of LoRa in UAV communications, setting a new standard for UAV operational communication frameworks.

Keywords: LoRa communication protocol, mobile network communication, UAV communication systems, search and rescue operations

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Authors: Mahdi Allaoua Seklab, Süleyman BaşTürk

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In the quest to enhance coastal environmental health, this study introduces a fully autonomous beach cleaning machine, a breakthrough in leveraging green energy and advanced artificial intelligence for ecological preservation. Designed to operate independently, the machine is propelled by a solar-powered system, underscoring a commitment to sustainability and the use of renewable energy in autonomous robotics. The vehicle's autonomous navigation is achieved through a sophisticated integration of LIDAR and a camera system, utilizing an SSD MobileNet V2 object detection model for accurate and real-time trash identification. The SSD framework, renowned for its efficiency in detecting objects in various scenarios, is coupled with the lightweight and precise highly MobileNet V2 architecture, making it particularly suited for the computational constraints of on-board processing in mobile robotics. Training of the SSD MobileNet V2 model was conducted on Google Colab, harnessing cloud-based GPU resources to facilitate a rapid and cost-effective learning process. The model was refined with an extensive dataset of annotated beach debris, optimizing the parameters using the Adam optimizer and a cross-entropy loss function to achieve high-precision trash detection. This capability allows the machine to intelligently categorize and target waste, leading to more effective cleaning operations. This paper details the design and functionality of the beach cleaning machine, emphasizing its autonomous operational capabilities and the novel application of AI in environmental robotics. The results showcase the potential of such technology to fill existing gaps in beach maintenance, offering a scalable and eco-friendly solution to the growing problem of coastal pollution. The deployment of this machine represents a significant advancement in the field, setting a new standard for the integration of autonomous systems in the service of environmental stewardship.

Keywords: autonomous beach cleaning machine, renewable energy systems, coastal management, environmental robotics

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Authors: Mehran Asadi, Afrand Agah

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In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.

Keywords: online social networks, data mining, social cloud computing, interaction and collaboration

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Authors: Michelangelo Sofo, Giuseppe Labianca

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In recent years, the scientific community's interest in the exploratory analysis of biomedical data has increased exponentially. Considering the field of research of nutritional biologists, the curative process, based on the analysis of clinical data, is a very delicate operation due to the fact that there are multiple solutions for the management of pathologies in the food sector (for example can recall intolerances and allergies, management of cholesterol metabolism, diabetic pathologies, arterial hypertension, up to obesity and breathing and sleep problems). In this regard, in this research work a system was created capable of evaluating various dietary regimes for specific patient pathologies. The system is founded on a mathematical-numerical model and has been created tailored for the real working needs of an expert in human nutrition using the human-centered design (ISO 9241-210), therefore it is in step with continuous scientific progress in the field and evolves through the experience of managed clinical cases (machine learning process). DietAdhoc® is a decision support system nutrition specialists for patients of both sexes (from 18 years of age) developed with an agile methodology. Its task consists in drawing up the biomedical and clinical profile of the specific patient by applying two algorithmic optimization approaches on nutritional data and a symbolic solution, obtained by transforming the relational database underlying the system into a deductive database. For all three solution approaches, particular emphasis has been given to the explainability of the suggested clinical decisions through flexible and customizable user interfaces. Furthermore, the system has multiple software modules based on time series and visual analytics techniques that allow to evaluate the complete picture of the situation and the evolution of the diet assigned for specific pathologies.

Keywords: medical decision support, physiological data extraction, data driven diagnosis, human centered AI, symbiotic AI paradigm

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Authors: Reyhan Tekin Sitrava

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

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

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Authors: Marie Sams, Kate Barnett-Richards, Jacqui Speculand, Gemma Tombs

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Business education in the United Kingdom has seen a number of improvements over the years in moving from delivering traditional chalk and talk lectures to using digital technologies and inviting guest lectures from industry to deliver sessions for students. Engaging topical industry talks to enhance course delivery is generally seen as a positive aspect of enhancing curriculum, however it is acknowledged that perhaps there are better ways in which industry can contribute to the quality of business programmes. Additionally, there is a consensus amongst UK industry managers that a bigger involvement in designing and inputting into business curriculum will have a greater impact on the quality of business ready graduates. Funded by the Disruptive Media Learning Lab at Coventry University in the UK, a project (SOPI - Student Online Projects with Industry) was initiated to enable students to work in project teams to respond and engage with real problems and challenges faced by five managers in various industries including retail, events and manufacturing. Over a semester, approximately 200 students were given the opportunity to develop their management, facilitation, problem solving and reflective skills, whilst having some exposure to real challenges in industry with a focus on supply chain and project management. Face to face seminars were re-designed to enable students to work on live issues in a competitive environment, and were guided to consider the theoretical aspects of their module delivery to underpin the solutions that they were generating. Dialogue between student groups and managers took place using Google+ community; an online social media tool which enables private discussions to take place and can be accessed on mobile devices. Results of the project will be shared in how this development has added value to students experience and understanding of the two subject areas. Student reflections will be analysed and evaluated to assess how the project has contributed to their perception of how the theoretical nature of these two business subjects are applied in practical situations.

Keywords: business, education, industry, projects

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Authors: Jigg Pelayo, Ricardo Villar

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The Philippines is long considered as a valuable producer of high value crops globally. The country’s employment and economy have been dependent on agriculture, thus increasing its demand for the efficient agricultural mechanism. Remote sensing and geographic information technology have proven to effectively provide applications for precision agriculture through image-processing technique considering the development of the aerial scanning technology in the country. Accurate information concerning the spatial correlation within the field is very important for precision farming of high value crops, especially. The availability of height information and high spatial resolution images obtained from aerial scanning together with the development of new image analysis methods are offering relevant influence to precision agriculture techniques and applications. In this study, an algorithm was developed and implemented to detect and count high value crops simultaneously through adaptive scaling of support vector machine (SVM) algorithm subjected to object-oriented approach combining watershed transformation and local maxima filter in enhancing tree counting and detection. The methodology is compared to cutting-edge template matching algorithm procedures to demonstrate its effectiveness on a demanding tree is counting recognition and delineation problem. Since common data and image processing techniques are utilized, thus can be easily implemented in production processes to cover large agricultural areas. The algorithm is tested on high value crops like Palm, Mango and Coconut located in Misamis Oriental, Philippines - showing a good performance in particular for young adult and adult trees, significantly 90% above. The s inventories or database updating, allowing for the reduction of field work and manual interpretation tasks.

Keywords: high value crop, LiDAR, OBIA, precision agriculture

Procedia PDF Downloads 398