Search results for: academic learning stress

Authors: Atakan Aral Ormancı, Tuğçe Arslantaş, Murat Özcü

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This study presents the design and implementation of an experimental chassis-level system for various control applications. Specifically, the height level of the chassis is controlled using proportional integrated derivative, fuzzy logic, and deep learning control methods. Real-time data obtained from height and pressure sensors installed in a 6x2 truck chassis, in combination with pulse-width modulation signal values, are utilized during the tests. A prototype pneumatic system of a 6x2 truck is added to the setup, which enables the Smart Pneumatic Actuators to function as if they were in a real-world setting. To obtain real-time signal data from height sensors, an Arduino Nano is utilized, while a Raspberry Pi processes the data using Matlab/Simulink and provides the correct output signals to control the Smart Pneumatic Actuator in the truck chassis. The objective of this research is to optimize the time it takes for the chassis to level down and up under various loads. To achieve this, proportional integrated derivative control, fuzzy logic control, and deep learning techniques are applied to the system. The results show that the deep learning method is superior in optimizing time for a non-linear system. Fuzzy logic control with a triangular membership function as the rule base achieves better outcomes than proportional integrated derivative control. Traditional proportional integrated derivative control improves the time it takes to level the chassis down and up compared to an uncontrolled system. The findings highlight the superiority of deep learning techniques in optimizing the time for a non-linear system, and the potential of fuzzy logic control. The proposed approach and the experimental results provide a valuable contribution to the field of control, automation, and systems engineering.

Keywords: automotive, chassis level control, control systems, pneumatic system control

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Authors: Saugata Bose, Ritambhra Korpal

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The internet has increased the copy-paste scenarios amongst students as well as amongst researchers leading to different levels of plagiarized documents. For this reason, much of research is focused on for detecting plagiarism automatically. In this paper, an initiative is discussed where Natural Language Processing (NLP) techniques as well as supervised machine learning algorithms have been combined to detect plagiarized texts. Here, the major emphasis is on to construct a framework which detects external plagiarism from monolingual texts successfully. For successfully detecting the plagiarism, n-gram frequency comparison approach has been implemented to construct the model framework. The framework is based on 120 characteristics which have been extracted during pre-processing the documents using NLP approach. Afterwards, filter metrics has been applied to select most relevant characteristics and then supervised classification learning algorithm has been used to classify the documents in four levels of plagiarism. Confusion matrix was built to estimate the false positives and false negatives. Our plagiarism framework achieved a very high the accuracy score.

Keywords: lexical matching, shallow NLP, supervised machine learning algorithm, word n-gram

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Authors: Abdel-Monem Sayed Mohamed

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Lake Aswan is one of the largest man-made reservoirs in the world. The reservoir began to fill in 1964 and the level rose gradually, with annual irrigation cycles, until it reached a maximum water level of 181.5 m in November 1999, with a capacity of 160 km3. The filling of such large reservoir changes the stress system either through increasing vertical compressional stress by loading and/or increased pore pressure through the decrease of the effective normal stress. The resulted effect on fault zones changes stability depending strongly on the orientation of pre-existing stress and geometry of the reservoir/fault system. The main earthquake occurred on November 14, 1981, with magnitude 5.5. This event occurred after 17 years of the reservoir began to fill, along the active part of the Kalabsha fault and located not far from the High Dam. Numerous of small earthquakes follow this earthquake and continue till now. For this reason, 13 seismograph stations (radio-telemetry network short-period seismometers) were installed around the northern part of Lake Aswan. The main purpose of the network is to monitor the earthquake activity continuously within Aswan region. The data described here are obtained from the continuous record of earthquake activity and lake-water level variation through the period from 1982 to 2015. The seismicity is concentrated in the Kalabsha area, where there is an intersection of the easterly trending Kalabsha fault with the northerly trending faults. The earthquake foci are distributed in two seismic zones, shallow and deep in the crust. Shallow events have focal depths of less than 12 km while deep events extend from 12 to 28 km. Correlation between the seismicity and the water level variation in the lake provides great suggestion to distinguish the micro-earthquakes, particularly, those in shallow seismic zone in the reservoir–triggered seismicity category. The water loading is one factor from several factors, as an activating medium in triggering earthquakes. The common factors for all cases of induced seismicity seem to be the presence of specific geological conditions, the tectonic setting and water loading. The role of the water loading is as a supplementary source of earthquake events. So, the earthquake activity in the area originated tectonically (ML ≥ 4) and the water factor works as an activating medium in triggering small earthquakes (ML ≤ 3). Study of the inducing seismicity from the water level variation in Aswan Lake is of great importance and play great roles necessity for the safety of the High Dam body and its economic resources.

Keywords: Aswan lake, Aswan seismic network, seismicity, water level variation

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Evridiki Zachopoulou, Vasilis Grammatikopoulos, Michail Vitoulis, Athanasios Gregoriadis

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In the last ten years, the recent economic crisis in Greece has decreased the financial ability and strength of several families when it comes to supporting their children’s studies. As a result, the number of students who are significantly delaying or even dropping out of their university studies is constantly increasing. The students who are at greater risk for academic failure are those who are facing various problems and social disadvantages, like health problems, special needs, family poverty or unemployment, single-parent students, immigrant students, etc. The ‘Umbrella’ project is a volunteer-based initiative to tackle this problem at International Hellenic University. The main purpose of the project is to provide support to disadvantaged students at a socio-emotional, academic, and practical level in order to help them complete their undergraduate studies. More specifically, the ‘Umbrella’ project has the following goals: (a) to develop a consulting-supporting network based on volunteering senior students, called ‘i-mentors’. (b) to train the volunteering i-mentors and create a systematic and consistent support procedure for students at-risk, (c), to develop a service that, parallel to the i-mentor network will be ensuring opportunities for at-risk students to find a job, (d) to support students who are coping with accessibility difficulties, (e) to secure the sustainability of the ‘Umbrella’ project after the completion of the funding of the project. The innovation of the Umbrella project is in its holistic-person-centered approach that will be providing individualized support -via the i-mentors network- to any disadvantaged student that will come ‘under the Umbrella.’

Keywords: peer mentoring, student support, socially disadvantaged students, volunteerism in higher education

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Authors: Syamimi Shamsuddin, Hanafiah Yussof, Fazah Akhtar Hanapiah, Salina Mohamed, Nur Farah Farhan Jamil, Farhana Wan Yunus

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Robot-based intervention for children with autism is an evolving research niche in human-robot interaction (HRI). Recent studies in this area mostly covered the role of robots in the clinical and experimental setting. Our previous work had shown that interaction with a robot pose no adverse effects on the children. Also, the presence of the robot, together with specific modules of interaction was associated with less autistic behavior. Extending this impact on school-going children, interactions that are in-tune with special education lessons are needed. This methodological paper focuses on how a robot can be incorporated in a current learning environment for autistic children. Six interaction scenarios had been designed based on the existing syllabus to teach communication skills, using the Applied Behavior Analysis (ABA) technique as the framework. Development of the robotic experience in class also covers the required set-up involving participation from teachers. The actual research conduct involving autistic children, teachers and robot shall take place in the next phase.

Keywords: autism spectrum disorder, ASD, humanoid robot, communication skills, robot-assisted learning

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Authors: Valentina Zhang

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While facial expression is a complex and individualized behavior, all facial emotion recognition (FER) systems known to us rely on a single facial representation and are trained on universal data. We conjecture that: (i) different facial representations can provide different, sometimes complementing views of emotions; (ii) when employed collectively in a discussion group setting, they enable more accurate emotion reading which is highly desirable in autism care and other applications context sensitive to errors. In this paper, we first study FER using pixel-based DL vs semantics-based DL in the context of deepfake videos. Our experiment indicates that while the semantics-trained model performs better with articulated facial feature changes, the pixel-trained model outperforms on subtle or rare facial expressions. Armed with these findings, we have constructed an adaptive FER system learning from both types of models for dyadic or small interacting groups and further leveraging the synthesized group emotions as the ground truth for individualized FER training. Using a collection of group conversation videos, we demonstrate that FER accuracy and personalization can benefit from such an approach.

Keywords: neurodivergence care, facial emotion recognition, deep learning, ground truth for supervised learning

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Authors: Banu Aytül Ekmekçi

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Salinity stress has negative effects on agricultural yield throughout the world, affecting production whether it is for subsistence or economic gain. This study investigates the inductive role of vitamin C and its application mode in mitigating the detrimental effects of irrigation with diluted (10, 20 and 30 %) NaCl + water on carthamus tinctorius plants. The results show that 10% of salt water exhibited insignificant changes, while the higher levels impaired growth by reducing seed germination, dry weights of shoot and root, water status and chlorophyll contents. However, irrigation with salt water enhanced carotenoids and antioxidant enzyme activities. The detrimental effects of salt water were ameliorated by application of 100 ppm ascorbic acid (vitamin C). The inductive role of vitamin was associated with the improvement of seed germination, growth, plant water status, carotenoids, endogenous ascorbic acid and antioxidant enzyme activities. Moreover, vitamin C alone or in combination with 30% NaCl water increased the intensity of protein bands as well as synthesized additional new proteins with molecular weights of 205, 87, 84, 65 and 45 kDa. This could increase tolerance mechanisms of treated plants towards water salinity.

Keywords: salinity, stress, vitamin c, antioxidant, NaCl, enzyme

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

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Authors: Ntite Orji Kalu, Martina Kurowski

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Until recently, the Czech Republic had an educational system dominated by indigenous people, who accounted for 95% of the school population. With the increasing influx of migrants and foreign students, especially from outside European Union, came a great disparity among the quality of learners and their learning needs and consideration for the challenges associated with being a minority and living within a foreign culture. This has prompted the research into ways of tailoring the educational system to meet the rising demand of learning styles and needs for the diverse learners in the Czech classrooms. Literature is reviewed regarding the various ways to accommodate the international students considering racial differences, focusing on theoretical approach and pedagogical principles. This study examines the compulsory educational system of the Czech Republic and the position and responsibility of the teacher in fostering a culturally sensitive and inclusive learning environment. Descriptive and content analysis is relied upon for this study. Recommendations are made for stakeholders to imbibe a more responsive environment that enhances the cultural and social integration of all learners.

Keywords: culturally responsive teaching, cultural competence, diversity, learners, inclusive education, Czech schools

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Authors: Agnes Anyango Andollo, Jane Achieng Achola

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The use of Information and Communication Technologies (ICTs) has become one of the driving forces in facilitation of learning in most colleges. The ability to effectively harness the technology varies from college to college. The study objective was to determine the lecturers’, institutional attributes and policies that influence the utilization of ICT by the lecturers’. A cross sectional survey design was employed in order to empirically investigate the extent to which lecturers’ personal, institutional attributes and policies influence the utilization of ICT to facilitate learning. The target population of the study was 295 lecturers who facilitate learning at KMTC-Nairobi. Structured self-administered questionnaire was given to the lecturers. Quantitative data was scrutinized for completeness, accuracy and uniformity then coded. Data were analyzed in frequencies and percentages using Statistical Package for Social Sciences (SPSS) version 19, this was a reliable tool for quantitative data analysis. A total of 155 completed questionnaires administered were obtained from the respondents for the study that were subjected to analysis. The study found out that 93 (60%) of the respondents were male while 62 (40%) of the respondents were female. Individual’s educational level, age, gender and educational experience had the greatest impact on use of ICT. Lecturers’ own beliefs, values, ideas and thinking had moderate impact on use of ICT. And that institutional support by provision of resources for ICT related training such as internet, computers, laptops and projectors had moderate impact (p = 0.049) at 5% significant level on use of ICT. The study concluded that institutional attributes and ICT policy were keys to utilization of ICT by lecturers at KMTC Nairobi also mandatory policy on use of ICT by lecturers to facilitate learning was key. It recommended that policies should be put in place for Technical support to lecturers when in problem during utilization of ICT and also a mechanism should be put in place to make the use of ICT in teaching and learning mandatory.

Keywords: policy, computers education, medical training institutions, ICTs

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Authors: Mabroka Omar Sherehe

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Despite the great efficacy of isoniazid (INH) and rifampicine (RIF) combination in the treatment of tuberculosis, hepatotoxicity is the most common serious complication. The potential protective effect of simvastatin (sim) against combination-induced hepatotoxicity was investigated in the present study. The administration of INH-RIF combination (50mg/kg each for 14 days) resulted in a significant increased activities of serum alanine and aspartate aminotransferases, such effects were further supported by histopathological studies. INH-RIF combination produced a significant increase in liver lipid, decreased SOD and CAT, and a significant depletion of GSH level. Additionally, treatment with INH-RIF combination resulted in a significant increase in liver MPO activity. The lipid-lowering drug, Sim demonstrated in the current study an evident antioxidant action, such effect was mediated via decreasing the elevated MDA, MPO, and restoring liver CAT activity. Additionally, Sim restored liver NO level to near basal value Furthermore, one cannot rule out the lipid-lowering effect of Sim that would probably add to its beneficial hepatoprotective antioxidant activity, where Sim decreased the elevated cholesterol, TGs and LDL cholesterol level and increased the serum HDL cholesterol level.

Keywords: isoniazid, rifampicine, oxidative stress, nitric oxide

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Authors: Albert Bou, Sebastian Dittert, Gianni de Fabritiis

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Advancing reinforcement learning (RL) requires tools that are flexible enough to easily prototype new methods while avoiding impractically slow experimental turnaround times. To match the first requirement, the most popular RL libraries advocate for highly modular agent composability, which facilitates experimentation and development. To solve challenging environments within reasonable time frames, scaling RL to large sampling and computing resources has proved a successful strategy. However, this capability has been so far difficult to combine with modularity. In this work, we explore design choices to allow agent composability both at a local and distributed level of execution. We propose a versatile approach that allows the definition of RL agents at different scales through independent, reusable components. We demonstrate experimentally that our design choices allow us to reproduce classical benchmarks, explore multiple distributed architectures, and solve novel and complex environments while giving full control to the user in the agent definition and training scheme definition. We believe this work can provide useful insights to the next generation of RL libraries.

Keywords: deep reinforcement learning, Python, PyTorch, distributed training, modularity, library

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Authors: Kaoutar Ben Azzou, Hanaa Talei

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Human resources is an important department in each organization as it manages the life cycle of employees from recruitment training to retirement or termination of contracts. The recruitment process starts with a job opening, followed by a selection of the best-fit candidates from all applicants. Matching the best profile for a job position requires a manual way of looking at many CVs, which requires hours of work that can sometimes lead to choosing not the best profile. The work presented in this paper aims at reducing the workload of HR personnel by automating the preliminary stages of the candidate screening process, thereby fostering a more streamlined recruitment workflow. This tool introduces an automated system designed to help with the recruitment process by scanning candidates' CVs, extracting pertinent features, and employing machine learning algorithms to decide the most fitting job profile for each candidate. Our work employs natural language processing (NLP) techniques to identify and extract key features from unstructured text extracted from a CV, such as education, work experience, and skills. Subsequently, the system utilizes these features to match candidates with job profiles, leveraging the power of classification algorithms.

Keywords: automated recruitment, candidate screening, machine learning, human resources management

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Suzi M. S. Cavalari, Tim Lewis

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Teletandem is a bilingual model of virtual exchange in which two partners from different countries( and speak different languages) meet synchronously and regularly over a period of 8 weeks to learn each other’s mother tongue (or the language of proficiency). At São Paulo State University (UNESP), participants should answer a questionnaire before starting the exchanges in which one of the questions refers to setting a goal to be accomplished with the help of the teletandem partner. In this context, the present presentation aims to examine the goal-setting activity of 79 Brazilians who participated in Portuguese-English teletandem exchanges over a period of four years (2012-2015). The theoretical background is based on goal setting and self-regulated learning theories that propose that appropriate efficient goals are focused on the learning process (not on the product) and are specific, proximal (short-term) and moderately difficult. The data set used was 79 initial questionnaires retrieved from the MulTeC (Multimodal Teletandem Corpus). Results show that only approximately 10% of goals can be considered appropriate. Features of these goals are described in relation to specificities of the teletandem context. Based on the results, three mechanisms that can help learners to set attainable goals are discussed.

Keywords: foreign language learning, goal setting, teletandem, virtual exchange

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Authors: Prasita Sooksamran, Wareerat Kaewurai

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STEM is an integrated teaching approach promoted by the Ministry of Education in Thailand. STEM Education is an integrated approach to teaching Science, Technology, Engineering, and Mathematics. It has been questioned by Thai teachers on the grounds of how to integrate STEM into the classroom. Therefore, the main objective of this study is to develop a science instructional model based on the STEM approach to enhance scientific mind and problem-solving skills for primary students. This study is participatory action research, and follows the following steps: 1) develop a model 2) seek the advice of experts regarding the teaching model. Developing the instructional model began with the collection and synthesis of information from relevant documents, related research and other sources in order to create prototype instructional model. 2) The examination of the validity and relevance of instructional model by a panel of nine experts. The findings were as follows: 1. The developed instructional model comprised of principles, objective, content, operational procedures and learning evaluation. There were 4 principles: 1) Learning based on the natural curiosity of primary school level children leading to knowledge inquiry, understanding and knowledge construction, 2) Learning based on the interrelation between people and environment, 3) Learning that is based on concrete learning experiences, exploration and the seeking of knowledge, 4) Learning based on the self-construction of knowledge, creativity, innovation and 5) relating their findings to real life and the solving of real-life problems. The objective of this construction model is to enhance scientific mind and problem-solving skills. Children will be evaluated according to their achievements. Lesson content is based on science as a core subject which is integrated with technology and mathematics at grade 6 level according to The Basic Education Core Curriculum 2008 guidelines. The operational procedures consisted of 6 steps: 1) Curiosity 2) Collection of data 3) Collaborative planning 4) Creativity and Innovation 5) Criticism and 6) Communication and Service. The learning evaluation is an authentic assessment based on continuous evaluation of all the material taught. 2. The experts agreed that the Science Instructional Model based on the STEM Education Approach had an excellent level of validity and relevance (4.67 S.D. 0.50).

Keywords: instructional model, STEM education, scientific mind, problem solving

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Authors: Simao E. Luis

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From the 1960s to the 1990s, the social-psychological framework of language attitudes that emerged from the Canadian research tradition was very influential. Integrativeness was one of the main variables in Gardner’s theory because refugees and immigrants were motivated to learn English and French to integrate into the Canadian community. Second language (L2) scholars have expressed concerns over integrativeness because it cannot explain the motivation of L2 learners in global contexts. This study aims to investigate student motivation to learn English as a foreign language in Mozambique, and to contribute to the ongoing validation of the L2 Motivational Self System theory in an under-researched country. One hundred thirty-seven (N=137) university students completed a well-established motivation questionnaire. The data were analyzed with SPSS, and descriptive statistics, correlations, multiple regressions, and MANOVA were conducted. Results show that many variables contribute to motivated learning behavior, particularly the L2 learning experience and attitudes towards the English language. Statistically significant differences were found between males and females, with males expressing more motivation to learn the English language for personal interests. Statistically significant differences were found between older and younger students, with older students reporting more vivid images of themselves as future English language users. These findings have pedagogical implications because motivational strategies are positively correlated with student motivated learning behavior. Therefore, teachers should design L2 tasks that can help students to develop their future L2 selves.

Keywords: English as a foreign language, L2 motivational self system, Mozambique, university students

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Authors: Bahar Ayhan

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The present paper is concerned with the numerical modeling of the inelastic behavior of the anisotropically damaged ductile materials, which are based on a generalized macroscopic theory within the framework of continuum damage mechanics. Kinematic decomposition of the strain rates into elastic, plastic and damage parts is basis for accomplishing the structure of continuum theory. The evolution of the damage strain rate tensor is detailed with the consideration of anisotropic effects. Helmholtz free energy functions are constructed separately for the elastic and inelastic behaviors in order to be able to address the plastic and damage process. Additionally, the constitutive structure, which is based on the standard dissipative material approach, is elaborated with stress tensor, a yield criterion for plasticity and a fracture criterion for damage besides the potential functions of each inelastic phenomenon. The finite element method is used to approximate the linearized variational problem. Stress and strain outcomes are solved by using the numerical integration algorithm based on operator split methodology with a plastic and damage (multiplicator) variable separately. Numerical simulations are proposed in order to demonstrate the efficiency of the formulation by comparing the examples in the literature.

Keywords: anisotropic damage, finite element method, plasticity, coupling

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Authors: Mark R. Skopec, Hamdi M. Issa, Henock B. Taddese, Kate Ippolito, Matthew J. Harris

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In peer review, there is a skew toward research from high-income countries, otherwise known as geographic bias. Research from well-known and prestigious institutions is often favored in the peer review process and is more frequently cited in biomedical research. English clinicians have been found to rate research from low-income countries worse compared to the same research presented as if from high-income countries. This entrenched bias, which is rooted in the perceived superiority of academic institutions in high-income countries is damaging in many regards. Crucially, it reinforces colonial roots by strengthening the dominance of knowledge bases in high-income contexts and perpetuates the perceived inferiority of research from low-income settings. We report on the interventions that Imperial College London is conducting to “decolonize” the higher education curriculum – a root and branch review of reading material in the Masters of Public Health course; identification of unconscious bias against low-income country research in faculty and staff; in-depth interviews with faculty members on their experiences and practices with respect to inclusion of low-income country research in their own teaching and learning practice; and exploring issues surrounding entrenched biases and structural impediments for enabling desirable changes. We intend to use these findings to develop frameworks and approaches, including workshops and online resources, to effect sustainable changes to diversify the curriculum at Imperial College London.

Keywords: curriculum design, diversity, geographic bias, higher education, implicit associations, inclusivity

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Authors: Santiago Moll Lopez, Erica Vega Fleitas, Dolors Rosello Ferragud, Luis Manuel Sanchez Ruiz, Jose Antonio Moraño Fernandez

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Game-based learning (GBL) is becoming popular in education. Learning through games offers students a motivating experience related to the social aspect of games. Among the significant positive outcomes are promoting positive emotions and collaboration, improving the assimilation of concepts, and creating an attractive and dynamic environment standout. This work presents a study of the design and implementation of games created with RPG Maker MZ software with a Sci-Fi storytelling environment for developing specific and transversal skills in a Mathematics subject at the Beng in Aerospace Engineering. Games were applied during regular classes and as a part of a Flip-Teaching methodology to increase the motivation and the assimilation of mathematical concepts in an engaging way. The key features of the games were the introduction of avatar design and the promotion of collaboration among students. Students' opinions and grades obtained in the activities and exams showed increased motivation and a significant improvement in their performance compared with other groups or past students' performances.

Keywords: game-based learning, rol games, mathematics, science fiction

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Authors: Wen Ma, Yong Peng, Zhixiang Li

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Security journey is a vital focus on the field of Rail Transportation. Accidents caused by the damage of the high-speed train windshield have occurred many times and have given rise to terrible consequences. Train windshield consists of tempered glass and polyvinyl butyral (PVB) film. In this work, the quasi-static tests and the split Hopkinson pressure bar (SHPB) tests were carried out first to obtain the mechanical properties and constitutive model for the tempered glass and PVB film. These tests results revealed that stress and Young’s modulus of tempered glass were wake-sensitive to strain rate, but stress and Young’s modulus of PVB film were strong-sensitive to strain rate. Then impact experiment of the windshield was carried out to investigate dynamic response and failure characteristics of train windshield. In addition, a finite element model based on the combined finite element method was proposed to investigate fracture and fragmentation responses of train windshield under different-velocity impact. The results can be used for further design and optimization of the windshield for high-speed train application.

Keywords: constitutive model, impact response, mechanism properties, PVB film, tempered glass

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

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Authors: Sarah Alshehri, Messedah Alqahtani

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Reading instruction in EFL classes is still challenging for teachers, and many students are still behind their expected level. Due to the Covid-19 pandemic, there was a shift in teaching English from face-to face to online classes. This paper will discover how the digital shift during and post pandemic has influenced English literacy instruction and what methods seem to be effective or challenging. Specifically, this paper will examine English language teachers' perspectives on the workable approaches and challenges that encountered them when teaching reading using E-Learning platform in Saudi Arabian Secondary and intermediate schools. The study explores public secondary school EFL teachers’ instructional practices and the challenges encountered when teaching reading online. Quantitative data will be collected through a 28 -item Likert type survey that will be administered to Saudi English teachers who work in public secondary and intermediate schools. The quantitative data will be analyzed using SPSS by conducting frequency distributions, descriptive statistics, reliability tests, and one-way ANOVA tests. The potential outcomes of this study will contribute to better understanding of digital literacy and technology integration in language teaching. Findings of this study can provide directions for professionals and policy makers to improve the quality of English teaching and learning. Limitations and results will be discussed, and suggestions for future directions will be offered.

Keywords: EFL reading, E-learning- EFL literacy, EFL workable approaches, EFL reading instruction

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Authors: Rui Wu

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In the volatile modern manufacturing environment, new orders randomly occur at any time, while the pre-emptive methods are infeasible. This leads to a real-time scheduling method that can produce a reasonably good schedule quickly. The dynamic Flexible Job Shop problem is an NP-hard scheduling problem that hybrid the dynamic Job Shop problem with the Parallel Machine problem. A Flexible Job Shop contains different work centres. Each work centre contains parallel machines that can process certain operations. Many algorithms, such as genetic algorithms or simulated annealing, have been proposed to solve the static Flexible Job Shop problems. However, the time efficiency of these methods is low, and these methods are not feasible in a dynamic scheduling problem. Therefore, a dynamic rule selection scheduling system based on the reinforcement learning method is proposed in this research, in which the dynamic Flexible Job Shop problem is divided into several parallel machine problems to decrease the complexity of the dynamic Flexible Job Shop problem. Firstly, the features of jobs, machines, work centres, and flexible job shops are selected to describe the status of the dynamic Flexible Job Shop problem at each decision point in each work centre. Secondly, a framework of reinforcement learning algorithm using a double-layer deep Q-learning network is applied to select proper composite dispatching rules based on the status of each work centre. Then, based on the selected composite dispatching rule, an available operation is selected from the waiting buffer and assigned to an available machine in each work centre. Finally, the proposed algorithm will be compared with well-known dispatching rules on objectives of mean tardiness, mean flow time, mean waiting time, or mean percentage of waiting time in the real-time Flexible Job Shop problem. The result of the simulations proved that the proposed framework has reasonable performance and time efficiency.

Keywords: dynamic scheduling problem, flexible job shop, dispatching rules, deep reinforcement learning

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Authors: Denise Levy, Anna Lucia C. H. Villavicencio

Abstract:

In a rapid-changing world, science teachers face considerable challenges. In addition to the basic curriculum, there must be included several transversal themes, which demand creative and innovative strategies to be arranged and integrated to traditional disciplines. In Brazil, nuclear science is still a controversial theme, and teachers themselves seem to be unaware of the issue, most often perpetuating prejudice, errors and misconceptions. This article presents the authors’ experience in the development of an interdisciplinary pedagogical proposal to include nuclear science in the basic curriculum, in a transversal and integrating way. The methodology applied was based on the analysis of several normative documents that define the requirements of essential learning, competences and skills of basic education for all schools in Brazil. The didactic materials and resources were developed according to the best practices to improve learning processes privileging constructivist educational techniques, with emphasis on active learning process, collaborative learning and learning through research. The material consists of an illustrated book for students, a book for teachers and a manual with activities that can articulate nuclear science to different disciplines: Portuguese, mathematics, science, art, English, history and geography. The content counts on high scientific rigor and articulate nuclear technology with topics of interest to society in the most diverse spheres, such as food supply, public health, food safety and foreign trade. Moreover, this pedagogical proposal takes advantage of the potential value of digital technologies, implementing QR codes that excite and challenge students of all ages, improving interaction and engagement. The expected results include the education of the educators for nuclear science communication in a transversal and integrating way, demystifying nuclear technology in a contextualized and significant approach. It is expected that the interdisciplinary pedagogical proposal contributes to improving attitudes towards knowledge construction, privileging reconstructive questioning, fostering a culture of systematic curiosity and encouraging critical thinking skills.

Keywords: science education, interdisciplinary learning, nuclear science, scientific literacy

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Authors: Richard O’Riordan, Saritha Unnikrishnan

Abstract:

Autonomous Vehicles (AVs) are incorporating an increasing number of ADAS features, including automated lane-keeping systems. In recent years, many research papers into lane detection algorithms have been published, varying from computer vision techniques to deep learning methods. The transition from lower levels of autonomy defined in the SAE framework and the progression to higher autonomy levels requires increasingly complex models and algorithms that must be highly reliable in their operation and functionality capacities. Furthermore, these algorithms have no room for error when operating at high levels of autonomy. Although the current research details existing computer vision and deep learning algorithms and their methodologies and individual results, the research also details challenges faced by the algorithms and the resources needed to operate, along with shortcomings experienced during their detection of lanes in certain weather and lighting conditions. This paper will explore these shortcomings and attempt to implement a lane detection algorithm that could be used to achieve improvements in AV lane detection systems. This paper uses a pre-trained LaneNet model to detect lane or non-lane pixels using binary segmentation as the base detection method using an existing dataset BDD100k followed by a custom dataset generated locally. The selected roads will be modern well-laid roads with up-to-date infrastructure and lane markings, while the second road network will be an older road with infrastructure and lane markings reflecting the road network's age. The performance of the proposed method will be evaluated on the custom dataset to compare its performance to the BDD100k dataset. In summary, this paper will use Transfer Learning to provide a fast and robust lane detection algorithm that can handle various road conditions and provide accurate lane detection.

Keywords: ADAS, autonomous vehicles, deep learning, LaneNet, lane detection

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Authors: Izuddin Fahmy Abu, Mohamad Haiqal Nizar Mohamad, Muhammad Fattah Fazel, Renu Agarwal, Igor Iezhitsa, Nor Salmah Bakar, Henrik Franzyk, Ian Mellor

Abstract:

Glaucoma is the global leading cause of irreversible blindness. Currently, the available treatment strategy only involves lowering intraocular pressure (IOP); however, the condition often progresses despite lowered or normal IOP in some patients. N-methyl-D-aspartate receptor (NMDAR) excitotoxicity often occurs in neurodegeneration-related glaucoma; thus it is a relevant target to develop a therapy based on neuroprotection approach. This study investigated the effects of Philanthotoxin-343 (PhTX-343), an NMDAR antagonist, on the neuroprotection of NMDA-induced glaucoma to alleviate visual impairments. Male Sprague-Dawley rats were equally divided: Groups 1 (control) and 2 (glaucoma) were intravitreally injected with phosphate buffer saline (PBS) and NMDA (160nM), respectively, while group 3 was pre-treated with PhTX-343 (160nM) 24 hours prior to NMDA injection. Seven days post-treatments, rats were subjected to visual behavior assessments and subsequently euthanized to harvest their retina and optic nerve tissues for histological analysis and determination of nitrosative stress level using 3-nitrotyrosine ELISA. Visual behavior assessments via open field, object, and color recognition tests demonstrated poor visual performance in glaucoma rats indicated by high exploratory behavior. PhTX-343 pre-treatment appeared to preserve visual abilities as all test results were significantly improved (p < 0.05). H&E staining of the retina showed a marked reduction of ganglion cell layer thickness in the glaucoma group; in contrast, PhTX-343 significantly increased the number by 1.28-folds (p < 0.05). PhTX-343 also increased the number of cell nuclei/100μm2 within inner retina by 1.82-folds compared to the glaucoma group (p < 0.05). Toluidine blue staining of optic nerve tissues showed that PhTX-343 reduced the degeneration changes compared to the glaucoma group which exhibited vacuolation overall sections. PhTX-343 also decreased retinal 3- nitrotyrosine concentration by 1.74-folds compared to the glaucoma group (p < 0.05). All results in PhTX-343 group were comparable to control (p > 0.05). We conclude that PhTX-343 protects against NMDA-induced changes and visual impairments in the rat model by reducing nitrosative stress levels.

Keywords: excitotoxicity, glaucoma, nitrosative stress , NMDA receptor , N-methyl-D-aspartate , philanthotoxin, visual behaviour

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Authors: Hexiong Liu

Abstract:

Powder metallurgy is the optimal method for the consolidation and preparation of W(Mo) alloys, which exhibit excellent application prospects at high temperatures. The properties of W(Mo) alloys are closely related to the sintered density. However, controlling the sintered density and porosity of these alloys is still challenging. In the past, the regulation methods mainly focused on time-consuming and costly trial-and-error experiments. In this study, the sintering data for more than a dozen W(Mo) alloys constituted a small-scale dataset, including both solid and liquid phases of sintering. Furthermore, simple descriptors were used to predict the sintered density of W(Mo) alloys based on the descriptor selection strategy and machine learning method (ML), where the ML algorithm included the least absolute shrinkage and selection operator (Lasso) regression, k-nearest neighbor (k-NN), random forest (RF), and multi-layer perceptron (MLP). The results showed that the interpretable descriptors extracted by our proposed selection strategy and the MLP neural network achieved a high prediction accuracy (R>0.950). By further predicting the sintered density of W(Mo) alloys using different sintering processes, the error between the predicted and experimental values was less than 0.063, confirming the application potential of the model.

Keywords: sintered density, machine learning, interpretable descriptors, W(Mo) alloy

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Authors: Sri Rahayu, M. Dwi Susan, Aris Soewondo, W. M. Agung Pramana

Abstract:

Semen is an organic fluid (seminal plasma) that contain spermatozoa. Proteins are one of the major seminal plasma components that modulate sperm functionality, influence sperm capacitation and maintaining the stability of the membrane. Semen freezing is a procedure to preserve sperm cells. The process causes decrease in sperm viability due to temperature shock and oxidation stress. Oxidation stress is a disturbance on phosphorylation that increases ROS concentration, and it produces lipid peroxide in spermatozoa membrane resulted in high MDA (malondialdehyde) concentration. The objective of this study was to examine the effect of freezing on protein and MDA profile of bovine sperm cell and seminal plasma after freezing. Protein and MDA of sperm cell and seminal plasma were isolated from 10 sample. Protein profiles was analyzed by SDS PAGE with separating gel 12,5 %. The concentration of MDA was measured by spectrophotometer. The results of the research indicated that freezing of semen cause lost of the seminal plasma proteins with molecular with 20, 10, and 9 kDa. In addition, the result research showed that protein of the sperm (26, 10, 9, 7, and 6 kDa) had been lost. There were difference MDA concentration of seminal plasma and sperm cell were increase after freezing. MDA concentration of seminal plasma before and after freezing were 2.2 and 2.4 nmol, respectively. MDA concentration of sperm cell before and after freezing were 1,5 and 1.8 nmol, respectively. In conclusion, there were differences protein profiles of spermatozoa before and after semen freezing and freezing cause increasing of the MDA concentration.

Keywords: MDA, semen freezing, SDS PAGE, protein profile

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