Search results for: chemical learning

Authors: Boukhiar Aissa, Halladj Fatima, Iguergaziz Nadia, Lamrani yasmina, Benamara Salem

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Determining the alcohol content in alcoholic fermentation bioprocess is of great importance. In fact, it is a key indicator for monitoring this bioprocess. Several methodologies (chemical, spectrophotometric, chromatographic) are used to the determination of this parameter. However, these techniques are very long and they require: rigorous preparations, sometimes dangerous chemical reagents and/or expensive equipment. In the present study, the date juice is used as the substrate of alcoholic fermentation. The extracted juice undergoes an alcoholic fermentation by Saccharomyces cerevisiae. The study of the possible use of refractometry as a sole means for the in situ control of alcoholic fermentation revealed a good correlation (R2=0.98) between initial and final °Brix: °Brixf=0.377×°Brixi. In addition, the relationship between Δ°Brix and alcoholic content of the final product (A,%) has been determined: Δ°Brix/A=1.1. The obtained results allowed us to establish iso-responses abacus, which can be used for the determination of alcohol and residual sugar content, with a mean relative error (MRE) of 5.35%.

Keywords: alcoholic fermentation, date juice, refractometry, residual sugar

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Authors: Behzad Behnia, Noah LaRussa-Trott

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In recent years, fibers have been successfully used as an additive to reinforce asphalt concrete materials and to enhance the sustainability and resiliency of transportation infrastructure. Roads covered with fiber-reinforced asphalt concrete (FRAC) require less frequent maintenance and tend to have a longer lifespan. The present work investigates the application of sasobit-coated aramid fibers in asphalt pavements and employs machine learning to develop prediction models to evaluate the cracking performance of FRAC materials. For the experimental part of the study, the effects of several important parameters such as fiber content, fiber length, and testing temperature on fracture characteristics of FRAC mixtures were thoroughly investigated. Two mechanical performance tests, i.e., the disk-shaped compact tension [DC(T)] and indirect tensile [ID(T)] strength tests, as well as the non-destructive acoustic emission test, were utilized to experimentally measure the cracking behavior of the FRAC material in both macro and micro level, respectively. The experimental results were used to train the supervised machine learning approach in order to establish prediction models for fracture performance of the FRAC mixtures in the field. Experimental results demonstrated that adding fibers improved the overall fracture performance of asphalt concrete materials by increasing their fracture energy, tensile strength and lowering their 'embrittlement temperature'. FRAC mixtures containing long-size fibers exhibited better cracking performance than regular-size fiber mixtures. The developed prediction models of this study could be easily employed by pavement engineers in the assessment of the FRAC pavements.

Keywords: fiber reinforced asphalt concrete, machine learning, cracking performance tests, prediction model

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Authors: Wei Hing Rosenkvist

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In the past few decades, concerns and demands of promoting student intercultural communicative competence in foreign language education have been increasing along with the rapid growth of information technologies and globalization in the 21st century. In Sweden, related concepts such as internationalization, global citizenship, multiculturalism, and intercultural communication etc., are also keywords that would be found in the written learning objectives of the foreign language education in all levels. Being one of the leading higher institutes in distance education in Europe, Dalarna University clearly states that after completion of the teacher education program, students shall understand the needs for integrating internationalization, intercultural and global perspective in teaching and learning in Swedish schools and implement their own studies to promote education in an international and global context. Despite the fact that many teachers and educators agree with the institutes’ mission and vision about the importance of internationalization and the need of increasing student understanding of intercultural and global perspective, they might find this objective unattainable and restricted due to the nature of the subject and their personal knowledge of intercultural competence. When conducting a comprehensive Chinese language course for the students who are going to become Chinese foreign language teachers, the researcher found that all the learning objectives are linguistic oriented while grammatical components dominate the entire course. Apparently, there is a gap between the learning objectives of the course and the DU’s mission of fostering an international learner with intercultural and globalized perspectives. How to include this macro-learning objective in a foreign language course is a great challenge to the educator. Although scholars from different academic domains have provided different theoretical frameworks and approaches for developing student intercultural competence, research that focuses on the didactic perspectives of developing student intercultural competence in teaching Chinese as a foreign language education (CFL) is limited and practical examples are rare. This has motivated the researcher to conduct an action research study that aims at integrating DU’s macro-learning objective in a current CFL course through different didactic practices with a purpose of developing the teacher student intercultural competence. This research study aims to, firstly, illustrate the cross-cultural knowledge integrated into the present Chinese language course for developing intercultural competence. Secondly, it investigates different didactic means that can be utilized to deliver cross-cultural knowledge to student teachers in the present course without generating dramatic disturbance of the syllabus. Thirdly, it examines the effectiveness of these didactic means in enhancing teacher student intercultural competence regarding the need for integrating and implementing internationalization, intercultural and global perspectives in teaching and learning in Swedish schools. Last but not least, it intends to serve as a practical example for developing the student teachers’ intercultural competence in foreign language education in DU and fill in the research gap of this academic domain worldwide.

Keywords: intercultural competence, foreign language education, action research, teacher education

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Authors: Maria Fontenele, Claude-Gilles Dussap, Vincent Dumouilla, Baptiste Boit

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Roquette Frères is a producer of plant-based ingredients that employs many processes to extract relevant molecules and often transforms them through chemical and physical processes to create desired ingredients with specific functionalities. In this context, Roquette encounters numerous multi-component complex systems in their processes, including fibers, proteins, and carbohydrates, in an aqueous environment. To develop, control, and optimize both new and old processes, Roquette aims to develop new in silico tools. Currently, Roquette uses process modelling tools which include specific thermodynamic models and is willing to develop computational methodologies such as molecular dynamics simulations to gain insights into the complex interactions in such complex media, and especially hydrogen bonding interactions. The issue at hand concerns aqueous mixtures of polyols with high dry matter content. The polyols mannitol and sorbitol molecules are diastereoisomers that have nearly identical chemical structures but very different physicochemical properties: for example, the solubility of sorbitol in water is 2.5 kg/kg of water, while mannitol has a solubility of 0.25 kg/kg of water at 25°C. Therefore, predicting liquid-solid equilibrium properties in this case requires sophisticated solution models that cannot be based solely on chemical group contributions, knowing that for mannitol and sorbitol, the chemical constitutive groups are the same. Recognizing the significance of solvation phenomena in polyols, the GePEB (Chemical Engineering, Applied Thermodynamics, and Biosystems) team at Institut Pascal has developed the COSMO-UCA model, which has the structural advantage of using quantum mechanics tools to predict formation and phase equilibrium properties. In this work, we use molecular dynamics simulations to elucidate the behavior of polyols in aqueous solution. Specifically, we employ simulations to compute essential metrics such as radial distribution functions and hydrogen bond autocorrelation functions. Our findings illuminate a fundamental contrast: sorbitol and mannitol exhibit disparate hydrogen bond lifetimes within aqueous environments. This observation serves as a cornerstone in elucidating the divergent physicochemical properties inherent to each compound, shedding light on the nuanced interplay between their molecular structures and water interactions. We also present a methodology to predict the physicochemical properties of complex solutions, taking as sole input the three-dimensional structure of the molecules in the medium. Finally, by developing knowledge models, we represent some physicochemical properties of aqueous solutions of sorbitol and mannitol.

Keywords: COSMO models, hydrogen bond, molecular dynamics, thermodynamics

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Authors: H. E. Elzain, S. Y. Chung, V. Senapathi, Kye-Hun Park

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Groundwater is considered a significant source for drinking and irrigation purposes in Miryang city, and it is attributed to a limited number of a surface water reservoirs and high seasonal variations in precipitation. Population growth in addition to the expansion of agricultural land uses and industrial development may affect the quality and management of groundwater. This research utilized multidisciplinary approaches of geostatistics such as multivariate statistics, factor analysis, cluster analysis and kriging technique in order to identify the hydrogeochemical process and characterizing the control factors of the groundwater geochemistry distribution for developing risk maps, exploiting data obtained from chemical investigation of groundwater samples under the area of study. A total of 79 samples have been collected and analyzed using atomic absorption spectrometer (AAS) for major and trace elements. Chemical maps using 2-D spatial Geographic Information System (GIS) of groundwater provided a powerful tool for detecting the possible potential sites of groundwater that involve the threat of contamination. GIS computer based map exhibited that the higher rate of contamination observed in the central and southern area with relatively less extent in the northern and southwestern parts. It could be attributed to the effect of irrigation, residual saline water, municipal sewage and livestock wastes. At wells elevation over than 85m, the scatter diagram represents that the groundwater of the research area was mainly influenced by saline water and NO3. Level of pH measurement revealed low acidic condition due to dissolved atmospheric CO2 in the soil, while the saline water had a major impact on the higher values of TDS and EC. Based on the cluster analysis results, the groundwater has been categorized into three group includes the CaHCO3 type of the fresh water, NaHCO3 type slightly influenced by sea water and Ca-Cl, Na-Cl types which are heavily affected by saline water. The most predominant water type was CaHCO3 in the study area. Contamination sources and chemical characteristics were identified from factor analysis interrelationship and cluster analysis. The chemical elements that belong to factor 1 analysis were related to the effect of sea water while the elements of factor 2 associated with agricultural fertilizers. The degree level, distribution, and location of groundwater contamination have been generated by using Kriging methods. Thus, geostatistics model provided more accurate results for identifying the source of contamination and evaluating the groundwater quality. GIS was also a creative tool to visualize and analyze the issues affecting water quality in the Miryang city.

Keywords: groundwater characteristics, GIS chemical maps, factor analysis, cluster analysis, Kriging techniques

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Authors: Jade V. Rushby, Tony Loughland, Tracy L. Durksen, Hoa Nguyen, Robert M. Klassen

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This paper reports a study investigating the development and implementation of an online multi-session ‘scenario-based learning’ (SBL) program administered to preservice teachers in Australia. The transition from initial teacher education to the teaching profession can present numerous cognitive and psychological challenges for early career teachers. Therefore, the identification of additional supports, such as scenario-based learning, that can supplement existing teacher education programs may help preservice teachers to feel more confident and prepared for the realities and complexities of teaching. Scenario-based learning is grounded in situated learning theory which holds that learning is most powerful when it is embedded within its authentic context. SBL exposes participants to complex and realistic workplace situations in a supportive environment and has been used extensively to help prepare students in other professions, such as legal and medical education. However, comparatively limited attention has been paid to investigating the effects of SBL in teacher education. In the present study, the SBL intervention provided participants with the opportunity to virtually engage with school-based scenarios, reflect on how they might respond to a series of plausible response options, and receive real-time feedback from experienced educators. The development process involved several stages, including collaboration with experienced educators to determine the scenario content based on ‘critical incidents’ they had encountered during their teaching careers, the establishment of the scoring key, the development of the expert feedback, and an extensive review process to refine the program content. The 4-part SBL program focused on areas that can be challenging in the beginning stages of a teaching career, including managing student behaviour and workload, differentiating the curriculum, and building relationships with colleagues, parents, and the community. Results from prior studies implemented by the research group using a similar 4-part format have shown a statistically significant increase in preservice teachers’ self-efficacy and classroom readiness from the pre-test to the final post-test. In the current research, professional teaching identity - incorporating self-efficacy, motivation, self-image, satisfaction, and commitment to teaching - was measured over six weeks at multiple time points: before, during, and after the 4-part scenario-based learning program. Analyses included latent growth curve modelling to assess the trajectory of change in the outcome variables throughout the intervention. The paper outlines (1) the theoretical underpinnings of SBL, (2) the development of the SBL program and methodology, and (3) the results from the study, including the impact of the SBL program on aspects of participating preservice teachers’ professional identity. The study shows how SBL interventions can be implemented alongside the initial teacher education curriculum to help prepare preservice teachers for the transition from student to teacher.

Keywords: classroom simulations, e-learning, initial teacher education, preservice teachers, professional learning, professional teaching identity, scenario-based learning, teacher development

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Authors: Rochelle Elva

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Faculty in Higher Education today are faced with the challenge of convincing their students of the importance of learning and mastery of skills. This is because most students often have a single motivation -to get high grades. If it appears that this goal will not be met, they lose their motivation, and their academic efforts wane. This is true even for students in the competitive fields of STEM, including Computer Science majors. As educators, we have to understand our students and leverage what motivates them to achieve our learning outcomes. This paper presents a case study that utilizes cognitive psychology’s Expectancy Value Theory and Motivation Theory to investigate the effect of sustained expectancy for success on students’ learning outcomes. In our case study, we explore how students’ motivation and persistence in their academic efforts are impacted by providing them with an unexpected possible path to success that continues to the end of the semester. The approach was tested in an undergraduate computer science course with n = 56. The results of the study indicate that when presented with the real possibility of success, despite existing low grades, both low and high-scoring students persisted in their efforts to improve their performance. Their final grades were, on average, one place higher on the +/-letter grade scale, with some students scoring as high as three places above their predicted grade.

Keywords: expectancy for success and persistence, motivation and performance, computer science education, motivation and performance in computer science

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Authors: Monika Konieczyńska, Joanna Fajfer, Olga Lipińska

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In Poland works related to the exploration and prospection of unconventional hydrocarbons (natural gas accumulated in the Silurian shale formations) started in 2007, based on the experience of the other countries that have created new possibilities for the use of existing hydrocarbons resources. The highly water-consuming process of hydraulic fracturing is required for the exploitation of shale gas which implies a need to ensure large volume of water available. As a result considerable amount of mining waste is generated, particularly liquid waste, i.e. flowback fluid with variable chemical composition. The chemical composition of the flowback fluid depends on the composition of the fracturing fluid and the chemistry of the fractured geological formations. Typically, flowback fluid is highly salinated, can be enriched in heavy metals, including rare earth elements, naturally occurring radioactive materials and organic compounds. The generated fluids considered as the extractive waste should be properly managed in the recovery or disposal facility. Problematic issue is both high hydration of waste as well as their variable chemical composition. Also the limited capacity of currently operating facilities is a growing problem. Based on the estimates, currently operating facilities will not be sufficient for the need of waste disposal when extraction of unconventional hydrocarbons starts. Further more, the content of metals in flowback fluids including rare earth elements is a considerable incentive to develop technology of metals recovery. Also recycling is a key factor in terms of selection of treatment process, which should provide that the thresholds required for reuse are met. The paper will present the study of the flowback fluids chemical composition, based on samples from hydraulic fracturing processes performed in Poland. The scheme of flowback fluid cleaning and recovering technology will be reviewed along with a discussion of the results and an assessment of environmental impact, including all generated by-products. The presented technology is innovative due to the metal recovery, as well as purified water supply for hydraulic fracturing process, which is significant contribution to reducing water consumption.

Keywords: environmental impact, flowback fluid, management of special waste streams, metals recovery, shale gas

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Authors: Georgi I. Petkov, Ivan I. Vankov, Yolina A. Petrova

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A structure-based model of category learning and categorization at different levels of abstraction is presented. The model compares different structures and expresses their similarity implicitly in the forms of mappings. Based on this similarity, the model can categorize different targets either as members of categories that it already has or creates new categories. The model is novel using two threshold parameters to evaluate the structural correspondence. If the similarity between two structures exceeds the higher threshold, a new sub-ordinate category is created. Vice versa, if the similarity does not exceed the higher threshold but does the lower one, the model creates a new category on higher level of abstraction.

Keywords: analogy-making, categorization, learning of categories, abstraction, hierarchical structure

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Authors: Jinwooung Kim, Jae-Hwan Jung, Seong-Jun Kim, Sung-Ah Kim

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Because the building envelope has a significant impact on the operation and maintenance stage of the building, designing the facade considering the performance can improve the performance of the building and lower the maintenance cost of the building. In general, however, optimizing two or more performance factors confronts the limits of time and computational tools. The optimization phase typically repeats infinitely until a series of processes that generate alternatives and analyze the generated alternatives achieve the desired performance. In particular, as complex geometry or precision increases, computational resources and time are prohibitive to find the required performance, so an optimization methodology is needed to deal with this. Instead of directly analyzing all the alternatives in the optimization process, applying experimental techniques (heuristic method) learned through experimentation and experience can reduce resource waste. This study proposes and verifies a method to optimize the double envelope of a building composed of a perforated panel using machine learning to the design geometry and quantitative performance. The proposed method is to achieve the required performance with fewer resources by supplementing the existing method which cannot calculate the complex shape of the perforated panel.

Keywords: building envelope, machine learning, perforated metal, multi-factor optimization, façade

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Authors: Lulu Liao, Huijing Chen, Yinan Zhao, Hongting Ning, Hui Feng

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Urinary tract infection rate is an important index of care quality in nursing homes. The aim of the study is to understand the nursing assistant's current knowledge and attitudes of urinary incontinence and to explore related stakeholders' viewpoint about urinary incontinence training. This explanatory sequential study used Knowledge, Practice, and Attitude Model (KAP) and Adult Learning Theories, as the conceptual framework. The researchers collected data from 509 nursing assistants in sixteen nursing homes in Hunan province in China. The questionnaire survey was to assess the knowledge and attitude of urinary incontinence of nursing assistants. On the basis of quantitative research and combined with focus group, training demands were identified, which nurse managers should adopt to improve nursing assistants’ professional practice ability in urinary incontinence. Most nursing assistants held the poor knowledge (14.0 ± 4.18) but had positive attitudes (35.5 ± 3.19) toward urinary incontinence. There was a significant positive correlation between urinary incontinence knowledge and nursing assistants' year of work and educational level, urinary incontinence attitude, and education level (p < 0.001). Despite a general awareness of the importance of prevention of urinary tract infections, not all nurse managers fully valued the training in urinary incontinence compared with daily care training. And the nursing assistants required simple education resources to equip them with skills to address problem about urinary incontinence. The variety of learning methods also highlighted the need for educational materials, and nursing assistants had shown a strong interest in online learning. Related education material should be developed to meet the learning need of nurse assistants and provide suitable training method for planned quality improvement in urinary incontinence.

Keywords: mixed methods, nursing assistants, nursing homes, urinary incontinence

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Authors: Alireza Hashemi

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In recent decades, language teaching methodology has undergone significant changes due to the advent of computers and the growth of educational software. French language education has also benefited from these developments, and various software has been produced to facilitate the learning of this language. However, the question arises whether these software programs meet the educational needs of Iranian learners, particularly in Semnan Province. The aim of this study is to evaluate the efficiency and effectiveness of French language educational software for learners in Semnan Province, considering educational, cultural, and technical criteria. In this study, content analysis and performance evaluation methods were used to examine the educational software ‘Français Facile’. This software was evaluated based on criteria such as teaching methods, cultural compatibility, and technical features. To collect data, standardized questionnaires and semi-structured interviews with learners in Semnan Province were used. Additionally, the SPSS statistical software was employed for quantitative data analysis, and the thematic analysis method was used for qualitative data. The results indicated that the ‘Français Facile’ software has strengths such as providing diverse educational content and an interactive learning environment. However, some weaknesses include the lack of alignment of educational content with the learning culture of learners in Semnan Province and technical issues in software execution. Statistical data showed that 65% of learners were satisfied with the educational content, but 55% reported issues related to cultural alignment with their needs. This study indicates that to enhance the efficiency of French language educational software, there is a need to localize educational content and improve technical infrastructure. Producing locally adapted educational software can improve the quality of language learning and increase the motivation of learners in Semnan Province. This research emphasizes the importance of understanding the cultural and educational needs of learners in the development of educational software and recommends that developers of educational software pay special attention to these aspects.

Keywords: educational software, French language, Iran, learners in Semnan province

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Authors: Samuel Marínez González

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In the last decades, the studies about humor have been increasing significantly in all areas. In the field of education and, specially, in the second language teaching, most research has concentrated on the beneficial effects that the introduction of humor in the process of teaching and learning a foreign language, as well as its impact on teachers and students. In the following research, we will try to know the learners’ perspectives about humor and its use in the Spanish as a Foreign Language classes. In order to do this, a different range of students from the Spanish courses at the University of Cape Town will participate in a survey that will reveal their beliefs about the frequency of humor in their daily lives and their Spanish lessons, their reactions to humorous situations, and the main advantages or disadvantages, from their point of view, to the introduction of humor in the teaching of Spanish as a Foreign Language.

Keywords: education, foreign languages, humor, pedagogy, Spanish as a Foreign Language, students’ perceptions

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Authors: K. Julia Rose Mary, Victor Arokia Doss

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Electrical and chemical stimulations up-regulate phosphorylaion of CREB, a transcriptional factor that induces its target gene production for memory consolidation and Late Long-Term Potentiation (L-LTP) in CA1 region of the hippocampus. L-LTP requires complex interactions among second-messenger signaling cascade molecules such as cAMP, CAMKII, CAMKIV, MAPK, RSK, PKA, all of which converge to phosphorylate CREB which along with CBP induces the transcription of target genes involved in memory consolidation. A differential equation based model for L-LTP representing stimulus-mediated activation of downstream mediators which confirms the steep, supralinear stimulus-response effects of activation and inhibition was used. The same was extended to accommodate the inhibitory effect of the Inducible cAMP Early Repressor (ICER). ICER is the natural inducible CREB antagonist represses CRE-Mediated gene transcription involved in long-term plasticity for learning and memory. After verifying the sensitivity and robustness of the model, we had simulated it with various empirical levels of repressor concentration to analyse their effect on the gene induction. The model appears to predict the regulatory dynamics of repression on the L-LTP and agrees with the experimental values. The flux data obtained in the simulations demonstrate various aspects of equilibrium between the gene induction and repression.

Keywords: CREB, L-LTP, mathematical modeling, simulation

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Authors: Aneek Kuila, Yaron Paz

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MIP 177 LT and HT are two-phase transformable metal organic frameworks consisting of a Ti12O15 oxocluster and a tetracarboxylate ligand that exhibits robust chemical stability and improved photoactivity. LT to HT only shows the changes in dimensionality from 0D to 1D without any change in the overall chemical structure. In terms of chemical and photoactivity MIP 177 LT is found to perform better than the MIP 177HT. Step-scan Fourier transform absorption difference time-resolved spectroscopy has been used to collect mid-IR time-resolved infrared spectra of the transient electronic excited states of a nano-porous metal–organic framework MIP 177-LT and HT with 2.5 ns time resolution. Analyzing the time-resolved vibrational data after 355nm LASER excitation reveals the presence of the temporal changes of ν (O-Ti-O) of Ti-O metal cluster and ν (-COO) of the ligand concluding the fact that these moieties are the ultimate acceptors of the excited charges which are localized over those regions on the nanosecond timescale. A direct negative correlation between the differential absorbance (Δ Absorbance) reveals the charge transfer relation among these two moieties. A longer-lived transient signal up to 180ns for MIP 177 LT compared to the 100 ns of MIP 177 HT shows the extended lifetime of the reactive charges over the surface that exerts in their effectivity. An ultrafast change of bidentate to monodentate bridging in the -COO-Ti-O ligand-metal coordination environment was observed after the photoexcitation of MIP 177 LT which remains and lives with for seconds after photoexcitation is halted. This phenomenon is very unique to MIP 177 LT but not observed with HT. This in-situ change in the coordination denticity during the photoexcitation was not observed previously which can rationalize the reason behind the ability of MIP 177 LT to accumulate electrons during continuous photoexcitation leading to a superior photocatalytic activity.

Keywords: time resolved FTIR, metal organic framework, denticity, photoacatalysis

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Authors: Waleed Afandi

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Artificial intelligence (AI) has rapidly transformed various sectors, including education. This paper explores the integration of AI in education, emphasizing its potential to revolutionize learning processes, enhance teaching methodologies, and personalize education. We examine the historical context of AI in education, current applications, and the potential challenges and ethical considerations associated with its implementation. By reviewing a wide range of literature, this study aims to provide a comprehensive understanding of how AI can be leveraged to improve educational outcomes and the future directions of AI-driven educational innovations. Additionally, the paper discusses the impact of AI on student engagement, teacher support, and administrative efficiency. Case studies highlighting successful AI applications in diverse educational settings are presented, showcasing the practical benefits and real-world implications. The analysis also addresses potential disparities in access to AI technologies and suggests strategies to ensure equitable implementation. Through a balanced examination of the promises and pitfalls of AI in education, this study seeks to inform educators, policymakers, and technologists about the optimal pathways for integrating AI to foster an inclusive, effective, and innovative educational environment.

Keywords: artificial intelligence, education, personalized learning, teaching methodologies, educational outcomes, AI applications, student engagement, teacher support, administrative efficiency, equity in education

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Authors: Anil Kumar, Rajnish Kaur, Mateusz Czyzycki, Alessandro Migilori, Andreas Germanos Karydas, Sanjiv Puri

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The radiative decay of Li(i=1-3) sub-shell vacancies produced through photoionization results in production of the characteristic emission spectrum comprising several X-ray lines, whereas non-radiative vacancy decay results in Auger electron spectrum. Accurate reliable data on the Li(i=1-3) sub-shell X-ray production (XRP) cross sections is of considerable importance for investigation of atomic inner-shell ionization processes as well as for quantitative elemental analysis of different types of samples employing the energy dispersive X-ray fluorescence (EDXRF) analysis technique. At incident photon energies in vicinity of the absorption edge energies of an element, the many body effects including the electron correlation, core relaxation, inter-channel coupling and post-collision interactions become significant in the photoionization of atomic inner-shells. Further, in case of compounds, the characteristic emission spectrum of the specific element is expected to get influenced by the chemical environment (coordination number, oxidation state, nature of ligand/functional groups attached to central atom, etc.). These chemical effects on L X-ray fluorescence parameters have been investigated by performing the measurements at incident photon energies much higher than the Li(i=1-3) sub-shell absorption edge energies using EDXRF spectrometers. In the present work, the cross sections for production of the Lk(k= γ2,3, γ4) X-rays have been measured for some compounds of 66Dy, namely, Dy2O3, Dy2(CO3)3, Dy2(SO4)3.8H2O, DyI2 and Dy metal by tuning the incident photon energies few eV above the L1 absorption-edge energy in order to investigate the influence of chemical effects on these cross sections in presence of the many body effects which become significant at photon energies close to the absorption-edge energies. The present measurements have been performed under vacuum at the IAEA end-station of the X-ray fluorescence beam line (10.1L) of ELETTRA synchrotron radiation facility (Trieste, Italy) using self-supporting pressed pellet targets (1.3 cm diameter, nominal thicknesses ~ 176 mg/cm2) of 66Dy compounds (procured from Sigma Aldrich) and a metallic foil of 66Dy (nominal thickness ~ 3.9 mg/cm2, procured from Good Fellow, UK). The present measured cross sections have been compared with theoretical values calculated using the Dirac-Hartree-Slater(DHS) model based fluorescence and Coster-Kronig yields, Dirac-Fock(DF) model based X-ray emission rates and two sets of L1 sub-shell photoionization cross sections based on the non-relativistic Hartree-Fock-Slater(HFS) model and those deduced from the self-consistent Dirac-Hartree-Fock(DHF) model based total photoionization cross sections. The present measured XRP cross sections for 66Dy as well as for its compounds for the L2,3 and L4 X-rays, are found to be higher by ~14-36% than the two calculated set values. It is worth to be mentioned that L2,3 and L4 X-ray lines are originated by filling up of the L1 sub-shell vacancies by the outer sub-shell (N2,3 and O2,3) electrons which are much more sensitive to the chemical environment around the central atom. The present observed differences between measured and theoretical values are expected due to combined influence of the many-body effects and the chemical effects.

Keywords: chemical effects, L X-ray production cross sections, Many body effects, Synchrotron radiation

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Authors: Wen-Wei Chiang

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In this study, the researcher developed a scale for use in measuring the degree to which high school students experience a state of flow. The researcher then verified its reliability and validity in an actual classroom setting. The ultimate objective was to identify feasible methods by which to promote the experience of a flow state among high school students engaged in the study of science. The nine indices identified in this study to assess the engagement of high school students focus primarily on the study of science-related topics; however, the principles on which they are based are applicable to a wide range of learning situations. Teachers must outline the goals of each lesson clearly and provide unambiguous feedback. They must also look for ways to make the lessons more fun and appealing.

Keywords: flow experience, positive psychology, questionnaire, science learning

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Authors: Rohit Mittal, Bright Keswani, Amit Mithal

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This paper provides a sense about the application of computational intelligence techniques used to develop computer games, especially car racing. For the deep sense and knowledge of artificial intelligence, this paper is divided into various sections that is optimization, imitation, innovation and combining approach of optimization and imitation. This paper is mainly concerned with combining approach which tells different aspects of using fitness measures and supervised learning techniques used to imitate aspects of behavior. The main achievement of this paper is based on modelling player behaviour and evolving new game content such as racing tracks as single car racing on single track.

Keywords: evolution algorithm, genetic, optimization, imitation, racing, innovation, gaming

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Authors: Adishirin Mammadov

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Polymers and polymer composites play vital roles in diverse industries, including food and beverage packaging, transportation innovations, and medical advancements. However, the advancements in polymer technology bring certain risks, particularly concerning water and soil pollution due to the presence of polymers. The creation of new polymers is a critical aspect of this field. While the primary focus is on improving their physical and chemical properties, ensuring their ecological compatibility is equally important. An advanced method for developing innovative polymer types involves integrating fillers with diverse characteristics, offering advantages such as cost reduction and improved quality indicators. In the conducted research, efforts were made to enhance environmental aspects by employing waste fillers. Specifically, low-density polyethylene (LDPE) was used as the polymer, and waste from cocoon factories was chosen as the filler. Following a process of cleaning, drying, and crushing the filler to specific dimensions, it was incorporated into polyethylene through a mechanical-chemical method under laboratory conditions. The varied rheological properties of the resulting polyethylene compositions examined at temperatures ranging from 145 to 165 degrees Celsius. These compositions demonstrated different rheological properties at various temperature intervals. Achieving homogeneity in the obtained compositions is crucial in the polymers mechanochemical process. Beyond rheological properties, swelling rates in different environments and percentages of mass loss at different temperatures learned using the differential thermal analysis method. The research revealed that, to a certain extent, the physico-chemical properties of polyethylene were not significantly affected by the polymer compositions. This suggests that incorporating cocoon waste enables cost reduction in composite production while positively impacting the environment.

Keywords: polyethylene, polymer, composites, filler, reology

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Siham Boulaknadel

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Every culture has its share of a rich history of storytelling in oral, visual, and textual form. The Amazigh language, as many languages, has its own which has entertained and informed across centuries and cultures, and its instructional potential continues to serve teachers. According to many researchers, listening to stories draws attention to the sounds of language and helps children develop sensitivity to the way language works. Stories including repetitive phrases, unique words, and enticing description encourage students to join in actively to repeat, chant, sing, or even retell the story. This kind of practice is important to language learners’ oral language development, which is believed to correlate completely with student’s academic success. Today, with the advent of multimedia, digital storytelling for instance can be a practical and powerful learning tool. It has the potential in transforming traditional learning into a world of unlimited imaginary environment. This paper reports on a research project on development of multimedia Storytelling Website using traditional Amazigh oral narratives called “tell me a story”. It is a didactic tool created for the learning of good moral values in an interactive multimedia environment combining on-screen text, graphics and audio in an enticing environment and enabling the positive values of stories to be projected. This Website developed in this study is based on various pedagogical approaches and learning theories deemed suitable for children age 8 to 9 year-old. The design and development of Website was based on a well-researched conceptual framework enabling users to: (1) re-play and share the stories in schools or at home, and (2) access the Website anytime and anywhere. Furthermore, the system stores the students work and activities over the system, allowing parents or teachers to monitor students’ works, and provide online feedback. The Website contains following main feature modules: Storytelling incorporates a variety of media such as audio, text and graphics in presenting the stories. It introduces the children to various kinds of traditional Amazigh oral narratives. The focus of this module is to project the positive values and images of stories using digital storytelling technique. Besides development good moral sense in children using projected positive images and moral values, it also allows children to practice their comprehending and listening skills. Reading module is developed based on multimedia material approach which offers the potential for addressing the challenges of reading instruction. This module is able to stimulate children and develop reading practice indirectly due to the tutoring strategies of scaffolding, self-explanation and hyperlinks offered in this module. Word Enhancement assists the children in understanding the story and appreciating the good moral values more efficiently. The difficult words or vocabularies are attached to present the explanation, which makes the children understand the vocabulary better. In conclusion, we believe that the interactive multimedia storytelling reveals an interesting and exciting tool for learning Amazigh. We plan to address some learning issues, in particularly the uses of activities to test and evaluate the children on their overall understanding of story and words presented in the learning modules.

Keywords: Amazigh language, e-learning, storytelling, language teaching

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Authors: Nermeen El Kashef, Yasser Fouad, Khaled Mahar

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No AI-complete system can model the human brain or behavior, without looking at the totality of the whole situation and incorporating a combination of senses. This paper proposes a Pattern Recognition model based on Simulation of Chemical Senses (SCS) for separation and classification of sign language. The model based on human taste controlling strategy. The main idea of the introduced model is motivated by the facts that the tongue cluster input substance into its basic tastes first, and then the brain recognizes its flavor. To implement this strategy, two level architecture is proposed (this is inspired from taste system). The separation-level of the architecture focuses on hand posture cluster, while the classification-level of the architecture to recognizes the sign language. The efficiency of proposed model is demonstrated experimentally by recognizing American Sign Language (ASL) data set. The recognition accuracy obtained for numbers of ASL is 92.9 percent.

Keywords: artificial intelligence, biocybernetics, gustatory system, sign language recognition, taste sense

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Authors: A. D. Tayal

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The energy industry is undergoing significant disruption. This research outlines that, whilst challenging; this disruption is also an emerging opportunity for electricity utilities. One such opportunity is leveraging the developments in data analytics and machine learning. As the uptake of renewable energy technologies and complimentary control systems increases, electricity grids will likely transform towards dense microgrids with high penetration of renewable generation sources, rich in network and customer data, and linked through intelligent, wireless communications. Data digitisation and analytics have already impacted numerous industries, and its influence on the energy sector is growing, as computational capabilities increase to manage big data, and as machines develop algorithms to solve the energy challenges of the future. The objective of this paper is to address how far the uptake of renewable technologies can go given the constraints of existing grid infrastructure and provides a qualitative assessment of how higher levels of renewable energy penetration can be facilitated by incorporating even broader technological advances in the fields of data analytics and machine learning. Western Australia is used as a contextualised case study, given its abundance and diverse renewable resources (solar, wind, biomass, and wave) and isolated networks, making a high penetration of renewables a feasible target for policy makers over coming decades.

Keywords: data, innovation, renewable, solar

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Authors: Marina Passero, Tianhua Zhai, Zuyi (Jacky) Huang

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Alzheimer’s disease has become a major public health issue, as indicated by the increasing populations of Americans living with Alzheimer’s disease. After decades of extensive research in Alzheimer’s disease, only seven drugs have been approved by Food and Drug Administration (FDA) to treat Alzheimer’s disease. Five of these drugs were designed to treat the dementia symptoms, and only two drugs (i.e., Aducanumab and Lecanemab) target the progression of Alzheimer’s disease, especially the accumulation of amyloid-b plaques. However, controversial comments were raised for the accelerated approvals of either Aducanumab or Lecanemab, especially with concerns on safety and side effects of these two drugs. There is still an urgent need for further drug discovery to target the biological processes involved in the progression of Alzheimer’s disease. Excessive cholesterol has been found to accumulate in the brain of those with Alzheimer’s disease. Cholesterol can be synthesized in both the blood and the brain, but the majority of biosynthesis in the adult brain takes place in astrocytes and is then transported to the neurons via ApoE. The blood brain barrier separates cholesterol metabolism in the brain from the rest of the body. Various proteins contribute to the metabolism of cholesterol in the brain, which offer potential targets for Alzheimer’s treatment. In the astrocytes, SREBP cleavage-activating protein (SCAP) binds to Sterol Regulatory Element-binding Protein 2 (SREBP2) in order to transport the complex from the endoplasmic reticulum to the Golgi apparatus. Cholesterol is secreted out of the astrocytes by ATP-Binding Cassette A1 (ABCA1) transporter. Lipoprotein receptors such as triggering receptor expressed on myeloid cells 2 (TREM2) internalize cholesterol into the microglia, while lipoprotein receptors such as Low-density lipoprotein receptor-related protein 1 (LRP1) internalize cholesterol into the neuron. Cytochrome P450 Family 46 Subfamily A Member 1 (CYP46A1) converts excess cholesterol to 24S-hydroxycholesterol (24S-OHC). Cholesterol has been approved for its direct effect on the production of amyloid-beta and tau proteins. The addition of cholesterol to the brain promotes the activity of beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), secretase, and amyloid precursor protein (APP), which all aid in amyloid-beta production. The reduction of cholesterol esters in the brain have been found to reduce phosphorylated tau levels in mice. In this work, a computational pipeline was developed to identify the protein targets involved in cholesterol regulation in brain and further to identify chemical compounds as the inhibitors of a selected protein target. Since extensive evidence shows the strong correlation between brain cholesterol regulation and Alzheimer’s disease, a detailed literature review on genes or pathways related to the brain cholesterol synthesis and regulation was first conducted in this work. An interaction network was then built for those genes so that the top gene targets were identified. The involvement of these genes in Alzheimer’s disease progression was discussed, which was followed by the investigation of existing clinical trials for those targets. A ligand-protein docking program was finally developed to screen 1.5 million chemical compounds for the selected protein target. A machine learning program was developed to evaluate and predict the binding interaction between chemical compounds and the protein target. The results from this work pave the way for further drug discovery to regulate brain cholesterol to combat Alzheimer’s disease.

Keywords: Alzheimer’s disease, drug discovery, ligand-protein docking, gene-network analysis, cholesterol regulation

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Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

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Authors: Yasser Y. Lenis, Amy Jo Montgomery, Diego F. Carrillo-Gonzalez

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Introduction: The use of didactic instruments in different learning environments plays a pivotal role in enhancing the level of knowledge in veterinary science students. The direct instruction of basic animal reproduction concepts in students enrolled in veterinary medicine programs allows them to elucidate the biological and molecular mechanisms that perpetuate the animal species in an ecosystem. Therefore, universities must implement didactic strategies that facilitate the teaching and learning processes for students and, in turn, enrich learning environments. Objective: to evaluate the effect of the use of a didactic textbook on the level of theoretical knowledge in embryo-maternal recognition for veterinary medicine students. Methods: the participants (n=24) were divided into two experimental groups: control (Ctrl) and treatment (Treat). Both groups received 4 hours of theoretical training regarding the basic concepts in bovine embryo-maternal recognition. However, the Treat group was also exposed to a guided lecture and the activity play-to-learn from a cow reproduction didactic textbook. A pre-test and a post-test were applied to assess the prior and subsequent knowledge in the participants. Descriptive statistics were applied to identify the success rates for each of the tests. Afterwards, a repeated measures model was applied where the effect of the intervention was considered. Results: no significant difference (p>0,05) was observed in the number of right answers for groups Ctrl (54,2%±12,7) and Treat (40,8%±16,8) in the pre-test. There was no difference (p>0,05) compering the number of right answers in Ctrl pre-test (54,2%±12,7) and post-test (60,8±18,8). However, the Treat group showed a significant (p>0,05) difference in the number of right answers when comparing pre-test (40,8%±16,8) and post-test (71,7%±14,7). Finally, after the theoretical training and the didactic activity in the Treat group, an increase of 10.9% (p<0,05) in the number of right answers was found when compared with the Ctrl group. Conclusion: the use of didactic tools that include guided lectures and activities like play-to-learn from a didactic textbook enhances the level of knowledge in an animal reproduction course for veterinary medicine students.

Keywords: animal reproduction, pedagogic, level of knowledge, learning environment

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Authors: Jayanth C., Anirudh Sai Yetikuri, Kavitha S. N.

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This paper explores the development and application of computer vision and machine learning techniques for real-time pose detection, facial recognition, and number plate extraction. Utilizing MediaPipe for pose estimation, the research presents methods for detecting hand raises and ducking postures through real-time video analysis. Complementarily, facial recognition is employed to compare and verify individual identities using the face recognition library. Additionally, the paper demonstrates a robust approach for extracting and storing vehicle number plates from images, integrating Optical Character Recognition (OCR) with a database management system. The study highlights the effectiveness and versatility of these technologies in practical scenarios, including security and surveillance applications. The findings underscore the potential of combining computer vision techniques to address diverse challenges and enhance automated systems for both individual and vehicular identification. This research contributes to the fields of computer vision and machine learning by providing scalable solutions and demonstrating their applicability in real-world contexts.

Keywords: computer vision, pose detection, facial recognition, number plate extraction, machine learning, real-time analysis, OCR, database management

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Authors: Ellina Zipman

Abstract:

This research investigates the place of music education in aged care facilities through the implementation of a program of regular piano lessons for residents. Using a qualitative case study methodology, the research explores aged care residents’ experiences in learning to play the piano. Since the aged care homes are unlikely places for formal learning and since older adults, especially in residential care, are not considered likely candidates for learning, this research opens the door for innovative and transformative thinking about where and to whom educational programs can be delivered. By addressing the educational needs of residents in aged care facilities, this research fills the gap in the literature. The research took place in Australia in two of Melbourne’s residential aged care facilities, engaging two residents (a nonagenarian female and an octogenarian male) to participate in 12-months weekly individual piano lessons. The data was collected through video recording of lessons, observations, interviews, emails, and a reflective journal. Data analysis was done using Nvivo and hard copy analysis with identifications of themes. The case studies revealed that passion for music was a major driver in participants’ motivation to engage in a long-term piano lessons program. This participation led to experiences of positive emotions, positive attitude, successes and challenges, the exercise of control, maintaining and building new relationships, improved self-confidence through autonomy and independent skills development, and discovering new identities through finding a new purpose and new roles in life. Speaking through participants’ voices, this research project demonstrates the importance of music education for older adults and hopes to influence transformation in the residential aged care sector.

Keywords: adult music education, quality of life, passion, positive ageing, wellbeing

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Authors: Xianglei Yin, Shen Wang, Baoyi Wang, Laihong Shen

Abstract:

Chemical looping partial oxidation of methane (CLPOM) is a novel technology to produce high-quality syngas with an auto-thermic process and low equipment investment. The development of oxygen carriers is important for the improvement of the CLPOM performance. In this work, the effect of the nickel-substitution proportion on the performance of LaMn₁₋ᵧNiᵧO₃₊δ perovskites for CLPOM was studied in the aspect of reactivity, syngas selectivity, resistance towards carbon deposition and thermal stability in cyclic redox process. The LaMn₁₋ₓNiₓO₃ perovskite oxides with x = 0, 0.1, 0.2 were prepared by the sol-gel method. The performance of LaMn₁₋ᵧNiᵧO₃₊δ perovskites for CLPOM was investigated through the characterization of XRD, H₂-TPR, XPS, and fixed-bed experiments. The characterization and test results suggest that the doping of nickel enhances the generation rate of syngas, leading to high syngas yield, methane conversion, and syngas selectivity. This is attributed to the that the introduction of nickel provides active sites to promote the methane activation on the surface and causes the addition of oxygen vacancies to accelerate the migration of oxygen anion in the bulk of oxygen carrier particles. On the other hand, the introduction of nickel causes carbon deposition to occur earlier. The best substitution proportion of nickel is y=0.1 and LaMn₀.₉Ni₀.₁O₃₊δ could produce high-quality syngas with a yield of 3.54 mmol·g⁻¹, methane conversion of 80.7%, and CO selectivity of 84.8% at 850℃. In addition, the LaMn₀.₉Ni₀.₁O₃₊δ oxygen carrier exhibits superior and stable performance in the cyclic redox process.

Keywords: chemical looping partial oxidation of methane, LaMnO₃₊δ, Ni doping, syngas, carbon deposition

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