Search results for: learning physical

Authors: Muhammad Mohsin, Fawad Ahmad, Fatima Batool, Muhammad Kaab Zarrar

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The integration of the Internet of Things (IoT) and edge computing devices offers a transformative approach to electronic waste management, particularly in the dismantling of printed circuit boards (PCBs). This paper explores how these technologies optimize operational efficiency and improve environmental sustainability by addressing challenges such as data security, interoperability, scalability, and real-time data processing. Proposed solutions include advanced machine learning algorithms for predictive maintenance, robust encryption protocols, and scalable architectures that incorporate edge computing. Case studies from leading e-waste management facilities illustrate benefits such as improved material recovery efficiency, reduced environmental impact, improved worker safety, and optimized resource utilization. The findings highlight the potential of IoT and edge computing to revolutionize e-waste dismantling and make the case for a collaborative approach between policymakers, waste management professionals, and technology developers. This research provides important insights into the use of IoT and edge computing to make significant progress in the sustainable management of electronic waste

Keywords: internet of Things, edge computing, waste PCB disassembly, electronic waste management, data security, interoperability, machine learning, predictive maintenance, sustainable development

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Authors: Mimi My Tse

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The prevalence of chronic non-cancer pain is high among community-dwelling older adults. Pain affects physical and psychosocial abilities. Older adults tend to be less mobile and have a high tendency to fall risk. In addition, older adults with pain are depressed, anxious, and not too willing to join social activities. This will make them feel very lonely and social isolation. Instead of giving pain management education and programs to older adults/clients, both older adults and their caregivers, it is sad to find that the majority of existing services are given to older adults only. Given the importance of family members in increasing compliance with health-promoting programs, we proposed to offer pain management programs to both older adults with his/her caregiver as a “dyad.” We used the Health Promotion Model and implemented a dyadic pain management program (DPM). The DPM is an 8-week group-based program. The DPM comprises 4 weeks of center-based, face-to-face activities and 4 weeks of digital-based activities delivered via a WhatsApp group. There were 30 dyads (15 in the experimental group with DPM and 15 in the control group with pain education pamphlets). Upon the completion of DPM, pain intensity and pain interference were significantly lower in the intervention group as compared to the control group. At the same time, physical function showed significant improvement and lower depression scores in the intervention group. In conclusion, the study highlights the potential benefits of involving caregivers in the management of chronic pain for older adults. This approach should be widely promoted in managing chronic pain situations for community-dwelling older adults and their caregivers.

Keywords: pain, older adults, dyadic approach, education

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Authors: Siveshnee Devar

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Background: The rate of absenteeism and prevalence of NCDs in South Africa is extremely high. This is consistent with other educational institutions and workplaces around the globe. In most cases the absence of health and the presence of one or more non communicable diseases coupled with the lack of physical exercise is a major factor in absenteeism. Absenteeism at the workplace comes at a huge cost to the employer and the country as a whole. Aim: Findings from this study was to develop a suitable nutritional wellness program for the workplace. Methodology: A needs analysis in the form of 24-hour recall, food frequency, health and socio demographic questionnaires was undertaken to determine the need for a wellness program for the institution. Anthropometric indices such as BMI, waist circumference and blood pressure were also undertaken to determine the state of health of the staff. Results: This study has found that obesity, central obesity, hypertension as well as deficiencies in nutrients and minerals were prevalent in this group. Fruit and vegetable consumption was also below the WHO recommendation. This study showed a link between diet, physical activity and diseases of lifestyle. There were positive correlations between age and systolic blood pressure, waist circumference and systolic blood pressure, waist circumference and diastolic blood pressure and waist-to-height ratio and BMI. Conclusion: The results indicated the need for immediate intervention in the form of a wellness program. Nutrition education is important for both the workplace and out. Education and knowledge are important factors for lifestyle changes. The proposed intervention is aimed at improving presenteeism and decreasing the incidence of non- communicable diseases. Presenteeism and good health are important factors for quality education at all educational institutions.

Keywords: absenteeism, non-communicable diseases, nutrition, wellness

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Authors: Zoltán Höhling, Sarah-Lu Oberschelp, Niklas Gilsdorf, Michael Wirsching, Andrea Kuhnert

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A majority of medical students at the University of Freiburg reported stress-related psychosomatic symptoms which are often associated with their studies. International research supports these findings, as medical students worldwide seem to be at special risk for mental health problems. In some countries and institutions, psychologically based interventions that assist medical students in coping with their stressors have been implemented. It turned out that anonymity is an important aspect here. Many students fear a potential damage of reputation when being associated with mental health problems, which may be due to a high level of competitiveness in classes. Therefore, we launched an online-platform where medical students could anonymously seek help and exchange their experiences with fellow students and experts. Medical students of all semesters have access to it through the university’s learning management system (called “ILIAS”). The informative part of the platform consists of exemplary videos showing medical students (actors) who act out scenes that demonstrate the antecedents of stress-related psychosomatic disorders. These videos are linked to different expert comments, describing the exhibited symptoms in an understandable and normalizing way. The (inter-)active part of the platform consists of self-help tools (such as meditation exercises or general tips for stress-coping) and an anonymous interactive forum where students can describe their stress-related problems and seek guidance from experts and/or share their experiences with fellow students. Besides creating an immediate proposal to help affected students, we expect that competitiveness between students might be diminished and bondage improved through mutual support between them. In the initial phase after the platform’s launch, it was accessed by a considerable number of medical students. On a closer look it appeared that platform sections like general information on psychosomatic-symptoms and self-treatment tools were accessed far more often than the online-forum during the first months after the platform launch. Although initial acceptance of the platform was relatively high, students showed a rather passive way of using our platform. While user statistics showed a clear demand for information on stress-related psychosomatic symptoms and its possible remedies, active engagement in the interactive online-forum was rare. We are currently advertising the platform intensively and trying to point out the assured anonymity of the platform and its interactive forum. Our plans, to assure students their anonymity through the use of an e-learning facility and promote active engagement in the online forum, did not (yet) turn out as expected. The reasons behind this may be manifold and based on either e-learning related issues or issues related to students’ individual needs. Students might, for example, question the assured anonymity due to a lack of trust in the technological functioning university’s learning management system. However, one may also conclude that reluctance to discuss stress-related psychosomatic symptoms with peer medical students may not be solely based on anonymity concerns, but could be rooted in more complex issues such as general mistrust between students.

Keywords: e-tutoring, stress-coping, student support, online forum

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Authors: Hassnen M. Jafer, W. Atherton, F. Ruddock

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This paper represents the results of experimental work to investigate the suitability of a waste material (WM) for soft soil stabilisation. In addition, the effect of particle size distribution (PSD) of the waste material on its performance as a soil stabiliser was investigated. The WM used in this study is produced from the incineration processes in domestic energy power plant and it is available in two different grades of fineness (coarse waste material (CWM) and fine waste material (FWM)). An intermediate plasticity silty clayey soil with medium organic matter content has been used in this study. The suitability of the CWM and FWM to improve the physical and engineering properties of the selected soil was evaluated dependant on the results obtained from the consistency limits, compaction characteristics (optimum moisture content (OMC) and maximum dry density (MDD)); along with the unconfined compressive strength test (UCS). Different percentages of CWM were added to the soft soil (3, 6, 9, 12 and 15%) to produce various admixtures. Then the UCS test was carried out on specimens under different curing periods (zero, 7, 14, and 28 days) to find the optimum percentage of CWM. The optimum and other two percentages (either side of the optimum content) were used for FWM to evaluate the effect of the fineness of the WM on UCS of the stabilised soil. Results indicated that both types of the WM used in this study improved the physical properties of the soft soil where the index of plasticity (IP) was decreased significantly. IP was decreased from 21 to 13.64 and 13.10 with 12% of CWM and 15% of FWM respectively. The results of the unconfined compressive strength test indicated that 12% of CWM was the optimum and this percentage developed the UCS value from 202kPa to 500kPa for 28 days cured samples, which is equal, approximately 2.5 times the UCS value for untreated soil. Moreover, this percentage provided 1.4 times the value of UCS for stabilized soil-CWA by using FWM which recorded just under 700kPa after 28 days curing.

Keywords: soft soil stabilisation, waste materials, fineness, unconfined compressive strength

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Authors: Sultan Ghaleb Aldaihani

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- The environment in which educational leadership takes place is becoming increasingly complex due to factors like globalization and rapid technological change. - This is creating a "leadership gap" where the complexity of the environment outpaces the ability of leaders to effectively respond. - Educational leadership involves guiding teachers and the broader school system towards improved student learning and achievement. 2. Implications of Artificial Intelligence (AI) in Educational Leadership: - AI has great potential to enhance education, such as through intelligent tutoring systems and automating routine tasks to free up teachers. - AI can also have significant implications for educational leadership by providing better information and data-driven decision-making capabilities. - Computer-adaptive testing can provide detailed, individualized data on student learning that leaders can use for instructional decisions and accountability. 3. Enhancing Decision-Making Processes: - Statistical models and data mining techniques can help identify at-risk students earlier, allowing for targeted interventions. - Probability-based models can diagnose students likely to drop out, enabling proactive support. - These data-driven approaches can make resource allocation and decision-making more effective. 4. Improving Efficiency and Productivity: - AI systems can automate tasks and change processes to improve the efficiency of educational leadership and administration. - Integrating AI can free up leaders to focus more on their role's human, interactive elements.

Keywords: Education, Leadership, Technology, Artificial Intelligence

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Authors: Amnah Moghees, Saima Abbas Dar, Muniba Saeed

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Teaching a second language to deaf learners has always been a challenge in Pakistan. Different approaches and strategies have been followed, but they have been resulted into partial or complete failure. The study aims to investigate the language problems faced by adult deaf learners of English as second language in mainstream classrooms. Moreover, the study also determines the factors which are very much involved in language teaching and learning in mainstream classes. To investigate the language problems, data will be collected through writing samples of ten deaf adult learners and ten normal ESL learners of the same class; whereas, observation in inclusive language teaching classrooms and interviews from five ESL teachers in inclusive classes will be conducted to know the factors which are directly or indirectly involved in inclusive language education. Keeping in view this study, qualitative research paradigm will be applied to analyse the corpus. The study figures out that deaf ESL learners face severe language issues such as; odd sentence structures, subject and verb agreement violation, misappropriation of verb forms and tenses as compared to normal ESL learners. The study also predicts that in mainstream classrooms there are multiple factors which are affecting the smoothness of teaching and learning procedure; role of mediator, level of deaf learners, empathy of normal learners towards deaf learners and language teacher’s training.

Keywords: deaf English language learner, empathy, mainstream classrooms, previous language knowledge of learners, role of mediator, language teachers' training

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Authors: Louis Botha

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Since the presentation of South Africa’s inclusive education strategy in Education White Paper 6 in 2001, very little has been accomplished in terms of its implementation. The failure to achieve the goals set by this policy document is related to teachers lacking confidence and knowledge about how to enact inclusive education, as well as challenges of inflexible curricula, limited resources in overcrowded classrooms, and so forth. This paper presents a socio-cultural approach to addressing these challenges of implementing inclusive education in the South African context. It takes its departure from the view that inclusive education has been adequately theorized and conceptualized in terms of its philosophical and ethical principles, especially in South African policy and debates. What is missing, however, are carefully theorized, practically implementable research interventions which can address the concerns mentioned above. Drawing on socio-cultural principles of learning and development and on cultural-historical activity theory (CHAT) in particular, this paper argues for the use of formative interventions which introduce appropriately constructed mediational artifacts that have the potential to initiate inclusive practices and pedagogies within South African schools and classrooms. It makes use of Vygotsky’s concept of double stimulation to show how the proposed artifacts could instigate forms of transformative agency which promote the adoption of inclusive cultures of learning and teaching.

Keywords: cultural-historical activity theory, double stimulation, formative interventions, transformative agency

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Authors: Juan Torralba

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Student digital artifacts were investigated, utilizing a qualitative exploratory research design to understand the ways in which students represented their knowledge of seventh-grade proportionality concepts as they participated in maker-based activities that culminated in the creation of digital 3-dimensional models of their dream homes. Representations of the geometric and numeric dimensions of proportionality were analyzed in the written, verbal, and visual data collected from the students. A directed content analysis approach was utilized in the data analysis, as this work aimed to build upon existing research in the field of maker-based STEAM Education. The results from this work show that students can represent their understanding of proportional reasoning through open-ended written responses more accurately than through verbal descriptions or digital artifacts. The geometric and numeric dimensions of proportionality and their respective components of attributes of similarity representation and percents, rates, and ratios representations were the most represented by the students than any other across the data, suggesting a maker-based instructional approach to teaching proportionality in the middle grades may be promising in helping students gain a solid foundation in those components. Recommendations for practice and research are discussed.

Keywords: learning through making, maker-based education, maker education in the middle grades, making in mathematics, the maker movement

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Authors: Babatunde Olumide Olawale, Oyebode Olumide Oyediran

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The security safe is a place or building where classified document and precious items are kept. To prevent unauthorised persons from gaining access to this safe a lot of technologies had been used. But frequent reports of an unauthorised person gaining access into security safes with the aim of removing document and items from the safes are pointers to the fact that there is still security gap in the recent technologies used as access control for the security safe. In this paper we try to solve this problem by developing a multimodal biometric system for physical access control into a security safe using face and voice recognition. The safe is accessed by the combination of face and speech pattern recognition and also in that sequential order. User authentication is achieved through the use of camera/sensor unit and a microphone unit both attached to the door of the safe. The user face was captured by the camera/sensor while the speech was captured by the use of the microphone unit. The Scale Invariance Feature Transform (SIFT) algorithm was used to train images to form templates for the face recognition system while the Mel-Frequency Cepitral Coefficients (MFCC) algorithm was used to train the speech recognition system to recognise authorise user’s speech. Both algorithms were hosted in two separate web based servers and for automatic analysis of our work; our developed system was simulated in a MATLAB environment. The results obtained shows that the developed system was able to give access to authorise users while declining unauthorised person access to the security safe.

Keywords: access control, multimodal biometrics, pattern recognition, security safe

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Authors: Yasamiyan Alolaywi

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The sources of second language acquisition vary and depend on a learner’s preferences and choices; however, undoubtedly, the most effective methods provide authentic language input. This current study explores the effectiveness of watching movies as a means of English language acquisition. It explores university students’ views on the impact of this method in improving English language skills. The participants in this study were 74 students (25 males and 49 females) from the Department of English Language and Translation at Qassim University, Saudi Arabia. Data for this research were collected from questionnaires and individual interviews with several selected students. The findings of this study showed that many students watch movies frequently and for various purposes, the most important of which is entertainment. The students also admitted that movies help them acquire a great deal of vocabulary and develop their listening and writing skills. Also, the participants believed that exposure to a target language by native speakers helps enhance language fluency and proficiency. The students learn not only linguistic aspects from films but also other aspects, such as culture, lifestyle, and ways of thinking, in addition to learning other languages such as Spanish. In light of these results, some recommendations are proposed, such as verifying the feasibility of integrating media into a foreign language classroom. While this study covers aspects of the relationship between watching movies and English language acquisition, knowledge gaps remain that need to be filled by further research, such as on incorporating media into the educational process and how movie subtitles can improve learners’ language skills.

Keywords: language acquisition, English movies, EFL learners, perceptions

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Authors: Khaled Igbaria

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Laila Al-Othman is a well-known feminist writer in Kuwait and the entire Arab world. She was born in 1943 in Kuwait to a large and wealthy family. The author has written several short stories, as well as novels, such as The Woman and the Cat (1985) and Wasumayya Comes out of the Sea (1986), which was chosen as one of the best 100 Arab novels of the 21st century. Another prominent novel of hers is Ṣamt al-Farāshāt [Silence of the Butterflies] (2007), which was highly controversial in her native Kuwait upon publication. For this study, her engagement in feminism was achieved by exploring the different ways in which her novel, Ṣamt al-Farāshāt [Silence of the Butterflies], addresses several feminist issues, mainly forced marriage, rape and sexual abuse, gender-based physical, sexual violence, and enforced silence. This paper focuses on demonstrating social obstacles and continuous trauma caused by a sequence of pain experienced by Arab females in their patriarchal society. This study argues that the novel reveals a sustained effort to raise the banner of feminism and a strong desire to liberate Arab women from patriarchal domination. Al-Othman successfully and uniquely represents women as gender-based traumatic victims of sexual and physical violence, forced silence, and general oppression in the patriarchal Arab society, as those needing help, support, protection, and liberation. They are not represented as independent or free. Methodologically, the study employs a qualitative literary analysis method in addition to trauma theory psychoanalysis, concentrating on feminist issues highlighted in the novel.

Keywords: Al-Othman, Arab women pain, trauma within narration., Silence of the Butterflies

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Authors: Alexander Vandesompele, Joni Dambre

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The diversity in dendritic arborization, as first illustrated by Santiago Ramon y Cajal, has always suggested a role for dendrites in the functionality of neurons. In the past decades, thanks to new recording techniques and optical stimulation methods, it has become clear that dendrites are not merely passive electrical components. They are observed to integrate inputs in a non-linear fashion and actively participate in computations. Regardless, in simulations of neural networks dendritic structure and functionality are often overlooked. Especially in a machine learning context, when designing artificial neural networks, point neuron models such as the leaky-integrate-and-fire (LIF) model are dominant. These models mimic the integration of inputs at the neuron soma, and ignore the existence of dendrites. In this work, the LIF point neuron model is extended with a simple form of dendritic computation. This gives the LIF neuron increased capacity to discriminate spatiotemporal input sequences, a dendritic functionality as observed in another study. Simulations of the spiking neurons are performed using the Bindsnet framework. In the common LIF model, incoming synapses are independent. Here, we introduce a dependency between incoming synapses such that the post-synaptic impact of a spike is not only determined by the weight of the synapse, but also by the activity of other synapses. This is a form of short term plasticity where synapses are potentiated or depressed by the preceding activity of neighbouring synapses. This is a straightforward way to prevent inputs from simply summing linearly at the soma. To implement this, each pair of synapses on a neuron is assigned a variable,representing the synaptic relation. This variable determines the magnitude ofthe short term plasticity. These variables can be chosen randomly or, more interestingly, can be learned using a form of Hebbian learning. We use Spike-Time-Dependent-Plasticity (STDP), commonly used to learn synaptic strength magnitudes. If all neurons in a layer receive the same input, they tend to learn the same through STDP. Adding inhibitory connections between the neurons creates a winner-take-all (WTA) network. This causes the different neurons to learn different input sequences. To illustrate the impact of the proposed dendritic mechanism, even without learning, we attach five input neurons to two output neurons. One output neuron isa regular LIF neuron, the other output neuron is a LIF neuron with dendritic relationships. Then, the five input neurons are allowed to fire in a particular order. The membrane potentials are reset and subsequently the five input neurons are fired in the reversed order. As the regular LIF neuron linearly integrates its inputs at the soma, the membrane potential response to both sequences is similar in magnitude. In the other output neuron, due to the dendritic mechanism, the membrane potential response is different for both sequences. Hence, the dendritic mechanism improves the neuron’s capacity for discriminating spa-tiotemporal sequences. Dendritic computations improve LIF neurons even if the relationships between synapses are established randomly. Ideally however, a learning rule is used to improve the dendritic relationships based on input data. It is possible to learn synaptic strength with STDP, to make a neuron more sensitive to its input. Similarly, it is possible to learn dendritic relationships with STDP, to make the neuron more sensitive to spatiotemporal input sequences. Feeding structured data to a WTA network with dendritic computation leads to a significantly higher number of discriminated input patterns. Without the dendritic computation, output neurons are less specific and may, for instance, be activated by a sequence in reverse order.

Keywords: dendritic computation, spiking neural networks, point neuron model

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Ábrám Zoltán

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Introduction: Climate change is now a major health risk, and its environmental and health effects have become frequently discussed topics. The consequences of climate change are clearly visible in natural disasters and excess deaths caused by extreme weather conditions. Global warming and the increasingly frequent extreme weather events have direct, immediate effects or long-term, indirect effects on health. For this reason, it is a need to teach the health effects of climate change in medical schools. Material and methods: We looked for various surveys, studies, and reports on the main pathways through which global warming affects health. Medical schools face the challenge of teaching the health implications of climate change and integrating knowledge about the health effects of climate change into medical training. For this purpose, there were organised World Café workshops for three target groups: medical students, academic staff, and practising medical doctors. Results: Among the goals of the research is the development of a detailed curriculum for medical students, which serves to expand their knowledge in basic education. At the same time, the project promotes the increase of teacher motivation and the development of methodological guidelines for university teachers; it also provides further training for practicing doctors. The planned teaching materials will be developed in a format suitable for traditional face-to-face teaching, as well as e-learning teaching materials. CLIMATEMED is a project based on the cooperation of six universities and institutions from four countries, the aim of which is to improve the curriculum and expand knowledge about the health effects of climate change at medical universities. Conclusions: In order to assess the needs, summarize the proposals, to develop the necessary strategy, World Café type, one-and-a-half to two-hour round table discussions will take place separately for medical students, academic staff, and practicing doctors. The CLIMATEMED project can facilitate the integration of knowledge about the health effects of climate change into curricula and can promote practical use. The avoidance of the unwanted effects of global warming and climate change is not only a public matter, but it is also a challenge to change our own lifestyle. It is the responsibility of all of us to protect the Earth's ecosystem and the physical and mental health of ourselves and future generations.

Keywords: climate change, health effects, medical schools, World Café, medical students

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Authors: Josip Karuc, Marjeta Mišigoj-Duraković, Goran Marković, Vedran Hadžić, Michael J. Duncan, Hrvoje Podnar, Maroje Sorić

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As a part of the CRO-PALS longitudinal study, this investigation aimed to examine the association between different levels of physical activity (PA) and movement quality in 16-years old school children. The total number of participants in this research was 725. Movement quality was assessed via the Functional Movement Screen (FMSTM), and the PA level was estimated using the School Health Action, Planning, and Evaluation System (SHAPES) questionnaire. In addition, body fat and socioeconomic status (SES) were assessed. In order to investigate the association between total FMS score and different levels of PA, multilevel modeling was employed for boys (n=359) and girls (n=366) separately. All models were adjusted for age, body fat, and SES. Among boys, MVPA, MPA, and VPA were not significant predictors of the total FMS score (β=0.000, p=0.78; β=-0.002, p=0.455; β=0.004, p=0.158, respectively). On the contrary, among girls, VPA and MVPA showed significant effects on the total FMS score (β=0.011, p=0.001, β=0.005, p=0.006, respectively). The findings of this research provide evidence that the intensity of PA is a minor but relevant factor in describing the association between PA and movement quality in adolescent girls but not in boys. This means that the PA level does not guarantee optimal functional movement patterns. Therefore, practicing functional movement patterns in an isolated manner and at moderate to vigorous intensity could be beneficial in order to reduce the risk of injury incidence and potential orthopedic abnormalities in later life. This work was supported by the Croatian Science Foundation, grant no: IP-2016-06-9926 and grant no: DOK-2018-01-2328.

Keywords: functional movement screen, fundamental movement patterns, movement quality, pediatric

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Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Saifur Rahman Sabuj

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This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth.

Keywords: k-nearest neighbour, support vector regression, random forest regression, long short-term memory network, earthquakes, solar activity, sunspot number, solar wind, solar flares

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Authors: Nora Gavira Duron, Cinthya Moreda Gonzalez-Ortega, Reyna Susana Garcia Ruiz

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This paper objective is to analyze the mathematics performance in the Learning Evaluation National Plan (PLANEA for its Spanish initials: Plan Nacional para la Evaluación de los Aprendizajes), applied to Mexican students who are enrolled in the last elementary-school year over the 2017-2018 academic year. Such test was conducted nationwide in 3,573 schools, using a sample of 108,083 students, whose average in mathematics, on a scale of 0 to 100, was 45.6 points. 75% of the sample analyzed did not reach the sufficiency level (60 points). It should be noted that only 2% got a 90 or higher score result. The performance is analyzed while considering whether there are differences in gender, marginalization level, public or private school enrollment, parents’ academic background, and living-with-parents situation. Likewise, this variable impact (among other variables) on school performance by gender is evaluated, considering multivariate logistic (Logit) regression analysis. The results show there are no significant differences in mathematics performance regarding gender in elementary school; nevertheless, the impact exerted by mothers who studied at least high school is of great relevance for students, particularly for girls. Other determining variables are students’ resilience, their parents’ economic status, and the fact they attend private schools, strengthened by the mother's education.

Keywords: multivariate regression analysis, academic performance, learning evaluation, mathematics result per gender

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Authors: Shreeyam, Ranjan Kumar Sah, Shivangi

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Recognizing and solving handwritten mathematical expressions can be a challenging task, particularly when certain characters are segmented and classified. This project proposes a solution that uses Convolutional Neural Network (CNN) and image processing techniques to accurately solve various types of equations, including arithmetic, quadratic, and trigonometric equations, as well as logical operations like logical AND, OR, NOT, NAND, XOR, and NOR. The proposed solution also provides a graphical solution, allowing users to visualize equations and their solutions. In addition to equation solving, the platform, called CNNCalc, offers a comprehensive learning experience for students. It provides educational content, a quiz platform, and a coding platform for practicing programming skills in different languages like C, Python, and Java. This all-in-one solution makes the learning process engaging and enjoyable for students. The proposed methodology includes horizontal compact projection analysis and survey for segmentation and binarization, as well as connected component analysis and integrated connected component analysis for character classification. The compact projection algorithm compresses the horizontal projections to remove noise and obtain a clearer image, contributing to the accuracy of character segmentation. Experimental results demonstrate the effectiveness of the proposed solution in solving a wide range of mathematical equations. CNNCalc provides a powerful and user-friendly platform for solving equations, learning, and practicing programming skills. With its comprehensive features and accurate results, CNNCalc is poised to revolutionize the way students learn and solve mathematical equations. The platform utilizes a custom-designed Convolutional Neural Network (CNN) with image processing techniques to accurately recognize and classify symbols within handwritten equations. The compact projection algorithm effectively removes noise from horizontal projections, leading to clearer images and improved character segmentation. Experimental results demonstrate the accuracy and effectiveness of the proposed solution in solving a wide range of equations, including arithmetic, quadratic, trigonometric, and logical operations. CNNCalc features a user-friendly interface with a graphical representation of equations being solved, making it an interactive and engaging learning experience for users. The platform also includes tutorials, testing capabilities, and programming features in languages such as C, Python, and Java. Users can track their progress and work towards improving their skills. CNNCalc is poised to revolutionize the way students learn and solve mathematical equations with its comprehensive features and accurate results.

Keywords: AL, ML, hand written equation solver, maths, computer, CNNCalc, convolutional neural networks

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Authors: Lily Ranjbar, Haori Yang

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Online education has been around for several decades, but the importance of online education became evident after the COVID-19 pandemic. Eventhough the online delivery approach works well for knowledge building through delivering content and oversight processes, it has limitations in developing hands-on laboratory skills, especially in the STEM field. During the pandemic, many education institutions faced numerous challenges in delivering lab-based courses, especially in the STEM field. Also, many students worldwide were unable to practice working with lab equipment due to social distancing or the significant cost of highly specialized equipment. The laboratory plays a crucial role in nuclear science and engineering education. It can engage students and improve their learning outcomes. In addition, online education and virtual labs have gained substantial popularity in engineering and science education. Therefore, developing virtual labs is vital for institutions to deliver high-class education to their students, including their online students. The School of Nuclear Science and Engineering (NSE) at Oregon State University, in partnership with SpectralLabs company, has developed an Advanced Virtual Radiation Detection and Measurement Lab (AVR-DML) to offer a fully online Master of Health Physics program. It was essential for us to use a system that could simulate nuclear modules that accurately replicate the underlying physics, the nature of radiation and radiation transport, and the mechanics of the instrumentations used in the real radiation detection lab. It was all accomplished using a Realistic, Adaptive, Interactive Learning System (RAILS). RAILS is a comprehensive software simulation-based learning system for use in training. It is comprised of a web-based learning management system that is located on a central server, as well as a 3D-simulation package that is downloaded locally to user machines. Users will find that the graphics, animations, and sounds in RAILS create a realistic, immersive environment to practice detecting different radiation sources. These features allow students to coexist, interact and engage with a real STEM lab in all its dimensions. It enables them to feel like they are in a real lab environment and to see the same system they would in a lab. Unique interactive interfaces were designed and developed by integrating all the tools and equipment needed to run each lab. These interfaces provide students full functionality for data collection, changing the experimental setup, and live data collection with real-time updates for each experiment. Students can manually do all experimental setups and parameter changes in this lab. Experimental results can then be tracked and analyzed in an oscilloscope, a multi-channel analyzer, or a single-channel analyzer (SCA). The advanced virtual radiation detection and measurement laboratory developed in this study enabled the NSE school to offer a fully online MHP program. This flexibility of course modality helped us to attract more non-traditional students, including international students. It is a valuable educational tool as students can walk around the virtual lab, make mistakes, and learn from them. They have an unlimited amount of time to repeat and engage in experiments. This lab will also help us speed up training in nuclear science and engineering.

Keywords: advanced radiation detection and measurement, virtual laboratory, realistic adaptive interactive learning system (rails), online education in stem fields, student engagement, stem online education, stem laboratory, online engineering education

Procedia PDF Downloads 94

Authors: Salma Akter, Md. Kamal Uddin

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Quality of life is a vast and comprehensive concept refers overall well-being of society. Current research and efforts of policymakers and planners are concerned to increase the urban quality of life through the sustainable development of city and country. While such efforts effectively improve the quality of life of urban dwellers through improved social, economic and housing infrastructures, very little has been paid to improve low-income settlement users more specifically government provided shelter projects. The top-down shelter policies and its objective indicators (physical design elements and physical environmental elements) indicators on low-income groups merely can ensure grassroots needs, aspiration and well-being refer as subjective qualities obliged to compromise with the quality of life. This research, therefore, aims to measure the quality of life of such government-provided low-income settlements. To do so, a conceptual framework has been developed to measure quality of life with arguing that quality of life depends on both objective and subjective indicators and needs to measure across three scales of living environment refers to macro (community), meso (neighborhood or shelter/built environment), and micro (family). The top-down shelter project, Dakshin Chandani Mahal Ashrayan Prakalpa is a resettlement/housing project of Government of Bangladesh for providing shelters and human resources development activities like education, microcredit, and training programme to landless, homeless and rootless people has been taken as case study. The study area is located at Dighalia Upazila, Khulna Bangladesh. In terms of methodology, this research is primarily exploratory and adopts a case study method and deductive approach for evaluating the quality of life. Data have been obtained from relevant literature review, key informant interview, focus group discussion, necessary drawings, photographs and participant observation across dwelling, neighborhood, and community level. Findings have revealed that Shelter users mostly compromise the quality of life at community level due to insufficient physical design elements and facilities while neighborhood and dwelling level have been manifested similar result like former ones. Thus, the outcome of this study can be beneficial for a global-level understating of the compromising the ‘quality of life’ under top-down shelter policy. Locally, for instance, in the context of Bangladesh, it can help policymakers and concerned authorities to formulate the shelter policies and take initiatives to improve the well-being of marginalized.

Keywords: Ashrayan Prakalpa, compromise, displaced people, quality of life

Procedia PDF Downloads 221

Authors: Pui-Lai To, Chechen Liao, Ming-Chi Sung

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Work and family life are the most important areas for men and women today. Every professional is required to meet and fulfill the responsibilities of work and family roles. Although the development and popularity of communication technology bring a lot of benefits, including effective and efficient communication, may also generate conflicts between work and family life. Since mobile devices and the applications of mobile devices, such as instant messages, are ubiquitous, the boundaries of work and family roles are increasingly blurred. Professionals may be in the risk of work over-loading and work-family conflict. This study examines the impact of work stress on professionals’ life and health in the context of instant messaging application of smart phone. This study uses a web-based questionnaire to collect samples. The questionnaires are sent via virtual community sites, instant messaging applications, and e-mail. The study develops and empirically validates a work-family conflict model by integrating the pressure theory and technostress factors. The causal relationship between variables in the research model is tested. In terms of data analysis, Partial Least Square (PLS) in Structural Equation Modeling (SEM) is used for sample analysis and research model testing. The results of this study are as follows. First, both the variables of work-related stress and technological violations positively affect the work-family conflict. Second, both the variables of work-loading and technology-overloading have no effect on work-family conflict. Third, work-family conflict has negative effect on job satisfaction, family satisfaction, physical health, and mental health.

Keywords: mental health, physical health, technostress, work-family conflict, work-related stress

Procedia PDF Downloads 306

Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

Procedia PDF Downloads 194

Authors: Vanessa Kummer, Maik Meusel

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Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

Procedia PDF Downloads 292

Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

Procedia PDF Downloads 105

Authors: Jelena Filipović, Milenko Košutić, Vladimir Filipović

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In the last decades urban population is characterized by sedentary lifestyles, low physical activity and "fast food". These changes in diet and physical non activity have been associated with the increase of chronic non diseases. Bread is one of the most popularly wheat products consumed worldwide. Spelt wheat has shown potential in various food applications, including bread, pasta, breakfast cereal and other products of altered nutritional characteristics compared to conventional wheat products. It has very high protein content and even 30 to 60% higher concentration of mineral elements Fe, Zn, Cu, Mg and P compared to Triticum Aestivum. Spelt wheat is growing without the use of pesticides in harsh ecological conditions and it is an old cultivar. So it can be used for organic and health safe food. Changes in the formulation of bread with the aim to improve their nutritional and functional properties usually lead to changes in the dough properties which is related reflected to the quality of the finished product. The aim of this paper is researching the impact of adding yeast extract to bread on sensory characteristics and consumer acceptance of a new product as a key factor for successful marketing of a new product. The sensory analysis of bread with 5% yeast extract is as follows: the technological quality is very good (3.8) and the color of the product is excellent (4.85). Based on data consumers survey declared that they liked the taste of bread with 5% yeast extract (74%), consumers marked the product as likeable (70%), and 75% of the total number of respondents would buy this new product. This paper is promoting a new type of bread with 5% yeast extract (Z score 0.80) to improve diet and novel functional product which intended for consumers conscious about their health and diet.

Keywords: bread, yeast extract, sensory analysis, consumer survey, score analysis Z

Procedia PDF Downloads 60

Authors: Tarmi A'Vard

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This paper discusses the transformative learning experience for students participating in the Inside-Out Prison Exchange Program (Inside-out) and explores the value this pedagogical approach may have in youth justice centers. Inside-Out is a semester-long university course which is unique as it takes 15 university students, with their textbook and theory-based knowledge, behind the walls to study alongside 15 incarcerated students, who have the lived experience of the criminal justice system. Inside-out is currently offered in three Victorian prisons, expanding to five in 2020. The Inside-out pedagogy which is based on transformative dialogic learning is reliant upon the participants sharing knowledge and experiences to develop an understanding and appreciation of the diversity and uniqueness of one another. Inside-out offers the class an opportunity to create its own guidelines for dialogue, which can lead to the student’s sense of equality, which is fundamental in the success of this program. Dialogue allows active participation by all parties in reconciling differences, collaborating ideas, critiquing and developing hypotheses and public policies, and encouraging self-reflection and exploration. The structure of the program incorporates the implementation of circular seating (where the students alternate between inside and outside), activities, individual reflective tasks, group work, and theory analysis. In this circle everyone is equal, this includes the educator, who serves as a facilitator more so than the traditional teacher role. A significant function of the circle is to develop a group consciousness, allowing the whole class to see itself as a collective, and no one person holds a superior role. This also encourages participants to be responsible and accountable for their behavior and contributions. Research indicates completing academic courses, like Inside-Out, contributes positively to reducing recidivism. Inside-Out’s benefits and success in many adult correctional institutions have been outlined in evaluation reports and scholarly articles. The key findings incorporate the learning experiences for the students in both an academic capability and professional practice and development. Furthermore, stereotypes and pre-determined ideas are challenged, and there is a promotion of critical thinking and evidence of self-discovery and growth. There is empirical data supporting positive outcomes of education in youth justice centers in reducing recidivism and increasing the likelihood of returning to education upon release. Hence, this research could provide the opportunity to increase young people’s engagement in education which is a known protective factor for assisting young people to move away from criminal behavior. In 2016, Tarmi completed the Inside-Out educator training in Philadelphia, Pennsylvania, and has developed an interest in exploring the pedagogy of Inside-Out, specifically targeting young offenders in a Youth Justice Centre.

Keywords: dialogic transformative learning, inside-out prison exchange program, prison education, youth justice

Procedia PDF Downloads 129

Authors: Nazia Mustafa, Aneela Maqsood

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Pediatric Quality of Life Inventory is the most widely used instrument for assessing children and adolescent health-related quality of life and has shown excellent markers of reliability and validity. The current study was carried out with the objectives of translation and cross-language validation along with the determination of factor Structure and psychometric properties of the Urdu version. It was administered on 154 Primary School Children with age range 10 to12 years (M= 10.86, S.D = 0.62); including boys (n=92) and girls (n = 62). The sample was recruited from two randomly selected schools from the Rawalpindi district of Pakistan. Results of the pilot phase revealed that the instrument had good reliability (Urdu Version α = 0.798; English Version α = 0.795) as well as test-retest correlation coefficients over a period of 15 days (r = 0.85). Exploratory factor analysis (EFA) resulted in three factorial structures; Social/School Functioning (k = 8), Psychological Functioning (k = 7) and Physical Functioning (k = 6) considered suitable for our sample instead of four factors. Bartlett's test of sphericity showed inter-correlation between variables. However, factor loadings for items 22 and 23 of the School Functioning subscale were problematic. The model was fit to the data after their removal with Cronbach’s Alpha Reliability coefficient of the scale (k = 21) as 0.87 and for subscales as 0.75, 0.77 and 0.73 for Social/School Scale, Psychological subscale and Physical subscale, respectively. These results supported the feasibility and reliability of the Urdu version of the Pediatric Quality of Life Inventory as a reliable and effective tool for the measurement of quality of life among Pediatrics Pakistani population.

Keywords: primary school children, paediatric quality of life, exploratory factor analysis, Pakistan

Procedia PDF Downloads 137

Authors: Patricia L. Fontanilla, David Nordstokke

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Introduction: Today’s children and youth face increasing social and behavioural challenges, leading to delays in social development and greater mental health needs. Early adolescents (aged 9 to 14) are experiencing a rise in mental health symptoms and diagnoses. This study examines the impact of HEROES, a social-emotional learning (SEL) program, on resilience and academic outcomes in early adolescents. The HEROES program is designed to enhance resilience the ability to adapt and thrive in the face of adversity, equipping youth to navigate developmental transitions and challenges. This study’s objective was to evaluate the program’s long-term effectiveness by measuring changes in resilience and academic resilience across 10 months. Methodology: This study collected data from 21 middle school students (grades 7 to 9) in a rural Canadian school. Quantitative data were gathered at four intervals: pre-intervention, post-intervention, and at 2- and 4-month follow-ups. Data were analyzed with linear mixed models (LMM). Results: Findings showed statistically significant increases in academic resilience over time and significant increases in resilience from pre-intervention to 2 and 4 months later. Limitations included a small sample size, which may affect generalizability. Conclusion: The HEROES program demonstrates promise in increasing resilience and academic resilience among early adolescents through SEL skill development.

Keywords: academic resilience, early adolescence, resilience, SEL, social-emotional learning program

Procedia PDF Downloads 18

Authors: Jaana Sepp, Ulvi Kõrgemaa, Kristi Puusepp, Õie Tähtla

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COVID-19 was officially recognized as a pandemic in late 2019 by the WHO, and it has led to changes in the education sector. Educational institutions were closed, and most schools adopted distance learning. Estonia is known as a digitally well-developed country. Based on that, in the pandemic time, nursing education continued, and new technological solutions were implemented. To provide nursing education, special focus was paid on quality and flexibility. The aim of this paper is to present administrative, digital, and technological solutions which support Estonian nursing educators to continue the study process in the pandemic time and to develop a sustainable solution for nursing education for the future. This paper includes the authors’ analysis of the documents and decisions implemented in the institutions through the pandemic time. It is a case study of Estonian nursing educators. Results of the analysis show that the implementation of distance learning principles challenges the development of innovative strategies and technics for the assessment of student performance and educational outcomes and implement new strategies to encourage student engagement in the virtual classroom. Additionally, hospital internships were canceled, and the simulation approach was deeply implemented as a new opportunity to develop and assess students’ practical skills. There are many other technical and administrative changes that have also been carried out, such as students’ support and assessment systems, the designing and conducting of hybrid and blended studies, etc. All services were redesigned and made more available, individual, and flexible. Hence, the feedback system was changed, the information was collected in parallel with educational activities. Experiences of nursing education during the pandemic time are widely presented in scientific literature. However, to conclude our study, authors have found evidence that solutions implemented in Estonian nursing education allowed the students to graduate within the nominal study period without any decline in education quality. Operative information system and flexibility provided the minimum distance between the students, support, and academic staff, and likewise, the changes were implemented quickly and efficiently. Institution memberships were updated with the appropriate information, and it positively affected their satisfaction, motivation, and commitment. We recommend that the feedback process and the system should be permanently changed in the future to place all members in the same information area, redefine the hospital internship process, implement hybrid learning, as well as to improve the communication system between stakeholders inside and outside the organization. The main limitation of this study relates to the size of Estonia. Nursing education is provided by two institutions only, and similarly, the number of students is low. The result could be generated to the institutions with a similar size and administrative system. In the future, the relationship between nurses’ performance and organizational outcomes should be deeply investigated and influences of the pandemic time education analyzed at workplaces.

Keywords: hybrid learning, nursing education, nursing, COVID-19

Procedia PDF Downloads 122