Search results for: learning delivery modes

Authors: Vidhu Mitha Goutham, Ruma Mukherjee

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The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.

Keywords: attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation

Procedia PDF Downloads 171

Authors: Y. Salfati, D. Sharef Bussel, J. Zamir

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In this paper, we present an Evaluation Case Study implementing the eight principles of Collaborative Approaches to Evaluation (CAE) as designed by Brad Cousins in the past decade. The focus of this paper is sharing a rich experience in which we achieved two main goals. The first was the development of a valuable and meaningful new teacher training program, and the second was a successful implementation of the CAE principles. The innovative teacher training program is based on the idea of including physical movement during the process of teaching and learning academic themes. The program is called Learning through Movement. This program is a response to a call from the Ministry of Education, claiming that today children sit in front of screens and do not exercise any physical activity. In order to contribute to children’s health, physical, and cognitive development, the Ministry of Education promotes learning through physical activities. Research supports the idea that sports and physical exercise improve academic achievements. The Learning through Movement program is operated by Kaye Academic College. Students in the Elementary School Training Program, together with students in the Physical Education Training Program, implement the program in collaboration with two mentors from the College. The program combines academic learning with physical activity. The evaluation began at the beginning of the program. During the evaluation process, data was collected by means of qualitative tools, including interviews with mentors, observations during the students’ collaborative planning, class observations at school and focus groups with students, as well as the collection of documentation related to the teamwork and to the program itself. The data was analyzed using content analysis and triangulation. The preliminary results show outcomes relating to the Teacher Training Programs, the student teachers, the pupils in class, the role of Physical Education teachers, and the evaluation. The Teacher Training Programs developed a collaborative approach to lesson planning. The students' teachers demonstrated a change in their basic attitudes towards the idea of integrating physical activities during the lessons. The pupils indicated higher motivation through full participation in classes. These three outcomes are indicators of the success of the program. An additional significant outcome of the program relates to the status and role of the physical education teachers, changing their role from marginal to central in the school. Concerning evaluation, a deep sense of trust and confidence was achieved, between the evaluator and the whole team. The paper includes the perspectives and challenges of the heads and mentors of the two programs as well as the evaluator’s conclusions. The evaluation unveils challenges in conducting a CAE evaluation in such a complex setting.

Keywords: collaborative evaluation, training teachers, learning through movement

Procedia PDF Downloads 140

Authors: Vinicius G. Goecks, Gregory M. Gremillion, William D. Nothwang

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Modern approaches to train intelligent agents rely on prolonged training sessions, high amounts of input data, and multiple interactions with the environment. This restricts the application of these learning algorithms in robotics and real-world applications, in which there is low tolerance to inadequate actions, interactions are expensive, and real-time processing and action are required. This paper addresses this issue introducing CyberSteer, a novel approach to efficiently design intrinsic reward functions based on human intention to guide deep reinforcement learning agents with no environment-dependent rewards. CyberSteer uses non-expert human operators for initial demonstration of a given task or desired behavior. The trajectories collected are used to train a behavior cloning deep neural network that asynchronously runs in the background and suggests actions to the deep reinforcement learning module. An intrinsic reward is computed based on the similarity between actions suggested and taken by the deep reinforcement learning algorithm commanding the agent. This intrinsic reward can also be reshaped through additional human demonstration or critique. This approach removes the need for environment-dependent or hand-engineered rewards while still being able to safely shape the behavior of autonomous robotic agents, in this case, based on human intention. CyberSteer is tested in a high-fidelity unmanned aerial vehicle simulation environment, the Microsoft AirSim. The simulated aerial robot performs collision avoidance through a clustered forest environment using forward-looking depth sensing and roll, pitch, and yaw references angle commands to the flight controller. This approach shows that the behavior of robotic systems can be shaped in a reduced amount of time when guided by a non-expert human, who is only aware of the high-level goals of the task. Decreasing the amount of training time required and increasing safety during training maneuvers will allow for faster deployment of intelligent robotic agents in dynamic real-world applications.

Keywords: human-robot interaction, intelligent robots, robot learning, semisupervised learning, unmanned aerial vehicles

Procedia PDF Downloads 256

Authors: Geoffrey A. Wright

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In an attempt to address shortcomings revealed in international assessments and lamented in legislation, many schools are reducing or eliminating elective courses, applying the rationale that replacing "non-essential" subjects with core subjects, such as mathematics and language arts, will better position students in the global market. However, there is evidence that systematically pairing a core subject with another complementary subject may lead to greater overall learning in both subjects. In this paper, we outline the methods and preliminary findings from a study we conducted analyzing the influence learning programming has on student mathematical comprehension and ability. The purpose of this research is to demonstrate in what ways two subjects might complement each other, and to better understand the principles and conditions that encourage what we call lateral transfer, the synergistic effect that occurs when a learner studies two complementary subjects.

Keywords: programming, engineering, technology, complementary subjects

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Authors: Nathan Labiosa, Aryan Kohli

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This study introduces a hybrid machine learning model for classifying proteins, developed to address the complexities of protein sequence and structural analysis. Utilizing an architecture that combines a lightweight transformer with a concurrent neural network, the hybrid model leverages both sequential and intrinsic physical properties of proteins. Trained on a comprehensive dataset from the Research Collaboratory for Structural Bioinformatics Protein Data Bank, the model demonstrates a classification accuracy of 95%, outperforming existing methods by at least 15%. The high accuracy achieved demonstrates the potential of this approach to innovate protein classification, facilitating advancements in drug discovery and the development of personalized medicine. By enabling precise protein function prediction, the hybrid model allows for specialized strategies in therapeutic targeting and the exploration of protein dynamics in biological systems. Future work will focus on enhancing the model’s generalizability across diverse datasets and exploring the integration of more machine learning techniques to refine predictive capabilities further. The implications of this research offer potential breakthroughs in biomedical research and the broader field of protein engineering.

Keywords: amino acids, deep learning, enzymes, neural networks, protein classification, proteins, transformers

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Authors: Athena Nguyen, Rojin Belganeh

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Detecting and identifying differences in like polymer materials are key factors in deformulation, comparative analysis as well as reverse engineering. Pyrolysis-GC/MS is an easy solid sample introduction technique which expands the application areas of gas chromatography and mass spectrometry. The Micro-furnace pyrolyzer is directly interfaced with the GC injector preventing any potential of cold spot, carryover, and cross contamination. This presentation demonstrates the study of two similar polymers by performing different mode of operations in the same system: Evolve gas analysis (EGA), Flash pyrolysis, Thermal desorption analysis, and Heart-cutting analysis. Unknown polymer materials and their chemical compositions are identified.

Keywords: gas chromatography/mass spectrometry, pyrolysis, pyrolyzer, thermal desorption-GC/MS

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Authors: M. Kumpugdee-Vollrath, M. Tabatabaeifar, M. Helmis

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Shellac is a natural polyester resin secreted by insects. Pectins are natural, non-toxic and water-soluble polysaccharides extracted from the peels of citrus fruits or the leftovers of apples. Both polymers are allowed for the use in the pharmaceutical industry and as a food additive. SSB Aquagold® is the aqueous solution of shellac and can be used for a coating process as an enteric or controlled drug release polymer. In this study, tablets containing 10 mg methylene blue as a model drug were prepared with a rotary press. Those tablets were coated with mixtures of shellac and one of the pectin different types (i.e. CU 201, CU 501, CU 701 and CU 020) mostly in a 2:1 ratio or with pure shellac in a small scale fluidized bed apparatus. A stable, simple and reproducible three-stage coating process was successfully developed. The drug contents of the coated tablets were determined using UV-VIS spectrophotometer. The characterization of the surface and the film thickness were performed with the scanning electron microscopy (SEM) and the light microscopy. Release studies were performed in a dissolution apparatus with a basket. Most of the formulations were enteric coated. The dissolution profiles showed a delayed or sustained release with a lagtime of at least 4 h. Dissolution profiles of coated tablets with pure shellac had a very long lagtime ranging from 13 to 17.5 h and the slopes were quite high. The duration of the lagtime and the slope of the dissolution profiles could be adjusted by adding the proper type of pectin to the shellac formulation and by variation of the coating amount. In order to apply a coating formulation as a colon delivery system, the prepared film should be resistant against gastric fluid for at least 2 h and against intestinal fluid for 4-6 h. The required delay time was gained with most of the shellac-pectin polymer mixtures. The release profiles were fitted with the modified model of the Korsmeyer-Peppas equation and the Hixson-Crowell model. A correlation coefficient (R²) > 0.99 was obtained by Korsmeyer-Peppas equation.

Keywords: shellac, pectin, coating, fluidized bed, release, colon delivery system, kinetic, SEM, methylene blue

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Authors: Xiaohua Zou, Yongxin Su

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The reasonable operation of heating, ventilation, and air conditioning (HVAC) is of great significance in improving the security, stability, and economy of power system operation. However, the uncertainty of the operating environment, thermal comfort varies by users and rapid decision-making pose challenges for HVAC demand response optimization. In this regard, this paper proposes a reinforcement learning-based method for HVAC demand response optimization considering few-shot personalized thermal comfort (PTC). First, an HVAC DR optimization framework based on few-shot PTC model and DRL is designed, in which the output of few-shot PTC model is regarded as the input of DRL. Then, a few-shot PTC model that distinguishes between awake and asleep states is established, which has excellent engineering usability. Next, based on soft actor criticism, an HVAC DR optimization algorithm considering the user’s PTC is designed to deal with uncertainty and make decisions rapidly. Experiment results show that the proposed method can efficiently obtain use’s PTC temperature, reduce energy cost while ensuring user’s PTC, and achieve rapid decision-making under uncertainty.

Keywords: HVAC, few-shot personalized thermal comfort, deep reinforcement learning, demand response

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Authors: Mei-Chun. Chang

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STEAM (Science, Technology, Engineering, Art, and Mathematics) represents a cross-disciplinary and learner-centered teaching model that cultivates students to link theory with the presentation of real situations, thereby improving their various abilities. This study explores students' learning performance after using the 6E model in STEAM teaching for a professional course in the digital media design department of technical colleges, as well as the difficulties and countermeasures faced by STEAM curriculum design and its implementation. In this study, through industry experts’ work experience, activity exchanges, course teaching, and experience, learners can think about the design and development value of virtual idol products that meet the needs of users and to employ AR/VR technology to innovate their product applications. Applying action research, the investigation has 35 junior students from the department of digital media design of the school where the researcher teaches as the research subjects. The teaching research was conducted over two stages spanning ten weeks and 30 sessions. This research collected the data and conducted quantitative and qualitative data sorting analyses through ‘design draft sheet’, ‘student interview record’, ‘STEAM Product Semantic Scale’, and ‘Creative Product Semantic Scale (CPSS)’. Research conclusions are presented, and relevant suggestions are proposed as a reference for teachers or follow-up researchers. The contribution of this study is to teach college students to develop original virtual idols and product designs, improve learning effectiveness through STEAM teaching activities, and effectively cultivate innovative and practical cross-disciplinary design talents.

Keywords: STEAM, 6E model, virtual idol, learning effectiveness, practical courses

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Authors: Md Rafiqul Islam, Ashir Ahmed, Anwaar Ulhaq, Abu Raihan M. Kamal, Yuan Miao, Hua Wang

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Mental health of people is equally important as of their physical health. Mental health and well-being are influenced not only by individual attributes but also by the social circumstances in which people find themselves and the environment in which they live. Like physical health, there is a number of internal and external factors such as biological, social and occupational factors that could influence the mental health of people. People living in poverty, suffering from chronic health conditions, minority groups, and those who exposed to/or displaced by war or conflict are generally more likely to develop mental health conditions. However, to authors’ best knowledge, there is dearth of knowledge on the impact of workplace (especially the highly stressed IT/Tech workplace) on the mental health of its workers. This study attempts to examine the factors influencing the mental health of tech workers. A publicly available dataset containing more than 65,000 cells and 100 attributes is examined for this purpose. Number of machine learning techniques such as ‘Decision Tree’, ‘K nearest neighbor’ ‘Support Vector Machine’ and ‘Ensemble’, are then applied to the selected dataset to draw the findings. It is anticipated that the analysis reported in this study would contribute in presenting useful insights on the attributes contributing in the mental health of tech workers using relevant machine learning techniques.

Keywords: mental disorder, diagnosis, occupational stress, IT workplace

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Authors: Bruna G. M. Araújo, Pedro M. M. Q. Cruz

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In this letter, we review the literature of the major concepts that govern Gaussian quantum information. As we work with quantum information and computation with continuous variables, Gaussian states are needed to better describe these systems. Analyzing a single ion locked in a Paul trap we use the interaction picture to obtain a toolbox of Gaussian operations with the ion-laser interaction Hamiltionian. This is achieved exciting the ion through the combination of two lasers of distinct frequencies corresponding to different sidebands of the external degrees of freedom. First we study the case of a trap with 1 mode and then the case with 2 modes. In this way, we achieve different continuous variables gates just by changing the external degrees of freedom of the trap and combining the Hamiltonians of blue and red sidebands.

Keywords: Paul trap, ion-laser interaction, Gaussian operations

Procedia PDF Downloads 679

Authors: Richa Raje, Anumol Antony

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Playing is the foremost stepping stone for childhood development. Play is an essential aspect of a child’s development and learning because it creates meaningful enduring environmental connections and increases children’s performance. The children’s proficiencies are ever varying in their course of growth. There is innovation in the activities, as it kindles the senses, surges the love for exploration, overcomes linguistic barriers and physiological development, which in turn allows them to find their own caliber, spontaneity, curiosity, cognitive skills, and creativity while learning during play. This paper aims to comprehend the learning in play which is the most essential underpinning aspect of the outdoor play area. It also assesses the trend of playgrounds design that is merely hammered with equipment's. It attempts to derive a relation between the natural environment and children’s activities and the emotions/senses that can be evoked in the process. One of the major concerns with our outdoor play is that it is limited to an area with a similar kind of equipment, thus making the play highly regimented and monotonous. This problem is often lead by the strict timetables of our education system that hardly accommodates play. Due to these reasons, the play areas remain neglected both in terms of design that allows learning and wellbeing. Poorly designed spaces fail to inspire the physical, emotional, social and psychological development of the young ones. Currently, the play space has been condensed to an enclosed playground, driveway or backyard which confines the children’s capability to leap the boundaries set for him. The paper emphasizes on study related to kids ranging from 5 to 11 years where the behaviors during their interactions in a playground are mapped and analyzed. The theory of affordance is applied to various outdoor play areas, in order to study and understand the children’s environment and how variedly they perceive and use them. A higher degree of affordance shall form the basis for designing the activities suitable in play spaces. It was observed during their play that, they choose certain spaces of interest majority being natural over other artificial equipment. The activities like rolling on the ground, jumping from a height, molding earth, hiding behind tree, etc. suggest that despite equipment they have an affinity towards nature. Therefore, we as designers need to take a cue from their behavior and practices to be able to design meaningful spaces for them, so the child gets the freedom to test their precincts.

Keywords: children, landscape design, learning environment, nature and play, outdoor play

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Authors: Ann P. Daunic, Nancy Corbett

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Background and Significance: Researchers emphasize the role that motivation, self-esteem, and self-regulation play in children’s early adjustment to the school culture, including skills such as identifying their own feelings and understanding the feelings of others. As social-emotional growth, academic learning, and successful integration within culture and society are inextricably connected, the Social-Emotional Learning Foundations (SELF) curriculum was designed to integrate social-emotional learning (SEL) instruction within early literacy instruction (specifically, reading) for Kindergarten and first-grade students at risk for emotional and behavioral difficulties. Storybook reading is a typically occurring activity in the primary grades; thus SELF provides an intervention that is both theoretically and practically sound. Methodology: The researchers will report on findings from the first two years of a three-year study funded by the US Department of Education’s Institute of Education Sciences to evaluate the effects of the SELF curriculum versus “business as usual” (BAU). SELF promotes the development of self-regulation by incorporating instructional strategies that support children’s use of SEL related vocabulary, self-talk, and critical thinking. The curriculum consists of a carefully coordinated set of materials and pedagogy designed specifically for primary grade children at early risk for emotional and behavioral difficulties. SELF lessons (approximately 50 at each grade level) are organized around 17 SEL topics within five critical competencies. SELF combines whole-group (the first in each topic) and small-group lessons (the 2nd and 3rd in each topic) to maximize opportunities for teacher modeling and language interactions. The researchers hypothesize that SELF offers a feasible and substantial opportunity within the classroom setting to provide a small-group social-emotional learning intervention integrated with K-1 literacy-related instruction. Participating target students (N = 876) were identified by their teachers as potentially at risk for emotional or behavioral issues. These students were selected from 122 Kindergarten and 100 first grade classrooms across diverse school districts in a southern state in the US. To measure the effectiveness of the SELF intervention, the researchers asked teachers to complete assessments related to social-emotional learning and adjustment to the school culture. A social-emotional learning related vocabulary assessment was administered directly to target students receiving small-group instruction. Data were analyzed using a 3-level MANOVA model with full information maximum likelihood to estimate coefficients and test hypotheses. Major Findings: SELF had significant positive effects on vocabulary, knowledge, and skills associated with social-emotional competencies, as evidenced by results from the measures administered. Effect sizes ranged from 0.41 for group (SELF vs. BAU) differences in vocabulary development to 0.68 for group differences in SEL related knowledge. Conclusion: Findings from two years of data collection indicate that SELF improved outcomes related to social-emotional learning and adjustment to the school culture. This study thus supports the integration of SEL with literacy instruction as a feasible and effective strategy to improve outcomes for K-1 students at risk for emotional and behavioral difficulties.

Keywords: Socio-cultural context for learning, social-emotional learning, social skills, vocabulary development

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Authors: Luanna B. Prevost, Kelli Carter, Margaurete Romero, Kirsti Martinez

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Writing is an essential scientific practice, yet, in several countries, the increasing university science class-size limits the use of written assessments. Written assessments allow students to demonstrate their learning in their own words and permit the faculty to evaluate students’ understanding. However, the time and resources required to grade written assessments prohibit their use in large-enrollment science courses. This study examined the use of machine learning algorithms to automatically analyze student writing and provide timely feedback to the faculty about students' writing in biology. Written responses to questions about matter and energy transformation were collected from large-enrollment undergraduate introductory biology classrooms. Responses were analyzed using the LightSide text mining and classification software. Cohen’s Kappa was used to measure agreement between the LightSide models and human raters. Predictive models achieved agreement with human coding of 0.7 Cohen’s Kappa or greater. Models captured that when writing about matter-energy transformation at the ecosystem level, students focused on primarily on the concepts of heat loss, recycling of matter, and conservation of matter and energy. Models were also produced to capture writing about processes such as decomposition and biochemical cycling. The models created in this study can be used to provide automatic feedback about students understanding of these concepts to biology faculty who desire to use formative written assessments in larger enrollment biology classes, but do not have the time or personnel for manual grading.

Keywords: machine learning, written assessment, biology education, text mining

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Authors: Ahmed Krobba, Meriem Touzene, Mohamed Debeyche

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The wireless communications system (5G) will provide more diverse applications and higher quality services for users compared to the long-term evolution 4G (LTE). 5G uses a higher carrier frequency, which suffers from information loss in 5G coverage. Most 5G users often cannot obtain high-quality communications due to transmission channel noise and channel complexity. Physical Uplink Control Channel (PUCCH-NR: Physical Uplink Control Channel New Radio) plays a crucial role in 5G NR telecommunication technology, which is mainly used to transmit link control information uplink (UCI: Uplink Control Information. This study based of evaluating the performance of channel physical uplink control PUCCH-NR under low Signal-to-Noise Ratios with various antenna numbers reception. We propose the artificial intelligence approach based on deep neural networks (Deep Learning) to estimate the PUCCH-NR channel in comparison with this approach with different conventional methods such as least-square (LS) and minimum-mean-square-error (MMSE). To evaluate the channel performance we use the block error rate (BLER) as an evaluation criterion of the communication system. The results show that the deep neural networks method gives best performance compared with MMSE and LS

Keywords: 5G network, uplink (Uplink), PUCCH channel, NR-PUCCH channel, deep learning

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Authors: Xiaoyan Dai, Hsieh Yisan

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Barcode processing of captured images is a huge challenge, as different shooting conditions can result in different barcode appearances. This paper proposes a deep learning-based barcode detection using synthetic-to-real data augmentation. We first augment barcodes themselves; we then augment images containing the barcodes to generate a large variety of data that is close to the actual shooting environments. Comparisons with previous works and evaluations with our original data show that this approach achieves state-of-the-art performance in various real images. In addition, the system uses hybrid resolution for barcode “scan” and is applicable to real-time applications.

Keywords: barcode detection, data augmentation, deep learning, image-based processing

Procedia PDF Downloads 159

Authors: Esra Ece Var

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Beekeeping plays essential role both in terms of agricultural yields and agricultural economy; they produce honey, wax, royal jelly, apitoxin, pollen, and propolis. Nowadays, these natural products become more importantly suitable and preferable for nutrition, food supplement, medicine, and industry. However, to produce organic honey, majority of the apiaries are located in remote or distant rural areas where utilities such as electricity and Internet network are not available. Additionally, due to colony failures, world honey production decreases year by year despite the increase in the number of beehives. The objective of this paper is to develop a smart beehive monitoring system for apiaries including those that do not have access to Internet network. In this context, temperature and humidity inside the beehive, and ambient temperature were measured with RFID sensors. Control center, where all sensor data was sent and stored at, has a GSM module used to warn the beekeeper via SMS when an anomaly is detected. Simultaneously, using the collected data, an unsupervised machine learning algorithm is used for detecting anomalies and calibrating the warning system. The results show that the smart beehive monitoring system can detect fatal anomalies up to 4 weeks prior to colony loss.

Keywords: beekeeping, smart systems, machine learning, anomaly detection, apiculture

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Authors: Ilyes Athimni, Mouna Bouzazi, Mongi Boulehmi, Ahmed Ferchichi

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The principles of good governance, universal values, and digitization are among the tools to fight corruption and improve the quality of service delivery. In recent years, these tools have become one of the most controversial topics in the field of education and a concern of many international organizations and institutions against the problem of corruption. Corruption in the education sector, particularly in higher education, has negative impacts on the quality of education systems and on the quality of administrative or educational services. Currently, the health crisis due to the spread of the COVID-19 pandemic reveals the difficulties encountered by education systems in most countries of the world. Due to the poor governance of these systems, many educational institutions were unable to continue working remotely. To respond to these problems encountered by most education systems in many countries of the world, our initiative is to propose a methodology to reinvent education systems based on global values and digital technologies. This methodology includes a work strategy for educational institutions, whether in the provision of administrative services or in the teaching method, based on information and communication technologies (ICTs), intelligence artificial, and intelligent agents. In addition, we will propose a supervisory law that will be implemented and monitored by intelligent agents to improve accountability, transparency, and accountability in educational institutions. On the other hand, we will implement and evaluate a field experience by applying the proposed methodology in the operation of an educational institution and comparing it to the traditional methodology through the results of teaching an educational program. With these specifications, we can reinvent quality education systems. We also expect the results of our proposal to play an important role at local, regional, and international levels in motivating governments of countries around the world to change their university governance policies.

Keywords: artificial intelligence, corruption in education, distance learning, education systems, ICTs, intelligent agents, good governance

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Authors: Tarana Siddika, Ilka U. Heinemann, Patrick O’Donoghue

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Protein kinase B (AKT1) is a serine/threonine kinase and central transducer of cell survival pathways. Typical approaches to study AKT1 biology in cells rely on growth factor or insulin stimulation that activates AKT1 via phosphorylation at two key regulatory sites (Threonine308, Serine473), yet cell stimulation also activates many other kinases and fails to differentiate the effect of the two main activating sites of AKT1 on downstream substrate phosphorylation and cell growth. While both AKT1 activating sites are associated with disease and used as clinical markers, in some cancers, high levels of Threonine308 phosphorylation are associated with poor prognosis while in others poor survival correlates with high Serine473 levels. To produce cells with specific AKT1 activity, a system was developed to deliver active AKT1 to human cells. AKT1 phospho-variants were produced from Escherichia coli with programmed phosphorylation by genetic code expansion. Tagging of AKT1 with an N-terminal cell penetrating peptide tag derived from the human immunodeficiency virus trans-activator of transcription (TAT) helped to enter AKT1 proteins in mammalian cells. The TAT-tag did not alter AKT1 kinase activity and was necessary and sufficient to rapidly deliver AKT1 protein variants that persisted in human cells for 24 h without the need to use transfection reagents. TAT-pAKT1T308, TAT-pAKT1S473 and TAT-pAKT1T308S473 proteins induced selective phosphorylation of the known AKT1 substrate GSK-3αβ, and downstream stimulation of the AKT1 pathway as evidenced by phosphorylation of ribosomal protein S6 at Serine240/244 in transfected cells. Increase in cell growth and proliferation was observed due to the transfection of different phosphorylated AKT1 protein variants compared to cells with TAT-AKT1 protein. The data demonstrate efficient delivery of AKT1 with programmed phosphorylation to human cells, thus establishing a cell-based model system to investigate signaling that is dependent on specific AKT1 activity and phosphorylation.

Keywords: cell penetrating peptide, cell signaling, protein kinase b (AKT1), phosphorylation

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Authors: Christos Chaschatzis, Chrysoula Karaiskou, Pantelis Angelidis, Sotirios K. Goudos, Igor Kotsiuba, Panagiotis Sarigiannidis

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Over the past few decades, the rapid growth of the global population has led to the need to increase agricultural production and improve the quality of agricultural goods. There is a growing focus on environmentally eco-friendly solutions, sustainable production, and biologically minimally fertilized products in contemporary society. Precision agriculture has the potential to incorporate a wide range of innovative solutions with the development of machine learning algorithms. YOLOv5 and YOLOv8 are two of the most advanced object detection algorithms capable of accurately recognizing objects in real time. Detecting tree diseases is crucial for improving the food production rate and ensuring sustainability. This research aims to evaluate the efficacy of YOLOv5 and YOLOv8 in detecting the symptoms of Armillaria spp. in sweet cherry trees and determining their health status, with the goal of enhancing the robustness of precision agriculture. Additionally, this study will explore Computer Vision (CV) techniques with machine learning algorithms to improve the detection process’s efficiency.

Keywords: Armillaria spp., machine learning, precision agriculture, smart farming, sweet cherries trees, YOLOv5, YOLOv8

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Authors: Sajed A. Habib

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Problem-based learning (PBL) is a student-centered pedagogy that originated in the medical field and has also been used extensively in other knowledge disciplines with recognized advantages and limitations. PBL has been used in various undergraduate engineering programs with mixed outcomes. The current fourth industrial revolution (digital era or Industry 4.0) has made it essential for many science and engineering students to receive effective training in advanced courses such as industrial automation and robotics. This paper presents a case study at Assumption University of Thailand, where a PBL-like approach was used to teach some aspects of automation and robotics to selected groups of undergraduate engineering students. These students were given some basic level training in automation prior to participating in a subsequent training session in order to solve technical problems with increased complexity. The participating students’ evaluation of the training sessions in terms of learning effectiveness, skills enhancement, and incremental knowledge following the problem-solving session was captured through a follow-up survey consisting of 14 questions and a 5-point scoring system. From the most recent training event, an overall 70% of the respondents indicated that their skill levels were enhanced to a much greater level than they had had before the training, whereas 60.4% of the respondents from the same event indicated that their incremental knowledge following the session was much greater than what they had prior to the training. The instructor-facilitator involved in the training events suggested that this method of learning was more suitable for senior/advanced level students than those at the freshmen level as certain skills to effectively participate in such problem-solving sessions are acquired over a period of time, and not instantly.

Keywords: automation, industry 4.0, model-based design training, problem-based learning

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Authors: Mitra Mesgar

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Online learning environment is becoming more popular among learners because of their multiple information representations. Despite the growing importance of online reading strategies among adult learners, little attention has been carried out to postgraduate EFL learners. This study is quantitative research designed and aimed to investigate metacognitive reading strategies employed by Iranian postgraduate learners to read online academic texts. This study is conducted by over 50 Iranian postgraduate students studying in different Malaysian universities. This study used two different survey questionnaires, namely, 1) background questionnaire and 2) OSORS questionnaire. The collected data were analyzed using SPSS. The findings of the study emphasized metacognitive reading strategies used by different aged adult learners. The results of the survey questionnaires revealed that adult learners use global reading strategies as well as problem-solving strategies and support reading strategies. Also, through one-way analysis of variance toward age factor revealed that it has no meaningful changes on metacognitive reading strategy usage. This means that metacognitive reading strategies used by adult learners are independent of age variable. Drawing from findings, adult learners have learning goals, and since they have more exposure to online academic texts, they are able to use different metacognitive online reading strategies that affect their understanding of academic texts.

Keywords: online reading strategies, metacognitive strategies, online learning, independent students, locus of control

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Authors: Sobhy Fathy A. Hashesh

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This study aimed at investigating the effect of Andalus Knowledge Phases and Times (ANPT) model of learning and the effect of 'Intel Education Contribution in ANPT' on the development of students’ academic performance and emotional quotient. The society of the study composed of Andalus Private Schools, elementary school students (N=700), while the sample of the study composed of four randomly assigned groups (N=80) with one experimental group and one control group to study "ANPT" effect and the "Intel Contribution in ANPT" effect respectively. The study followed the quantitative and qualitative approaches in collecting and analyzing data to answer the study questions. Results of the study revealed that there were significant statistical differences between students’ academic performances and emotional quotients for the favor of the experimental groups. The study recommended applying this model on different educational variables and on other age groups to generate more data leading to more educational results for the favor of students’ learning outcomes.

Keywords: Al Andalus, emotional quotient, students, academic performance development

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Authors: Y. Gholipour, E. Rezazadeh

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Delay is one of the most serious and common problems of construction project that can affect project delivery unfavorably. This research presents the most important causes of delay in large dam projects based on a survey on some executed dam construction in Iran. In this survey a randomly selected samples of owners, consultants and contractors have been involved. The outcome of this survey revealed that scheduled payments, site management, shop drawing review process, unforeseen ground conditions and contractor experience as the most important factors affecting on delay in dam construction projects.

Keywords: delay, dam construction, project management, Iran

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Authors: Kitti Supchukun, Kris Angkanaporn, Teerapong Yata

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The product innovation using a nutraceutical delivery system on improving the growth performance of broilers is the product planning and development to solve the antibiotics banning policy incurred in the local and global livestock production system. Restricting the use of antibiotics can reduce the quality of chicken meat and increase pathogenic bacterial contamination. Although other alternatives were used to replace antibiotics, the efficacy was inconsistent, reflecting on low chicken growth performance and contaminated products. The product innovation aims to effectively deliver the selected active ingredients into the body. This product is tested on the pharmaceutical lab scale and on the farm-scale for market feasibility in order to create product innovation using the nutraceutical delivery system model. The model establishes the product standardization and traceable quality control process for farmers. The study is performed using mixed methods. Starting with a qualitative method to find the farmers' (consumers) demands and the product standard, then the researcher used the quantitative research method to develop and conclude the findings regarding the acceptance of the technology and product performance. The survey has been sent to different organizations by random sampling among the entrepreneur’s population including integrated broiler farm, broiler farm, and other related organizations. The mixed-method results, both qualitative and quantitative, verify the user and lead users' demands since they provide information about the industry standard, technology preference, developing the right product according to the market, and solutions for the industry problems. The product innovation selected nutraceutical ingredients that can solve the following problems in livestock; bactericidal, anti-inflammation, gut health, antioxidant. The combinations of the selected nutraceutical and nanostructured lipid carriers (NLC) technology aim to improve chemical and pharmaceutical components by changing the structure of active ingredients into nanoparticle, which will be released in the targeted location with accurate concentration. The active ingredients in nanoparticle form are more stable, elicit antibacterial activity against pathogenic Salmonella spp and E.coli, balance gut health, have antioxidant and anti-inflammation activity. The experiment results have proven that the nutraceuticals have an antioxidant and antibacterial activity which also increases the average daily gain (ADG), reduces feed conversion ratio (FCR). The results also show a significant impact on the higher European Performance Index that can increase the farmers' profit when exporting. The product innovation will be tested in technology acceptance management methods from farmers and industry. The production of broiler and commercialization analyses are useful to reduce the importation of animal supplements. Most importantly, product innovation is protected by intellectual property.

Keywords: nutraceutical, nano structure lipid carrier, anti-microbial drug resistance, broiler, Salmonella

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Authors: Sujata Kulkarni, Udhav Bhosle, Vijaykumar T.

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Due to the closeness between seismic signals and non-seismic signals, it is vital to detect earthquakes using conventional methods. In order to distinguish between seismic events and non-seismic events depending on their amplitude, our study processes the data that come from seismic sensors. The authors suggest a robust noise suppression technique that makes use of a bandpass filter, an IIR Wiener filter, recursive short-term average/long-term average (STA/LTA), and Carl short-term average (STA)/long-term average for event identification (LTA). The trigger ratio used in the proposed study to differentiate between seismic and non-seismic activity is determined. The proposed work focuses on significant feature extraction for machine learning-based seismic event detection. This serves as motivation for compiling a dataset of all features for the identification and forecasting of seismic signals. We place a focus on feature vector dimension reduction techniques due to the temporal complexity. The proposed notable features were experimentally tested using a machine learning model, and the results on unseen data are optimal. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model may also be employed in a real-time setting while lowering false alarm rates. The planned study is based on the examination of seismic signals obtained from both individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station sensors, respectively located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset.

Keywords: Carl STA/LTA, features extraction, real time, dataset, machine learning, seismic detection

Procedia PDF Downloads 117

Authors: Rosy Makeresemese Qhosola

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South Africa has maintained the effort to uphold its guiding principles in terms of its constitution. The constitution upholds principles such as equity, social justice, peace, freedom and hope, to mention but a few. So, such principles are made to form the basis for any legislation and policies that are in place to guide all fields/departments of government. Education is one of those departments or fields and is expected to abide by such principles as outlined in their policies. Therefore, as expected education policies and legislation outline their intentions to ensure the development of students’ clear critical thinking capacity as well as their creative capacities by creating learning contexts and opportunities that accommodate the effective teaching and learning strategies, that are learner centered and are compatible with the prescripts of a democratic constitution of the country. The paper aims at exploring and analyzing the progress of conventional accounting in terms of its adherence to the effective use of principles of good teaching, as per policy expectations in South Africa. The progress is traced by comparing conventional accounting to Critical Accounting Research (CAR), where the history of accounting as intended in the curriculum of SA and CAR are highlighted. Critical Accounting Research framework is used as a lens and mode of teaching in this paper, since it can create a space for the learning of accounting that is optimal marked by the use of more learner-centred methods of teaching. The Curriculum of South Africa also emphasises the use of more learner-centred methods of teaching that encourage an active and critical approach to learning, rather than rote and uncritical learning of given truths. The study seeks to maintain that conventional accounting is in contrast with principles of good teaching as per South African policy expectations. The paper further maintains that, the possible move beyond it and the adherence to the effective use of good teaching, could be when CAR forms the basis of teaching. Data is generated through Participatory Action Research where the meetings, dialogues and discussions with the focused groups are conducted, which consists of lecturers, students, subject heads, coordinators and NGO’s as well as departmental officials. The results are analysed through Critical Discourse Analysis since it allows for the use of text by participants. The study concludes that any teacher who aspires to achieve in the teaching and learning of accounting should first meet the minimum requirements as stated in the NQF level 4, which forms the basic principles of good teaching and are in line with Critical Accounting Research.

Keywords: critical accounting research, critical discourse analysis, participatory action research, principles of good teaching

Procedia PDF Downloads 301

Authors: M. A. Riurean, S. Heijnen, C. A. Knott, J. Makinde, D. Gotti, J. VD. Kamp

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Objectives: The current study is done in preparation for a randomized controlled study investigating the effects of an implicit motor learning protocol implemented using an extension-supporting glove. It will explore different protocols to find out which is preferred when studying motor learn-ing in the chronic stroke population that struggles with hand spasticity. Design: This exploratory study will follow 24 individuals who have a chronic stroke (> 6 months) during their usual care journey. We will record the results of two 9-Hole Peg Tests (9HPT) done during their therapy ses-sions with a physiotherapist or in their home before and after 4 weeks of them wearing an exten-sion-supporting glove used to employ the to-be-studied protocols. The participants will wear the glove 3 times/week for one hour while performing their activities of daily living and record the times they wore it in a diary. Their experience will be monitored through telecommunication once every week. Subjects: Individuals that have had a stroke at least 6 months prior to participation, hand spasticity measured on the modified Ashworth Scale of maximum 3, and finger flexion motor control measured on the Motricity Index of at least 19/33. Exclusion criteria: extreme hemi-neglect. Methods: The participants will be randomly divided into 3 groups: one group using the glove in a pre-set way of decreasing support (implicit motor learning), one group using the glove in a self-controlled way of decreasing support (autonomous motor learning), and the third using the glove with constant support (as control). Before and after the 4-week period, there will be an intake session and a post-assessment session. Analysis: We will compare the results of the two 9HPTs to check whether the protocols were effective. Furthermore, we will compare the results between the three groups to find the preferred one. A qualitative analysis will be run of the experience of participants throughout the 4-week period. Expected results: We expect that the group using the implicit learning protocol will show superior results.

Keywords: implicit learning, hand spasticity, stroke, error minimization, motor task

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Authors: Tolga Aydin, M. Fatih Alaeddinoğlu

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People, throughout the history, have made estimates and inferences about the future by using their past experiences. Developing information technologies and the improvements in the database management systems make it possible to extract useful information from knowledge in hand for the strategic decisions. Therefore, different methods have been developed. Data mining by association rules learning is one of such methods. Apriori algorithm, one of the well-known association rules learning algorithms, is not commonly used in spatio-temporal data sets. However, it is possible to embed time and space features into the data sets and make Apriori algorithm a suitable data mining technique for learning spatio-temporal association rules. Lake Van, the largest lake of Turkey, is a closed basin. This feature causes the volume of the lake to increase or decrease as a result of change in water amount it holds. In this study, evaporation, humidity, lake altitude, amount of rainfall and temperature parameters recorded in Lake Van region throughout the years are used by the Apriori algorithm and a spatio-temporal data mining application is developed to identify overflows and newly-formed soil regions (underflows) occurring in the coastal parts of Lake Van. Identifying possible reasons of overflows and underflows may be used to alert the experts to take precautions and make the necessary investments.

Keywords: apriori algorithm, association rules, data mining, spatio-temporal data

Procedia PDF Downloads 367

Authors: C. Treadgold, S. Sivaraman

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The Starlight Children's Foundation (Starlight) is an Australian non-profit organisation that delivers programs, in partnership with health professionals, to support children, adolescents, and their families who are living with a serious illness. While supporting children and adolescents with life-limiting conditions has always been a feature of Starlight's work, providing a dedicated program, specifically targeting and meeting the needs of the paediatric palliative population, is a recent area of focus. Recognising the challenges in providing children’s palliative services, Starlight initiated a research and development project to better understand and meet the needs of this group. The aim was to create a program which enhances the wellbeing of children, adolescents, and their families receiving paediatric palliative care in their community through the provision of on-going, tailored, positive experiences or 'moments'. This paper will present the results of the formative evaluation of this unique program, highlighting the development processes and outcomes of the pilot. The pilot was designed using an innovation methodology, which included a number of research components. There was a strong belief that it needed to be delivered in partnership with a dedicated palliative care team, helping to ensure the best interests of the family were always represented. This resulted in Starlight collaborating with both the Victorian Paediatric Palliative Care Program (VPPCP) at the Royal Children's Hospital, Melbourne, and the Sydney Children's Hospital Network (SCHN) to pilot the 'Moments' program. As experts in 'positive disruption', with a long history of collaborating with health professionals, Starlight was well placed to deliver a program which helps children, adolescents, and their families to experience moments of joy, connection and achieve their own sense of accomplishment. Building on Starlight’s evidence-based approach and experience in creative service delivery, the program aims to use the power of 'positive disruption' to brighten the lives of this group and create important memories. The clinical and Starlight team members collaborate to ensure that the child and family are at the centre of the program. The design of each experience is specific to their needs and ensures the creation of positive memories and family connection. It aims for each moment to enhance quality of life. The partnership with the VPPCP and SCHN has allowed the program to reach families across metropolitan and regional locations. In late 2019 a formative evaluation of the pilot was conducted utilising both quantitative and qualitative methodologies to document both the delivery and outcomes of the program. Central to the evaluation was the interviews conducted with both clinical teams and families in order to gain a comprehensive understanding of the impact of and satisfaction with the program. The findings, which will be shared in this presentation, provide practical insight into the delivery of the program, the key elements for its success with families, and areas which could benefit from additional research and focus. It will use stories and case studies from the pilot to highlight the impact of the program and discuss what opportunities, challenges, and learnings emerged.

Keywords: children, families, memory making, pediatric palliative care, support

Procedia PDF Downloads 97