Search results for: specific learning disability

Authors: Ivana Hrebickova, Maria Sevcikova, Romana Slamberova

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The present study was aimed at evaluation of cognitive changes following scheduled neonatal methamphetamine exposure in combination with long-term exposure in adulthood of female Wistar rats. Pregnant mothers were divided into two groups: group with indirect exposure (methamphetamine in dose 5 mg/ml/kg, saline in dose 1 ml/kg) during early lactation period (postnatal day 1–11) - progeny of these mothers were exposed to the effects of methamphetamine or saline indirectly via the breast milk; and the second group with direct exposure – all mothers were left intact for the entire lactation period, while progeny was treated with methamphetamine (5 mg/ml/kg) by injection or the control group, which was received needle pick (shame, not saline) at the same time each day of period of application (postnatal day 1–11). Learning ability and memory consolidation were tested in the Morris Water Maze, which consisted of three types of tests: ‘Place Navigation Test ‘; ‘Probe Test ‘; and ‘Memory Recall Test ‘. Adult female progeny were injected daily, after completion last trial with saline or methamphetamine (1 mg/ml/kg). We compared the effects of indirect/direct neonatal methamphetamine exposure and adult methamphetamine treatment on cognitive function of female rats. Statistical analyses showed that neonatal methamphetamine exposure worsened spatial learning and ability to remember the position of the platform. The present study demonstrated that direct methamphetamine exposure has more significant impact on process of learning and memory than indirect exposure. Analyses of search strategies (thigmotaxis, scanning) used by females during the Place Navigation Test and Memory Recall Test confirm all these results.

Keywords: methamphetamine, Morris water maze, neonatal exposure, strategies, Wistar rats

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Yi-Ju Lee

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This study identified the hypothesised relationship between creative experience, and revisit intention of handmade oriental parasol umbrella in Kaohsiung, Taiwan. A face-to-face questionnaire survey was administered in Meinong town, Kaohsiung. The components of creative experience were found as “sense of achievement”, “unique learning” and “interaction with instructors” in creative tourism. The result also revealed significant positive relationships between creative experience and revisit intention in handmade activities. This paper provides additional suggestions for enhancing revisit intention and guidance regarding creative tourism.

Keywords: creative tourism, sense of achievement, unique learning, interaction with instructors, folk art

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Authors: Joel S. Mtebe, Aron Kondoro, Mussa M. Kissaka, Elia Kibga

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Sub-Saharan Africa is described as the second fastest growing mobile phone penetration in the world more than in the United States or the European Union. Mobile phones have been used to provide a lot of opportunities to improve people’s lives in the region such as in banking, marketing, entertainment, and paying various bills such as water, TV, and electricity. However, the potential of using mobile phones to enhance teaching and learning has not been explored. This study presents an experience of developing and delivering SMS quizzes questions that were used to assess mastery of the subject content knowledge of science and mathematics secondary school teachers in Tanzania. The SMS quizzes were used as a follow up support mechanism to 500 teachers who participated in a project to upgrade subject content knowledge of science and mathematics subjects. Quizzes of 10-15 questions were sent to teachers each week for 8 weeks and the results were analyzed using SPSS. The results showed that chemistry and biology had better performance compared to mathematics and physics. Teachers reported some challenges that led to poor performance, invalid answers, and non-responses and they are presented. This research has several practical implications for those who are implementing or planning to use mobile phones for teaching and learning especially in rural secondary schools in sub-Saharan Africa.

Keywords: mobile learning, elearning, educational technolgies, SMS, secondary education, assessment

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Authors: Rujia Chen, Ajit Narayanan

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Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

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Authors: Kwame Amoah

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Artificial intelligence (AI) has the prospect of transforming project management, significantly improving efficiency and accuracy. By automating specific tasks with defined guidelines, AI can assist project managers in making better decisions and allocating resources efficiently, with possible risk mitigation. This study explores how AI is already impacting project management and likely future AI's impact on the field. The AI's reaction has been a divided opinion; while others picture it as a destroyer of jobs, some welcome it as an innovation advocate. Both sides agree that AI will be disruptive and revolutionize PM's functions. If current research is to go by, AI or some form will replace one-third of all learning graduate PM jobs by as early as 2030. A recent survey indicates AI spending will reach $97.9 billion by the end of 2023. Considering such a profound impact, the project management profession will also see a paradigm shift driven by AI. The study examines what the project management profession will look like in the next 5-10 years after this technological disruption. The research methods incorporate existing literature, develop trend analysis, and conduct structured interviews with project management stakeholders from North America to gauge the trend. PM professionals can harness the power of AI, ensuring a smooth transition and positive outcomes. AI adoption will maximize benefits, minimize adverse consequences, and uphold ethical standards, leading to improved project performance.

Keywords: project management, disruptive teacnologies, project management function, AL applications, artificial intelligence

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Authors: V. Mantey, N. Findlay, I. Maddox

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The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

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Authors: Alejandra Yanez, Teresa Benavides, Zita Lopez

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Even before COVID-19, one of the most important challenges that Higher Education faces today is the need for innovative educational methodologies and flexibility. We could all agree that one of the objectives of Higher Education is to provide students with a variety of intellectual and practical skills that, at the same time, will help them develop competitive advantages such as adaptation and critical thinking. Among the strategic objectives of Universidad de Monterrey (UDEM) has been to provide flexibility and satisfaction to students in the delivery modes of the academic offer. UDEM implemented a methodology that combines face to face with synchronous and asynchronous as delivery modes. UDEM goal, in this case, was to implement new technologies and different teaching methodologies that will improve the students learning experience. In this study, the experience of students during courses implemented in new delivery mode was compared with students in courses with traditional delivery modes. Students chose openly either way freely. After everything students around the world lived in 2020 and 2021, one can think that the face to face (traditional) delivery mode would be the one chosen by students. The results obtained in this study reveal that both delivery modes satisfy students and favor their learning process. We will show how the combination of delivery modes provides flexibility, so the proposal is that universities can include them in their academic offer as a response to the current student's learning interests and needs.

Keywords: flexibility, new delivery modes, student satisfaction, academic offer

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Sanaa Rashed Abdallah, Yasser F. Hassan

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The paper will introduce an approach where a rough sets, gene expression programming and rough neural networks are used cooperatively for learning and classification support. The Objective of gene expression programming rough neural networks (GEP-RNN) approach is to obtain new classified data with minimum error in training and testing process. Starting point of gene expression programming rough neural networks (GEP-RNN) approach is an information system and the output from this approach is a structure of rough neural networks which is including the weights and thresholds with minimum classification error.

Keywords: rough sets, gene expression programming, rough neural networks, classification

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Authors: Johanna Gamboa-Kroesen

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School connectedness, or the sense of belonging at school, is a critical factor in adolescent health, academic achievement, and socioemotional well-being. In educational research, the construct of the psychological sense of school membership is often referred to as school engagement, school bonding, or school attachment. While current research recognizes school connectedness as integral to a child’s mental health and academic success, many schools have yet to develop adequate interventions to promote a child’s overall sense of belonging at school. However, prior researches in music education indicates that, among other benefits, music classrooms may provide an environment where students feel they belong. While studies indicates that music learning environments, specifically performing ensemble learning environments, instill a sense of school connectedness and, more broadly, contribute to a student’s socio-emotional development, there has been inadequate research on how the actions of music teachers contribute to this phenomenon. The purpose of this study was to examine the relationship between school connectedness and music learning environments with middle school music students enrolled in a school-based music ensemble. In addition, the study aimed to provide a descriptive analysis of the instructional practices that music teachers use to promote an inclusive environment in their classrooms and an overall sense of belonging in their students. Using 191 student surveys of school membership, student reflective writings, 5 teacher interviews, and 10 classroom observations, this study examined the relationship between 7th and 8th-grade student-reported levels of connectedness within their school-based music ensemble and teacher instructional practice. The study found that students reported high levels of positive school membership within their music classes. Students who participate in school-based orchestra ensembles reported a positive change in emotional state during music instruction. In addition, evidence in this study found that music teachers use instructional practices to build connectedness through de-emphasizing competition and strengthening a student’s sense of relational value within their music learning experience. The findings offer implications for future music teacher instruction to create environments of inclusion, strengthen student-teacher relationships, and promote strategies that enhance student connection to school.

Keywords: music education, belonging, instructional practice, school connectedness

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Authors: Mohammad Khatib, Salam Hadid

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Introduction: Developing nurses' cultural competence begins in their basic training and requires them to participate in an array of activities which raise their awareness and stimulate their interest, desire and curiosity about different cultures, by creating opportunities for intercultural meetings promoting the concept of 'culture' and its components, including recognition of cultural diversity and the legitimacy of the other. Importantly, professionals need to acquire specific cultural knowledge and thorough understanding of the values, norms, customs, beliefs and symbols of different cultures. Similarly, they need to be given opportunities to practice the verbal and non-verbal communication skills of other cultures according to their cultural codes. Such a system is being implemented as part of nursing studies at Zefat Academic College in two study frameworks; firstly, a course integrating nursing theory and practice in multicultural nursing; secondly, a course in learning the languages spoken in Israel focusing on medical and nursing terminology. Methods: Students participating in the 'Transcultural Nursing' course come from a variety of backgrounds: Jews, or Arabs, religious, or secular; Muslim, Christian, new immigrants, Ethiopians or from other cultural affiliations. They are required to present and discuss cultural practices that affect health. In addition, as part of the language course, students learn and teach their friends 5 spoken languages (Arabic, Russian, Amharian, Yidish, and Sign language) focusing on therapeutic interaction and communication using the vocabulary and concepts necessary for the therapeutic encounter. An evaluation of the process and the results was done using a structured questionnaire which includes series of questions relating to the contributions of the courses to their cultural knowledge, awareness and skills. 155 students completed the questionnaire. Results: A preliminary assessment of this educational system points an increase in cultural awareness and knowledge among the students as well as in their willingness to recognize the other's difference. A positive atmosphere of multiculturalism is reflected in students' mutual interest and respect was created. Students showed a deep understanding of cultural issues relating to health and care (consanguinity and genetics, food customs; cultural events, reincarnation, traditional treatments etc.). Most of the students were willing to recommend the courses to others and suggest some changes relating learning methods (more simulations, role playing and activities).

Keywords: cultural competence, nursing education, culture, language

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Authors: Grace karimi Muriithi, Behnaz Papari, Ali Arsalan, Christopher Shannon Edrington

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Complexity and nonlinearity of the control system design is increasing for DC microgrid applications when the cyber concept associated with the technology constraints will added to the picture. Controllers’ functionality during the critical operation mode is required to guaranteed specifically for a high profile applications such as NAVY DC ship power system (SPS) as an small-scaled DC microgrid. Thus, SPS is susceptible to cyber-attacks and, accordingly, can provide the disastrous effects. In this study, a machine learning (ML) approach is demonstrated to offer the promising performance of SPS for developing an effective and robust functionality over attacks time. Simulation results analysis demonstrate that the proposed method can improve the controllability successfully.

Keywords: controlability, cyber attacks, distribute control, machine learning

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Authors: Jose A. Diaz-Olivares, Nafise Mahdavian, Farhad Abtahi, Kaj Lindecrantz, Abdelakram Hafid, Fernando Seoane

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Despite new recommendations and guidelines for the evaluation of occupational risk assessments and their prevention, work-related musculoskeletal disorders are still one of the biggest causes of work activity disruption, productivity loss, sick leave and chronic work disability. It affects millions of workers throughout Europe, with a large-scale economic and social burden. These specific efforts have failed to produce significant results yet, probably due to the limited availability and high costs of occupational risk assessment at work, especially when the methods are complex, consume excessive resources or depend on self-evaluations and observations of poor accuracy. To overcome these limitations, a pervasive system of risk assessment tools in real time has been developed, which has the characteristics of a systematic approach, with good precision, usability and resource efficiency, essential to facilitate the prevention of musculoskeletal disorders in the long term. The system allows the combination of different wearable sensors, placed on different limbs, to be used for data collection and evaluation by a software solution, according to the needs and requirements in each individual working environment. This is done in a non-disruptive manner for both the occupational health expert and the workers. The creation of this solution allows us to attend different research activities that require, as an essential starting point, the recording of data with ergonomic value of very diverse origin, especially in real work environments. The software platform is here presented with a complimentary smart clothing system for data acquisition, comprised of a T-shirt containing inertial measurement units (IMU), a vest sensorized with textile electronics, a wireless electrocardiogram (ECG) and thoracic electrical bio-impedance (TEB) recorder and a glove sensorized with variable resistors, dependent on the angular position of the wrist. The collected data is processed in real-time through a mobile application software solution, implemented in commercially available Android-based smartphones and tablet platforms. Based on the collection of this information and its analysis, real-time risk assessment and feedback about postural improvement is possible, adapted to different contexts. The result is a tool which provides added value to ergonomists and occupational health agents, as in situ analysis of postural behavior can assist in a quantitative manner in the evaluation of work techniques and the occupational environment.

Keywords: ergonomics, mobile technologies, risk assessment, smart textiles

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Authors: Dimitrios Vlachopoulos, George Tsokkas

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Although it is not intended to identify distance education students as a homogeneous group, recent research has demonstrated that there are some demographic and personality common traits among most of them that provide the basis for the description of a typical distance learning student. The purpose of this paper is to describe these common traits and to facilitate their learning journey within a distance education program. The described research is an initiative of the Distance Education Unit at the European University Cyprus (Laureate International Universities) in the context of its action for the improvement of the students’ performance.

Keywords: distance education students, successful student performance, European University Cyprus, common traits

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Authors: Sayor Ajfar Aaron, Mushfiqur Rahman, Sajjat Hossain Abir, Ashif Newaz

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This study delves into the development of an artificial intelligence application designed for real-time emotion recognition from human vocal patterns. Utilizing advanced machine learning algorithms, including deep learning and neural networks, the paper highlights both the technical challenges and potential opportunities in accurately interpreting emotional cues from speech. Key findings demonstrate the critical role of diverse training datasets and the impact of ambient noise on recognition accuracy, offering insights into future directions for improving robustness and applicability in real-world scenarios.

Keywords: artificial intelligence, convolutional neural network, emotion recognition, vocal patterns

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Manolo Dulva Hina, Clement Thierry, Assia Soukane, Amar Ramdane-Cherif

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In this paper, we presented our innovative way of determining the driving context for a driving assistance system. We invoke the fusion of all parameters that describe the context of the environment, the vehicle and the driver to obtain the driving context. We created a training set that stores driving situation patterns and from which the system consults to determine the driving situation. A machine-learning algorithm predicts the driving situation. The driving situation is an input to the fission process that yields the action that must be implemented when the driver needs to be informed or assisted from the given the driving situation. The action may be directed towards the driver, the vehicle or both. This is an ongoing work whose goal is to offer an alternative driving assistance system for safe driving, green driving and comfortable driving. Here, ontologies are used for knowledge representation.

Keywords: cognitive driving, intelligent transportation system, multimodal system, ontology, machine learning

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Authors: Fahad Suleiman

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Queuing, in medical system is the process of moving patients in a specific sequence to a specific service according to the patients’ nature of illness. The term scheduling stands for the process of computing a schedule. This may be done by a queuing based scheduler. This paper focuses on the medical consultancy system, the different queuing algorithms that are used in healthcare system to serve the patients, and the average waiting time. The aim of this paper is to build automatic queuing system for organizing the medical queuing system that can analyses the queue status and take decision which patient to serve. The new queuing architecture model can switch between different scheduling algorithms according to the testing results and the factor of the average waiting time. The main innovation of this work concerns the modeling of the average waiting time is taken into processing, in addition with the process of switching to the scheduling algorithm that gives the best average waiting time.

Keywords: queuing systems, queuing system models, scheduling algorithms, patients

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Authors: Ralph Barnes

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The internationalization and globalization of education are now a huge, multi-million dollar industry. The movement of international students across the globe has provided a rich vein of revenue for universities and institutions of higher learning to exploit and harvest. A concerted effort has been made by universities worldwide to court students from overseas, with some countries relying up to one-third of student fees, coming from international students. Australian universities and English Language Centres are coming under increased government scrutiny in respect to such areas as the academic progression of international students, management and understanding of student visa requirements and the design of higher education courses and effective assessment regimes. As such, universities and other higher education institutions are restructuring themselves more as service providers rather than as strictly education providers. In this paper, the high-touch, tailored academic model currently followed by some Australian educational institutions to support international students, is examined and challenged. Academic support services offered to international students need to be coordinated, sustained and reviewed regularly, in order to assess their effectiveness. Maintaining the delivery of high-quality educational programs and learning outcomes for this high income-generating student cohort is vital, in order to continue the successful academic and social engagement by international students across the Australian university and higher education landscape.

Keywords: ESL, engagement, tertiary, learning

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Authors: Ene Rosemary Ndidiamaka, Nwangwu Florence Chinyere

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The physiochemical properties of the melon seed oil was studied to determine its potentials as viable feed stock for biodisel production. The melon seed was extracted by solvent extraction using n-hexane as the extracting solvent. In this research, methanol was the alcohol used in the production of biodiesel, although alcohols like ethanol, propanol may also be used. Sodium hydroxide was employed for the catalysis. The melon seed oil was characterized for specific gravity, pH, ash content, iodine value, acid value, saponification value, peroxide value, free fatty acid value, flash point, viscosity, and refractive index using standard methods. The melon seed oil had very high oil content. Specific gravity and flash point of the oil is satisfactory. However, moisture content of the oil exceeded the stipulated ASRTM standard for biodiesel production. The overall results indicates that the melon seed oil is suitable for single-stage transesterification process to biodiesel production.

Keywords: biodiesel, catalyst, melon seed, transesterification

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Authors: Alaa Yehia, Ayatollah Yehia, Sherif Yehia

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There is a recurring issue of low enrollments across many civil engineering departments in postsecondary institutions. While there have been moments where enrollments begin to increase, civil engineering departments find themselves facing low enrollments at around 60% over the last five years across the Middle East. There are many reasons that could be attributed to this decline, such as low entry-level salaries, over-saturation of civil engineering graduates in the job market, and a lack of construction projects due to the impending or current recession. However, this recurring problem alludes to an intrinsic issue of the curriculum. The societal shift to the usage of high technology such as machine learning (ML) and artificial intelligence (AI) demands individuals who are proficient at utilizing it. Therefore, existing curriculums must adapt to this change in order to provide an education that is suitable for potential and current students. In this paper, In order to provide potential solutions for this issue, the analysis considers two possible implementations of high technology into the civil engineering curriculum. The first approach is to implement a course that introduces applications of high technology in Civil Engineering contexts. While the other approach is to intertwine applications of high technology throughout the degree. Both approaches, however, should meet requirements of accreditation agencies. In addition to the proposed improvement in civil engineering curriculum, a different pedagogical practice must be adapted as well. The passive learning approach might not be appropriate for Gen Z students; current students, now more than ever, need to be introduced to engineering topics and practice following different learning methods to ensure they will have the necessary skills for the job market. Different learning methods that incorporate high technology applications, like AI, must be integrated throughout the curriculum to make the civil engineering degree more attractive to prospective students. Moreover, the paper provides insight on the importance and approach of adapting the Civil Engineering curriculum to address the current low enrollment crisis that civil engineering departments globally, but specifically in the Middle East, are facing.

Keywords: artificial intelligence (AI), civil engineering curriculum, high technology, low enrollment, pedagogy

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Authors: Marcos Devaner, Marcela Alves, Cledson Braga, Fabiano Alves, Wilton Bezerra

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This article discusses the inclusion of people with disabilities in the process of testing an accessible system solution for distance education. The accessible system, team profile, methodologies and techniques covered in the testing process are presented. The testing process shown in this paper was designed from the experience with user. The testing process emerged from lessons learned from past experiences and the end user is present at all stages of the tests. Also, lessons learned are reported and how it was possible the maturing of the team and the methods resulting in a simple, productive and effective process.

Keywords: experience report, accessible systems, software testing, testing process, systems, e-learning

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Authors: Eyup Ozkan, Ozkan U. Nalbantoglu, Aycan Gundogdu, Mehmet Hora, A. Emre Onuk

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The human microbiome has been associated with cardiological conditions and this relationship is becoming to be defined beyond the gastrointestinal track. In this study, we investigate the alteration in circulatory microbiota in the context of Coronary Artery Disease (CAD). We received circulatory blood samples from suspected CAD patients and maintain 16S ribosomal RNA sequencing to identify each patient’s microbiome. It was found that Corynebacterium and Methanobacteria genera show statistically significant differences between healthy and CAD patients. The overall biodiversities between the groups were observed to be different revealed by machine learning classification models. We also achieve and demonstrate the performance of a diagnostic method using circulatory blood microbiome-based estimation.

Keywords: coronary artery disease, blood microbiome, machine learning, angiography, next-generation sequencing

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Authors: Amina Babou

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Gender and language is a moot and miscellaneous arena in the sphere of socio-linguistics, which has been proliferated so widely and rapidly in recent years. The dawn of research on gender and foreign language education was against the feminist researchers who allowed space for the bustling concourse of voices and perspectives in the arena of gender and language differences, in the early to the mid-1970. The objective of this scrutiny is to explore to what extent teaching gender and language in the English as a Foreign Language (EFL) classroom plays a pivotal role in learning language information and skills. Moreover, the gist of this paper is to investigate how EFL students in Algeria conflate their gender identities with the linguistic practices and scholastic expertise. To grapple with the full range of issues about the EFL students’ awareness about the negotiation of meanings in the classroom, we opt for observing, interviewing, and questioning later to check using ‘how-do-you do’ procedure. The analysis of the EFL classroom discourse, from five Algerian universities, reveals that speaking strategies such as the manners students make an abrupt topic shifts, respond spontaneously to the teacher, ask more questions, interrupt others to seize control of conversations and monopolize the speaking floor through denying what others have said, do not sit very lightly on 80.4% of female students’ shoulders. The data indicate that female students display the assertive style as a strategy of learning to subvert the norms of femininity, especially in the speaking module.

Keywords: EFL students, gender identity, linguistic styles, foreign language

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Authors: Kari Björn

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Introduction of games has expanded the application area of computer-aided learning tools to wide variety of age groups of learners. Serious games engage the learners into a real-world -type of simulation and potentially enrich the learning experience. Institutional background of a Bachelor’s level engineering program in Information and Communication Technology is introduced, with detailed focus on one of its majors, Health Technology. As part of a Customer Oriented Software Application thematic semester, one particular course of “eHealth Business and Solutions” is described and reflected in a gamified framework. Learning a consistent view into vast literature of business management, strategies, marketing and finance in a very limited time enforces selection of topics relevant to the industry. Health Technology is a novel and growing industry with a growing sector in consumer wearable devices and homecare applications. The business sector is attracting new entrepreneurs and impatient investor funds. From engineering education point of view the sector is driven by miniaturizing electronics, sensors and wireless applications. However, the market is highly consumer-driven and usability, safety and data integrity requirements are extremely high. When the same technology is used in analysis or treatment of patients, very strict regulatory measures are enforced. The paper introduces a course structure using gamification as a tool to learn the most essential in a new market: customer value proposition design, followed by a market entry game. Students analyze the existing market size and pricing structure of eHealth web-service market and enter the market as a steering group of their company, competing against the legacy players and with each other. The market is growing but has its rules of demand and supply balance. New products can be developed with an R&D-investment, and targeted to market with unique quality- and price-combinations. Product cost structure can be improved by investing to enhanced production capacity. Investments can be funded optionally by foreign capital. Students make management decisions and face the dynamics of the market competition in form of income statement and balance sheet after each decision cycle. The focus of the learning outcome is to understand customer value creation to be the source of cash flow. The benefit of gamification is to enrich the learning experience on structure and meaning of financial statements. The paper describes the gamification approach and discusses outcomes after two course implementations. Along the case description of learning challenges, some unexpected misconceptions are noted. Improvements of the game or the semi-gamified teaching pedagogy are discussed. The case description serves as an additional support to new game coordinator, as well as helps to improve the method. Overall, the gamified approach has helped to engage engineering student to business studies in an energizing way.

Keywords: engineering education, integrated curriculum, learning experience, learning outcomes

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Authors: Prakash Singh

Abstract:

This study focused on the importance of collegiality to improve the effectiveness of a poorly resourced school (PRS). In an effective school that embraces collegiality as its culture, one can expect to find a teaching staff and a management team that shares responsibilities and accountabilities through the development of a common purpose and vision, regardless of whether the school is considered to be poorly resourced or not. Working together in collegial teams is a more effective way to accomplish tasks and to create a climate for effective learning, even for learners in PRSs from poor communities. The main aim of this study was therefore to determine whether collegiality as a leadership strategy could extract the best from people in a PRS, and consequently create the most effective and efficient educational climate possible. The responses received from the teachers and the principal at the PRS supports the notion that collegiality does have a positive influence on learning, as demonstrated by the improved academic achievement of the learners. The teachers were now more involved in the school. They agreed that this was a positive development. Their descriptions of increased involvement, shared accountability and shared decision-making identified important aspects of collegiality that transformed the school from being dysfunctional. Hence, it is abundantly clear that a collegial leadership style can help extract the best from people because the most effective and efficient educational climate can be created at a school when collegiality is employed. Collegial leadership demonstrates that even in PRSs, there are boundless opportunities to improve teaching and learning.

Keywords: collegiality, collegial leadership, effective educational climate, poorly resourced school

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Authors: Inci Develioglu, Hasan Firat Pulat

Abstract:

Sustainable development is one of the most important topics in today's world, and it is also an important research topic for geoenvironmental engineering. Dredging process is performed to expand the river and port channel, flood control and accessing harbors. Every year large amount of sediment are dredged for these purposes. Dredged marine soils can be reused as filling materials, road and foundation embankments, construction materials and wildlife habitat developments. In this study, geotechnical engineering properties and compressibility behavior of dredged soil obtained from the Izmir Bay were investigated. The samples with four different organic matter contents were obtained and particle size distributions, consistency limits, pH and specific gravity tests were performed. The consolidation tests were conducted to examine organic matter content (OMC) effects on compressibility behavior of dredged soil. This study has shown that the OMC has an important effect on the engineering properties of dredged soils. The liquid and plastic limits increased with increasing OMC. The lowest specific gravity belonged to sample which has the maximum OMC. The specific gravity values ranged between 2.76 and 2.52. The maximum void ratio difference belongs to sample with the highest OMC (De_11% = 0.38)_. As the organic matter content of the samples increases, the change in the void ratio has also increased. The compression index increases with increasing OMC.

Keywords: compressibility, consolidation, geotechnical properties, organic matter content, dredged soil

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Authors: Marilei Amadeu Sabino

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Before the advent of Cognitive Linguistics, metaphor was considered a stylistic issue, but now it is viewed as a critical component of everyday language and a fundamental mechanism of human conceptualizations of the world. It means that human beings' conceptual system (the way we think and act) is metaphorical in nature. Another interesting hypothesis in Cognitive Linguistics is that cognition is embodied, that is, our cognition is influenced by our experiences in the physical world: the mind is connected to the body and the body influences the mind. In this sense, it is believed that many conceptual metaphors appear to be potentially universal or near-universal, because people across the world share certain bodily experiences. In these terms, many metaphors may be identical or very similar in several languages. Thus, in this study, we analyzed some somatic (also called body) idioms of Italian and Portuguese languages, in order to investigate the proportion in which their metaphors are the same, similar or different in both languages. It was selected hundreds of Italian idioms in dictionaries and indicated their corresponding idioms in Portuguese. The analysis allowed to conclude that much of the studied expressions are really structurally, semantically and metaphorically identical or similar in both languages. We also contrasted some Portuguese and Italian somatic expressions to their corresponding English idioms to have a multilingual perspective of the issue, and it also led to the conclusion that the most common idioms based on metaphors are probably those that have to do with the human body. Although this is mere speculation and needs more study, the results found incite relevant discussions on issues that matter Foreign and Second Language Teaching and Learning, including the retention of vocabulary. The teaching of the metaphorically different body idioms also plays an important role in language learning and teaching as it will be shown in this paper. Acknowledgments: FAPESP – São Paulo State Research Support Foundation –the financial support offered (proc. n° 2017/02064-7).

Keywords: body idioms, cognitive linguistics, metaphor, vocabulary teaching and learning

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Authors: Massimiliano Condotta, Giovanni Borga, Chiara Scanagatta

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Urban systems are places where the various actors involved interact and enter in conflict, in particular with reference to topics such as traffic congestion and security. But topics of discussion, and often clash because of their strong complexity, are air and noise pollution. For air pollution, the complexity stems from the fact that atmospheric pollution is due to many factors, but above all, the observation and measurement of the amount of pollution of a transparent, mobile and ethereal element like air is very difficult. Often the perceived condition of the inhabitants does not coincide with the real conditions, because it is conditioned - sometimes in positive ways other in negative ways - from many other factors such as the presence, or absence, of natural elements such as trees or rivers. These problems are seen with noise pollution as well, which is also less considered as an issue even if it’s problematic just as much as air quality. Starting from these opposite positions, it is difficult to identify and implement valid, and at the same time shared, mitigation solutions for the problem of urban pollution (air and noise pollution). The LOOPER (Learning Loops in the Public Realm) project –described in this paper – wants to build and test a methodology and a platform for participatory co-design, planning, and construction process inside a learning loop process. Novelties in this approach are various; the most relevant are three. The first is that citizens participation starts since from the research of problems and air quality analysis through a participatory data collection, and that continues in all process steps (design and construction). The second is that the methodology is characterized by a learning loop process. It means that after the first cycle of (1) problems identification, (2) planning and definition of design solution and (3) construction and implementation of mitigation measures, the effectiveness of implemented solutions is measured and verified through a new participatory data collection campaign. In this way, it is possible to understand if the policies and design solution had a positive impact on the territory. As a result of the learning process produced by the first loop, it will be possible to improve the design of the mitigation measures and start the second loop with new and more effective measures. The third relevant aspect is that the citizens' participation is carried out via Urban Living Labs that involve all stakeholder of the city (citizens, public administrators, associations of all urban stakeholders,…) and that the Urban Living Labs last for all the cycling of the design, planning and construction process. The paper will describe in detail the LOOPER methodology and the technical solution adopted for the participatory data collection and design and construction phases.

Keywords: air quality, co-design, learning loops, noise pollution, urban living labs

Procedia PDF Downloads 365