Search results for: effect of collaborative learning

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: 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: Béla Kovács, Éva Bódi, Farzaneh Garousi, Szilvia Várallyay, Áron Soós, Xénia Vágó, Dávid Andrási

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In agriculture for analysis of elements in different food and food raw materials, moreover environmental samples generally flame atomic absorption spectrometers (FAAS), graphite furnace atomic absorption spectrometers (GF-AAS), inductively coupled plasma optical emission spectrometers (ICP-OES) and inductively coupled plasma mass spectrometers (ICP-MS) are routinely applied. An inductively coupled plasma mass spectrometer (ICP-MS) is capable for analysis of 70-80 elements in multielemental mode, from 1-5 cm3 volume of a sample, moreover the detection limits of elements are in µg/kg-ng/kg (ppb-ppt) concentration range. All the analytical instruments have different physical and chemical interfering effects analysing the above types of samples. The smaller the concentration of an analyte and the larger the concentration of the matrix the larger the interfering effects. Nowadays there is very important to analyse growingly smaller concentrations of elements. From the above analytical instruments generally the inductively coupled plasma mass spectrometer is capable of analysing the smallest concentration of elements. The applied ICP-MS instrument has Collision Cell Technology (CCT) also. Using CCT mode certain elements have better (smaller) detection limits with 1-3 magnitudes comparing to a normal ICP-MS analytical method. The CCT mode has better detection limits mainly for analysis of selenium, arsenic, germanium, vanadium and chromium. To elaborate an analytical method for trace elements with an inductively coupled plasma mass spectrometer the most important interfering effects (problems) were evaluated: 1) Physical interferences; 2) Spectral interferences (elemental and molecular isobaric); 3) Effect of easily ionisable elements; 4) Memory interferences. Analysing food and food raw materials, moreover environmental samples an other (new) interfering effect emerged in ICP-MS, namely the effect of various matrixes having different evaporation and nebulization effectiveness, moreover having different quantity of carbon content of food and food raw materials, moreover environmental samples. In our research work the effect of different water-soluble compounds furthermore the effect of various quantity of carbon content (as sample matrix) were examined on changes of intensity of the applied elements. So finally we could find “opportunities” to decrease or eliminate the error of the analyses of applied elements (Cr, Co, Ni, Cu, Zn, Ge, As, Se, Mo, Cd, Sn, Sb, Te, Hg, Pb, Bi). To analyse these elements in the above samples, the most appropriate inductively coupled plasma mass spectrometer is a quadrupole instrument applying a collision cell technique (CCT). The extent of interfering effect of carbon content depends on the type of compounds. The carbon content significantly affects the measured concentration (intensities) of the above elements, which can be corrected using different internal standards.

Keywords: elements, environmental and food samples, ICP-MS, interference effects

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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: Motahar Reza, Rajni Chahal, Neha Sharma

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This article addresses the boundary layer flow and heat transfer of Casson fluid over a nonlinearly permeable stretching surface with chemical reaction in the presence of variable magnetic field. The effect of thermal radiation is considered to control the rate of heat transfer at the surface. Using similarity transformations, the governing partial differential equations of this problem are reduced into a set of non-linear ordinary differential equations which are solved by finite difference method. It is observed that the velocity at fixed point decreases with increasing the nonlinear stretching parameter but the temperature increases with nonlinear stretching parameter.

Keywords: boundary layer flow, nonlinear stretching, Casson fluid, heat transfer, radiation

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Authors: Yusra Pintaningrum, Ilma Fahira Basyir, Sony Hilal Wicaksono, Vito A. Damay

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Background: Coronary artery calcium (CAC) scores play an important role in improving prognostic accuracy and can be selectively used to guide the allocation of statin therapy for atherosclerotic cardiovascular disease outcomes and potentially associated with the occurrence of MACE (Major Adverse Cardiovascular Event) and MI (Myocardial Infarction). Objective: This systematic review and meta-analysis aim to analyze the findings of a study about CAC Score and statin treatment effect on MI and MACE risk. Methods: Search for published scientific articles using the PRISMA (Preferred Reporting, Items for Systematic Reviews and Meta-Analysis) method conducted on PubMed, Cochrane Library, and Medline databases published in the last 20 years on “coronary artery calcium” AND “statin” AND “cardiovascular disease” Further systematic review and meta-analysis using RevMan version 5.4 were performed based on the included published scientific articles. Results: Based on 11 studies included with a total of 1055 participants, we performed a meta-analysis and found that individuals with CAC score > 0 increased risk ratio of MI 8.48 (RR = 9.48: 95% CI: 6.22 – 14.45) times and MACE 2.48 (RR = 3.48: 95% CI: 2.98 – 4.05) times higher than CAC score 0 individual. Statin compared against non-statin treatment showed a statistically insignificant overall effect on the risk of MI (P = 0.81) and MACE (P = 0.89) in an individual with elevated CAC score 1 – 100 (P = 0.65) and > 100 (P = 0.11). Conclusions: This study found that an elevated CAC scores individual has a higher risk of MI and MACE than a non-elevated CAC score individual. There is no significant effect of statin compared against non-statin treatment to reduce MI and MACE in elevated CAC score individuals of 1 – 100 or > 100.

Keywords: coronary artery calcium, statin, cardiovascular disease, myocardial infarction, MACE

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Authors: Iman A. A. Kassem, Ayman A. Farghaly, Zeinab M. Hassan, Farouk R. Melek, Neveen S. Ghaly

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Lipidium sativum L, an annual herb that grows to 50 cm, is known as an important member of family Brassicaceae. Besides its nutritional value, the seeds were widely used in folk medicine for treatment of cough, asthma, and headache. It was also reported to possess hypocholesterolemic, anti-inflammatory, antidiarrheal, antimicrobial and anticancer activities. In this study, the genoprotective properties of L. sativum seed methanolic extract (LSME) were evaluated in vivo. Three groups of mice were given LSME for five consecutive days at the three dose levels 25, 50 and 100 mg/kg b.wt. The three groups were then injected intraperitoneally with cyclophosphamide at a dose of 20 mg/kg b.wt. to induce DNA damage. A group received only cyclophosphamide (20 mg/kg b.wt.) served as control. LSME significantly inhibited the DNA aberrations in mice caused by cyclophosphamide in a dose-dependent manner in the two groups that received LSME at 50 and 100 mg/kg b.wt. dose levels. The chromosomal aberrations' inhibitory indices were calculated as 18 and 31 in mice bone marrow cells and 27 and 48 in mice spermatocytes, respectively. Phytochemical examination carried out by us revealed that flavonoids were the main chemical constituents of LSME. The major flavonoids kaempferol, kaempferol-3-O-rhamnoside, kaempferol-3-O-glucoside, quercetin, and quercetin-3-O-glucoside were isolated and characterized. It was concluded that the genoprotective effect of LSME might be attributed to the presence of flavonoids which are well-known for their antioxidant properties.

Keywords: cyclophosphamide, flavonoids, genoprotective effect, Lepidium sativum

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Authors: K. Akhil, J. Jayakumar, S. Sudheer Khan

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Zinc oxide nanoparticles (ZnO NPs) are extensively used in a wide variety of commercial products including sunscreen, textile and paints. The present study evaluated the effect of surface capping agents including polyethylene glycol (EG), gelatin, polyvinyl alcohol(PVA) and poly vinyl pyrrolidone(PVP) on photocatalytic activity of ZnO NPs. The particles were also tested for its antibacterial and antibiofilm activity against Staphylococcus aureus (MTCC 3160) and Pseudomonas aeruginosa (MTCC 1688). Preliminary characterization was done by UV-Visible spectroscopy. Electron microscopic analysis showed that the particles were hexagonal in shape. The hydrodynamic size distribution was analyzed by using dynamic light scattering method and crystalline nature was determined by X-Ray diffraction method.

Keywords: antibacterial, antibiofilm, capping agents, photodegradation, surface coating, zinc oxide nanoparticles

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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: P. Murugesan, G. Venkateswaran, V. A. Shanmuga Selvan

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Water used for brewing tea plays a major role in the quality of tea. Water with higher hardness gives very dark coloured brew. This study was conducted to determine the effect of water hardness and free residual chlorine on the quality of black tea liquor. Theaflavin (TF) and Thearubigin (TR) levels are lower in comparison with the tea brewed in distilled water. At the same time, there is an increase in High Polymerized Substance (HPS) and Total Liquor Colour (TLC). While water with higher hardness has a negative impact on tea brew, water with high concentration of free residual chlorine did not affect the quality of tea brew.

Keywords: Theaflavin, Thearubigin, high polymerised substance, total liquor colour, hardness, residual chlorine

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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: Ramesh Bhat, Ammu Somanath, A. K. Nayanatara

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Background: The increasing prevalence of overweight and obesity is a critical public health problem for women. The negative effect of stress on memory and cognitive functions has been widely explored for decades in numerous research projects using a wide range of methodology. Deterioration of memory and other brain functions are hallmarks of Alzheimer’s disease. Estrogen fluctuations and withdrawal have myriad direct effects on the central nervous system that have the potential to influence cognitive functions. Aim: The present study aims to compare the effect of stress on the cognitive functions in overweight/obese women before and after menopause. Material and Methods: A total of 142 female subjects constituting women before menopause between the age group of 18–44 years and women after menopause between the age group of 45–60 years were included in the sample. Participants were categorized into overweight/obese groups based on the body mass index. The Perceived Stress Scale (PSS) the major tool was used for measuring the perception of stress. Based on the stress scale measurement each group was classified into with stress and without stress. Addenbrooke’s cognitive Examination-III was used for measuring the cognitive functions. Results: Premenopausal women with stress showed a significant (P<0.05) decrease in the cognitive parameters such as attention and orientation Fluency, language and visuospatial ability. Memory did not show any significant change in this group. Whereas, in the postmenopausal stressed women all the cognitive functions except fluency showed a significant (P<0.05) decrease after menopause stressed group. Conclusion: Stress is a significant factor on the cognitive functions of obese and overweight women before and after menopause. Practice of Yoga, Encouragement in activities like gardening, embroidery, games and relaxation techniques should be recommended to prevent stress. Insights into the neurobiology before and after menopause can be gained from future studies examining the effect on the HPA axis in relation to cognition and stress.

Keywords: cognition, stress, premenopausal, body mass index

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Authors: Chih-Fong Tsai, Ya-Han Hu

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The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

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Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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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

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Authors: Tanveer Rasool, A. K. Dhiman

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The paper reports 2-D numerical study used to investigate the effect of changing working fluids with Prandtl numbers 0.71, 10 and 50 on the flow and convective heat transfer across an equilateral triangular cylinder placed in a horizontal channel with its apex facing the flow. Numerical results have been generated for fixed blockage ratio of 50% and for three Reynolds numbers of 50, 75, and 100 for each Prandtl numbers respectively. The studies show that for above range of Reynolds numbers, the overall drag coefficient is insensitive to the Prandtl number changes while as the heat transfer characteristics change drastically with changing Prandtl number of the working fluid. The results generated are in complete agreement with the previous literature available.

Keywords: Prandtl number, Reynolds number, drag coefficient, flow and isothermal patterns

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Authors: Arife Simsek, Gulfem Bakan

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Sediment refers to the accumulation of varying amounts of sediment material in natural waters and the formation of bottom sludge. Sediments are the most important sources of pollutants as well as important future sources and carriers of pollutants. The accumulation of pollutants in sediments can cause serious environmental problems for the surrounding areas. Heavy metals (such as Cr, Cd, Al, Pb, Cu, Al, Zn) disrupt the water quality, affect the useful use of sediment, affect the ecosystem and have a toxic effect on the life of the sediment layer. This effect, which accumulates in the aquatic organisms, can enter the human body with the food chain and affect health seriously. Potential metal toxicity can be determined by comparing acid volatile sulfides (AVS) – simultaneously extracted metal (SEM) ratio in anoxic sediments to determine the effect of metals. Determination of the concentration of SEM and AVS is useful in screening sediments for potential toxicity due to the high metal concentration. In the case of SEM/AVS < 0 (anoxic sediment); in terms of AVS biomass production, its toxicity can be controlled. No toxic effects may be observed when SEM / AVS < 0. SEM / AVS > 0 (in the case of oxic sediment); metals with sensitive fraction such as Cu, As, Ag, Zn are stored. In this study, AVS and SEM measurements of sediment samples collected from five different points in the district of Tekkeköy in Samsun province were performed. The SEM - AVS ratio was greater than 0 in all samples. Therefore, it is necessary to test the toxicity against the risks that may occur in the ecosystem.

Keywords: AVS-SEM, Black Sea, heavy metal, sediment, toxicity

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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

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Authors: Ehsan Asgari, Meisam Afazeli, Nezhla Attarchian

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Post-earthquake fire is considered as a major threat in seismic areas. After an earthquake, fire is possible in structures. In this research, the effect of post-earthquake fire on steel moment frames with and without fireproofing coating is investigated. For this purpose, finite element method is employed. For the verification of finite element results, the results of an experimental study carried out by previous researchers are used, and the predicted FE results are compared with the test results, and good agreement is observed. After ensuring the accuracy of the predictions of finite element models, the effect of post-earthquake fire on the frames is investigated taking into account the parameters including the presence or absence of fire protection, frame design assumptions, earthquake type and different fire scenario. Ordinary fire and post-earthquake fire effect on the frames is also studied. The plastic hinges induced by earthquake in the structure are determined in the beam to the column connection and in panel zone. These areas should be accurately considered when providing fireproofing coatings. The results of the study show that the occurrence of fire beside corner columns is the most damaging scenario that results in progressive collapse of structure. It was also concluded that the behavior of structure in fire after a strong ground motion is significantly different from that in a normal fire.

Keywords: post earthquake fire, moment frame, finite element simulation, coupled temperature-displacement analysis, fire scenario

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Authors: D. Triana, I. N. Rachmawati, L. Sabri

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Maternal breastfeeding self-efficacy is positively associated with increased duration of breastfeeding in different cultures and age groups. This study aims to determine the effect of skin-to-skin contact immediately after the cesarean section on maternal breastfeeding self-efficacy. The research design is Posttest quasi-experimental research design only with control groups involving 52 women with consecutive sampling in Langsa-Aceh. The data collected through breastfeeding Self-Efficacy Scale-Short Form. The results of Independent t-test showed a significant difference in the mean values of maternal breastfeeding self-efficacy in the intervention group and the control group (59.00 ± 6.54; 49.62 ± 7.78; p= 0.001). Skin to skin contact is proven to affect the maternal breastfeeding self-efficacy after cesarean section significantly.

Keywords: breastfeeding self-efficacy, cesarean section, skin to skin contact, immediately

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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

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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

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Authors: Aliyu B. Mustapha, Poul A. Gida

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Pot experiment was conducted at the landscape unit of Modibbo Adama University of Technology, Yola in 2015 and 2016 to determine the effect of some chemicals namely glyphosate, salt and detergent on Golden dodder (Cuscuta campestris Yunk). The experiment was laid in a completely randomized design (CRD) with three replications. The treatments include the following: glyphosate-T0= (control),(Og a.i/ha-1) T1=35g a.i/ha-1, T2=70g a.i/ha-1, T3=105g a.i/ha-1, T4=140 a.i/ha-1 and T5=175g a.i/ha-1: Salt (T0=control O mole/ha-1 T1=1mole/ha-1 T2=2mole/ha-1, T3=3mole/ha-1 , T4=4mole/ha-1 and T5=5mole/ha-1:washing detergent T0=Og/ha-1(control), T1=30ml detergent +70ml distilled water T2=45ml detergent+65ml distilled water T3=60ml detergent+40ml distilled water, T4=75ml detergent+25ml distilled water and T5=90ml detergent +10mldistilled water, the treatments were replicated three times. Data were collected include: plant height, number of leaves, leaf area, leaf area index and Cuscuta cover score at 3,6,9and 12 weeks after sprouting(WAS). Biomas of Vitex was also collected at the end of the experiment. Data collected were analyzed using software Genstat version 8.0. Results showed that glyphosate gave the least Cuscuta cover score and the tallest Vitex plant. However, detergent mildly controlled Cuscuta, while salt has no effect on Cuscuta campestris indicating that glyphosate could be used in the control of parasitic dodder (Cuscuta campestris) on Vitex plant.

Keywords: chemical, control, dudder, Vitex

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Authors: Aylin Mohammadalipour Tofighi

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Marine-based geoengineering methods, proposed to mitigate climate change, operate primarily through two mechanisms: reducing atmospheric carbon dioxide levels and diminishing solar absorption by the oceans. While these approaches promise beneficial outcomes, they are fraught with environmental, legal, ethical, and political challenges, necessitating robust international governance. This paper underscores the critical role of international cooperation within the governance framework, offering a focused analysis of existing international environmental mechanisms applicable to marine-based geoengineering governance. It evaluates the efficacy and limitations of current international legal structures, including treaties and organizations, in managing marine-based geoengineering, noting significant gaps such as the absence of specific regulations, dedicated international entities, and explicit governance mechanisms such as monitoring. To rectify these problems, the paper advocates for concrete steps to bolster international cooperation. These include the formulation of dedicated marine-based geoengineering guidelines within international agreements, the establishment of specialized supervisory entities, and the promotion of transparent, global consensus-building. These recommendations aim to foster governance that is environmentally sustainable, ethically sound, and politically feasible, thereby enhancing knowledge exchange, spurring innovation, and advancing the development of marine-based geoengineering approaches. This study emphasizes the importance of collaborative approaches in managing the complexities of marine-based geoengineering, contributing significantly to the discourse on international environmental governance in the face of rapid climate and technological changes.

Keywords: climate change, environmental law, international cooperation, international governance, international law, marine-based geoengineering, marine law, regulatory frameworks

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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

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Authors: Divya Kanchibhotla, Shashank Kulkarni, Shweta Singh

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Chronic stress and sleep quality reduces mental health and increases the risk of developing depression and anxiety as well. There is increasing evidence for the utility of meditation as an adjunct clinical intervention for conditions like depression and anxiety. The present study is an attempt to explore the impact of Sahaj Samadhi Meditation (SSM), a category of Automatic Self Transcending Meditation (ASTM), on perceived stress and sleep quality in adults. The study design was a single group pre-post assessment. Perceived Stress Scale (PSS) and the Pittsburgh Sleep Quality Index (PSQI) were used in this study. Fifty-two participants filled PSS, and 60 participants filled PSQI at the beginning of the program (day 0), after two weeks (day 16) and at two months (day 60). Significant pre-post differences for the perceived stress level on Day 0 - Day 16 (p < 0.01; Cohen's d = 0.46) and Day 0 - Day 60 (p < 0.01; Cohen's d = 0.76) clearly demonstrated that by practicing SSM, participants experienced reduction in the perceived stress. The effect size of the intervention observed on the 16th day of assessment was small to medium, but on the 60th day, a medium to large effect size of the intervention was observed. In addition to this, significant pre-post differences for the sleep quality on Day 0 - Day 16 and Day 0 - Day 60 (p < 0.05) clearly demonstrated that by practicing SSM, participants experienced improvement in the sleep quality. Compared with Day 0 assessment, participants demonstrated significant improvement in the quality of sleep on Day 16 and Day 60. The effect size of the intervention observed on the 16th day of assessment was small, but on the 60th day, a small to medium effect size of the intervention was observed. In the current study we found out that after practicing SSM for two months, participants reported a reduction in the perceived stress, they felt that they are more confident about their ability to handle personal problems, were able to cope with all the things that they had to do, felt that they were on top of the things, and felt less angered. Participants also reported that their overall sleep quality improved; they took less time to fall asleep; they had less disturbances in sleep and less daytime dysfunction due to sleep deprivation. The present study provides clear evidence of the efficacy and safety of non-pharmacological interventions such as SSM in reducing stress and improving sleep quality. Thus, ASTM may be considered a useful intervention to reduce psychological distress in healthy, non-clinical populations, and it can be an alternative remedy for treating poor sleep among individuals and decreasing the use of harmful sedatives.

Keywords: automatic self transcending meditation, Sahaj Samadhi meditation, sleep, stress

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Authors: Natalia Kajka, Agnieszka Kulik

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The aim of the study is to present the results regarding differences in perception of cognitive progress of children with Attention Deficit Hyperactivity Disorder (ADHD) by adults and children themselves. The experiment was attended by 45 children with ADHD, their parents and teachers. The children attended the 3-month metacognitive training. Both children and adults were examined before and after joining this project. In order to show significant differences between the first and second measurement of the test, non-parametric Wilcoxon tests were performed. The analysis showed statistically significant differences in the change of cognitive functioning in children with ADHD participating in metacognitive training, this was also confirmed by the results of the parents' research. There were no significant differences in the teachers' assessment of these children.

Keywords: ADHD, executive function, Golem effect metacognitive training

Procedia PDF Downloads 173