Search results for: physical learning environment

Authors: Chingwen Yeh, Yu Ru Chen

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This paper aims to explore the growth and development of children in the creative dance class of Cloud Gate Dance School in Taichung Taiwan. Professor Chingwen Yeh’s qualitative research method was applied in this study. First of all, application of Dalcroze Eurhythmic teaching materials such as music, teaching aids, speaking language through classroom situation was collected and exam. Second, the in-class observation on the participation of the young children's learning situation was recorded both by words and on video screen as the research data. Finally, data analysis was categorized into the following aspects: children's body movement coordination, children’s mind concentration and imagination and children’s verbal expression. Through the in-depth interviews with the in-class teachers, parents of participating children and other in class observers were conducted from time to time; this research found the children's body rhythm, language skills, and social learning growth were improved in certain degree through the creative dance training. These authors hope the study can contribute as the further research reference on the related topic.

Keywords: Cloud Gate Dance School, creative dance, Dalcroze, Eurhythmic

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Authors: Suegnet Smit

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Prior to 1994, special education in South Africa was marginalized and fragmented. Moving away from a Medical model approach to special education, the Government, after 1994, promoted an Inclusive approach, as a means to transform education in general, and special education in particular. This transformation, however, is moving at too a slow pace for learners with barriers to learning and development to benefit fully from their education. The goal of the Department of Basic Education is to minimize, remove, and prevent barriers to learning and development in the educational setting, by attending to the unique needs of the individual learner. However, the implementation of Inclusive education is problematic, and general education remains poor. This paper highlights the historical development of special education in South Africa, underpinned by a bio-ecological perspective. Problematic areas within the systemic levels of the education system are highlighted in order to indicate how the interactive processes within the systemic levels affect special needs learners on the personal dimension of the bio-ecological approach. As part of the methodology, thorough document analysis was conducted on information collected from a large body of research literature, which included academic articles, reports, policies, and policy reviews. Through a qualitative analysis, data were grouped and categorized according to the bio-ecological model systems, which revealed various successes and challenges within the education system. The challenges inhibit change, growth, and development for the child, who experience barriers to learning. From these findings, it is established that special education in South Africa has been, and still is, on a bumpy road. Sadly, the transformation process of change, envisaged by implementing Inclusive education, is still yet a dream, not fully realized. Special education seems to be stuck at what is, and the education system has not moved forward significantly enough to reach what special education should and could be. The gap that exists between a vision of Inclusive quality education for all, and the current reality, is still too wide. Problems encountered in all the education system levels, causes a funnel-effect downward to learners with special educational needs, with negative effects for the development of these learners.

Keywords: bio-ecological perspective, education systems, inclusive education, special education

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Authors: Marzieh Karimihaghighi, Carlos Castillo

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This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

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Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

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Authors: Isaac Kayode Ogunlade

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Data acquisition (DAQ) is the process by which physical phenomena from the real world are transformed into an electrical signal(s) that are measured and converted into a digital format for processing, analysis, and storage by a computer. The DAQ is designed using PIC18F4550 microcontroller, communicating with Personal Computer (PC) through USB (Universal Serial Bus). The research deployed initial knowledge of data acquisition system and embedded system to develop a weather data acquisition device using LM35 sensor to measure weather parameters and the use of Artificial Intelligence(Artificial Neural Network - ANN)and statistical approach(Autoregressive Integrated Moving Average – ARIMA) to predict precipitation (rainfall). The device is placed by a standard device in the Department of Meteorology, Federal University of Technology, Akure (FUTA) to know the performance evaluation of the device. Both devices (standard and designed) were subjected to 180 days with the same atmospheric condition for data mining (temperature, relative humidity, and pressure). The acquired data is trained in MATLAB R2012b environment using ANN, and ARIMAto predict precipitation (rainfall). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correction Square (R2), and Mean Percentage Error (MPE) was deplored as standardize evaluation to know the performance of the models in the prediction of precipitation. The results from the working of the developed device show that the device has an efficiency of 96% and is also compatible with Personal Computer (PC) and laptops. The simulation result for acquired data shows that ANN models precipitation (rainfall) prediction for two months (May and June 2017) revealed a disparity error of 1.59%; while ARIMA is 2.63%, respectively. The device will be useful in research, practical laboratories, and industrial environments.

Keywords: data acquisition system, design device, weather development, predict precipitation and (FUTA) standard device

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Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

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Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

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Authors: Shoba Rathilal

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High unemployment rates and a shortage of experienced and qualified employees appear to be a paradox that currently plagues most countries worldwide. In a developing country like South Africa, the rate of unemployment is reported to be approximately 35%, the highest recorded globally. At the same time, a countrywide deficit in experienced and qualified potential employees is reported in South Africa, which is causing fierce rivalry among firms. Employers have reported that graduates are very rarely able to meet the demands of the job as there are gaps in their knowledge and conceptual understanding and other 21st-century competencies, attributes, and dispositions required to successfully negotiate the multiple responsibilities of employees in organizations. In addition, the rates of unemployment and suitability of graduates appear to be skewed by race and social class, the continued effects of a legacy of inequitable educational access. Higher Education in the current technologically advanced and dynamic world needs to serve as an agent of transformation, aspiring to develop graduates to be creative, flexible, critical, and with entrepreneurial acumen. This requires that higher education curricula and pedagogy require a re-envisioning of our selection, sequencing, and pacing of the learning, teaching, and assessment. At a particular Higher education Institution in South Africa, Design Thinking and Project Based learning are being adopted as two approaches that aim to enhance the student experience through the provision of a “distinctive education” that brings together disciplinary knowledge, professional engagement, technology, innovation, and entrepreneurship. Using these methodologies forces the students to solve real-time applied problems using various forms of knowledge and finding innovative solutions that can result in new products and services. The intention is to promote the development of skills for self-directed learning, facilitate the development of self-awareness, and contribute to students being active partners in the application and production of knowledge. These approaches emphasize active and collaborative learning, teamwork, conflict resolution, and problem-solving through effective integration of theory and practice. In principle, both these approaches are extremely impactful. However, at the institution in this study, the implementation of the PBL and DT was not as “smooth” as anticipated. This presentation reports on the analysis of the implementation of these two approaches within higher education curricula at a particular university in South Africa. The study adopts a qualitative case study design. Data were generated through the use of surveys, evaluation feedback at workshops, and content analysis of project reports. Data were analyzed using document analysis, content, and thematic analysis. Initial analysis shows that the forces constraining the implementation of PBL and DT range from the capacity to engage with DT and PBL, both from staff and students, educational contextual realities of higher education institutions, administrative processes, and resources. At the same time, the implementation of DT and PBL was enabled through the allocation of strategic funding and capacity development workshops. These factors, however, could not achieve maximum impact. In addition, the presentation will include recommendations on how DT and PBL could be adapted for differing contexts will be explored.

Keywords: design thinking, project based learning, innovative higher education pedagogy, student and staff capacity development

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Authors: Jin-Hee Ahn, Seok-Hyeon Jeon, Young-Bin Lee, Min-Gyun Ha, Yu-Chan Hong

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Corrosion durability of steel bridges can be generally affected by atmospheric environments of bridge installation, since corrosion problem is related to environmental factors such as humidity, temperature, airborne salt, chemical components as SO₂, chlorides, etc. Thus, atmospheric environment condition should be measured to estimate corrosion condition of steel bridges as well as measurement of actual corrosion damage of structural members of steel bridge. Even in the same atmospheric environment, the corrosion environment may be different depending on the installation direction of structural members. In this study, therefore, atmospheric corrosion monitoring was conducted using atmospheric corrosion monitoring sensor, hygrometer, thermometer and airborne salt collection device to examine the corrosion durability of steel bridges. As a target steel bridge for corrosion durability monitoring, a cable-stayed bridge with truss steel members was selected. This cable-stayed bridge was located on the coast to connect the islands with the islands. Especially, atmospheric corrosion monitoring was carried out depending on structural direction of a cable-stayed bridge with truss type girders since it consists of structural members with various directions. For atmospheric corrosion monitoring, daily average electricity (corrosion current) was measured at each monitoring members to evaluate corrosion environments and corrosion level depending on structural members with various direction which have different corrosion environment in the same installed area. To compare corrosion durability connected with monitoring data depending on corrosion monitoring members, monitoring steel plate was additionally installed in same monitoring members. Monitoring steel plates of carbon steel was fabricated with dimension of 60mm width and 3mm thickness. And its surface was cleaned for removing rust on the surface by blasting, and its weight was measured before its installation on each structural members. After a 3 month exposure period on real atmospheric corrosion environment at bridge, surface condition of atmospheric corrosion monitoring sensors and monitoring steel plates were observed for corrosion damage. When severe deterioration of atmospheric corrosion monitoring sensors or corrosion damage of monitoring steel plates were found, they were replaced or collected. From 3month exposure tests in the actual steel bridge with various structural member with various direction, the rust on the surface of monitoring steel plate was found, and the difference in the corrosion rate was found depending on the direction of structural member from their visual inspection. And daily average electricity (corrosion current) was changed depending on the direction of structural member. However, it is difficult to identify the relative differences in corrosion durability of steel structural members using short-term monitoring results. After long exposure tests in this corrosion environments, it can be clearly evaluated the difference in corrosion durability depending on installed conditions of steel bridges. Acknowledgements: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03028755).

Keywords: corrosion, atmospheric environments, steel bridge, monitoring

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Authors: Shirka Kassam Jwasshaka

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Graduates from higher educational institutions in Nigeria need to leave with the necessary skills to be independent in the emergence work environment. The goal of this study is to develop a framework of digital skills that Nigerian graduates in building construction need to be proficient in various digital skills to comfortably fit into the global advances in a technological labour market. The descriptive survey design was used in this investigation. The study's population consisted of building construction experts selected from different sites within the North Central geographical zones of Nigeria. Using random sampling approaches, 120 seasoned experts were chosen. Three research questions raised by the researchers guided the study. The data was gathered using a 60-item, structured questionnaire. The questions were formulated around three key skill areas such as digital skills related to ICT, digital skills related to general workforce, and basic digital literacy skills that students should have. A building construction specialist validated the questionnaire. Winstep in conjunction with SPSS was used to determine the Cronbach Alpha reliability of the items' internal consistency and person separation,item measure, item fit based on PTMEA CORR, polarity items, misfit items, unidimensionality, and a person-item map. The Cronbach Coefficient reliability of items for the three sub constructs was 0.70. The results showed nearly every sub component within the three areas of digital skills was regarded as significant to be learn by experts. The researchers recommended among other things, that all parties involved in the education sector should work together to develop a curriculum that covers digital skills which can meet employer’s' needs.

Keywords: lifelong learning, digital skill, framework, building technology

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Authors: Hamad Albadr

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With the growth that has occurred in the orientation of universities from liability cognitive and maintain the culture of the community to liability functional creating graduates to work according to the needs of the community development; representing the university in today's world, the prime mover of the wheel of development in the community and find appropriate solutions to the problems they are facing and adapt to the demands of the changing environment. This paper deals with the role of Saudi universities as institutions, government agencies, non-governmental organizations and the extent of its responsibility to the environmental awareness of the community members in various segments, where it will use the research methodology descriptive and analytical, to gather information, data and analysis answers the study sample consisting of 1500 people from the staff of the Saudi universities: The members of the faculty, and members of the administrative and technical bodies, and current students, 500 of them are responsible for employment in the labor sector and graduates. Where will review the paper to identify the extent to which the objectives of the academic programs in Saudi universities to the needs of the community, and the extent to which the course content submitted to the characteristics of the local environment for the community and how he benefited them to ensure its conformity with the changing needs of society, and how to address the research presented in academic programs to the needs of society and its problems , and the extent to which graduate students to deal enough of social responsibility and commitment to community service and the preservation of the environment, and the availability of a clear policy to serve the community with the academic programs, and how to encourage faculty, staff and students to participate in community service. And the availability of counseling services for school students for vocational guidance, scientific, environmental, and the contribution of Academic Programs publishes scientific and technical culture and the environmental community members.

Keywords: universities in Saudi Arabia, environmental awareness, academic programs, environmental community

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Authors: Khashane Stephen Malatji, Makwalete Johanna Malatji

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Student evaluation of teaching (SET) is the common way of assessing teaching quality at universities and tracing the professional growth of lecturers. The aim of this study was to investigate the role played by student evaluation in the teaching and learning agenda at one South African University. The researchers used a qualitative approach and a case study research design. With regards to data collection, document analysis was used. Evaluation reports were reviewed to monitor the growth of lecturers who were evaluated during the academic years 2020 and 2021 in one faculty. The results of the study reveal that student evaluation remains the most relevant tool to inform the teaching agenda at a university. Lecturers who were evaluated were found to grow academically. All lecturers evaluated during 2020 have shown great improvement when evaluated repeatedly during 2021. Therefore, it can be concluded that student evaluation helps to improve the pedagogical and professional proficiency of lecturers. The study therefore, recommends that lecturers conduct an evaluation for each module they teach every semester or annually in case of year modules. The study also recommends that lecturers attend to all areas that draw negative comments from students in order to improve.

Keywords: students’ voices, teaching agenda, evaluation, feedback, responses

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Authors: Shubham Gogri, Lucia Florescu

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Medical diagnosis and radiotherapy treatment planning using Computed Tomography (CT) rely on the quantitative accuracy and quality of the CT images. At the same time, requirements for CT imaging include reducing the radiation dose exposure to patients and minimizing scanning time. A solution to this is the sparse-view CT technique, based on a reduced number of projection views. This, however, introduces a new problem— the incomplete projection data results in lower quality of the reconstructed images. To tackle this issue, deep learning methods have been applied to enhance the quality of the sparse-view CT images. A first approach involved employing Mir-Net, a dedicated deep neural network designed for image enhancement. This showed promise, utilizing an intricate architecture comprising encoder and decoder networks, along with the incorporation of the Charbonnier Loss. However, this approach was computationally demanding. Subsequently, a specialized Generative Adversarial Network (GAN) architecture, rooted in the Pix2Pix framework, was implemented. This GAN framework involves a U-Net-based Generator and a Discriminator based on Convolutional Neural Networks. To bolster the GAN's performance, both Charbonnier and Wasserstein loss functions were introduced, collectively focusing on capturing minute details while ensuring training stability. The integration of the perceptual loss, calculated based on feature vectors extracted from the VGG16 network pretrained on the ImageNet dataset, further enhanced the network's ability to synthesize relevant images. A series of comprehensive experiments with clinical CT data were conducted, exploring various GAN loss functions, including Wasserstein, Charbonnier, and perceptual loss. The outcomes demonstrated significant image quality improvements, confirmed through pertinent metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the corrected images and the ground truth. Furthermore, learning curves and qualitative comparisons added evidence of the enhanced image quality and the network's increased stability, while preserving pixel value intensity. The experiments underscored the potential of deep learning frameworks in enhancing the visual interpretation of CT scans, achieving outcomes with SSIM values close to one and PSNR values reaching up to 76.

Keywords: generative adversarial networks, sparse view computed tomography, CT image correction, Mir-Net

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Authors: Martha Hough RN

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Background: New nursing graduates lack the critical thinking skills required to provide safe nursing care. Critical thinking is essential in providing safe, competent, and skillful nursing interventions. Educational institutions must provide a curriculum that improves nursing students' critical thinking abilities. In addition, the recent pandemic resulted in nursing students who previously received in-person clinical but now most clinical has been converted to remote learning, increasing the use of simulations. Unfolding medium and high-fidelity simulations and Socratic questioning are used in many simulations debriefing sessions. Methodology: Google Scholar was researched with the keywords: critical thinking of nursing students with unfolding simulation, which resulted in 22,000 articles; three were used. A second search was implemented with critical thinking of nursing students Socratic questioning, which resulted in two articles being used. Conclusion: Unfolding simulations increase nursing students' critical thinking, especially during the briefing (pre-briefing and debriefing) phases, where most learning occurs. In addition, the use of Socratic questions during the briefing phases motivates other questions, helps the student analyze and critique their thinking, and assists educators in probing students' thinking, which further increases critical thinking.

Keywords: briefing, critical thinking, Socratic thinking, unfolding simulations

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Authors: Ibrahim Khan, Waqas Khalid

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The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.

Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning

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Authors: Fatemah Al-Baroud, Sudharani Shreenivas Kshatriya

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Kuwait Oil Company (KOC) is one of the companies for gas & oil production in Kuwait. The oil and gas industry is truly global; with operations conducted in every corner of the globe, the global community will rely heavily on oil and gas supplies. KOC has made many commitments to protect all due to KOC’s operations and operational releases. As per KOC’s strategy, the substantial increase in production activities will bring many challenges in managing various environmental hazards and stresses in the company. In order to handle those environmental challenges, the need of implementing effectively the health, safety, and environmental management system (HSEMS) is significant. And by implementing the HSEMS system properly, the environmental aspects of the activities, products, and services were identified, evaluated, and controlled in order to (i) Comply with local regulatory and other obligatory requirements; (ii) Comply with company policy and business requirements; and (iii) Reduce adverse environmental impact, including adverse impact to company reputation. Assessments for the Jurassic Production Facilities are being carried out as a part of the KOC HSEMS procedural requirement and monitoring the implementation of the relevant HSEMS procedures in the facilities. The assessments have been done by conducting series of theme audits using KOC’s audit protocol at JPFs. The objectives of the audits are to evaluate the compliance of the facilities towards the implementation of environmental procedures and the status of the KEPA requirement at all JPFs. The list of the facilities that were covered during the theme audit program are the following: (1) Jurassic Production Facility (JPF) – Sabriya (2) Jurassic Production Facility (JPF) – East Raudhatian (3) Jurassic Production Facility (JPF) – West Raudhatian (4)Early Production Facility (EPF 50). The auditing process comprehensively focuses on the application of KOC HSE MS procedures at JPFs and their ability to reduce the resultant negative impacts on the environment from the operations. Number of findings and observations were noted and highlighted in the audit reports and sent to all concerned controlling teams. The results of these audits indicated that the facilities, in general view, were in line with KOC HSE Procedures, and there were commitments in documenting all the HSE issues in the right records and plans. Further, implemented several control measures at JPFs that minimized/reduced the environmental impact, such as SRU were installed for sulphur recovery. Future scope and monitoring audit after a sufficient period of time will be carried out in conjunction with the controlling teams in order to verify the current status of the recommendations and evaluate the contractors' performance towards the required actions in preserving the environment.

Keywords: assessment of the environmental compliance, environmental and social impact assessment, kuwait environment public authority regulations, health, safety and environment management procedures, jurassic production facilities

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Authors: Musa Auwal Mamman, S. G. Isa

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This study was carried out in order to investigate the effectiveness of GeoGebra software in teaching and learning of linear and quadratic equations amongst senior secondary school students in Fagge Local Government Area, Kano State–Nigeria. Five research items were raised in objectives, research questions and hypotheses respectively. A random sampling method was used in selecting 398 students from a population of 2098 of SS2 students. The experimental group was taught using the GeoGebra software while the control group was taught using the conventional teaching method. The instrument used for the study was the mathematics performance test (MPT) which was administered at the beginning and at the end of the study. The results of the study revealed that students taught with GeoGebra software (experimental group) performed better than students taught with traditional teaching method. The t- test was used to analyze the data obtained from the study.

Keywords: GeoGebra Software, mathematics performance, random sampling, mathematics teaching

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Authors: Agni Stylianou-Georgiou, Eliza Pitri

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This study is a design narrative that discusses researchers’ new learning based on changes made in pedagogies and learning opportunities in the context of a Cognitive Psychology and an Art History undergraduate course. The purpose of this study was to investigate how to encourage creative problem-based play in tertiary education engaging instructors and student-teachers in designing educational games. Course instructors modified content to encourage flexible thinking during game design problem-solving. Qualitative analyses of data sources indicated that Thinking Birds’ questions could encourage flexible thinking as instructors engaged in creative problem-based play. However, student-teachers demonstrated weakness in adopting flexible thinking during game design problem solving. Further studies of student-teachers’ shifting perspectives during different instructional design tasks would provide insights for developing the Thinking Birds’ questions as tools for creative problem solving.

Keywords: creative problem-based play, educational games, flexible thinking, tertiary education

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Authors: Abubakar Umar Birnin-yauri

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Plastic wastes arising from Polyethylene (PE)-based materials are increasingly becoming environmental problem, this is owed to the fact that these PE waste materials will only decompose over hundreds, or even thousands of years, during which they cause serious environmental problems. In this research, Polymer blends prepared from PE, Cashewnut flour (CNF) and Gum Arabic (GA) were studied in order to assay their biodegradation potentials via composting method. Different sample formulations were made i.e., X1= (70% PE, 25% CNF and 5% GA, X2= (70% PE, 20% CNF and 10% GA), X3= (70% PE, 15% CNF and 15% GA), X4 = (70% PE, 10% CNF and 20% GA) and X5 = (70% PE, 5% CNF and 25% GA) respectively. The results obtained showed that X1 recorded weight loss of 9.89% of its original weight after the first 20 days and 37.45% after 100 day, and X2 lost 12.67 % after the first 20 days and 42.56% after 100day, sample X5 experienced the greatest weight lost in the two methods adopted which are 52.9% and 57.89%. Instrumental analysis such as Fourier Transform Infrared Spectroscopy, Thermogravimetric analysis and Scanning electron microscopy were performed on the polymer blends before and after biodegradation. The study revealed that the biodegradation of the polymer blends is influenced by the contents of both the CNF and GA added into the blends.

Keywords: polyethylene, cashewnut, gum Arabic, biodegradation, blend, environment

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Authors: Noah G. Akhimien, Eshrar Latif

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The natural environment is in need for an urgent rescue due to dilapidation and recession of resources. Passive design strategies have proven to be one of the effective ways to reduce CO₂ emissions and to improve building performance. On the other hand, there is a huge drop in material availability due to poor recycling culture. Consequently, building waste pose environmental hazard due to unrecycled building materials from construction and deconstruction. Buildings are seen to be material banks for a circular economy, therefore incorporating circular economy into passive housing will not only safe guide the climate but also improve resource efficiency. The study focuses on incorporating a circular economy in passive design strategies for an affordable energy and resource efficient residential building in Nigeria. Carbon dioxide (CO₂) concentration is still on the increase as buildings are responsible for a significant amount of this emission globally. Therefore, prompt measures need to be taken to combat the effect of global warming and associated threats. Nigeria is rapidly growing in human population, resources on the other hand have receded greatly, and there is an abrupt need for recycling even in the built environment. It is necessary that Nigeria responds to these challenges effectively and efficiently considering building resource and energy. Passive design strategies were assessed using simulations to obtain qualitative and quantitative data which were inferred to case studies as it relates to the Nigeria climate. Building materials were analysed using the ReSOLVE model in order to explore possible recycling phase. This provided relevant information and strategies to illustrate the possibility of circular economy in passive buildings. The study offers an alternative approach, as it is the general principle for the reworking of an economy on ecological lines in passive housing and by closing material loops in circular economy.

Keywords: building, circular, efficiency, environment, sustainability

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Authors: Christos Karolemeas, Avgi Vassi, Georgia Christodoulopoulou

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Although the past decades, planning a cycle network became an inseparable part of all transportation plans, there is still a lot of room for improvement in the way planning is made, in order to create safe and direct cycling networks that gather the parameters that positively influence one's decision to cycle. The aim of this article is to study, evaluate and visualize the bikeability of cities. This term is often used as the 'the ability of a person to bike' but this study, however, adopts the term in the sense of bikeability as 'the ability of the urban landscape to be biked'. The methodology used included assessing cities' accessibility by cycling, based on international literature and corresponding walkability methods and the creation of a 'bikeability index'. Initially, a literature review was made to identify the factors that positively affect the use of bicycle infrastructure. Those factors were used in order to create the spatial index and quantitatively compare the city network. Finally, the bikeability index was applied in two case studies: two Greek municipalities that, although, they have similarities in terms of land uses, population density and traffic congestion, they are totally different in terms of geomorphology. The factors suggested by international literature were (a) safety, (b) directness, (c) comfort and (d) the quality of the urban environment. Those factors were quantified through the following parameters: slope, junction density, traffic density, traffic speed, natural environment, built environment, activities coverage, centrality and accessibility to public transport stations. Each road section was graded for the above-mentioned parameters, and the overall grade shows the level of bicycle accessibility (low, medium, high). Each parameter, as well as the overall accessibility levels, were analyzed and visualized through Geographic Information Systems. This paper presents the bikeability index, its' results, the problems that have arisen and the conclusions from its' implementation through Strengths-Weaknesses-Opportunities-Threats analysis. The purpose of this index is to make it easy for researchers, practitioners, politicians, and stakeholders to quantify, visualize and understand which parts of the urban fabric are suitable for cycling.

Keywords: accessibility, cycling, green spaces, spatial data, urban environment

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Authors: Abdessalam Hijab, Hafida Boulekbache, Eric Henry

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The objective of this article is to explain the use of geographic information system (GIS) and information and communication technologies (ICTs) in the real-time processing and analysis of data on the status of an urban sanitation network by integrating professional actors in sanitation for sustainable management in urban areas. Indeed, it is a smart geo-collaboration based on the complementarity of ICTs and GIS. This multi-actor reflection was built with the objective of contributing to the development of complementary solutions to the existing technologies to better protect the urban environment, with the help of a checklist with the spatial reference "E-Géo-LD" dedicated to the "professional/professional" actors in sanitation, for intelligent monitoring of liquid sanitation networks in urban areas. In addition, this research provides a good understanding and assimilation of liquid sanitation schemes in the "Lamkansa" sampling area of the city of Casablanca, and spatially evaluates these schemes. Downstream, it represents a guide to assess the environmental impacts of the liquid sanitation scheme.

Keywords: ICT, GIS, spatial checklist, liquid sanitation, environment

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Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

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Authors: Arjumand Warsy

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The Holy Quran was revealed to Prophet Mohammad (May Peace and Blessings of Allah be upon Him) over fourteen hundred years ago, at a time when majority of the people in Arabia were illiterate and very few could read or write. Any knowledge about medicine, anatomy, biology, astronomy, physics, geology, geophysics or other sciences were almost non-existent. Many superstitious and groundless believes were prevalent and these believes were passed down through past generations. At that time, the Holy Quran was revealed and it presented several phenomenon that have been only currently unveiled, as scientists have worked endlessly to provide explanation for these physical and biological phenomenon applying scientific technologies. Many important discoveries were made during the 20th century and it is interesting to note that many of these discoveries were already present in the Holy Quran fourteen hundred years ago. The Scientific phenomenon, mentioned in the Holy Quran, cover many different fields in biological and physical sciences and have been the source of guidance for a number of scientists. A perfect description of the creation of the universe, the orbits in space, the development process, development of hearing process prior to sight, importance of the skin in sensing pain, uniqueness of fingerprints, role of males in selection of the sex of the baby, are just a few of the many facts present in the Quran that have astonished many scientists. The Quran in Chapter 20, verse 50 states: قَالَ رَبُّنَا الَّذِيۤ اَعْطٰى كُلَّ شَيْءٍ خَلْقَهٗ ثُمَّ هَدٰى ۰۰ (He said "Our Lord is He, Who has given a distinctive form to everything and then guided it aright”). Explaining this brief statement in the light of the modern day Molecular Genetics unveils the entire genetic basis of life and how guidance is stored in the genetic material (DNA) present in the nucleus. This thread like structure, made of only six molecules (sugar, phosphate, adenine, thymine, cytosine and guanine), is so brilliantly structured by the Creator that it holds all the information about each and every living thing, whether it is viruses, bacteria, fungi, plants, animals or humans or any other living being. This paper will present an update on some of the physical and biological phenomena’ presented in the Holy Quran, unveiled using advanced technologies during the last century and will discuss how the need to incorporate this information in the curricula.

Keywords: The Holy Quran, scientific facts, curriculum, Muslims

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Authors: Edwin Cartledge, Jack Clark, Mazaher Molaei-Chalchooghi

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The mixing in the crystallization step of active pharmaceutical ingredient manufacturers was studied via advanced modeling tools to enable a successful scale-up. A virtual representation of the vessel was created, and computational fluid dynamics were used to simulate multiphase flow and, thus, the mixing environment within this vessel. The study identified a significant dead zone in the vessel underneath the impeller and found that increasing the impeller speed and power did not improve the mixing. A series of sensitivity analyses found that to improve mixing, the vessel had to be redesigned, and found that optimal mixing could be obtained by adding two extra cylindrical baffles. The same two baffles from the simulated environment were then constructed and added to the process vessel. By identifying these potential issues before starting the manufacture and modifying the vessel to ensure good mixing, this study mitigated a failed crystallization and potential batch disposal, which could have resulted in a significant loss of high-value material.

Keywords: active pharmaceutical ingredient, baffles, computational fluid dynamics, mixing, modelling

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Authors: Jin Song Gui, Han Li, Rui Jin Zhang, Heng Jiang Cai

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There is a large deviation between the measured wave power at the vertical breast wall and the calculated one according to current specification in the case of overtopping. In order to investigate the reasons for the deviation, the wave forces of vertical breast wall under overtopping conditions have been measured through physical model experiment and compared with the calculated results. The effect of water depth, period and the wave height on the wave forces of the vertical breast wall have been also investigated. The distribution of wave pressure under different wave actions was tested based on the force sensor which is installed in the vertical breakwater. By comparing and analyzing the measured values and norms calculated values, the applicability of the existing norms recommended method were discussed and a reference for the design of vertical breakwater was provided. Experiment results show that with the decrease of the water depth, the gap is growing between the actual wave forces and the specification values, and there are no obvious regulations between these two values with the variation of period while wave force greatly reduces with the overtopping reducing. The amount of water depth and wave overtopping has a significant impact on the wave force of overtopping section while the period has no obvious influence on the wave force. Finally, some favorable recommendations for the overtopping wave force design of the vertical breakwater according to the model experiment results are provided.

Keywords: overtopping wave, physical model experiment, vertical breakwater, wave forces

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Authors: Mouli Banga, Chaya Ravindra

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Wound classification is a crucial step in wound diagnosis. An effective classifier can aid wound specialists in identifying wound types with reduced financial and time investments, facilitating the determination of optimal treatment procedures. This study presents a deep neural network-based classifier that leverages wound images and their corresponding locations to categorize wounds into various classes, such as diabetic, pressure, surgical, and venous ulcers. By incorporating a developed body map, the process of tagging wound locations is significantly enhanced, providing healthcare specialists with a more efficient tool for wound analysis. We conducted a comparative analysis between two prominent convolutional neural network models, ResNet50 and MobileNetV2, utilizing a dataset of 730 images. Our findings reveal that the RestNet50 outperforms MovileNetV2, achieving an accuracy of approximately 90%, compared to MobileNetV2’s 83%. This disparity highlights the superior capability of ResNet50 in the context of this dataset. The results underscore the potential of integrating deep learning with spatial data to improve the precision and efficiency of wound diagnosis, ultimately contributing to better patient outcomes and reducing healthcare costs.

Keywords: wound classification, MobileNetV2, ResNet50, multimodel

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Authors: Chingwen Yeh, I. Chen Huang

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This paper aims to conduct a case study on kinesthetic intelligence development intervention with a child who has Asperger symptom identified by physician. First, the characteristics of Asperger were defined based on the related literature. Some Asperger's people are born with outstanding insight and are good at solving complex and difficult problems. In contrast to high-functioning autistic, Asperger children do not lose their ability to express themselves verbally. However in the cognitive function, they focus mainly on the things they are interested in instead of paying attention to the whole surrounding situation. Thus it is difficult for them not only to focus on things that they are not interesting in, but also to interact with people. Secondly, 8-weeks of kinesthetic intelligence development courses were designed within a series of physical action that including the following sections: limbs coordination, various parts of body rhythm changes, strength and space awareness and breathing practice. In classroom observations were recorded both on words and with video as the qualitative research data. Finally, in-depth interview with the case child’s teachers, parents and other in class observers were documented on a weekly base in order to examine the effectiveness of before and after the kinesthetic intelligence development course and to testify the usefulness of the lesson plan. This research found that the case child has improved significantly in terms of attention span and body movement creativity. In the beginning of intervention, the case child made less eyes contact with others. The instructor needed to face the case child to confirm the eyes contact. And the instructor also used various adjective words as guiding language for all kinds of movement sequence practice. The result can cause the case child’s attention and learning motivation. And the case child understand what to do to enhance kinesthetic intelligence. These authors hope findings of this study can contribute as reference for the further research on the related topic.

Keywords: asperger symptom, body rhythm, kinesthetic intelligence, space awareness

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Authors: Leone Sherman

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While the democratic rights of a citizen may be very clearly outlined in a country’s constitution, it’s not uncommon for political elite to undermine those rights and gain more power and control over a country than it is allowed by this constitution. Moreover, while such a change in some smaller states may not have a substantial impact on the international community, the same change in countries with vast resources and political influence, such as Russia, is always a considerable factor for the world policy. This article aims to research the weaknesses of the Russian democratic system and their effect on the international policy through the three key aspects: The Russian people’s ability to produce the required political will to control their government’s decisions, the current development of the Russian political environment, and the affection of this environment on the world community as a whole during the recent years. The used methodology is a narrative analysis of recent political events, official statistics, international investigations and media statements. As a result, the ever-widening gap between the people and the government becomes evidently seen, as well as the challenges it imposes on the political world arena, both current and those that still lie ahead of us.

Keywords: Russia, political analysis, democratic weaknesses, international society

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Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Salise Oktay, Nilgun Kizilcan, Basak Bengu

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At present, formaldehyde-based adhesives such as urea-formaldehyde (UF), melamine-formaldehyde (MF), melamine – urea-formaldehyde (MUF), etc. are mostly used in wood-based panel industry because of their high reactivity, chemical versatility, and economic competitiveness. However, formaldehyde-based wood adhesives are produced from non- renewable resources and also formaldehyde is classified as a probable human carcinogen (Group B1) by the U.S. Environmental Protection Agency (EPA). Therefore, there has been a growing interest in the development of environment-friendly, economically competitive, bio-based wood adhesives to meet wood-based panel industry requirements. In this study, like a formaldehyde-free adhesive, oxidized starch – urea wood adhesives was synthesized. In this scope, firstly, acid hydrolysis of corn starch was conducted and then acid thinned corn starch was oxidized by using hydrogen peroxide and CuSO₄ as an oxidizer and catalyst, respectively. Secondly, the polycondensation reaction between oxidized starch and urea conducted. Finally, nano – TiO₂ was added to the reaction system to strengthen the adhesive network. Solid content, viscosity, and gel time analyses of the prepared adhesive were performed to evaluate the adhesive processability. FTIR, DSC, TGA, SEM characterization techniques were used to investigate chemical structures, thermal, and morphological properties of the adhesive, respectively. Rheological analysis of the adhesive was also performed. In order to evaluate the quality of oxidized corn starch – urea adhesives, particleboards were produced in laboratory scale and mechanical and physical properties of the boards were investigated such as an internal bond, modulus of rupture, modulus of elasticity, formaldehyde emission, etc. The obtained results revealed that oxidized starch – urea adhesives were synthesized successfully and it can be a good potential candidate to use the wood-based panel industry with some developments.

Keywords: nano-TiO₂, corn starch, formaldehyde emission, wood adhesives

Procedia PDF Downloads 147