Search results for: interdisciplinary learning

Authors: O. Wright, N. Perkins, M. Donn, M. Halstead

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This research responded to anecdotal evidence that suggested inefficiencies within the Architect and Acoustician relationship may lead to ineffective acoustic design decisions.  The acoustician spoken to believed that he was approached too late in the design phase. The approached architect valued acoustical qualities, yet, struggled to interpret common measurement parameters. The preliminary investigation of these opinions indicated a gap in the current New Zealand Architectural discourse and currently informs the creation of a 2016 Master of Architecture (Prof) thesis research. Little meaningful information about acoustic intervention in the early design phase could be found from past literature. In the information that was sourced, authors focus on software as an incorporation tool without investigating why the flaws in the relationship originally exist. To further explore this relationship, a survey was designed. It underwent three phases to ensure its consistency, and was delivered to a group of 51 acousticians from one international Acoustics company. The results were then separated between New Zealand and off-shore to identify trends. The survey results suggest that 75% of acousticians meet the architect less than 5 times per project. Instead of regular contact, a mediated method is adopted though a mix of telecommunication and written reports. Acousticians tend to be introduced later into New Zealand building project than the corresponding off-shore building. This delay corresponds to an increase in remedial action for each of the building types in the survey except Auditoria and Office Buildings. 31 participants have had their specifications challenged by an architect. Furthermore, 71% of the acousticians believe that architects do not have the knowledge to understand why the acoustic specifications are in place. The issues raised in this investigation align to the colloquial evidence expressed by the two consultants. It identifies a larger gap in the industry were acoustics is remedially treated rather than identified as a possible design driver. Further research through design is suggested to understand the role of acoustics within architectural design and potential tools for its inclusion during, not after, the design process.

Keywords: architectural acoustics, early-design, interdisciplinary communication, remedial response

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Authors: Weber-Lewerenz Bianca

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Digitization is developing fast and has become a powerful tool for digital planning, construction, and operations. Its transformation bears high potentials for companies, is critical for success, and thus, requires responsible handling. This study provides an assessment of calls made in the sustainable development goals by the United Nations (SDGs), White Papers on AI by international institutions, EU-Commission and German Government requesting for the consideration and protection of values and fundamental rights, the careful demarcation between machine (artificial) and human intelligence and the careful use of such technologies. The study discusses digitization and the impacts of artificial intelligence (AI) in construction engineering from an ethical perspective by generating data via conducting case studies and interviewing experts as part of the qualitative method. This research evaluates critically opportunities and risks revolving around corporate digital responsibility (CDR) in the construction industry. To the author's knowledge, no study has set out to investigate how CDR in construction could be conceptualized, especially in relation to the digitization and AI, to mitigate digital transformation both in large, medium-sized, and small companies. No study addressed the key research question: Where can CDR be allocated, how shall its adequate ethical framework be designed to support digital innovations in order to make full use of the potentials of digitization and AI? Now is the right timing for constructive approaches and apply ethics-by-design in order to develop and implement a safe and efficient AI. This represents the first study in construction engineering applying a holistic, interdisciplinary, inclusive approach to provide guidelines for orientation, examine benefits of AI and define ethical principles as the key driver for success, resources-cost-time efficiency, and sustainability using digital technologies and AI in construction engineering to enhance digital transformation. Innovative corporate organizations starting new business models are more likely to succeed than those dominated by conservative, traditional attitudes.

Keywords: construction engineering, digitization, digital transformation, artificial intelligence, ethics, corporate digital responsibility, digital innovation

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Authors: Rick Denley

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Urban planning today faces unprecedented challenges as cities strive for sustainability in response to climate change, rapid population growth, and the increasing demand for green infrastructure. In this context, effective leadership becomes as essential as innovative design and technology. Rick Denley’s keynote, Resilient Leadership in Sustainable Urban Planning: Embracing Change to Shape Future Cities, focuses on equipping urban planners, academics, and industry leaders with the leadership tools necessary to guide their teams and projects through complex transitions. His session addresses the essential role of leadership in driving sustainable urban transformations, adapting to changing environmental demands, and fostering collaborative approaches to green infrastructure initiatives. Rick’s keynote is grounded in his Change Growth Formula, a practical framework he has developed over years of leading corporate transformations and advising on resilience and growth. His talk will focus on how urban planning professionals can cultivate adaptability, inspire innovative thinking, and lead their teams to achieve impactful urban projects that prioritize sustainable landscapes, water management, and green spaces. Attendees will gain actionable insights on building a resilient mindset, leveraging collaborative partnerships, and aligning urban planning initiatives with environmental goals. This session is aligned with the conference’s objectives to share interdisciplinary knowledge, explore innovative solutions, and address critical challenges in urban landscape and urban planning. Rick’s approach combines insights from leadership theory with real-world applications in urban planning, making his talk relevant for professionals seeking both inspiration and practical tools to lead sustainable transformations.

Keywords: resilient leadership, change management, collaborative planning, adaptive leadership, community engagement, leadership in urban design

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Authors: Elisa Monteiro, Kiiko Ikegami

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The inclusion of children with diverse learning needs and/or disabilities in regular classrooms has been identified as crucial to the provision of educational equity and quality for all students. In this, teachers play an essential role, as they have a strong impact on student attainment. Whilst the adoption of inclusive practice is increasing, with potential benefits for the teaching profession, there is also a rise in the level of its challenges in Macao as many more students with learning disabilities are now being included in general education classes. Consequently, there has been a significant focus on teacher professional development to ensure that teachers are adequately prepared to teach in inclusive classrooms that give access to diverse students. Major changes in teacher education will need to take place to include more inclusive education content and to equip teachers with the necessary skills in the area of inclusive practice. This paper draws on data from in-depth interviews with 20 teachers to examine teachers’ views of support, challenges, and barriers to inclusive practices at the school and classroom levels. Thematic analysis was utilised to determine major themes within the data. Several themes emerged and serve to illustrate the identified barriers and the potential value of effective teacher education. Suggestions for increased professional development opportunities for inclusive education specific to higher education institutions are presented and the implications for practice and teacher education are discussed.

Keywords: inclusion, inclusive practice, teacher education, higher education

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Authors: Amr Sadek, Abdelrahaman Al-Qahtany, Turkey Salem Al-Qahtany

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In this study, a backpropagation artificial neural network (ANN) model has been used to predict the cooling and heating capacities of air conditioners (AC) under different conditions. Sufficiently large measurement results were obtained from the national energy-efficiency laboratories in Saudi Arabia and were used for the learning process of the ANN model. The parameters affecting the performance of the AC, including temperature, humidity level, specific heat enthalpy indoors and outdoors, and the air volume flow rate of indoor units, have been considered. These parameters were used as inputs for the ANN model, while the cooling and heating capacity values were set as the targets. A backpropagation ANN model with two hidden layers and one output layer could successfully correlate the input parameters with the targets. The characteristics of the ANN model including the input-processing, transfer, neurons-distance, topology, and training functions have been discussed. The performance of the ANN model was monitored over the training epochs and assessed using the mean squared error function. The model was then used to predict the performance of the AC under conditions that were not included in the measurement results. The optimum performance of the AC was also predicted under the different environmental conditions in Saudi Arabia. The uncertainty of the ANN model predictions has been evaluated taking into account the randomness of the data and lack of learning.

Keywords: artificial neural network, uncertainty of model predictions, efficiency of air conditioners, cooling and heating capacities

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Authors: Panaya Sudta, Wanchalerm Patanacharoenwong, Prachya Bumrungkun

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As With the evolution of smart grids, ensuring the reliability and efficiency of electrical systems in smart cities has become crucial. This paper proposes a distinct approach that combines advanced machine learning techniques to accurately predict electrical failures and address voltage reliability issues. This approach aims to improve the accuracy and efficiency of reliability evaluations in smart cities. The aim of this research is to develop a comprehensive predictive model that accurately predicts electrical failures and voltage reliability in smart cities. This model integrates RFM analysis, K-means clustering, and LSTM networks to achieve this objective. The research utilizes RFM analysis, traditionally used in customer value assessment, to categorize and analyze electrical components based on their failure recency, frequency, and monetary impact. K-means clustering is employed to segment electrical components into distinct groups with similar characteristics and failure patterns. LSTM networks are used to capture the temporal dependencies and patterns in customer data. This integration of RFM, K-means, and LSTM results in a robust predictive tool for electrical failures and voltage reliability. The proposed model has been tested and validated on diverse electrical utility datasets. The results show a significant improvement in prediction accuracy and reliability compared to traditional methods, achieving an accuracy of 92.78% and an F1-score of 0.83. This research contributes to the proactive maintenance and optimization of electrical infrastructures in smart cities. It also enhances overall energy management and sustainability. The integration of advanced machine learning techniques in the predictive model demonstrates the potential for transforming the landscape of electrical system management within smart cities. The research utilizes diverse electrical utility datasets to develop and validate the predictive model. RFM analysis, K-means clustering, and LSTM networks are applied to these datasets to analyze and predict electrical failures and voltage reliability. The research addresses the question of how accurately electrical failures and voltage reliability can be predicted in smart cities. It also investigates the effectiveness of integrating RFM analysis, K-means clustering, and LSTM networks in achieving this goal. The proposed approach presents a distinct, efficient, and effective solution for predicting and mitigating electrical failures and voltage issues in smart cities. It significantly improves prediction accuracy and reliability compared to traditional methods. This advancement contributes to the proactive maintenance and optimization of electrical infrastructures, overall energy management, and sustainability in smart cities.

Keywords: electrical state prediction, smart grids, data-driven method, long short-term memory, RFM, k-means, machine learning

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Authors: Anna Sanczyk

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Diverse students may experience different forms of discrimination. Some of the oppression students experience in schools are racism, sexism, classism, or homophobia that may affect their achievement, and teachers need to make sure they create inclusive, equitable classroom environments. The broader literature on social change in education shows that teachers who challenge oppression and want to promote equitable and transformative education face institutional, social, and political constraints. This paper discusses research on teachers’ work to create socially just and culturally inclusive classrooms and schools. The practical contribution of this literature review is that it provides a comprehensive compilation of the studies presenting teachers’ roles and efforts in affecting social change. The examination of the research on social change in education points to the urgency of teachers addressing the needs of marginalized students and resisting systemic oppression in schools. The implications of this literature review relate to the concerns that schools should provide greater advocacy for marginalized students in diverse learning contexts, and teacher education programs should prepare teachers to be active advocates for diverse students. The literature review has the potential to inform educators to enhance educational equity and improve the learning environment. This literature review illustrates teachers as agents of change in diverse classrooms and contributes to understanding various ways of taking action towards fostering more equitable and transformative education in today’s schools.

Keywords: agents of change, diversity, opression, social change

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Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

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This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

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Authors: Natalie Delia

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This academic paper investigates the transformative potential of incorporating alternative ways of knowing within the framework of Critical Studies departments. Traditional academic paradigms often prioritize empirical evidence and established methodologies, potentially limiting the scope of critical inquiry. In response to this, our research seeks to illuminate the benefits and challenges associated with integrating alternative epistemologies, such as indigenous knowledge systems, artistic expressions, and experiential narratives. Drawing upon a comprehensive review of literature and case studies, we examine how alternative ways of knowing can enrich and diversify the intellectual landscape of Critical Studies departments. By embracing perspectives that extend beyond conventional boundaries, departments may foster a more inclusive and holistic understanding of critical issues. Additionally, we explore the potential impact on pedagogical approaches, suggesting that alternative ways of knowing can stimulate alternative way of teaching methods and enhance student engagement. Our investigation also delves into the institutional and cultural shifts necessary to support the integration of alternative epistemologies within academic settings. We address concerns related to validation, legitimacy, and the potential clash with established norms, offering insights into fostering an environment that encourages intellectual pluralism. Furthermore, the paper considers the implications for interdisciplinary collaboration and the potential for cultivating a more responsive and socially engaged scholarship. By encouraging a synthesis of diverse perspectives, Critical Studies departments may be better equipped to address the complexities of contemporary issues, encouraging a dynamic and evolving field of study. In conclusion, this paper advocates for a paradigm shift within Critical Studies departments towards a more inclusive and expansive approach to knowledge production. By embracing alternative ways of knowing, departments have the opportunity to not only diversify their intellectual landscape but also to contribute meaningfully to broader societal dialogues, addressing pressing issues with renewed depth and insight.

Keywords: critical studies, alternative ways of knowing, academic department, Wallerstein

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Authors: Daniel R. dos Santos, Mateus S. Maldonado, Estevão J. R. Batista

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The military commanders increasingly dependent on spatial awareness, as knowing where enemy are, understanding how war battle scenarios change over time, and visualizing these trends in ways that offer insights for decision-making. Thanks to advancements in geospatial technologies and artificial intelligence algorithms, the commanders are now able to modernize military operations on a universal scale. Thus, geovisualisation has become an essential asset in the defense sector. It has become indispensable for better decisionmaking in dynamic/temporal scenarios, operation planning and management for the war field, situational awareness, effective planning, monitoring, and others. For example, a 3D visualization of war field data contributes to intelligence analysis, evaluation of postmission outcomes, and creation of predictive models to enhance decision-making and strategic planning capabilities. However, old-school visualization methods are slow, expensive, and unscalable. Despite modern technologies in generating 3D point clouds, such as LIDAR and stereo sensors, monocular depth values based on deep learning can offer a faster and more detailed view of the environment, transforming single images into visual information for valuable insights. We propose a dedicated monocular depth reconstruction approach via deep learning techniques for 3D geovisualisation of satellite images. It introduces scalability in terrain reconstruction and data visualization. First, a dataset with more than 7,000 satellite images and associated digital elevation model (DEM) is created. It is based on high resolution optical and radar imageries collected from Planet and Copernicus, on which we fuse highresolution topographic data obtained using technologies such as LiDAR and the associated geographic coordinates. Second, we developed an imagery-DEM fusion strategy that combine feature maps from two encoder-decoder networks. One network is trained with radar and optical bands, while the other is trained with DEM features to compute dense 3D depth. Finally, we constructed a benchmark with sparse depth annotations to facilitate future research. To demonstrate the proposed method's versatility, we evaluated its performance on no annotated satellite images and implemented an enclosed environment useful for Geovisualisation applications. The algorithms were developed in Python 3.0, employing open-source computing libraries, i.e., Open3D, TensorFlow, and Pythorch3D. The proposed method provides fast and accurate decision-making with GIS for localization of troops, position of the enemy, terrain and climate conditions. This analysis enhances situational consciousness, enabling commanders to fine-tune the strategies and distribute the resources proficiently.

Keywords: depth, deep learning, geovisualisation, satellite images

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Authors: Selinda England, Ben Bodnaryk

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With an unprecedented increase in provincial immigration and government support for greater international and culturally diverse learners, a trade/applied learning-focused polytechnic with four campuses within one Canadian province saw the need for intercultural awareness and an intercultural teaching competence strategy for faculty training. An institution-wide pre-assessment needs survey was conducted in 2018, in which 87% of faculty professed to have some/no training when working with international and/or culturally diverse learners. After researching fellow Polytechnics in Canada and seeing very little in the way of faculty support for intercultural competence, an institutional project team comprised of members from all facets of the Polytechnic was created and included: Indigenous experts, Academic Chairs, Directors, Human Resource Managers, and international/settlement subject matter experts. The project team was organized to develop and implement a new academic model focused on enriching intercultural competence among faculty. Utilizing a competency based model, the project team incorporated inclusive terminology into competency indicators and devised a four-phase proposal for implementing intercultural teacher training: a series of workshops focused on the needs of international and culturally diverse learners, including teaching strategies based on current TESOL methodologies, literature and online resources for quick access when planning lessons, faculty assessment examples and models of interculturally proficient instructors, and future job descriptions - all which promote and encourage development of specific intercultural skills. Results from a post-assessment survey (to be conducted in Spring 2020) and caveats regarding improvements and next steps will be shared. The project team believes its intercultural and inclusive teaching competency-based model is one of the first, institution-wide faculty supported initiatives within the Canadian college and Polytechnic post-secondary educational environment; it aims to become a leader in both the province and nation regarding intercultural competency training for trades, industry, and business minded community colleges and applied learning institutions.

Keywords: cultural diversity and education, diversity training teacher training, teaching and learning, teacher training

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Authors: Jana Beresova

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The paper presents a study carried out in 2015-2016 within the national scheme of research - VEGA 1/0106/15 based on theoretical research and empirical verification of the concept of intercultural communicative competence. It focuses on the current conception concerning target languages teaching compatible with the Common European Framework of Reference for Languages: Learning, teaching, assessment. Our research had revealed how the concept of intercultural communicative competence had been perceived by secondary-school teachers of English in Slovakia before they were intensively trained. Intensive workshops were based on the use of both authentic and instructional materials with the goal to support interculturally oriented language teaching aimed at challenging thinking. The former concept that supported the development of the students´ linguistic knowledge and the use of a target language to obtain information about the culture of the country whose language learners were learning was expanded by the meaning-making framework which views language as a typical means by which culture is mediated. The goal of the workshop was to influence English teachers to better understand the concept of intercultural communicative competence, combining theory and practice optimally. The results of the study will be presented and analysed, providing particular recommendations for language teachers and suggesting some changes in the National Educational Programme from which English learners should benefit in their future studies or professional careers.

Keywords: authentic materials, English language teaching, instructional materials, intercultural communicative competence

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Authors: Fengjuan Wang, Azilawati Jamaludin

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Developmental Dyscalculia (DD) is defined as a particular learning difficulty with continuous challenges in learning requisite math skills that cannot be explained by intellectual disability or educational deprivation. Recent studies have increasingly recognized that DD is a heterogeneous, instead of monolithic, learning disorder with not only cognitive and behavioral deficits but so too neural dysfunction. In recent years, neuroimaging studies employed group comparison to explore the neural underpinnings of DD, which contradicted the heterogenous nature of DD and may obfuscate critical individual differences. This research aimed to investigate the neural heterogeneity of DD using case studies with functional near-infrared spectroscopy (fNIRS). A total of 54 aged 6-7 years old of children participated in this study, comprising two comprehensive cognitive assessments, an 8-minute resting state, and an 8-minute one-digit addition task. Nine children met the criteria of DD and scored at or below 85 (i.e., the 16th percentile) on the Mathematics or Math Fluency subtest of the Wechsler Individual Achievement Test, Third Edition (WIAT-III) (both subtest scores were 90 and below). The remaining 45 children formed the typically developing (TD) group. Resting-state data and brain activation in the inferior frontal gyrus (IFG), superior frontal gyrus (SFG), and intraparietal sulcus (IPS) were collected for comparison between each case and the TD group. Graph theory was used to analyze the brain network under the resting state. This theory represents the brain network as a set of nodes--brain regions—and edges—pairwise interactions across areas to reveal the architectural organizations of the nervous network. Next, a single-case methodology developed by Crawford et al. in 2010 was used to compare each case’s brain network indicators and brain activation against 45 TD children’s average data. Results showed that three out of the nine DD children displayed significant deviation from TD children’s brain indicators. Case 1 had inefficient nodal network properties. Case 2 showed inefficient brain network properties and weaker activation in the IFG and IPS areas. Case 3 displayed inefficient brain network properties with no differences in activation patterns. As a rise above, the present study was able to distill differences in architectural organizations and brain activation of DD vis-à-vis TD children using fNIRS and single-case methodology. Although DD is regarded as a heterogeneous learning difficulty, it is noted that all three cases showed lower nodal efficiency in the brain network, which may be one of the neural sources of DD. Importantly, although the current “brain norm” established for the 45 children is tentative, the results from this study provide insights not only for future work in “developmental brain norm” with reliable brain indicators but so too the viability of single-case methodology, which could be used to detect differential brain indicators of DD children for early detection and interventions.

Keywords: brain activation, brain network, case study, developmental dyscalculia, functional near-infrared spectroscopy, graph theory, neural heterogeneity

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Authors: Sahithi Yarlagadda

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The design of antenna is constrained by mathematical and geometrical parameters. Though there are diverse antenna structures with wide range of feeds yet, there are many geometries to be tried, which cannot be customized into predefined computational methods. The antenna design and optimization qualify to apply evolutionary algorithmic approach since the antenna parameters weights dependent on geometric characteristics directly. The evolutionary algorithm can be explained simply for a given quality function to be maximized. We can randomly create a set of candidate solutions, elements of the function's domain, and apply the quality function as an abstract fitness measure. Based on this fitness, some of the better candidates are chosen to seed the next generation by applying recombination and permutation to them. In conventional approach, the quality function is unaltered for any iteration. But the antenna parameters and geometries are wide to fit into single function. So, the weight coefficients are obtained for all possible antenna electrical parameters and geometries; the variation is learnt by mining the data obtained for an optimized algorithm. The weight and covariant coefficients of corresponding parameters are logged for learning and future use as datasets. This paper drafts an approach to obtain the requirements to study and methodize the evolutionary approach to automated antenna design for our past work on Vivaldi antenna as test candidate. The antenna parameters like gain, directivity, etc. are directly caged by geometries, materials, and dimensions. The design equations are to be noted here and valuated for all possible conditions to get maxima and minima for given frequency band. The boundary conditions are thus obtained prior to implementation, easing the optimization. The implementation mainly aimed to study the practical computational, processing, and design complexities that incur while simulations. HFSS is chosen for simulations and results. MATLAB is used to generate the computations, combinations, and data logging. MATLAB is also used to apply machine learning algorithms and plotting the data to design the algorithm. The number of combinations is to be tested manually, so HFSS API is used to call HFSS functions from MATLAB itself. MATLAB parallel processing tool box is used to run multiple simulations in parallel. The aim is to develop an add-in to antenna design software like HFSS, CSTor, a standalone application to optimize pre-identified common parameters of wide range of antennas available. In this paper, we have used MATLAB to calculate Vivaldi antenna parameters like slot line characteristic impedance, impedance of stripline, slot line width, flare aperture size, dielectric and K means, and Hamming window are applied to obtain the best test parameters. HFSS API is used to calculate the radiation, bandwidth, directivity, and efficiency, and data is logged for applying the Evolutionary genetic algorithm in MATLAB. The paper demonstrates the computational weights and Machine Learning approach for automated antenna optimizing for Vivaldi antenna.

Keywords: machine learning, Vivaldi, evolutionary algorithm, genetic algorithm

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Authors: Aimen Mohamed Almaloul

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The main focus of this study is investigating the interference of Arabic in the use of English prepositions by Libyan university students. Prepositions in the tests used in the study were categorized, according to their relation to Arabic, into similar Arabic and English prepositions (SAEP), dissimilar Arabic and English prepositions (DAEP), Arabic prepositions with no English counterparts (APEC), and English prepositions with no Arabic counterparts (EPAC). The subjects of the study were the first year university students of the English department, Sabrata Faculty of Arts, Azzawia University; both males and females, and they were 100 students. The basic tool for data collection was a test of English prepositions; students are instructed to fill in the blanks with the correct prepositions and to put a zero (0) if no preposition was needed. The test was then handed to the subjects of the study. The test was then scored and quantitative as well as qualitative results were obtained. Quantitative results indicated the number, percentages and rank order of errors in each of the categories and qualitative results indicated the nature and significance of those errors and their possible sources. Based on the obtained results the researcher could detect that students made more errors in the EPAC category than the other three categories and these errors could be attributed to the lack of knowledge of the different meanings of English prepositions. This lack of knowledge forced the students to adopt what is called the strategy of transfer.

Keywords: foreign language acquisition, foreign language learning, interference system, interlanguage system, mother tongue interference

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Authors: Yilin Liao, Hewen Li, Paula McConvey

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Speed skaters often face a risk of concussion when they fall on the ice floor and impact crash mats during practices and competitive races. Several variables, including those related to the skater, the crash mat, and the impact position (body side/head/feet impact), are believed to influence the severity of the skater's concussion. While computer simulation modeling can be employed to analyze these accidents, the simulation process is time-consuming and does not provide rapid information for coaches and teams to assess the skater's injury risk in competitive events. This research paper promotes the exploration of the feasibility of using AI techniques for evaluating skater’s potential concussion severity, and to develop a fast concussion prediction tool using artificial neural networks to reduce the risk of treatment delays for injured skaters. The primary data is collected through virtual tests and physical experiments designed to simulate skater-mat impact. It is then analyzed to identify patterns and correlations; finally, it is used to train and fine-tune the artificial neural networks for accurate prediction. The development of the prediction tool by employing machine learning strategies contributes to the application of AI methods in sports science and has theoretical involvements for using AI techniques in predicting and preventing sports-related injuries.

Keywords: artificial neural networks, concussion, machine learning, impact, speed skater

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Authors: Zahid Ullah, Atlas Khan

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In today's data-driven world, the role of mathematics in bridging the gap between theory and applications is becoming increasingly vital. This abstract highlights the significance of mathematics as a powerful tool for analyzing, interpreting, and extracting meaningful insights from vast amounts of data. By integrating mathematical principles with real-world applications, researchers can unlock the full potential of data-driven decision-making processes. This abstract delves into the various ways mathematics acts as a bridge connecting theoretical frameworks to practical applications. It explores the utilization of mathematical models, algorithms, and statistical techniques to uncover hidden patterns, trends, and correlations within complex datasets. Furthermore, it investigates the role of mathematics in enhancing predictive modeling, optimization, and risk assessment methodologies for improved decision-making in diverse fields such as finance, healthcare, engineering, and social sciences. The abstract also emphasizes the need for interdisciplinary collaboration between mathematicians, statisticians, computer scientists, and domain experts to tackle the challenges posed by the data-driven landscape. By fostering synergies between these disciplines, novel approaches can be developed to address complex problems and make data-driven insights accessible and actionable. Moreover, this abstract underscores the importance of robust mathematical foundations for ensuring the reliability and validity of data analysis. Rigorous mathematical frameworks not only provide a solid basis for understanding and interpreting results but also contribute to the development of innovative methodologies and techniques. In summary, this abstract advocates for the pivotal role of mathematics in bridging theory and applications in a data-driven world. By harnessing mathematical principles, researchers can unlock the transformative potential of data analysis, paving the way for evidence-based decision-making, optimized processes, and innovative solutions to the challenges of our rapidly evolving society.

Keywords: mathematics, bridging theory and applications, data-driven world, mathematical models

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Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

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Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Rudolf Egger

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This article shows some results of an ongoing project in Kosova. This project deals with the meaning of social transformation processes in the life-courses of Kosova people. One goal is to create an oral history archive in this country. In the last seven years we did some interpretative work (using narrative interviews) concerning the experiences and meanings of social changes from the perspective of life course. We want to reconstruct the individual possibilities in creating one's life in new social structures. After the terrible massacres of ethnical-territorially defined nationalism in former Yugoslavia it is the main focus to find out something about the many small daily steps which must be done, to build up a kind of “normality” in this country. These steps can be very well reconstructed by narrations, by life stories, because personal experiences are naturally linked with social orders. Each individual story is connected with further stories, in which the collective history will be negotiated and reflected. The view on the biographical narration opens the possibility to analyze the concreteness of the “individual case” in the complexity of collective history. Life stories contain thereby a kind of a transition character, that’s why they can be used for the reconstruction of periods of political transformation. For example: In the individual story we can find very clear the national or mythological character of the Albanian people in Kosova. The shown narrations can be read also as narrative lines in relation to the (re-)interpretation of the past, in which lived life is fixed into history in the so-called collective memory in Kosova.

Keywords: biographical learning, adult education, social change, post war societies

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

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

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

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

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

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

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Authors: Sultan Ghaleb Aldaihani

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

Keywords: Education, Leadership, Technology, Artificial Intelligence

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Authors: Amnah Moghees, Saima Abbas Dar, Muniba Saeed

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

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

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Authors: Louis Botha

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

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

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Authors: Juan Torralba

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

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

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Authors: 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: 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: 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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