Search results for: module based teaching and learning

Authors: Marcelo Pereira Introini

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The global value chains (GVC) approach contributes to a better understanding of the international production organization amid globalization’s second unbundling from the 1970s on. Mainly due to the tools that help to understand the importance of critical competences, technological capabilities, and functions performed by each player, GVC research flourished in recent years, rooted in discussing the possibilities of integration and repositioning along regional and global value chains. Regarding this context, part of the literature endorsed a more optimistic view that engaging in fragmented production networks could represent learning opportunities for developing countries’ firms, since the relationship with transnational corporations could allow them build skills and competences. Increasing recognition that GVCs are based on asymmetric power relations provided another sight about benefits, costs, and development possibilities though. Once leading companies tend to restrict the replication of their technologies and capabilities by their suppliers, alternative strategies beyond the functional specialization, seen as a way to integrate value chains, began to be broadly highlighted. This paper organizes a coherent narrative about the shortcomings of the GVC analytical framework, while recognizing its multidimensional contributions and recent developments. We adopt two different and complementary perspectives to explore the idea of integration in the international production. On one hand, we emphasize obstacles beyond production components, analyzing the role played by intangible assets and intellectual property regimes. On the other hand, we consider the importance of domestic production and innovation systems for technological development. In order to provide a deeper understanding of the restrictions on technological learning of developing countries’ firms, we firstly build from the notion of intellectual monopoly to analyze how flagship companies can prevent subordinated firms from improving their positions in fragmented production networks. Based on intellectual property protection regimes we discuss the increasing asymmetries between these players and the decreasing access of part of them to strategic intangible assets. Second, we debate the role of productive-technological ecosystems and of interactive and systemic technological development processes, as concepts of the Innovation Systems approach. Supporting the idea that not only endogenous advantages are important for international competition of developing countries’ firms, but also that the building of these advantages itself can be a source of technological learning, we focus on local efforts as a crucial element, which is not replaceable for technology imported from abroad. Finally, the paper contributes to the discussion about technological development as a two-dimensional dynamic. If GVC analysis tends to underline a company-based perspective, stressing the learning opportunities associated to GVC integration, historical involvement of national States brings up the debate about technology as a central aspect of interstate disputes. In this sense, technology is seen as part of military modernization before being also used in civil contexts, what presupposes its role for national security and productive autonomy strategies. From this outlook, it is important to consider it as an asset that, incorporated in sophisticated machinery, can be the target of state policies besides the protection provided by intellectual property regimes, such as in export controls and inward-investment restrictions.

Keywords: global value chains, innovation systems, intellectual monopoly, technological development

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Authors: Imran Khan Swati, Mohammad Younas

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This work aims to explore the potential applications of polymeric hydrophobic membranes and green ionic liquids (ILs). Protic and aprotic ILs were synthesized in the lab., characterized, and tested for CO₂/N₂ and CO₂/CH₄ separation using hydrophobic polymeric membranes via supported ionic liquid membrane (SILM). ILs were verified by FTIR spectroscopy. The SILMs were stable at room temperature up to 0.5 MPa. For CO₂, [BSmim][tos] had the greatest coefficient of solubility and permeability, along with all ILs. At 0.5 MPa, IL [BSmim][tos] was found with a selectivity of 56.2 and 47.5 for pure CO₂/N₂ and CO₂/CH₄, respectively. The ILs synthesized for this study are rated as [BSmim][tos]>[BSmpy][tos]>[Bmim][Cl]>[Bpy][Cl] based on their SILM separation performance. Furthermore, high values of selectivity of [BSmim][tos] and [BSmpy][tos] support the use of ILs for CO₂ separation using SILMs. The study was extended to synthesize and test the ammonium-based ILs, [2-HEA][f] and [2-HEA][Hs]. These ILs achieved 50 % less selectivity for CO₂/N₂ as compared to [BSmim][tos] and [BSmpy][tos]. Nevertheless, the permeability of CO₂ achieved with [2-HEA][f] and [2-HEA][Hs] is more than 20 times higher than the [BSmim][tos] and [BSmpy][tos]. Later, the CO₂/N₂ permeability and selectivity study was extended using a flat sheet membrane contactor with recirculated IL. The contact angle effects, liquid entry pressure (LEP), initial CO₂ concentration, and type of solvents and membrane material on the CO₂ capture efficiency and membrane wetting in the post-combustion capture (PCC) process have been experimentally investigated and evaluated. Polytetrafluoroethylene (PTFE) has shown the most hydrophobic property with 6-170 loss in the contact angle. Furthermore, [Omim][BF4] and [Bmim][BF6] have exhibited only 5-8 % loss in LEP using PTFE membrane support. The CO₂ capture efficiency has been achieved as 80.8-99.8 % in different combinations of ILs and membrane support, keeping all other variables constant. While increasing CO₂ concentration from 15 to 45 % vol., an increase of nearly three folds in the CO₂ mass transfer flux was observed. The combination of [Omim][BF4] and PTFE membrane witnessed good long-term stability with only a 20 % loss in CO₂ capture efficiency in 480 min of continuous operation. A 3- D simulation model for non-dispersive solvent absorption in membrane contactors provides insight into the optimum design of a separation system for a specific application minimizing the overall cost and making the process environment-friendly.

Keywords: Post-combustion CO2 capture, membrane synthesis, process development, permeability and selectivity, ionic liquids

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Feng Wang, Shencheng Ni, Shuying Han, Shanhong You

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With the development of optical communication, optical performance monitoring (OPM) has received more and more attentions. Since optical signal-to-noise ratio (OSNR) is directly related to bit error rate (BER), it is one of the important parameters in optical networks. Recently, artificial neural network (ANN) has been greatly developed. ANN has strong learning and generalization ability. In this paper, a method of OSNR monitoring based on delay-tap sampling (DTS) and ANN has been proposed. DTS technique is used to extract the eigenvalues of the signal. Then, the eigenvalues are input into the ANN to realize the OSNR monitoring. The experiments of 10 Gb/s non-return-to-zero (NRZ) on–off keying (OOK), 20 Gb/s pulse amplitude modulation (PAM4) and 20 Gb/s return-to-zero (RZ) differential phase-shift keying (DPSK) systems are demonstrated for the OSNR monitoring based on the proposed method. The experimental results show that the range of OSNR monitoring is from 15 to 30 dB and the root-mean-square errors (RMSEs) for 10 Gb/s NRZ-OOK, 20 Gb/s PAM4 and 20 Gb/s RZ-DPSK systems are 0.36 dB, 0.45 dB and 0.48 dB respectively. The impact of chromatic dispersion (CD) on the accuracy of OSNR monitoring is also investigated in the three experimental systems mentioned above.

Keywords: artificial neural network (ANN), chromatic dispersion (CD), delay-tap sampling (DTS), optical signal-to-noise ratio (OSNR)

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Authors: Sabitha Rahim

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In the context of teaching vocabulary of English as Foreign Language (EFL), the confluence of memory and retention is one of the most significant factors in students' language acquisition. The progress of students engaged in foreign language acquisition is often stymied by vocabulary attrition, which leads to learners' lack of confidence and motivation. However, among other factors, little research has investigated the importance of neuroplasticity in Foreign Language acquisition and how underused neural pathways lead to the loss of plasticity, thereby affecting the learners’ vocabulary retention and motivation. This research explored the effect of enhancing vocabulary acquisition of EFL students in the Foundation Year at King Abdulaziz University through various methods and neuroplasticity exercises that reinforced their attention, motivation, and engagement. It analyzed the results to determine if stimulating the brain of EFL learners by various physical and mental activities led to the improvement in short and long term memory in vocabulary retention. The main data collection methods were student surveys, assessment records of teachers, student achievement test results, and students' follow-up interviews. A key implication of this research is for the institutions to consider having multiple varieties of student activities promoting brain plasticity within the classrooms as an effective tool for foreign language acquisition. Building awareness among the faculty and adapting the curriculum to include activities that promote brain plasticity ensures an enhanced learning environment and effective language acquisition in EFL classrooms.

Keywords: language acquisition, neural paths, neuroplasticity, vocabulary attrition

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Authors: Kuznetsova Tamara, Sametova Fauziya

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Modern period of educational development is characterized by various attempts in higher quality and effective result provision. Having acquired the modernized educational paradigm, our academic community placed the personality development through language and culture under the focus of primary research. The competency-based concept claims for professionally ready specialists who are capable of solving practical problems. In this sense, under the circumstances of the current development of Kazakhstani society, it is inevitable to form the ability to conduct argumentative discourse as the crucial element of intercultural communicative competence. This article particularly states the necessity of the culture of argumentative discourse formation presents theoretical background of its organization and aims at identifying important argumentative skills within educational process.

Keywords: argumentative discourse, teaching process, skills, competency

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Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Tayo P. Ogundunmade, Adedayo A. Adepoju

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Terrorist attacks in liberal democracies bring about a few pessimistic results, for example, sabotaged public support in the governments they target, disturbing the peace of a protected environment underwritten by the state, and a limitation of individuals from adding to the advancement of the country, among others. Hence, seeking for techniques to understand the different factors involved in terrorism and how to deal with those factors in order to completely stop or reduce terrorist activities is the topmost priority of the government in every country. This research aim is to develop an efficient deep learning-based predictive model for the prediction of future terrorist activities in Nigeria, addressing low-quality prediction accuracy problems associated with the existing solution methods. The proposed predictive AI-based model as a counterterrorism tool will be useful by governments and law enforcement agencies to protect the lives of individuals in society and to improve the quality of life in general. A Heterogeneous Bayesian Neural Network (HETBNN) model was derived with Gaussian error normal distribution. Three primary transfer functions (HOTTFs), as well as two derived transfer functions (HETTFs) arising from the convolution of the HOTTFs, are namely; Symmetric Saturated Linear transfer function (SATLINS ), Hyperbolic Tangent transfer function (TANH), Hyperbolic Tangent sigmoid transfer function (TANSIG), Symmetric Saturated Linear and Hyperbolic Tangent transfer function (SATLINS-TANH) and Symmetric Saturated Linear and Hyperbolic Tangent Sigmoid transfer function (SATLINS-TANSIG). Data on the Terrorist activities in Nigeria gathered through questionnaires for the purpose of this study were used. Mean Square Error (MSE), Mean Absolute Error (MAE) and Test Error are the forecast prediction criteria. The results showed that the HETFs performed better in terms of prediction and factors associated with terrorist activities in Nigeria were determined. The proposed predictive deep learning-based model will be useful to governments and law enforcement agencies as an effective counterterrorism mechanism to understand the parameters of terrorism and to design strategies to deal with terrorism before an incident actually happens and potentially causes the loss of precious lives. The proposed predictive AI-based model will reduce the chances of terrorist activities and is particularly helpful for security agencies to predict future terrorist activities.

Keywords: activation functions, Bayesian neural network, mean square error, test error, terrorism

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Authors: Oznur Ispir, Oya Celebi Cakiroglu, Esengul Elibol, Emine Ceribas, Gizem Acikgoz, Hande Yesilbas, Merve Tarhan

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This study was conducted to evaluate the academic staff teaching the 'Nursing Management' course in the undergraduate nursing programs of the state universities in Turkey and to assess the current content of the course. Design of the study is descriptive. Population of the study consists of seventy-eight undergraduate nursing programs in the state universities in Turkey. The questionnaire/survey prepared by the researchers was used as a data collection tool. The data were obtained by screening the content of the websites of nursing education programs between March and May 2016. Descriptive statistics were used to analyze the data. The research performed within the study indicated that 58% of the undergraduate nursing programs from which the data were derived were included in the school of health, 81% of the academic staff graduated from the undergraduate nursing programs, 40% worked as a lecturer and 37% specialized in a field other than the nursing. The research also implied that the above-mentioned course was included in 98% of the programs from which it was possible to obtain data. The full name of the course was 'Nursing Management' in 95% of the programs and 98% stated that the course was compulsory. Theory and application hours were 3.13 and 2.91, respectively. Moreover, the content of the course was not shared in 65% of the programs reviewed. This study demonstrated that the experience and expertise of the academic staff teaching the 'Nursing Management' course was not sufficient in the management area, and the schedule and content of the course were not sufficient although many nursing education programs provided the course. Comparison between the curricula of the course revealed significant differences.

Keywords: nursing, nursing management, nursing management course, undergraduate program

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Rajesh Kumar, Puneet Aggarwal

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In context of understanding problems faced by undergraduate students while carrying out laboratory experiments dealing with high voltages, it was found that most of the students are hesitant to work directly on machine. The reason is that error in the circuitry might lead to deterioration of machine and laboratory instruments. So, it has become inevitable to include modern pedagogic techniques for undergraduate students, which would help them to first carry out experiment in virtual system and then to work on live circuit. Further advantages include that students can try out their intuitive ideas and perform in virtual environment, hence leading to new research and innovations. In this paper, virtual environment used is of MATLAB/Simulink for three-phase induction machines. The performance analysis of three-phase induction machine is carried out using virtual environment which includes Direct Current (DC) Test, No-Load Test, and Block Rotor Test along with speed torque characteristics for different rotor resistances and input voltage, respectively. Further, this paper carries out computer aided teaching of basic Voltage Source Inverter (VSI) drive circuitry. Hence, this paper gave undergraduates a clearer view of experiments performed on virtual machine (No-Load test, Block Rotor test and DC test, respectively). After successful implementation of basic tests, VSI circuitry is implemented, and related harmonic distortion (THD) and Fast Fourier Transform (FFT) of current and voltage waveform are studied.

Keywords: block rotor test, DC test, no load test, virtual environment, voltage source inverter

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Authors: Yash Bingi, Yiqiao Yin

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Reduction of child mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this issue, Cardiotocograms (CTGs) have emerged as a leading tool to determine fetal health. By using ultrasound pulses and reading the responses, CTGs help healthcare professionals assess the overall health of the fetus to determine the risk of child mortality. However, interpreting the results of the CTGs is time-consuming and inefficient, especially in underdeveloped areas where an expert obstetrician is hard to come by. Using a support vector machine (SVM) and oversampling, this paper proposed a model that classifies fetal health with an accuracy of 99.59%. To further explain the CTG measurements, an algorithm based on Randomized Input Sampling for Explanation ((RISE) of Black-box Models was created, called Feature Alteration for explanation of Black Box Models (FAB), and compared the findings to Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME). This allows doctors and medical professionals to classify fetal health with high accuracy and determine which features were most influential in the process.

Keywords: machine learning, fetal health, gradient boosting, support vector machine, Shapley values, local interpretable model agnostic explanations

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Authors: Zeina Bou Diab, Arij Daou

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The neural mechanisms of sequential behaviors are intensively studied, with songbirds a focus for learned vocal production. We are studying the premotor nucleus HVC at a nexus of multiple pathways contributing to song learning and production. The HVC consists of multiple classes of neuronal populations, each has its own cellular, electrophysiological and functional properties. During singing, a large subset of motor cortex analog-projecting HVCRA neurons emit a single 6-10 ms burst of spikes at the same time during each rendition of song, a large subset of basal ganglia-projecting HVCX neurons fire 1 to 4 bursts that are similarly time locked to vocalizations, while HVCINT neurons fire tonically at average high frequency throughout song with prominent modulations whose timing in relation to song remains unresolved. This opens the opportunity to define models relating explicit HVC circuitry to how these neurons work cooperatively to control learning and singing. We developed conductance-based Hodgkin-Huxley models for the three classes of HVC neurons (based on the ion channels previously identified from in vitro recordings) and connected them in several physiologically realistic networks (based on the known synaptic connectivity and specific glutaminergic and gabaergic pharmacology) via different architecture patterning scenarios with the aim to replicate the in vivo firing patterning behaviors. We are able, through these networks, to reproduce the in vivo behavior of each class of HVC neurons, as shown by the experimental recordings. The different network architectures developed highlight different mechanisms that might be contributing to the propagation of sequential neural activity (continuous or punctate) in the HVC and to the distinctive firing patterns that each class exhibits during singing. Examples of such possible mechanisms include: 1) post-inhibitory rebound in HVCX and their population patterns during singing, 2) different subclasses of HVCINT interacting via inhibitory-inhibitory loops, 3) mono-synaptic HVCX to HVCRA excitatory connectivity, and 4) structured many-to-one inhibitory synapses from interneurons to projection neurons, and others. Replication is only a preliminary step that must be followed by model prediction and testing.

Keywords: computational modeling, neural networks, temporal neural sequences, ionic currents, songbird

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Authors: Manal Suleiman, George Abu-Aqil, Uraib Sharaha, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman, Mahmoud Huleihel

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Klebsiella pneumoniae is one of the most aggressive multidrug-resistant bacteria associated with human infections resulting in high mortality and morbidity. Thus, for an effective treatment, it is important to diagnose both the species of infecting bacteria and their susceptibility to antibiotics. Current used methods for diagnosing the bacterial susceptibility to antibiotics are time-consuming (about 24h following the first culture). Thus, there is a clear need for rapid methods to determine the bacterial susceptibility to antibiotics. Infrared spectroscopy is a well-known method that is known as sensitive and simple which is able to detect minor biomolecular changes in biological samples associated with developing abnormalities. The main goal of this study is to evaluate the potential of infrared spectroscopy in tandem with Random Forest and XGBoost machine learning algorithms to diagnose the susceptibility of Klebsiella pneumoniae to antibiotics within approximately 20 minutes following the first culture. In this study, 1190 Klebsiella pneumoniae isolates were obtained from different patients with urinary tract infections. The isolates were measured by the infrared spectrometer, and the spectra were analyzed by machine learning algorithms Random Forest and XGBoost to determine their susceptibility regarding nine specific antibiotics. Our results confirm that it was possible to classify the isolates into sensitive and resistant to specific antibiotics with a success rate range of 80%-85% for the different tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful diagnostic method for determining the Klebsiella pneumoniae susceptibility to antibiotics.

Keywords: urinary tract infection (UTI), Klebsiella pneumoniae, bacterial susceptibility, infrared spectroscopy, machine learning

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Authors: Alisha Sinha, Laxmi Kant Sharma

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Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). This study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Rajasthan, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Sariska National Park, Rajasthan (India), before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature, to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters.

Keywords: wildfire susceptibility mapping, LST, random forest, GEE, MODIS, climatic parameters

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Authors: Slim Chokri

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Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

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Authors: Anamika Devi

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Based on Vygotsky’s cultural, historical theory and Hedegaard’s concept of transition, this study aims to investigate to propose strategies to maintain digital wellbeing of children and parents during and post COVID pandemic. Due COVID 19 pandemic, children and families have been facing new challenges and sudden changes in their everyday life. While children are juggling to adjust themselves in new circumstance of onsite and online learning settings, parents are juggling with their work-life balance. A number of papers have identified that the COVID-19 pandemic has affected the lives of many families around the world in many ways, for example, the stress level of many parents increased, families faced financial difficulties, uncertainty impacted on long term effects on their emotional and social wellbeing. After searching and doing an intensive literature review from 2020 and 2021, this study has found some scholarly articles provided solution or strategies of reducing stress levels of parents and children in this unprecedented time. However, most of them are not underpinned by proper theoretical lens to ensure they validity and success. Therefore, this study has proposed strategies that are underpinned by theoretical lens to ensure their impact on children’s and parents' emotional wellbeing during and post COVID-19 era. The strategies will highlight on activities for positive coping strategies to the best use of family values and digital technologies.

Keywords: onsite and online learning, strategies, emotional wellbeing, tips, and strategies, COVID19

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Authors: Kafeero Frank

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This study aimed at developing a frame work for open source software adoption in an institution of higher learning in Uganda, with the case of KIU as a study area. There were mainly four research questions based on; individual staff interaction with open source software forum, perceived FOSS characteristics, organizational characteristics and external characteristics as factors that affect open source software adoption. The researcher used causal-correlation research design to study effects of these variables on open source software adoption. A quantitative approach was used in this study with self-administered questionnaire on a purposively and randomly sampled sample of university ICT staff. Resultant data was analyzed using means, correlation coefficients and multivariate multiple regression analysis as statistical tools. The study reveals that individual staff interaction with open source software forum and perceived FOSS characteristics were the primary factors that significantly affect FOSS adoption while organizational and external factors were secondary with no significant effect but significant correlation to open source software adoption. It was concluded that for effective open source software adoption to occur there must be more effort on primary factors with subsequent reinforcement of secondary factors to fulfill the primary factors and adoption of open source software. Lastly recommendations were made in line with conclusions for coming up with Kyambogo University frame work for open source software adoption in institutions of higher learning. Areas of further research recommended include; Stakeholders’ analysis of open source software adoption in Uganda; Challenges and way forward. Evaluation of Kyambogo University frame work for open source software adoption in institutions of higher learning. Framework development for cloud computing adoption in Ugandan universities. Framework for FOSS development in Uganda IT industry

Keywords: open source software., organisational characteristics, external characteristics, cloud computing adoption

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Authors: Sabina Mohsin

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The need for grounding of teachers and the trend of using innovative ways to deal with students of various abilities in schools, colleges and universities has always been essential in any part of the world. Teacher edification programs, and qualifications standards, all too repeatedly lack enough rigidity, extensiveness and profundity, resulting in high levels of unskilled teachers and squat student performance. Accordingly, the solution, from this viewpoint, lies in making the entry and training necessities for teaching deeper and more exact. Teachers’ continuous professional development is necessary to reach all kinds of learners in class. The training provided is a direct opportunity for new teachers to interact better and motivate students in a two way discussion class. The intention of the study was to scrutinize whether the teachers’ training played an important role to fabricate lectures and classroom activities and reflected the objectives of the training provided in various schools and universities. It also aims to examine the current practices used in the various teacher training programs and if there is any other method that can be associated to enhance the effectiveness of these programs further. This research uses qualitative data collected from interviews, peer discussions, classroom observations, reviews, feedback of students and teachers to study teacher training and teaching methods used in school and universities in Bangladesh. The study finds teacher training to be effective though it has some limitations. It also includes some suggestions to make teacher training more effective.

Keywords: current practices in teacher training, enhancing effectiveness, limitation, student motivation, teacher training

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Authors: William Anderson, Kyle Kobold, Oleg Yakimenko

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Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.

Keywords: microgrid, renewable energy, complex systems, optimization, predictive modeling, neural networks

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

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We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: situation-awareness, smart home, IoT, machine learning, classifier

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Authors: K. P. v. R. de Silva, R. W. Senevirathna, M. M. A. J. Kumara, J. P. M. Kumarasinghe, R. L. Gunawardana, S. M. Uluwitiya, G. C. P. Jayawickrama, W. K. T. I. Madushani

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Introduction: The limited literature supporting the utilization of mechanical bowel preparation (MBP) for patients undergoing laparoscopic anterior resection (LAR) remains a notable issue. This study was conducted to examine the clinical consequences of anastomosis in colorectal surgery with MBP compared to cases where MBP was not utilized (no-MBP) in the context of LAR. Methods: This was a retrospective comparative study conducted in the professorial surgical wards of the teaching hospital karapitiya (THK). Colorectal cancer patients(n=306) participated in the study, including 151 MBP patients and 155 no-MBP patients, where the postoperative complications and mortality rates were compared. Results: The anastomotic leakage rate was 2.6%(n=4) in the no-MBP group and 6.0%(n=9) in the MBP group (p=0.143). The postoperative paralytic ileus rate was 18.5%(n=28) and 5.8%(n=9) in the MBP group and no-MBP group, respectively, displaying a statistically significant difference (p=0.001). Wound infection, pneumonia, urinary tract infection, and cardiac complication rates also were higher in the MBP group. The overall mortality rate was 1.3%(n=3) in the no-MBP group and 2.0%(n=2) in the MBP group. Conclusions: The evidence concludes that MBP increases post-operative complications. Therefore, prophylactic MBP in LAR has not been proven to benefit patients. However, further research is necessary to understand the comparative effects of MBP versus no preparation comprehensively.

Keywords: MBP, anastomosis, LAR, paralytic ileus

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Authors: Maryam Alkhalosi, Ahmad Naddaf, Rana Alani

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Qatar National Library has its main accounts on social media, which presents the general image of the library and its daily news. A paper will be presented based on a case study researching the outcome of having a separate Instagram account led by librarians, not the Communication Department of the library. The main purpose of the librarians-led account is to promote librarians’ services and events, such as research consultation, reference questions, community engagement programs, collection marketing, etc. all in the way that librarians think it reflects their role in the community. Librarians had several obstacles to help users understanding librarians' roles. As was noticed that Instagram is the most popular social media platform in Qatar, it was selected to promote how librarians can help users through a focused account to create a direct channel between librarians and users. Which helps librarians understand users’ needs and interests. This research will use a quantitative approach depending on the case study, librarians have used their case in the department of Research and learning to find out the best practices might help in promoting the librarians' services and reaching out to a bigger number of users. Through the descriptive method, this research will describe the changes observed in the numbers of community users who interact with the Instagram account and engaged in librarians’ events. Statistics of this study are based on three main sources: 1. The internal monthly statistics sheet of events and programs held by the Research and Learning Department. 2. The weekly tracking of the Instagram account statistics. 3. Instagram’s tools such as polls, quizzes, questions, etc. This study will show the direct effect of a librarian-led Instagram account on the number of community members who participate and engage in librarian-led programs and services. In addition to highlighting the librarians' role directly with the community members. The study will also show the best practices on Instagram, which helps reaching a wider community of users. This study is important because, in the region, there is a lack of studies focusing on librarianship, especially on contemporary problems and its solution. Besides, there is a lack of understanding of the role of a librarian in the Arab region. The research will also highlight how librarians can help the public and researchers as well. All of these benefits can come through one popular easy channel in social media. From another side, this paper is a chance to share the details of this experience starting from scratch, including the phase of setting the policy and guidelines of managing the social media account, until librarians reached to a point where the benefits of this experience are in reality. This experience had even added many skills to the librarians.

Keywords: librarian’s role, social media, instagram and libraries, promoting libraries’ services

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Authors: Zhengrong Wu, Haibo Yang

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In the context of escalating global climate change and environmental degradation, the complexity and frequency of natural disasters are continually increasing. Confronted with an abundance of information regarding natural disasters, traditional knowledge graph construction methods, which heavily rely on grammatical rules and prior knowledge, demonstrate suboptimal performance in processing complex, multi-source disaster information. This study, drawing upon past natural disaster reports, disaster-related literature in both English and Chinese, and data from various disaster monitoring stations, constructs question-answer templates based on large language models. Utilizing the P-Tune method, the ChatGLM2-6B model is fine-tuned, leading to the development of a disaster knowledge graph based on large language models. This serves as a knowledge database support for disaster emergency response.

Keywords: large language model, knowledge graph, disaster, deep learning

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Michael Karnes, Alper Yilmaz

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This paper presents the application of the omni-modeler towards pedestrian redetection. The pedestrian redetection task creates several challenges when applying deep neural networks (DNN) due to the variety of pedestrian appearance with camera position, the variety of environmental conditions, and the specificity required to recognize one pedestrian from another. DNNs require significant training sets and are not easily adapted for changes in class appearances or changes in the set of classes held in its knowledge domain. Pedestrian redetection requires an algorithm that can actively manage its knowledge domain as individuals move in and out of the scene, as well as learn individual appearances from a few frames of a video. The Omni-Modeler is a dynamically learning few-shot visual recognition algorithm developed for tasks with limited training data availability. The Omni-Modeler adapts the knowledge domain of pre-trained deep neural networks to novel concepts with a calculated localized language encoder. The Omni-Modeler knowledge domain is generated by creating a dynamic dictionary of concept definitions, which are directly updatable as new information becomes available. Query images are identified through nearest neighbor comparison to the learned object definitions. The study presented in this paper evaluates its performance in re-identifying individuals as they move through a scene in both single-camera and multi-camera tracking applications. The results demonstrate that the Omni-Modeler shows potential for across-camera view pedestrian redetection and is highly effective for single-camera redetection with a 93% accuracy across 30 individuals using 64 example images for each individual.

Keywords: dynamic learning, few-shot learning, pedestrian redetection, visual recognition

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Authors: Michael Lousis

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The systematic identification of the most conspicuous and significant errors made by learners during three-years of testing of their progress in learning Arithmetic are presented in this article. How these errors have changed over three-years of school instruction of Arithmetic also is shown. The sample is comprised of two hundred (200) English students and one hundred and fifty (150) Greek students. These students were purposefully selected according to their participation in each testing session in the development of the three-year Kassel Project in England and Greece, in both domains simultaneously in Arithmetic and Algebra. The data sample includes six test-scripts corresponding to three testing sessions in both Arithmetic and Algebra respectively.

Keywords: arithmetic, errors, Kassel Project, progress of learning

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Authors: Milad Abbasi

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Carbon fiber-reinforced polymer (CFRP) composite structures are increasingly being utilized in the automotive industry due to their lightweight and specific energy absorption capabilities. Although it is impossible to predict composite mechanical properties directly using theoretical methods, various research has been conducted so far in the literature for accurate simulation of CFRP structures' energy-absorbing behavior. In this research, axial compression experiments were carried out on hand lay-up unidirectional CFRP composite tubes. The fabrication method allowed the authors to extract the material properties of the CFRPs using ASTM D3039, D3410, and D3518 standards. A neural network machine learning algorithm was then utilized to build a robust prediction model to forecast the axial compressive properties of CFRP tubes while reducing high-cost experimental efforts. The predicted results have been compared with the experimental outcomes in terms of load-carrying capacity and energy absorption capability. The results showed high accuracy and precision in the prediction of the energy-absorption capacity of the CFRP tubes. This research also demonstrates the effectiveness and challenges of machine learning techniques in the robust simulation of composites' energy-absorption behavior. Interestingly, the proposed method considerably condensed numerical and experimental efforts in the simulation and calibration of CFRP composite tubes subjected to compressive loading.

Keywords: CFRP composite tubes, energy absorption, crushing behavior, machine learning, neural network

Procedia PDF Downloads 157

Authors: Mahmoud B. Rokaya

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The study of opinion mining and sentiment analysis includes analysis of opinions, sentiments, evaluations, attitudes, and emotions. The rapid growth of social media, social networks, reviews, forum discussions, microblogs, and Twitter, leads to a parallel growth in the field of sentiment analysis. The field of sentiment analysis tries to develop effective tools to make it possible to capture the trends of people. There are two approaches in the field, lexicon-based and corpus-based methods. A lexicon-based method uses a sentiment lexicon which includes sentiment words and phrases with assigned numeric scores. These scores reveal if sentiment phrases are positive or negative, their intensity, and/or their emotional orientations. Creation of manual lexicons is hard. This brings the need for adaptive automated methods for generating a lexicon. The proposed method generates dynamic lexicons based on the corpus and then classifies text using these lexicons. In the proposed method, different approaches are combined to generate lexicons from text. The proposed method classifies the tweets into 5 classes instead of +ve or –ve classes. The sentiment classification problem is written as an optimization problem, finding optimum sentiment lexicons are the goal of the optimization process. The solution was produced based on mathematical programming approaches to find the best lexicon to classify texts. A genetic algorithm was written to find the optimal lexicon. Then, extraction of a meta-level feature was done based on the optimal lexicon. The experiments were conducted on several datasets. Results, in terms of accuracy, recall and F measure, outperformed the state-of-the-art methods proposed in the literature in some of the datasets. A better understanding of the Arabic language and culture of Arab Twitter users and sentiment orientation of words in different contexts can be achieved based on the sentiment lexicons proposed by the algorithm.

Keywords: social media, Twitter sentiment, sentiment analysis, lexicon, genetic algorithm, evolutionary computation

Procedia PDF Downloads 192

Authors: Christine Okello

Abstract:

Physical Education (PE) curriculum provides school age students to explore issues that are likely to impact on health, safety, and well-being. The current curriculum includes the physical activity component, intended to improve physical fitness, social skills as well as building confidence. While this viewpoint is vital, there are challenges and stigma attached when specific issues are either ignored, inadequately addressed, or not seen to be important. The department stipulates that students attend a school that is closest to their home, to access available government transportation to and from school. Equivalently, parents of students with a disability decide where their children attend school. A choice between a regular classroom, mainstream Special Unit classroom, or a School for Specific Purposes (SSP). Parents who take their children to regular schools may be oblivious of the details of the curriculum. Physical Education outcomes does not stipulate the extent to which a student must perform or expected to perform. It is therefore due to the classroom teacher to adjust their teaching goals or outcomes to suit all students in their classroom. A student who can run a hundred meters race in 20 seconds may belong in the same classroom as a student in a wheelchair. While these students are challenged because of a lack of performance, teachers are challenged to effectively teach successful PE lessons, and on the other hand students without a disability may not be able to attain their optimum. This paper will identify areas of need, address the challenges, and explore a possible solution.

Keywords: special needs, disability, challenges, physical education

Procedia PDF Downloads 65