Search results for: module based teaching and learning

Authors: Iris Alkaher, Nurit Carmi

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It is widely agreed among scientists that population growth (PG) is a major factor that drives the global environmental crisis. Many researchers recognize that explicitly addressing the impact of PG on the environment and human quality of life through education systems worldwide could play a significant role in improving understanding regarding the links between rapid PG and environmental degradation and changing perceptions, attitudes, and behaviors concerning the necessity to reduce the fertility rate. However, the issue of PG is still rarely included in schools' curricula, mainly because of its complexity and controversiality. This study aims to explore the perspectives of teachers with an academic background in environmental and sustainability education (ESEteachers) and teachers with no such background (non-ESE teachers) regarding PG as an environmental risk. The study also explores the teachers’ willingness to include PG in their teaching and identifies what predicts their inclusion of it. In this mixed-methods research study, data were collected using questionnaires and interviews. The findings portray a complex picture concerning the debate aboutPG as a major factor that drives the global environmental crisis in the Israeli context. Consistent with other countries, we found that the deep-rooted pronatalist culture in the Israeli society, as well as a robust national pronatalist agenda and policies, have a tremendous impact on the education system. Therefore, we found that an academic background in ESE had a limited impact on teachers' perceptions concerning PG as a problem and on their willingness to include it in their teaching and discuss its controversiality. Teachers' attitudes related to PG demonstrated social, cultural, and politically oriented disavowal justification regarding the negative impacts of rapid PG, identified in the literature as population-skepticism and population-fatalism. Specifically, factors such as the ongoing Israeli-Palestinian conflict, the Jewish anxiety of destruction, and the religious command to“be fruitful and multiply”influenced the perceptions of both ESE and non-ESE teachers. While these arguments are unique to the Israeli context, pronatalist policies are international. In accordance with the pronatalist policy, we also found that the absence of PG from both school curricula and the Israeli public discourse was reported by ESE and non-ESE teachers as major reasons for their disregarding PG in their teaching. Under these circumstances, the role of the education system to bring the population question to the front stage in Israel and elsewhere is more challenging. To encourage science and social studies teachers to incorporate the controversial issue of PG in their teaching and successfully confront dominant pronatalist cultures, they need strong and ongoing scaffolding and support. In accordance with scientists' agreement regarding the role of PG as a major factor that drives the global environmental crisis, we call on stakeholders and policymakers in the education system to bring the population debate into schools' curricula, the sooner, the better. And not only as part of human efforts to mitigate environmental degradation but also to use this controversial topic as a platform for shaping critical learners and responsible and active citizens who are tolerant of different people’s opinions.

Keywords: population growth, environmental and sustainability education, controversial environmental sustainability issues, pronatalism

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Authors: Dauda Ibrahim Adagye

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Effective internal control system in the bursary unit of tertiary educational institutions is geared toward achieving quality teaching, learning, and research environment and as well assist the management of the institutions, particularly when decisions are to be made. While internal control system exists in all institutions, the outlined objectives above are far from being achieved. The paper; therefore, assesses the effectiveness of internal control system in tertiary educational institutions in Nasarawa State, Nigeria with the specific focus on the Nasarawa state Polytechnic, Lafia. The study is survey; hence, a simple closed-ended questionnaire was developed and administered to a sample of twenty-seven (27) member staff from the Bursary and the internal audit unit of the Nasarawa State Polytechnic, Lafia to obtain data for analysis purposes and to test the study hypothesis. Responses from the questionnaire were analyzed using a simple percentage and chi-square. Findings shows that the right people are not assigned to the right job in the department, budget, and management accounting were never used in the institution’s operations and checking of subordinate by their superior officers is not regular. This renders the current internal control structure of the Polytechnic as ineffective and weak. The paper therefore, recommends that: transparency should be seen as significant, as the institution work toward meeting its objectives, therefore, it means that the right staff is assigned to the right job and regular checking of the subordinates by their ensued superiors.

Keywords: internal control, tertiary educational intuitions, efficiency

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Authors: Isabelle Lorge, Napoleon Katsos

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BACKGROUND: Children growing up in a multilingual environment face challenges related to the need to monitor the speaker’s linguistic abilities, more frequent communication failures, and having to acquire a large number of words in a limited amount of time compared to monolinguals. As a result, bilingual learners may develop different word learning strategies, rely more on some strategies than others, and engage cognitive resources such as theory of mind and attention skills in different ways. HYPOTHESIS: The goal of our study is to investigate whether multilingual exposure leads to improvements in the ability to use pragmatic inference for word learning, i.e., to use speaker cues to derive their referring intentions, often by overcoming lower level salience effects. The speaker cues we identified as relevant are (a) use of a modifier with or without stress (‘the WET dax’ prompting the choice of the referent which has a dry counterpart), (b) referent extension (‘this is a kitten with a fep’ prompting the choice of the unique rather than shared object), (c) referent novelty (choosing novel action rather than novel object which has been manipulated already), (d) teacher versus random sampling (assuming the choice of specific examples for a novel word to be relevant to the extension of that new category), and finally (e) emotional affect (‘look at the figoo’ uttered in a sad or happy voice) . METHOD: To this end, we implemented on a touchscreen computer a task corresponding to each of the cues above, where the child had to pick the referent of a novel word. These word learning tasks (a), (b), (c), (d) and (e) were adapted from previous word learning studies. 113 children have been tested (54 reception and 59 year 1, ranging from 4 to 6 years old) in a London primary school. Bilingual or monolingual status and other relevant information (age of onset, proficiency, literacy for bilinguals) is ascertained through language questionnaires from parents (34 out of 113 received to date). While we do not yet have the data that will allow us to test for effect of bilingualism, we can already see that performances are far from approaching ceiling in any of the tasks. In some cases the children’s performances radically differ from adults’ in a qualitative way, which means that there is scope for quantitative and qualitative effects to arise between language groups. The findings should contribute to explain the puzzling speed and efficiency that bilinguals demonstrate in acquiring competence in two languages.

Keywords: bilingualism, pragmatics, word learning, attention

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Authors: Prabhvir Singh Marway, Kai Lok Chan, Maria-Ruxandra Jinga, Rachel Bok Ying Lee, Matthew Bok Kit Lee, Krishan Nandapalan, Sze Yi Beh, Harry Carr, Christopher Kui

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We piloted a structured series of online workshops on the 3D segmentation of anatomical structures from CT scans. 33 participants were recruited from four UK universities for two-day workshops between 2020 and 2021. Open-source software (3D-Slicer) was used. We hypothesized that active participation via real-time screen-sharing and voice-communication via Discord would enable improved engagement and learning, despite national lockdowns. Written feedback indicated positive learning experiences, with subjective measures of anatomical understanding and software confidence improving.

Keywords: medical education, workshop, segmentation, anatomy

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Authors: Rodchares Hanrinth, Theerapong Srisil, Peeraya Sriphong, Pawich Paktipat

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The trigger tool is the low-cost, low-tech method to detect adverse events through clues called triggers. The Institute for Healthcare Improvement (IHI) has developed the Global Trigger Tool for measuring and preventing adverse events. However, this tool is not specific for detecting adverse drug events. The pharmacy-based trigger tool is needed to detect adverse drug events (ADEs). Group concept mapping is an effective method for conceptualizing various ideas from diverse stakeholders. This technique was used to identify a pharmacy-based trigger to detect adverse drug events (ADEs). The aim of this study was to involve the pharmacists in conceptualizing, developing, and prioritizing a feasible trigger tool to detect adverse drug events in a provincial hospital, the northeastern part of Thailand. The study was conducted during the 6-month period between April 1 and September 30, 2017. Study participants involved 20 pharmacists (17 hospital pharmacists and 3 pharmacy lecturers) engaging in three concept mapping workshops. In this meeting, the concept mapping technique created by Trochim, a highly constructed qualitative group technic for idea generating and sharing, was used to produce and construct participants' views on what triggers were potential to detect ADEs. During the workshops, participants (n = 20) were asked to individually rate the feasibility and potentiality of each trigger and to group them into relevant categories to enable multidimensional scaling and hierarchical cluster analysis. The outputs of analysis included the trigger list, cluster list, point map, point rating map, cluster map, and cluster rating map. The three workshops together resulted in 21 different triggers that were structured in a framework forming 5 clusters: drug allergy, drugs induced diseases, dosage adjustment in renal diseases, potassium concerning, and drug overdose. The first cluster is drug allergy such as the doctor’s orders for dexamethasone injection combined with chlorpheniramine injection. Later, the diagnosis of drug-induced hepatitis in a patient taking anti-tuberculosis drugs is one trigger in the ‘drugs induced diseases’ cluster. Then, for the third cluster, the doctor’s orders for enalapril combined with ibuprofen in a patient with chronic kidney disease is the example of a trigger. The doctor’s orders for digoxin in a patient with hypokalemia is a trigger in a cluster. Finally, the doctor’s orders for naloxone with narcotic overdose was classified as a trigger in a cluster. This study generated triggers that are similar to some of IHI Global trigger tool, especially in the medication module such as drug allergy and drug overdose. However, there are some specific aspects of this tool, including drug-induced diseases, dosage adjustment in renal diseases, and potassium concerning which do not contain in any trigger tools. The pharmacy-based trigger tool is suitable for pharmacists in hospitals to detect potential adverse drug events using clues of triggers.

Keywords: adverse drug events, concept mapping, hospital, pharmacy-based trigger tool

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Authors: Andrey Khalov

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The rapid proliferation of unstructured data in IT infrastructure management demands innovative approaches for extracting actionable knowledge. This paper presents a framework for ontology-based knowledge extraction that combines relational graph neural networks (R-GNN) with large language models (LLMs). The proposed method leverages the DOLCE framework as the foundational ontology, extending it with concepts from ITSMO for domain-specific applications in IT service management and outsourcing. A key component of this research is the use of transformer-based models, such as DeBERTa-v3-large, for automatic entity and relationship extraction from unstructured texts. Furthermore, the paper explores how transfer learning techniques can be applied to fine-tune large language models (LLaMA) for using to generate synthetic datasets to improve precision in BERT-based entity recognition and ontology alignment. The resulting IT Ontology (ITO) serves as a comprehensive knowledge base that integrates domain-specific insights from ITIL processes, enabling more efficient decision-making. Experimental results demonstrate significant improvements in knowledge extraction and relationship mapping, offering a cutting-edge solution for enhancing cognitive computing in IT service environments.

Keywords: ontology expansion, synthetic dataset, transformer fine-tuning, concept extraction, DOLCE, BERT, taxonomy, LLM, NER

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Authors: Amanuel Berhanu Kesamo

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This paper presents both circuit simulation and hardware implementation of a robot vehicle that can be either controlled manually via Bluetooth with video streaming or navigate autonomously to a target point by avoiding obstacles. In manual mode, the user controls the mobile robot using C# windows form interfaced via Bluetooth. The camera mounted on the robot is used to capture and send the real time video to the user. In autonomous mode, the robot plans the shortest path to the target point while avoiding obstacles along the way. Ultrasonic sensor is used for sensing the obstacle in its environment. An efficient path planning algorithm is implemented to navigate the robot along optimal route.

Keywords: Arduino Uno, autonomous, Bluetooth module, path planning, remote controlled robot, ultra sonic sensor

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Authors: Maciej Zareba, Pawel Dawid

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The integration of serious games into the onboarding process is radically changing the way organizations seek to engage and retain new employees, especially in digital generations such as Millennials (Generation Y) and Generation Z. Serious gamification uses game design elements - such as points, leaderboards and progress indicators - to create interactive, goal-oriented and engaging experiences that facilitate smoother transitions to new roles and acceptance of organizational cultures. The use of serious games in onboarding reduces the stress of starting a new job while accelerating the learning curve through mechanisms that reward achievements, such as completing milestones, connecting with other team members or learning about company values. These tools promote immediate recognition and a sense of belonging to the team and organization, thereby significantly increasing retention and engagement rates. The article also outlines the benefits of using serious games in the onboarding process. It focuses on increasing employee motivation, accelerating learning about the organization and increasing engagement in the long term. In addition, the paper outlines the potential of using a serious game - 4FactoryManager - in the onboarding process. The article provides useful information for HR professionals who are looking for innovative ways to recruit, onboard and keep the best employees in a changing labor market.

Keywords: HR, oboarding, digital generation, serious games

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Authors: Derejaw Lake Melie, Alemu Kumlachew Tegegne

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The increasing prevalence of social media in Ethiopia has exacerbated societal challenges by fostering the proliferation of negative emotional posts and comments. Illicit use of social media has further exacerbated divisions among the population. Addressing these issues through manual identification and aggregation of emotions from millions of users for swift decision-making poses significant challenges, particularly given the rapid growth of Amharic language usage on social platforms. Consequently, there is a critical need to develop an intelligent system capable of automatically detecting and categorizing negative emotional content into social, religious, and political categories while also filtering out toxic online content. This paper aims to leverage sentiment analysis techniques to achieve automatic detection and filtering of negative emotional content from Amharic social media texts, employing a comparative study of deep learning algorithms. The study utilized a dataset comprising 29,962 comments collected from social media platforms using comment exporter software. Data pre-processing techniques were applied to enhance data quality, followed by the implementation of deep learning methods for training, testing, and evaluation. The results showed that CNN, GRU, LSTM, and Bi-LSTM classification models achieved accuracies of 83%, 50%, 84%, and 86%, respectively. Among these models, Bi-LSTM demonstrated the highest accuracy of 86% in the experiment.

Keywords: negative emotion, emotion detection, social media filtering sentiment analysis, deep learning.

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Authors: Yinghui Shang, Ziqing Zhou, Rong Han, Hang Wang, Xiaodi Liu, Yong Yang

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The design of advanced amorphous solids, such as metallic glasses, with targeted properties through artificial intelligence signifies a paradigmatic shift in physical metallurgy and materials technology. Here, we developed a machine-learning architecture that facilitates the generation of metallic glasses with targeted multifunctional properties. Our architecture integrates the state-of-the-art unsupervised generative adversarial network model with supervised models, allowing the incorporation of general prior knowledge derived from thousands of data points across a vast range of alloy compositions, into the creation of data points for a specific type of composition, which overcame the common issue of data scarcity typically encountered in the design of a given type of metallic glasses. Using our generative model, we have successfully designed copper-based metallic glasses, which display exceptionally high hardness or a remarkably low modulus. Notably, our architecture can not only explore uncharted regions in the targeted compositional space but also permits self-improvement after experimentally validated data points are added to the initial dataset for subsequent cycles of data generation, hence paving the way for the customized design of amorphous solids without human intervention.

Keywords: metallic glass, artificial intelligence, mechanical property, automated generation

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Authors: K. Y. Chen, H. Chua, C. W. Kan

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In this study, we will analyze Germany’s environmental situation such as water and air quality and review its environmental policy. In addition, we will collect the yearly environmental data as well as information concerning public environmental investment. Based on the data collect, we try to find out the relationship between public environmental investment and sustainable development in Germany. In addition, after comparing the trend of environmental quality and situation of environmental policy and investment, we may have some conclusions and learnable aspects to refer to. Based upon the data collected, it was revealed that Germany has established a well-developed institutionalization of environmental education. And the ecological culture at school is dynamic and continuous renewal. The booming of green markets in Germany is a very successful experience for learning. The green market not only creates a number of job opportunities, but also helps the government to improve and protect the environment. Acknowledgement: Authors would like to thank the financial support from the Hong Kong Polytechnic University for this work.

Keywords: Germany, public environmental investment, environment quality, sustainable development

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: F. Rashno Seydari, M. Nikafrooz

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This paper intends to study needs analysis of hearing-impaired students’ teachers in elementary schools all over Iran. The subjects of this study were 275 teachers who were teaching hearing-impaired students in elementary schools. The participants were selected by a quota sampling method. To collect the data, questionnaires of training needs consisting of 41 knowledge items and 31 performance items were used. The collected data were analyzed by using SPSS software in the form of descriptive analyses (frequency and mean) and inferential analyses (one sample t-test, paired t-test, independent t-test, and Pearson correlation coefficient). The findings of the study indicated that teachers generally have considerable needs in knowledge and performance domains. In 32 items out of the total 41 knowledge domain items and in the 27 items out of the total 31 performance domain items, the teachers had considerable needs. From the quantitative point of view, the needs of the performance domain were more than those of the knowledge domain, so they have to be considered as the first priority in training these teachers. There was no difference between the level of the needs of male and female teachers. There was a significant difference between the knowledge and performance domain needs and the teachers’ teaching experience, 0.354 and 0.322 respectively. The teachers who had been trained in working with hearing-impaired students expressed more training needs (both knowledge and performance).

Keywords: educational needs analysis, teachers of hearing impaired students, knowledge domain, function domain

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Authors: Gokcen Ogruk-Maz, Sinan Yildirim

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Introduction: Exchange rate forecasting is pivotal for informed financial decision-making, encompassing risk management, investment strategies, and international trade planning. However, traditional forecasting models often fail to capture the complexity and volatility of currency markets. This study explores the potential of ensemble learning techniques such as Random Forest, Gradient Boosting, and AdaBoost to enhance the accuracy and robustness of exchange rate predictions. Research Objectives The primary objective is to evaluate the performance of ensemble methods in comparison to traditional econometric models such as Uncovered Interest Rate Parity, Purchasing Power Parity, and Monetary Models. By integrating advanced machine learning techniques with fundamental macroeconomic indicators, this research seeks to identify optimal approaches for predicting exchange rate movements across major currency pairs. Methodology: Using historical exchange rate data and economic indicators such as interest rates, inflation, money supply, and GDP, the study develops forecasting models leveraging ensemble techniques. Comparative analysis is performed against traditional models and hybrid approaches incorporating Facebook Prophet, Artificial Neural Networks, and XGBoost. The models are evaluated using statistical metrics like Mean Squared Error, Theil Ratio, and Diebold-Mariano tests across five currency pairs (JPY to USD, AUD to USD, CAD to USD, GBP to USD, and NZD to USD). Preliminary Results: Results indicate that ensemble learning models consistently outperform traditional methods in predictive accuracy. XGBoost shows the strongest performance among the techniques evaluated, achieving significant improvements in forecast precision with consistently low p-values and Theil Ratios. Hybrid models integrating macroeconomic fundamentals into machine learning frameworks further enhance predictive accuracy. Discussion: The findings show the potential of ensemble methods to address the limitations of traditional models by capturing non-linear relationships and complex dynamics in exchange rate movements. While Random Forest and Gradient Boosting are effective, the superior performance of XGBoost suggests that its capacity for handling sparse and irregular data offers a distinct advantage in financial forecasting. Conclusion and Implications: This research demonstrates that ensemble learning techniques, particularly when combined with traditional macroeconomic fundamentals, provide a robust framework for improving exchange rate forecasting. The study offers actionable insights for financial practitioners and policymakers, emphasizing the value of integrating machine learning approaches into predictive modeling for monetary economics.

Keywords: exchange rate forecasting, ensemble learning, financial modeling, machine learning, monetary economics, XGBoost

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Authors: Kshama Sharma

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Education is one of the most powerful tools to transform the world into a just, sustainable, and more peaceful place for existing lives across the globe. However, its recent objective approach focused on materialistic, factual, and existing knowledge, has a constraint of human experiences that is limited to certain dimensions only. And leads to a materialistic world which is deprived of spiritual approaches and makes it less compassionate, and more grades oriented. To make it more comprehensive, education should explore the subjective approaches towards spiritualism to connect lives with the greater self and consciousness of cosmic intelligence. This approach will bring a major shift in the orientation of pedagogical processes, assessment strategies, and administrative management of the present education system. Spirituality often related to the religious aspect of human civilization and development, however, when universal consciousness /cosmic intelligence (which is often claimed as dark energy) and the human mind competencies works in coherence and coordination then the efficiency of human mind reaches to a different dimension and achieve extraordinary level of human understanding. Quantitative analysis of the existing secondary data from the different agencies working in the field of meditation had been analyzed to conclude its implications on human mind and further how it can effectively use in education to bring the desired and expected results. Any kind of meditation practice affects the cognitive, mental, physical, emotional, and conscious state of mind. If aligned with the teaching and learning methodology will lead to conscious learner and peaceful world.

Keywords: spirituality, cosmic intelligence, consciousness, mind competencies

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Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

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Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

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Authors: Salah Belaidi

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We have carried out extensive and in-depth research to search for bioactive compounds based on Algerian plants. A selection of 50 ligands from Algerian medicinal plants. Several compounds used in herbal medicine have been drawn using Marvin Sketch software. We determined the three-dimensional structures of the ligands with the MMFF94 force field in order to prepare these ligands for molecular docking. The 3D protein structure of the SARS-CoV-2 main protease was taken from the Protein Data Bank. We used AutoDockVina software to apply molecular docking. The hydrogen atoms were added during the molecular docking process, and all the twist bonds of the ligands were added using the (ligand) module in the AutoDock software. The COVID-19 main protease (Mpro) is a key enzyme that plays a vital role in viral transcription and mediating replication, so it is a very attractive drug target for SARS-CoV-2. In this work, an evaluation was carried out on the biologically active compounds present in these selected medicinal plants as effective inhibitors of the protease enzyme of COVID-19, with an in-depth computational calculation of the molecular docking using the Autodock Vina software. The top 7 ligands: Phloroglucinol, Afzelin, Myricetin-3-O- rutinosidTricin 7-neohesperidoside, Silybin, Silychristinthat and Kaempferol are selected among the 50 molecules studied which are Algerian medicinal plants, whose selection is based on the best binding energy which is relatively low compared to the reference molecule with binding affinities of -9.3, -9.3, -9, -8.9, -8 .5, 8.3 and -8.3 kcal mol-1 respectively. Then, we analyzed the ADME properties of the best7 ligands using the web server SwissADME. Two ligands (Silybin, Silychristin) were found to be potential candidates for the discovery and design of novel drug inhibitors of the protease enzyme of SARS-CoV-2. The stability of the two ligands in complexing with the Mpro protease was validated by molecular dynamics simulation; they revealed a stable trajectory in both techniques, RMSD and RMSF, by showing molecular properties with coherent interactions in molecular dynamics simulations. Finally, we conclude that the Silybin ligand forms a more stable complex with the Mpro protease compared to the Silychristin ligand.

Keywords: COVID-19, medicinal plants, molecular docking, ADME properties, molecular dynamics

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Authors: Rungrot Trongsakul

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This paper was aimed to study the strategic cooperation development of Thailand in accordance with the door open policy of Myanmar, by use of DIMES Model: Diplomacy, Information, Military and Economics, Socio-Culture. This research employed qualitative method, aiming to study, analyze and synthesize the content of laws, policies, relevant research papers and documents, and relevant theories, and to study external environment and national power based on DIMES Model. The five steps of strategic development utilized in this study included (1) conceptual framework and definition; (2) environmental scanning; (3) assessing; (4) determining; and (5) drafting strategic plan. The suggested strategies were based on the concept of 'Soft Power'. Therefore, the determination of measures, action plans or projects as strategic means of public and private organizations should be based on sincere participation among people and communities living on the borders shared by both countries. Adoption of education, learning and sharing process is a key to building sustainability of the countries’ strategic cooperation, while an application of 'Soft Power' in all dimensions of the cooperation between the two countries was suggested.

Keywords: education, strategic cooperation, Thailand-Myanmar borders, sustainability

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Authors: Troy A. Tyre

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Organizational behavior and organizational change are elements of the ever-changing global business environment. Leadership and organizational behavior are 21st century disciplines. Network marketing organizations need to understand the ever-changing nature of global business and be ready and willing to adapt to the environment. Network marketing organizations have a challenge keeping up with a rapid escalation in global growth. Network marketing growth has been steady and global. Network marketing organizations have been slow to develop a 21st century global strategy to manage the rapid escalation of growth degrading organizational behavior, job satisfaction, increasing attrition, and degrading customer service. Development of an organizational behavior and leadership theory for the 21st century to help network marketing develops a global business strategy to manage the rapid escalation in growth that affects organizational behavior. Managing growth means organizational leadership must develop and adapt to the organizational environment. Growth comes with an open mind and one’s departure from the comfort zone. Leadership growth operates in the tacit dimension. Systems thinking and adaptation of mental models can help shift organizational behavior. Shifting the organizational behavior requires organizational learning. Organizational learning occurs through single-loop, double-loop, and triple-loop learning. Triple-loop learning is the most difficult, but the most rewarding. Tools such as theory U can aid in developing a landscape for organizational behavioral development. Additionally, awareness to espoused and portrayed actions is imperatives. Theories of motivation, cross-cultural diversity, and communications are instrumental in founding an organizational behavior suited for the 21st century.

Keywords: global, leadership, network marketing, organizational behavior

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Authors: Joud Zagzoog, Amira Aldabbagh, Radiyah Hamidaddin

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This research aims to test out and observe whether artificial intelligence (AI) software and applications could actually be effective, useful, and time-saving for those who use them. Shaguf Bites, a web application that uses AI technology, claims to help students study and memorize information more effectively in less time. The website uses smart learning, or AI-powered bite-sized repetitive learning, by transforming documents or PDFs with the help of AI into summarized interactive smart flashcards (Bites, n.d.). To properly test out the websites’ effectiveness, both qualitative and quantitative methods were used in this research. An experiment was conducted on a number of students where they were first requested to use Shaguf Bites without any prior knowledge or explanation of how to use it. Second, they were asked for feedback through a survey on how their experience was after using it and whether it was helpful, efficient, time-saving, and easy to use for studying. After reviewing the collected data, we found out that the majority of students found the website to be straightforward and easy to use. 58% of the respondents agreed that the website accurately formulated the flashcard questions. And 53% of them reported that they are most likely to use the website again in the future as well as recommend it to others. Overall, from the given results, it is clear that Shaguf Bites have proved to be very beneficial, accurate, and time saving for the majority of the students.

Keywords: artificial intelligence (AI), education, memorization, spaced repetition, flashcards.

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Authors: Kritchakhris Na-Wattanaprasert

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The objective of this research is to design and develop a prototype of a key performance indicator system this is suitable for warehouse management in a case study and use requirement. In this study, we design a prototype of key performance indicator system (KPI) for warehouse case study of furniture business by methodology in step of identify scope of the research and study related papers, gather necessary data and users requirement, develop key performance indicator base on balance scorecard, design pro and database for key performance indicator, coding the program and set relationship of database and finally testing and debugging each module. This study use Balance Scorecard (BSC) for selecting and grouping key performance indicator. The system developed by using Microsoft SQL Server 2010 is used to create the system database. In regard to visual-programming language, Microsoft Visual C# 2010 is chosen as the graphic user interface development tool. This system consists of six main menus: menu login, menu main data, menu financial perspective, menu customer perspective, menu internal, and menu learning and growth perspective. Each menu consists of key performance indicator form. Each form contains a data import section, a data input section, a data searches – edit section, and a report section. The system generates outputs in 5 main reports, the KPI detail reports, KPI summary report, KPI graph report, benchmarking summary report and benchmarking graph report. The user will select the condition of the report and period time. As the system has been developed and tested, discovers that it is one of the ways to judging the extent to warehouse objectives had been achieved. Moreover, it encourages the warehouse functional proceed with more efficiency. In order to be useful propose for other industries, can adjust this system appropriately. To increase the usefulness of the key performance indicator system, the recommendations for further development are as follows: -The warehouse should review the target value and set the better suitable target periodically under the situation fluctuated in the future. -The warehouse should review the key performance indicators and set the better suitable key performance indicators periodically under the situation fluctuated in the future for increasing competitiveness and take advantage of new opportunities.

Keywords: key performance indicator, warehouse management, warehouse operation, logistics management

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Authors: Ankit Kamboj, Shikha Talwar, Nilesh Powar

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To successfully deliver our products in the market, the employees need to be in a safe environment, especially in an industrial and manufacturing environment. The consequences of delinquency in wearing safety glasses while working in industrial plants could be high risk to employees, hence the need to develop a real-time automatic detection system which detects the persons (violators) not wearing safety glasses. In this study a convolutional neural network (CNN) algorithm called faster region based CNN (Faster RCNN) with rotated bounding box has been used for detecting safety glasses on persons; the algorithm has an advantage of detecting safety glasses with different orientation angles on the persons. The proposed method of rotational bounding boxes with a convolutional neural network first detects a person from the images, and then the method detects whether the person is wearing safety glasses or not. The video data is captured at the entrance of restricted zones of the industrial environment (manufacturing plant), which is further converted into images at 2 frames per second. In the first step, the CNN with pre-trained weights on COCO dataset is used for person detection where the detections are cropped as images. Then the safety goggles are labelled on the cropped images using the image labelling tool called roLabelImg, which is used to annotate the ground truth values of rotated objects more accurately, and the annotations obtained are further modified to depict four coordinates of the rectangular bounding box. Next, the faster RCNN with rotated bounding box is used to detect safety goggles, which is then compared with traditional bounding box faster RCNN in terms of detection accuracy (average precision), which shows the effectiveness of the proposed method for detection of rotatory objects. The deep learning benchmarking is done on a Dell workstation with a 16GB Nvidia GPU.

Keywords: CNN, deep learning, faster RCNN, roLabelImg rotated bounding box, safety goggle detection

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Authors: Louise Fitzgerald, John Allman

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Introduction: Having previously engaged in a meditation-based programme (MBP) for staff in general practice, we explore the evidence and extent to which MBPs are employed in the workplace. Aim of the study: We aim to understand the current workplace MBP intervention literature, which will help inform the suitability of these interventions within the workplace domain. Objectives: Uptake of MBPs in the workplace has grown as organizations look to support employee health, wellbeing, and performance. We will discuss the current MBP literature, including the large variability across MBPs and the associated difficulties in evaluating their efficacy. Learning points: 1) MBPs have a positive impact on cognitive function including concentration and memory and as such job performance. MBPs appear to have a positive impact on objective and subjective job satisfaction, productivity, motivation and work engagement. Meditation in the workplace may have positive impacts on mental health issues - including stress reduction and depression. 2) From our review MBPs appear to be implementable in a wide range of professions and work contexts - regardless of individual factors. Given many companies are focusing on health and wellbeing of employees, this could be included in employee wellbeing programmes. 3) Despite the benefits of mindfulness and meditation interventions in psychosocial workplace health and work performance the long-term efficacy has yet to be fully determined.

Keywords: meditation-based programmes, mindfulness, meditation, well-being

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Authors: Ahmad Tijani Surajudeen, Muhammad Zahiri Awang Mat, Aliy Abdulwahid Adebisi

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With arrival of Islam in the South-Western Nigeria in the late fifteenth and early sixteenth centuries, various masājid established in different parts of the area played vital roles towards the betterment and unity of the Muslims. However, despite the fact that the masājid in the South-Western part of Nigeria contributed immensely to the spiritual and educational enhancement of the Muslims, it has not fully captured the holistic educational roles as a unique model used by the Prophet (S.A.W). Therefore, the primary objective of this paper is to investigate and broaden the roles of masjid towards its compartmentalized and holistic contributions among the Muslims in the south-western Nigeria. The findings from the paper have identified five holistic roles of masjid, namely, spiritual, intellectual, physical, social and emotional contributions which have been exemplified in the prophetic model of masjid. The paper has argued that the five factors must be unreservedly unified towards the betterment of the Muslims and enhancement of Islamic education and Arabic Language in the South-Western Nigeria. However, the challenges of masjid management in the South-Western Nigeria are the main hindrance in achieving the holistic roles of masjid. It is thereby suggested that, the management of masjid should take the identified prophetic model of masjid into account in order to positively improve the affairs of Muslims as well as promoting the teaching and learning of Islamic education and Arabic language among the Muslims in the South-Western Nigeria.

Keywords: worship, Islamic education, Arabic language, prophetic holistic model

Procedia PDF Downloads 335

Authors: Sirine Sayed, Mostapha Tarfaoui, Abdelmalek Toumi, Youssef Qarssis, Mohamed Daly, Chokri Bouraoui

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This paper delves into the combination of additive manufacturing (AM) and artificial intelligence (AI) to solve challenges related to the mechanical behavior of AM-produced parts. The article highlights the fundamentals and benefits of additive manufacturing, including creating complex geometries, optimizing material use, and streamlining manufacturing processes. The paper also addresses the challenges associated with additive manufacturing, such as ensuring stable mechanical performance and material properties. The role of AI in improving the static behavior of AM-produced parts, including machine learning, especially the neural network, is to make regression models to analyze the large amounts of data generated during experimental tests. It investigates the potential synergies between AM and AI to achieve enhanced functions and personalized mechanical properties. The mechanical behavior of parts produced using additive manufacturing methods can be further improved using design optimization, structural analysis, and AI-based adaptive manufacturing. The article concludes by emphasizing the importance of integrating AM and AI to enhance mechanical operations, increase reliability, and perform advanced functions, paving the way for innovative applications in different fields.

Keywords: additive manufacturing, mechanical behavior, artificial intelligence, machine learning, neural networks, reliability, advanced functionalities

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Authors: Obe Olumide Olayinka, Victor Balanica, Eugen Neagoe

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The effects of hypertension are often lethal thus its early detection and prevention is very important for everybody. In this paper, a neural network (NN) model was developed and trained based on a dataset of hypertension causative parameters in order to forecast the likelihood of occurrence of hypertension in patients. Our research goal was to analyze the potential of the presented NN to predict, for a period of time, the risk of hypertension or the risk of developing this disease for patients that are or not currently hypertensive. The results of the analysis for a given patient can support doctors in taking pro-active measures for averting the occurrence of hypertension such as recommendations regarding the patient behavior in order to lower his hypertension risk. Moreover, the paper envisages a set of three example scenarios in order to determine the age when the patient becomes hypertensive, i.e. determine the threshold for hypertensive age, to analyze what happens if the threshold hypertensive age is set to a certain age and the weight of the patient if being varied, and, to set the ideal weight for the patient and analyze what happens with the threshold of hypertensive age.

Keywords: neural network, hypertension, data set, training set, supervised learning

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Authors: Adi Gielgun-Katz, Alina S. Rusu

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In the last two decades, education systems worldwide have integrated new pedagogical methods and strategies in lesson plans, such as innovative technologies, social-emotional learning (SEL), gamification, mixed learning, multiple literacies, and many others. Visual language, such as photographs, is known to transcend cultures and languages, and it is commonly used by youth to express positions and affective states in social networks. Therefore, visual language needs more educational attention as a linguistic and communicative component that can create connectedness among the students and their teachers. Nowadays, when SEL is gaining more and more space and meaning in the area of academic improvement in relation to social well-being, and taking and sharing pictures is part of the everyday life of the majority of people, it becomes natural to add the visual language to SEL approach as a reinforcement strategy for connecting education to the contemporary culture and language of the youth. This article presents a program conducted in a high school class in Israel, which combines the five SEL with photography techniques, i.e., Social-Emotional Learning Visual Empowerments (SELVEs) program (experimental group). Another class of students from the same institution represents the control group, which is participating in the SEL program without the photography component. The SEL component of the programs addresses skills such as: troubleshooting, uncertainty, personal strengths and collaboration, accepting others, control of impulses, communication, self-perception, and conflict resolution. The aim of the study is to examine the effects of programs on the level of the five SEL aspects in the two groups of high school students: Self-Awareness, Social Awareness, Self-Management, Responsible Decision Making, and Relationship Skills. The study presents a quantitative assessment of the SEL programs’ impact on the students. The main hypothesis is that the students’ questionnaires' analysis will reveal a better understanding and improvement of the five aspects of the SEL in the group of students involved in the photography-enhanced SEL program.

Keywords: social-emotional learning, photography, education program, adolescents

Procedia PDF Downloads 92

Authors: Raveendranath Veeramani, Sunil Jonathan Holla, Parkash Chand, Sunil Chumber

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Gray’s Anatomy for Students has been a well-appreciated book among undergraduate students of anatomy in Asia. However, the current curricular requirements of anatomy require a more focused and organized approach. The editors of the first South Asia edition of Gray’s Anatomy for Students hereby highlight the modifications and importance of this edition. There is an emphasis on active learning by making the clinical relevance of anatomy explicit. Learning anatomy in context has been fostered by the association between anatomists and clinicians in keeping with the emerging integrated curriculum of the 21st century. The language has been simplified to aid students who have studied in the vernacular. The original illustrations have been retained, and few illustrations have been added. There are more figure numbers mentioned in the text to encourage students to refer to the illustrations while learning. The text has been made more student-friendly by adding generalizations, classifications and summaries. There are useful review materials at the beginning of the chapters which include digital resources for self-study. There are updates on imaging techniques to encourage students to appreciate the importance of essential knowledge of the relevant anatomy to interpret images, due emphasis has been laid on dissection. Additional importance has been given to the cranial nerves, by describing their relevant details with several additional illustrations and flowcharts. This new edition includes innovative features such as set inductions, outlines for subchapters and flowcharts to facilitate learning. Set inductions are mostly clinical scenarios to create interest in the need to study anatomy for healthcare professions. The outlines are a modern multimodal facilitating approach towards various topics to empower students to explore content and direct their learning and include learning objectives and material for review. The components of the outline encourage the student to be aware of the need to create solutions to clinical problems. The outlines help students direct their learning to recall facts, demonstrate and analyze relationships, use reason to explain concepts, appreciate the significance of structures and their relationships and apply anatomical knowledge. The 'structures to be identified in a dissection' are given as Level I, II and III which represent the 'must know, desirable to know and nice to know' content respectively. The flowcharts have been added to get an overview of the course of a structure, recapitulate important details about structures, and as an aid to recall. There has been a great effort to balance the need to have content that would enable students to understand concepts as well as get the basic material for the current condensed curriculum.

Keywords: Grays anatomy, South Asia, human anatomy, students anatomy

Procedia PDF Downloads 207

Authors: Smita Singh

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Objective: The Faculty of Chemistry Education at Humboldt University has developed ‘Studium’, a web application designed to enhance long-term self-regulated learning (SRL) and academic achievement. Leveraging advanced generative AI, ‘Studium’ offers a dynamic and adaptive educational experience tailored to individual learning preferences and languages. The application includes evolving tools for personalized notetaking from preferred sources, customizable presentation capabilities, and AI-assisted guidance from academic documents or textbooks. It also features workflow automation and seamless integration with collaborative platforms like Miro, powered by AI. This study aims to propose a model that combines generative AI with traditional features and customization options, empowering students to create personalized learning environments that effectively address the challenges of SRL. Method: To achieve this, the study included graduate and undergraduate students from diverse subject streams, with 15 participants each from Germany and India, ensuring a diverse educational background. An exploratory design was employed using a speed dating method with enactment, where different scenario sessions were created to allow participants to experience various features of ‘Studium’. The session lasted for 50 minutes, providing an in-depth exploration of the platform's capabilities. Participants interacted with Studium’s features via Zoom conferencing and were then engaged in semi-structured interviews lasting 10-15 minutes to gain deeper insights into the effectiveness of ‘Studium’. Additionally, online questionnaire surveys were conducted before and after the session to gather feedback and evaluate satisfaction with self-regulated learning (SRL) after using ‘Studium’. The response rate of this survey was 100%. Results: The findings of this study indicate that students widely acknowledged the positive impact of ‘Studium’ on their learning experience, particularly its adaptability and intuitive design. They expressed a desire for more tools like ‘Studium’ to support self-regulated learning in the future. The application significantly fostered students' independence in organizing information and planning study workflows, which in turn enhanced their confidence in mastering complex concepts. Additionally, ‘Studium’ promoted strategic decision-making and helped students overcome various learning challenges, reinforcing their self-regulation, organization, and motivation skills. Conclusion: This proposed model emphasizes the need for effective integration of personalized AI tools into active learning and SRL environments. By addressing key research questions, our framework aims to demonstrate how AI-assisted platforms like “Studium” can facilitate deeper understanding, maintain student motivation, and support the achievement of academic goals. Thus, our ideal model for AI-assisted educational platforms provides a strategic approach to enhance student's learning experiences and promote their development as self-regulated learners. This proposed model emphasizes the need for effective integration of personalized AI tools into active learning and SRL environments. By addressing key research questions, our framework aims to demonstrate how AI-assisted platforms like ‘Studium’ can facilitate deeper understanding, maintain student motivation, and support the achievement of academic goals. Thus, our ideal model for AI-assisted educational platforms provides a strategic approach to enhance student's learning experiences and promote their development as self-regulated learners.

Keywords: self-regulated learning (SRL), generative AI, AI-assisted educational platforms

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Authors: Ai Chin Wong, Wan Ahmad Jaafar Wan Yahaya, Balakrishnan Muniandy

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This study aims to explore the impact of Virtual Reality (VR) using visual signaling principles in learning about the science laboratory safety guide; this study involves students with different spatial ability. There are two types of science laboratory safety lessons, which are Virtual Reality with Signaling (VRS) and Virtual Reality Non Signaling (VRNS). This research has adopted a 2 x 2 quasi-experimental factorial design. There are two types of variables involved in this research. The two modes of courseware form the independent variables with the spatial ability as the moderator variable. The dependent variable is the students’ performance. This study sample consisted of 141 students. Descriptive and inferential statistics were conducted to analyze the collected data. The major effects and the interaction effects of the independent variables on the independent variable were explored using the Analyses of Covariance (ANCOVA). Based on the findings of this research, the results exhibited low spatial ability students in VRS outperformed their counterparts in VRNS. However, there was no significant difference in students with high spatial ability using VRS and VRNS. Effective learning in students with different spatial ability can be boosted by implementing the Virtual Reality with Signaling (VRS) in the design as well as the development of Virtual Science Laboratory (ViSLab).

Keywords: spatial ability, science laboratory safety, visual signaling principles, virtual reality

Procedia PDF Downloads 262