Search results for: blended learning and teaching

Authors: Julia Wimmers-Klick

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At the Canadian University of British Columbia's Faculty of Medicine, anatomy has traditionally been taught through a combination of lectures and dissection labs in the first two years, with radiology taught separately through lectures and online modules. However, this separation may leave students underprepared for medical practice, as medical imaging is essential for diagnosing anatomical and pathological conditions. To address this, a pilot project was initiated aimed at integrating radiological imaging into anatomy dissection labs from day one of medical school. The incorporated radiological images correlated with the current dissection areas. Additional stations were added within the lab, tailored to the specific content being covered. These stations focused on bones, and quiz questions, along with light-box exercises using radiographs, CT scans, and MRIs provided by the radiology department. The images used were free of pathologies. Examples of these will be presented in the poster. Feedback from short interviews with students and instructors has been positive, particularly among second-year students who appreciated the integration compared to their first-year experience. This low-budget approach was easy to implement but faced challenges, as lab instructors were not radiologists and occasionally struggled to answer students' questions. Instructors expressed a desire for basic training or a refresher course in radiology image reading, particularly focused on identifying healthy landmarks. Overall, all participants agreed that integrating radiology with anatomy reinforces learning during dissection, enhancing students' understanding and preparation for clinical practice.

Keywords: quality improvement, radiology education, anatomy education, integration

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Authors: Jamunarani Appalasamy, Tha Kyi Kyi, Quek Kia Fatt, Joyce Pauline Joseph, Anuar Zaini M. Zain

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Background: The Health Belief Theory had always been associated with chronic disease management. Various health behaviour concepts and perception branching from this Health Belief Theory had involved with medication understanding, use, and self-efficacy which directly link to medication adherence. In a previous quantitative and qualitative study, stroke patients in Malaysia were found to be strongly believing information obtained by various sources such as the internet and social communication. This action leads to lower perception of their stroke preventative medication benefit which in long-term creates non-adherence. Hence, this study intends to pilot an intervention which uses audio-visual concept incorporated with mHealth service to enhance learning and self-reflection among stroke patients to manage their disease. Methods/Design: Twenty patients will be allocated to a proposed intervention whereas another twenty patients are allocated to the usual treatment. The intervention involves a series of developed audio-visual videos sent via mobile phone which later await for responses and feedback from the receiver (patient) via SMS or recorded calls. The primary outcome would be the medication understanding, use and self-efficacy measured over two months pre and post intervention. Secondary outcome is measured from changes of blood parameters and other self-reported questionnaires. Discussion: This study shall also assess uptake/attrition, feasibility, and acceptability of this intervention. Trial Registration: NMRR-15-851-24737 (IIR)

Keywords: health belief, medication understanding, medication use, self-efficacy

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Authors: Zhu Weimin, Allegorico Carmine, Ruggiero Gionata

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During decades of Baker Hughes (BH) iCenter experience, it is demonstrated that in addition to conventional insights on equipment steady operation conditions, insights on transient conditions can add significant and exclusive value for anomaly detection, downtime saving, and predictive maintenance. Our work shows examples from the BH iCenter experience to introduce the advantages and features of using transient condition analytics: (i) Operation under critical engine conditions: e.g., high level or high change rate of temperature, pressure, flow, vibration, etc., that would not be reachable in normal operation, (ii) Management of dedicated sub-systems or components, many of which are often bottlenecks for reliability and maintenance, (iii) Indirect detection of anomalies in the absence of instrumentation, (iv) Repetitive sequences: if data is properly processed, the engineering features of transients provide not only anomaly detection but also problem characterization and prognostic indicators for predictive maintenance, (v) Engine variables accounting for fatigue analysis. iCenter has been developing and deploying a series of analytics based on transient conditions. They are contributing to exclusive value adding in the following areas: (i) Reliability improvement, (ii) Startup reliability improvement, (iii) Predictive maintenance, (iv) Repair/overhaul cost down. Illustrative examples for each of the above areas are presented in our study, focusing on challenges and adopted techniques ranging from purely statistical approaches to the implementation of machine learning algorithms. The obtained results demonstrate how the value is obtained using transient condition analytics in the BH iCenter experience.

Keywords: analytics, diagnostics, monitoring, turbomachinery

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Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

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Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

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Authors: Kothuri Sriraman, Mattupalli Komal Teja

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In this paper, a novel algorithm is proposed for the recognition of hulls in a hand written images that might be irregular or digit or character shape. Identification of objects and internal objects is quite difficult to extract, when the structure of the image is having bulk of clusters. The estimation results are easily obtained while going through identifying the sub-regional objects by using the SASK algorithm. Focusing mainly to recognize the number of internal objects exist in a given image, so as it is shadow-free and error-free. The hard clustering and density clustering process of obtained image rough set is used to recognize the differentiated internal objects, if any. In order to find out the internal hull regions it involves three steps pre-processing, Boundary Extraction and finally, apply the Hull Detection system. By detecting the sub-regional hulls it can increase the machine learning capability in detection of characters and it can also be extend in order to get the hull recognition even in irregular shape objects like wise black holes in the space exploration with their intensities. Layered hulls are those having the structured layers inside while it is useful in the Military Services and Traffic to identify the number of vehicles or persons. This proposed SASK algorithm is helpful in making of that kind of identifying the regions and can useful in undergo for the decision process (to clear the traffic, to identify the number of persons in the opponent’s in the war).

Keywords: chain code, Hull regions, Hough transform, Hull recognition, Layered Outline Extraction, SASK algorithm

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Authors: Jianhong Xiang, Rui Sun, Linyu Wang

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In recent years, due to the continuous improvement of deep learning theory, Convolutional Neural Network (CNN) has played a great superior performance in the research of Hyperspectral Image (HSI) classification. Since HSI has rich spatial-spectral information, only utilizing a single dimensional or single size convolutional kernel will limit the detailed feature information received by CNN, which limits the classification accuracy of HSI. In this paper, we design a multi-scale CNN with MLP based on residual and attention modules (INRAM-3DCNN) for the HSI classification task. We propose to use multiple 3D convolutional kernels to extract the packet feature information and fully learn the spatial-spectral features of HSI while designing residual 3D convolutional branches to avoid the decline of classification accuracy due to network degradation. Secondly, we also design the 2D Inception module with a joint channel attention mechanism to quickly extract key spatial feature information at different scales of HSI and reduce the complexity of the 3D model. Due to the high parallel processing capability and nonlinear global action of the Multilayer Perceptron (MLP), we use it in combination with the previous CNN structure for the final classification process. The experimental results on two HSI datasets show that the proposed INRAM-3DCNN method has superior classification performance and can perform the classification task excellently.

Keywords: INRAM-3DCNN, residual, channel attention, hyperspectral image classification

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: Nina Leila Mussa

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Background: The devastating effects of the war on Syria’s educational infrastructure has been widely reported, with millions of children denied access. However, among those who resettled in Lebanon, the impact of receiving educational assistance on their abilities to pass the English entrance exam is not well described. The aim of this study was to identify predictors of success among Syrian refugees receiving English language courses in a Lebanese university. Methods: The database of Syrian refugee girls matriculated in English courses at the American University of Beirut (AUB) was reviewed. The study period was 7/2018-09/2020. Variables compared included: family size and income, welfare status, parents’ education, English proficiency, access to the internet, and need for external help with homework. Results: For the study period, there were 28 girls enrolled. The average family size was 6 (range 4-9), with eight having completed primary, 14 secondary education, and 6 graduated high school. Eighteen were single-income families. After 12 weeks of English courses, 16 passed the Test of English as Foreign Language (TOEFL) from the first attempt, and 12 failed. Out of the 12, 8 received external help, and 6 passed on the second attempt, which brings the total number of successful passing to 22. Conclusion: Despite the tragedy of war, girls receiving assistance in learning English in Lebanon are able to pass the basic language test. Investment in enhancing those educational experiences will be determinantal in achieving widespread progress among those at-risk children.

Keywords: refugee girls, TOEFL, education, success

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Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

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With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

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Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

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Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

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Authors: Muhammad Zafarullah Khan, Sumeera Abbasi

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The Farmers Field School (FFS) learning approach aims to improve knowledge of the farmers through integrated crop management and provide leadership in their decision making process. The study was conducted to assess the impact of FFS on vegetables production before and after FFS intervention in four villages of district Peshawar in cropping season 2012, by interviewing 80 FFS respondents, twenty from each selected village. It was observed from the study results that all the respondents were satisfied from the impact of FFS and they informed an increased in production in vegetables. It was further observed that after the implementation of FFS the sowing seed rate of tomato and cucumber were decreased from 0.185kg/kanal to 0.100 kg/ kanal and 0.120kg/kanal to 0.010kg/kanal where as the production of tomato and cucumber were increased from 8158.75kgs/kanal to 10302. 5kgs/kanal and 3230kgs/kanal to 5340kgs/kanal, respectively. The cost of agriculture inputs per kanal including seed cost, crop management, Farm Yard Manure, and weedicides in case of tomato were reduced by Rs.28, Rs. 3170, Rs.658and Rs 205 whereas in cucumber reduced by Rs.35, Rs.570, Rs 80 and Rs.430 respectively. Only fertilizers cost was increased by Rs. 2200 in case of tomato and Rs 465 in case of cucumber. Overall the cost was reduced to Rs 545 in tomato and Rs 490 in cucumber production.FFS provided a healthy vegetables and also reduced input cost by adopting integrated crop management. Therefore the promotion of FFS is needed to be planned for farmers to reduce cost of production, so that the more farmers should be benefited.

Keywords: impact, farmer field schools, vegetable production, Peshawar Khyber Pakhtunkhwa

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: Hanadi Khadawardi

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The current study contributes to research on foreign/second language (L2) academic reading by presenting a significant case study, which seeks to investigate specific groups of international (Arab) postgraduate students’ L2 academic reading practices in the UK educational context. In particular, the study scrutinises postgraduate students’ L2 paper-based and digital-based academic reading strategies, and their use of digital aids while engaged in L2 academic reading. To this end, the study investigates Arab readers’ attitudes toward digital L2 academic reading. The study aims to compare between paper and digital L2 academic reading strategies that the students employ and which reading formats they prefer. This study tracks Masters-level students and examines the way in which their reading strategies and attitudes change throughout their Masters programme in the UK educational context. The academic reading strategies and attitudes of five students from four different disciplines (Health Science, Psychology, Management, and Education) are investigated at two points during their one-year Masters programmes. In addition, the study investigates the same phenomenon with 15 Saudi PhD students drawn from seven different disciplines (Computer Science, Engineering, Psychology, Management, Marketing, Health Science, and Applied Linguistics) at one period of their study in the same context. The study uses think-aloud protocol, field notes, stimulated recall, and semi-structured interviews to collect data. The data is analysed qualitatively. The results of the study will explain the process of learning in terms of reading L2 paper and digital academic texts in the L2 context.

Keywords: EFL: English as a foreign language, ESL: English as a second language, L: Language

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Authors: Derjew Ayele Ejigu, Houde Song, Xiaojing Liu

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This work presents the particle swarm optimization trained neural network (PSO-NN) supervisory proportional integral derivative (PID) control method to monitor the pressurized water reactor (PWR) core power for safe operation. The proposed control approach is implemented on the transfer function of the PWR core, which is computed from the state-space model. The PWR core state-space model is designed from the neutronics, thermal-hydraulics, and reactivity models using perturbation around the equilibrium value. The proposed control approach computes the control rod speed to maneuver the core power to track the reference in a closed-loop scheme. The particle swarm optimization (PSO) algorithm is used to train the neural network (NN) and to tune the PID simultaneously. The controller performance is examined using integral absolute error, integral time absolute error, integral square error, and integral time square error functions, and the stability of the system is analyzed by using the Bode diagram. The simulation results indicated that the controller shows satisfactory performance to control and track the load power effectively and smoothly as compared to the PSO-PID control technique. This study will give benefit to design a supervisory controller for nuclear engineering research fields for control application.

Keywords: machine learning, neural network, pressurized water reactor, supervisory controller

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Authors: Arunima Verma, Padmabati Mondal

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Serotonin-receptor binding plays a key role in several neurological and biological processes, including mood, sleep, hunger, cognition, learning, and memory. In this article, we performed molecular dynamics simulation to examine the key residues that play an essential role in the binding of serotonin to the G-protein-coupled 5-HT₁ᴮ receptor (5-HT₁ᴮ R) via electrostatic interactions. An end-point free energy calculation method (MM-PBSA) determines the stability of the 5-HT1B R due to serotonin binding. The single-point mutation of the polar or charged amino acid residues (Asp129, Thr134) on the binding sites and the calculation of binding free energy validate the importance of these residues in the stability of the serotonin-receptor complex. Principal component analysis indicates the serotonin-bound 5-HT1BR is more stabilized than the apo-receptor in terms of dynamical changes. The difference dynamic cross-correlations map shows the correlation between the transmembrane and mini-Go, which indicates signal transduction happening between mini-Go and the receptor. Allosteric communication reveals the key nodes for signal transduction in 5-HT1BR. These results provide useful insights into the signal transduction pathways and mutagenesis study to regulate the functionality of the complex. The developed protocols can be applied to study local non-covalent interactions and long-range allosteric communications in any protein-ligand system for computer-aided drug design.

Keywords: allostery, CADD, MD simulations, MM-PBSA

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: Hela Ghali, Sihem Ben Fredj, Mohamed Ben Rejeb, Sawssen Layouni, Salwa Khefacha, Lamine Dhidah, Houyem Said Laatiri

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Background: Antimicrobial resistance is a growing threat to public health and motivates to improve prevention and control programs both at international (WHO) and national levels. Despite their low pathogenicity, vancomycin-resistant enterococci (VRE) are common nosocomial pathogens in several countries. The high potential for transmission of VRE between patients and the threat to send its resistance genes to other bacteria such as staphylococcus aureus already resistant to meticilin, justify strict control measures. Indeed, in Europe, the proportion of Enterococcus faecium responsible for invasive infections, varies from 1% to 35% in 2011 and less than 5% were resistant to vancomycin. In addition, it represents the second cause of urinary tract and wound infections and the third cause of nosocomial bacteremia in the United States. The nosocomial outbreaks of VRE have been mainly described in intensive care services, hematology-oncology and haemodialysis. An epidemic of VRE has affected our hospital and the objective of this work is to describe the measures put in place. Materials/Methods: Following the alert given by the service of plastic surgery concerning a patient carrier of VRE, a team of the prevention and healthcare security service (doctor + technician) made an investigation. A review of files was conducted to draw the synoptic table and the table of cases. Results: By contacting the microbiology laboratory, we have identified four other cases of VRE and who were hospitalized in Medical resuscitation department (2 cases, one of them was transferred to the Physical rehabilitation department), and Nephrology department (2 cases). The visit has allowed to detect several malfunctions in professional practice. A crisis cell has allowed to validate, coordinate and implement control measures following the recommendations of the Technical Center of nosocomial infections. In fact, the process was to technically isolate cases in their sector of hospitalization, to restrict the use of antibiotics, to strength measures of basic hygiene, and to make a screening by rectal swab for both cases and contacts (other patients and health staff). These measures have helped to control the situation and no other case has been reported for a month. 2 new cases have been detected in the intensive care unit after a month. However, these are short-term strategies, and other measures in the medium and long term should be taken into account in order to face similar outbreaks. Conclusion: The efforts to control the outbreak were not efficient since 2 new cases have been reported after a month. Therefore, a continuous monitoring in order to detect new cases earlier is crucial to minimize the dissemination of VRE.

Keywords: hospitals, nosocomial infection, outbreak, vancomycin-resistant enterococci

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Authors: Ewa M. Laskowska, Jorn Vatn

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Industry 4.0 focuses on digital optimization of industrial processes. The idea is to use extracted data in order to build a decision support model enabling use of those data for real time decision making. In terms of predictive maintenance, the desired decision support tool would be a model enabling prognostics of system's health based on the current condition of considered equipment. Within area of system prognostics and health management, a commonly used health indicator is Remaining Useful Lifetime (RUL) of a system. Because the RUL is a random variable, it has to be estimated based on available health indicators. Health indicators can be of different types and come from different sources. They can be process variables, equipment performance variables, data related to number of experienced failures, etc. The aim of this study is the analysis of performance variables of emergency shutdown valves (ESV) used in oil and gas industry. ESV is inspected periodically, and at each inspection torque and time of valve operation are registered. The data will be analyzed by means of machine learning or statistical analysis. The purpose is to investigate whether the available data could be used as a health indicator for a prognostic purpose. The second objective is to examine what is the most efficient way to incorporate the data into predictive model. The idea is to check whether the data can be applied in form of explanatory variables in Markov process or whether other stochastic processes would be a more convenient to build an RUL model based on the information coming from registered data.

Keywords: emergency shutdown valves, health indicator, prognostics, remaining useful lifetime, RUL

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Authors: Benyapa Thitimapong

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Teenage pregnancy and adolescent mothers have become a matter of increasing concern in Thailand. Since adolescent mothers have been a big problem for two main consequences; health outcomes and socio-economic impacts. Adolescent mothers often endure poor living conditions; limited financial resources while also experience high stress, family instability, and limited educational opportunities. These disadvantages are negative and have long-term effects on adolescent mothers, their families, and the community. The majority of pregnant students and adolescent mothers dropped out of school after becoming pregnant, and some of them return to study again after they gave birth. This research aimed to explain the meaning of adolescent mothers who had undergone with childrearing and studying simultaneously after childbirth. A phenomenological qualitative approach was undertaken to investigate this study. The participants were 20 adolescent mothers each of whom became a mother and a student concurrently within less than 2 years after giving birth to a healthy baby and had also undergone the experience of childrearing and studying in non-formal education. In-depth interview was carried out for data collection, and the data were analyzed using content analysis method. ‘Learning to move forward’ was the meaning of adolescent mothers who experienced with childrearing and studying simultaneously. Their expressions were classified into two categories 1) having more responsibility, and 2) conceding and going on. The result of this study can be used as evidence for health care providers, especially nurses to facilitate and support pregnant adolescents and adolescent mothers to continue their education. Also, it can be used to guide policy to promote in all educational system to enable these groups to remain in school for their life-long success in the future.

Keywords: adolescent mothers, childrearing, studying, teenage pregnancy

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Authors: Sylvanus N. Wosu

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Research shows that factors, such as low motivation, preparation, resources, emotional and social integration, and fears of risk-taking, are the most common barriers to access, matriculation, and retention into science, technology, engineering, and mathematics (STEM) disciplines for underrepresented (URM) students. These factors have been shown to impact students’ attraction and success in STEM fields. Standardized tests such as the SAT and ACT often used as predictor of success, are not always true predictors of success for African and Hispanic American students. Without an adequate academic support environment, even a high SAT score does not guarantee academic success in science and engineering. This paper proposes a model for Academic Coaching for building success and inclusive excellence in STEM education. Academic coaching is framed as a process of motivating students to be independent learners through relational mentorship, facilitating learning supports inside and outside of the classroom or school environment, and developing problem-solving skills and success attitudes that lead to higher performance in the specific subjects. The model is formulated based on best strategies and practices for enriching Academic Performance Impact skills and motivating students’ interests in STEM. A scaled model for measuring the Academic Performance Impact (API) index and STEM is discussed. The study correlates API with state standardized test and shows that the average impact of those skills can be predicted by the Academic Performance Impact (API) index or Academic Preparedness Index.

Keywords: diversity, equity, graduate education, inclusion, inclusive excellence, model

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Authors: Lal Hussain, Wajid Aziz, Imtiaz Ahmed Awan, Sharjeel Saeed

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Multiscale entropy analysis (MSE) is a useful technique recently developed to quantify the dynamics of physiological signals at different time scales. This study is aimed at investigating the electroencephalogram (EEG) signals to analyze the background activity of alcoholic and control subjects by inspecting various coarse-grained sequences formed at different time scales. EEG recordings of alcoholic and control subjects were taken from the publically available machine learning repository of University of California (UCI) acquired using 64 electrodes. The MSE analysis was performed on the EEG data acquired from all the electrodes of alcoholic and control subjects. Mann-Whitney rank test was used to find significant differences between the groups and result were considered statistically significant for p-values<0.05. The area under receiver operator curve was computed to find the degree separation between the groups. The mean ranks of MSE values at all the times scales for all electrodes were higher control subject as compared to alcoholic subjects. Higher mean ranks represent higher complexity and vice versa. The finding indicated that EEG signals acquired through electrodes C3, C4, F3, F7, F8, O1, O2, P3, T7 showed significant differences between alcoholic and control subjects at time scales 1 to 5. Moreover, all electrodes exhibit significance level at different time scales. Likewise, the highest accuracy and separation was obtained at the central region (C3 and C4), front polar regions (P3, O1, F3, F7, F8 and T8) while other electrodes such asFp1, Fp2, P4 and F4 shows no significant results.

Keywords: electroencephalogram (EEG), multiscale sample entropy (MSE), Mann-Whitney test (MMT), Receiver Operator Curve (ROC), complexity analysis

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Authors: Fujia Yang

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Alongside technological innovation, the current “double reduction” policy and English Curriculum Standards for Compulsory Education in China both emphasise and encourage appropriately integrating educational technologies into the classroom. Therefore, schools are increasingly using digital means to engage students in English reading, but the impact of such technologies on Chinese pupils’ reading achievement remains unclear. To serve as a reference for reforming English reading education in primary schools under the double reduction policy, this study investigates the effects and primary influencing factors of a specific reading programme, Accelerated Reader (AR), on Chinese primary school students’ reading achievement. A quantitative online survey was used to collect 37 valid questionnaires from teachers, and the results demonstrate that, from teachers’ perspectives, the AR program seemed to positively affect students’ reading achievement by recommending material at the appropriate reading levels and developing students’ reading habits. Although the reading enjoyment derived from the AR program does not directly influence students’ reading achievement, these factors are strongly correlated. This can be explained by the self-paced, independent learning AR format, its high accuracy in predicting reading level, the quiz format and external motivation, and the importance of examinations and resource limitations in China. The results of this study may support reforming English reading education in Chinese primary schools.

Keywords: educational technology, reading programme, primary students, accelerated reader, reading effects

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Authors: Hamed Yousefi, Farnaz Mohammadi, Niloufar Mirian, Navid Amini

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Investigating possible capacities of visual functions where adapted mechanisms can enhance the capability of sports trainees is a promising area of research, not only from the cognitive viewpoint but also in terms of unlimited applications in sports training. In this paper, the visual evoked potential (VEP) and event-related potential (ERP) signals of amateur and trained volleyball players in a pilot study were processed. Two groups of amateur and trained subjects are asked to imagine themselves in the state of receiving a ball while they are shown a simulated volleyball field. The proposed method is based on a set of time-frequency features using algorithms such as Gabor filter, continuous wavelet transform, and a multi-stage wavelet decomposition that are extracted from VEP signals that can be indicative of being amateur or trained. The linear discriminant classifier achieves the accuracy, sensitivity, and specificity of 100% when the average of the repetitions of the signal corresponding to the task is used. The main purpose of this study is to investigate the feasibility of a fast, robust, and reliable feature/model determination as a neurofeedback parameter to be utilized for improving the volleyball players’ performance. The proposed measure has potential applications in brain-computer interface technology where a real-time biomarker is needed.

Keywords: visual evoked potential, time-frequency feature extraction, short-time Fourier transform, event-related spectrum potential classification, linear discriminant analysis

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Authors: Jonathan Munyoro, Takawira Machimbidza, Stephen Mutula

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The study investigates the current status of Intellectual Property (IP) information services in Zimbabwe's university libraries. Specifically, the study assesses the current IP information services offered in Zimbabwe’s university libraries, identifies challenges to the development of comprehensive IP information services in Zimbabwe’s university libraries, and suggests solutions for the development of IP information services in Zimbabwe’s university libraries. The study is born out of a realisation that research on IP information services in university libraries has received little attention, especially in developing country contexts, despite the fact that there are calls for heightened participation of university libraries in IP information services. In Zimbabwe, the launch of the National Intellectual Property Policy and Implementation Strategy 2018-2022 and the introduction of the Education 5.0 concept are set to significantly change the IP landscape in the country. Education 5.0 places more emphasis on innovation and industrialisation (in addition to teaching, community service, and research), and has the potential to shift the focus and level of IP output produced in higher and tertiary education institutions beyond copyrights and more towards commercially exploited patents, utility models, and industrial designs. The growing importance of IP commercialisation in universities creates a need for appropriate IP information services to assist students, academics, researchers, administrators, start-ups, entrepreneurs, and inventors. The critical challenge for university libraries is to reposition themselves and remain relevant in the new trajectory. Designing specialised information services to support increased IP generation and commercialisation appears to be an opportunity for university libraries to stay relevant in the knowledge economy. However, IP information services in Zimbabwe’s universities appear to be incomplete and focused mostly on assisting with research publications and copyright-related activities. Research on the existing status of IP services in university libraries in Zimbabwe is therefore necessary to help identify gaps and provide solutions in order to stimulate the growth of new forms of such services. The study employed a quantitative approach. An online questionnaire was administered to 57 academic librarians from 15 university libraries. Findings show that the current focus of the surveyed institutions is on providing scientific research support services (15); disseminating/sharing university research output (14); and copyright activities (12). More specialised IP information services such as IP education and training, patent information services, IP consulting services, IP online service platforms, and web-based IP information services are largely unavailable in Zimbabwean university libraries. Results reveal that the underlying challenge in the development of IP information services in Zimbabwe's university libraries is insufficient IP knowledge among academic librarians, which is exacerbated by inadequate IP management frameworks in university institutions. The study proposes a framework for the entrenchment of IP information services in Zimbabwe's university libraries.

Keywords: academic libraries, information services, intellectual property, IP knowledge, university libraries, Zimbabwe

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Authors: Sonny Nyeko, Moses Niwe

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Purpose: This research paper examined the mediation effect of organization culture in the relationship between information technology (IT) competence and IT governance effectiveness in Ugandan public universities. The purpose of the research paper is to examine the role of organizational culture in the relationship between IT competence and IT governance effectiveness. Design/methodology/approach: The paper adopted the MedGraph program, Sobel tests and Kenny and Baron Approach for testing the mediation effects. Findings: It is impeccable that IT competence and organization culture are true drivers of IT governance effectiveness in Ugandan public universities. However, organizational culture reveals partial mediation in the IT competence and IT governance effectiveness relationship. Research limitations/implications: The empirical investigation in this research depends profoundly on public universities. Future research in Ugandan private universities could be undertaken to compare results. Practical implications: To effectively achieve IT governance effectiveness, it means senior management requires IT knowledge which is a vital ingredient of IT competence. Moreover, organizations today ought to adopt cultures that are intended to have them competitive in their businesses, with IT operations not in isolation. Originality/value: Spending thousands of dollars on IT resources in advanced institutes of learning necessitates IT control. Preliminary studies in Ugandan public universities have revealed the ineffective utilization of IT resources. Besides, IT governance issues with IT competence and organization culture remain outstanding. Thus, it’s a new study testing the mediating outcome of organization culture in the association between IT competence and IT governance effectiveness in the Ugandan universities.

Keywords: organization culture, IT competence, IT governance, effectiveness, mediating effect, universities, Uganda

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Authors: Abiodun Ezekiel Adesina, Alice M. Olagunju

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This study determined the effects of Computer Assisted Instructions (CAI) and Academic Self-Concepts (ASC) on pre-service teachers’ achievement in AE concepts in CoE in Southwest, Nigeria. The study adopted pretest-posttest, control group, quasi-experimental design. Six CoE with e-library facilities were purposively selected. Two hundred and thirty-two intact 200 level Agricultural education students offering introduction to AE course across the six CoE were participants. The participants were assigned to three groups (D&PM, 77, TM, 73 and control, 82). Treatment lasted eight weeks. The AE achievement test (r=0.76), pre-service teachers’ ASC Scale (r=0.81); instructional guides for tutorial (r=0.76), drill and practice (r=0.81) and conventional lecture modes (r=0.83), and teacher performance assessment sheet were used for data collection. Data were analysed using analysis of covariance and Scheffe post-hoc at 0.05 level of significance. The participants were 55.6% female with mean age of 20.8 years. Treatment had significant main effects on pre-service teachers’ achievement (F(2,207)=60.52; η²=0.21; p < 0.05). Participants in D&PM (x̄ =27.83) had the best achievement compared to those in TM (x̄ =25.41) and control (x̄ =18.64) groups. ASC had significant main effect on pre-service teachers’ achievement (F(1,207)=22.011; η²=0.166; p < 0.05). Participants with high ASC (x̄ =27.52) had better achievement compared to those with low ASC (x̄ =22.37). The drill and practice and tutorial instructional modes enhanced students’ achievement in Agricultural Economics concepts. Therefore, the two instructional modes should be adopted for improved learning outcomes in agricultural economics concepts among pre-service teachers.

Keywords: achievement in agricultural economics concepts, colleges of education in southwestern Nigeria, computer-assisted instruction, drill and practice instructional mode, tutorial instructional mode

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Authors: Daudi S. Simbeye, Mbazingwa E. Mkiramweni

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Plant diseases have the potential to drastically diminish the quantity and quality of agricultural products. Guava (Psidium guajava), sometimes known as the apple of the tropics, is one of the most widely cultivated fruits in tropical regions. Monitoring plant health and diagnosing illnesses is an essential matter for sustainable agriculture, requiring the inspection of visually evident patterns on plant leaves and fruits. Due to minor variations in the symptoms of various guava illnesses, a professional opinion is required for disease diagnosis. Due to improper pesticide application by farmers, erroneous diagnoses may result in economic losses. This study proposes a method that uses artificial intelligence (AI) to detect and classify the most widespread guava plant by comparing images of its leaves and fruits to datasets. ESP32 CAM is responsible for data collection, which includes images of guava leaves and fruits. By comparing the datasets, these image formats are used as datasets to help in the diagnosis of plant diseases through the leaves and fruits, which is vital for the development of an effective automated agricultural system. The system test yielded the most accurate identification findings (99 percent accuracy in differentiating four guava fruit diseases (Canker, Mummification, Dot, and Rust) from healthy fruit). The proposed model has been interfaced with a mobile application to be used by smartphones to make a quick and responsible judgment, which can help the farmers instantly detect and prevent future production losses by enabling them to take precautions beforehand.

Keywords: early identification, guava plants, fruit diseases, deep learning

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Authors: Aisultan Shoiynbek, Darkhan Kuanyshbay, Paulo Menezes, Akbayan Bekarystankyzy, Assylbek Mukhametzhanov, Temirlan Shoiynbek

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Speech emotion recognition (SER) has received increasing research interest in recent years. It is a common practice to utilize emotional speech collected under controlled conditions recorded by actors imitating and artificially producing emotions in front of a microphone. There are four issues related to that approach: emotions are not natural, meaning that machines are learning to recognize fake emotions; emotions are very limited in quantity and poor in variety of speaking; there is some language dependency in SER; consequently, each time researchers want to start work with SER, they need to find a good emotional database in their language. This paper proposes an approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describes the sequence of actions involved in the proposed approach. One of the first objectives in the sequence of actions is the speech detection issue. The paper provides a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To investigate the working capacity of the developed model, an analysis of speech detection and extraction from real tasks has been performed.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Juhi Faridi, Mohd. Ajmal Kafeel

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The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution of an intelligent era. Neural networks, having the computational power and learning ability similar to the brain is one of the key AI technologies. Neuromorphic computing system (NCS) consists of the synaptic device, neuronal circuit, and neuromorphic architecture. Memristor are a promising candidate for neuromorphic computing systems, but when it comes to neuromorphic computing, the conductance behavior of the synaptic memristor or neuronal memristor needs to be studied thoroughly in order to fathom the neuroscience or computer science. Furthermore, there is a need of more simulation work for utilizing the existing device properties and providing guidance to the development of future devices for different performance requirements. Hence, development of NCS needs more simulation work to make use of existing device properties. This work aims to provide an insight to build neuronal circuits using memristors to achieve a Memristor based NCS.  Here we throw a light on the research conducted in the field of memristors for building analog and digital circuits in order to motivate the research in the field of NCS by building memristor based neural circuits for advanced AI applications. This literature is a step in the direction where we describe the various Key findings about memristors and its analog and digital circuits implemented over the years which can be further utilized in implementing the neuronal circuits in the NCS. This work aims to help the electronic circuit designers to understand how the research progressed in memristors and how these findings can be used in implementing the neuronal circuits meant for the recent progress in the NCS.

Keywords: analog circuits, digital circuits, memristors, neuromorphic computing systems

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Authors: Sanghoon Shim, Jaehwan Jung, Sung-Ah Kim

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As the digital technology develops, studies on the TUI (Tangible User Interface) that links the physical environment utilizing the human senses with the virtual environment through the computer are actively being conducted. In addition, there has been a tremendous advance in computer design making through the use of computer-aided design techniques, which enable optimized decision-making through comparison with machine learning and parallel comparison of alternatives. However, a complex design that can respond to user requirements or performance can emerge through the intuition of the designer, but it is difficult to actualize the emerged design by the designer's ability alone. Ancillary tools such as Gaudí's Sandbag can be an instrument to reinforce and evolve emerged ideas from designers. With the advent of many commercial tools that support 3D objects, designers' intentions are easily reflected in their designs, but the degree of their reflection reflects their intentions according to the proficiency of design tools. This study embodies the environment in which the form can be implemented by the fingers of the most basic designer in the initial design phase of the complex type building design. Leapmotion is used as a sensor to recognize the hand motions of the designer, and it is converted into digital information to realize an environment that can be linked in real time in virtual reality (VR). In addition, the implemented design can be linked with Rhino™, a 3D authoring tool, and its plug-in Grasshopper™ in real time. As a result, it is possible to design sensibly using TUI, and it can serve as a tool for assisting designer intuition.

Keywords: design environment, digital modeling, hand gesture, TUI, virtual reality

Procedia PDF Downloads 366