Integrating Artificial Intelligence in Teacher Education: A Systematic Analysis

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The current work is a systematic review paper that examines the function and significance of artificial intelligence (AI) in teacher education. The researcher gathered almost fifty articles from various platforms, including Google Scholar, Science Direct, Research Gate, and others, on AI and teacher education. Additionally, those publications’ analysis reveals a few key areas and their significance for teacher preparation. By delivering tailored learning experiences, improving instructional strategies, and providing data-driven insights etc. After collecting the article from the above sources, the investigator analyzed all the article on four major points e.g. AI and digital learning, AI and Teacher Education, AI and pedagogical leaning, AI and challenges in teaching learning process systematically, where the investigator found few points and analyzed vividly, at the end the view concern to the Artificial intelligence (AI) has the potential to completely transform Teacher Education. But in order to fully enjoy these advantages, the ethical, equitable, and preparedness issues around AI must be resolved.

Analysis of The Usage of The Shopee Live Streaming Application, The Effectiveness of Promotion on Customer Repurchase Decisions Mediated by User Satisfaction

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This study investigates the impact of live streaming, promotion effectiveness, and user satisfaction on customers’ repurchase decisions, based on data from 105 respondents through an online questionnaire. The analysis focuses on both direct and mediated effects, with quantitative methods applied.

The results reveal that live streaming significantly enhances user satisfaction (p-value = 0.000), but does not have a direct effect on repurchase decisions (p-value = 0.084). Promotion effectiveness, while not significantly impacting user satisfaction (p-value = 0.103), does have a significant influence on repurchase decisions (p-value = 0.032).

User satisfaction plays a crucial role in driving repurchase decisions (p-value = 0.038) and mediates the effect of live streaming on repurchase behavior (p-value = 0.049). In contrast, promotion effectiveness does not significantly influence repurchase decisions through user satisfaction (p-value = 0.184).

This study underscores the importance of user satisfaction as a key mediator in encouraging repurchase behavior. E-commerce platforms should enhance the engagement and familiarity of live streaming, ensuring it aligns with users’ shopping habits. Moreover, promotional strategies must be tailored to customer expectations in order to increase satisfaction and reduce disappointment. By optimizing these aspects, platforms can better cultivate customer loyalty and drive repurchase decisions.

Hyperparameter Tuning of Random Forest Algorithm for Diabetes Classification

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This study aims to optimize the hyperparameters of the Random Forest model in diabetes classification using the Pima Indian Diabetes dataset, given the importance of early diabetes diagnosis to mitigate serious health impacts. While Random Forest is a popular algorithm for classification due to its resistance to overfitting, the selection of the right hyperparameters significantly affects its performance. Therefore, this research utilizes Grid Search and Random Search techniques for hyperparameter tuning to improve model accuracy. The research methodology includes data collection, preprocessing, dataset splitting (80% for training and 20% for testing), feature scaling using Standard Scaler, and the application of the Random Forest algorithm with hyperparameter tuning and model evaluation based on accuracy, precision, recall, and F1-Score. The results show that Random Forest, when tuned with Grid Search and Random Search, significantly improved model performance, with Random Search yielding the best results, achieving an accuracy of 0.75, precision of 0.64, and recall of 0.69. This study demonstrates that hyperparameter tuning can significantly enhance the performance of the Random Forest model, contributing to the development of machine learning applications for medical diabetes diagnosis.

Analytical Modelling of Resistive Load Effect on Transient Voltage and Power Output from d_33-mode Piezoelectric Vibration Energy Harvester

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This research analytically investigates the effect of resistive load on the transient performance of a -mode piezoelectric vibration energy harvester. Through normalized voltage and power analyses under varying normalized resistive loads and excitation frequencies, it is observed that the normalized voltage peaks consistently at resonance frequency, with its magnitude increasing as load resistance decreases. The normalized power, however, exhibits a maximum at an optimal load resistance that aligns with the system’s internal impedance, underscoring the significance of impedance matching for efficient energy harvesting. Transient voltage and power dynamics reveal faster energy dissipation and higher fluctuations at lower resistances, while higher resistances yield smoother but less efficient energy transfer. These findings provide crucial insights into the interplay between load resistance, transient dynamics, and power optimization, paving the way for improved design and application of piezoelectric energy harvesters in real-world systems.

Electricity Supply for Hydrogen Plant through Power Swap Based Reliability and Risk Case Study Pertamina Power’s Electrolyzer in Sumatra

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The Puzzle of Mosaicism: Mechanisms and Clinical Implications in Preimplantation Genetic Testing

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Chromosomal mosaicism in preimplantation embryos, characterized by cells with varying genotypes, arises due to mitotic errors after fertilization. These errors, including anaphase lag, mitotic nondisjunction, or cytokinesis failure, typically occur during early embryonic divisions when maternal factors predominantly regulate development. The impact of mosaicism on embryonic viability varies based on the stage and nature of the chromosomal errors, potentially reducing implantation rates and increasing miscarriage risks. However, mosaic embryos can occasionally self-correct through apoptosis of abnormal cells or selective growth of euploid cells, enabling the development of healthy blastocysts.

Advancements in genetic screening, particularly next-generation sequencing (NGS), have improved mosaicism detection, although challenges remain in accurately interpreting its clinical significance. NGS identifies mosaicism with higher sensitivity than previous methods like fluorescence in situ hybridization (FISH) or array comparative genomic hybridization (aCGH). Nonetheless, discrepancies in detection rates and sampling errors complicate clinical decision-making.

Patients undergoing preimplantation genetic testing must be counseled on the potential outcomes of transferring mosaic embryos, especially when euploid options are unavailable. While mosaic embryos offer a chance for pregnancy, they carry an increased risk of miscarriage and uncertain long-term outcomes. Personalized genetic counseling and improved screening methodologies are essential for refining patient care and optimizing in vitro fertilization (IVF) outcomes. Further research is needed to understand the mechanisms and implications of mosaicism, ensuring evidence-based practices in embryo selection and transfer.

Workplace Learning and Collaborative Learning: Insights and Applications in Azerbaijan’s Education System

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This article explores the significance of workplace learning and collaborative learning, focusing on their applications within Azerbaijan’s education system. By analyzing global best practices and local contexts, the article identifies key strategies to enhance the professional development of educators and students. Workplace learning is examined as a mechanism for continuous professional growth through structured and unstructured experiences in educational settings. Collaborative learning is discussed as a dynamic process that enhances critical thinking, teamwork, and adaptability among students and educators alike. Findings emphasize the integration of collaborative methodologies into workplace environments, fostering innovation and adaptability in educational practices. The article also highlights the challenges that hinder the full implementation of these methods, including cultural barriers, resource limitations, and policy gaps. Actionable solutions, such as infrastructure development, targeted training programs, and cultural shifts toward teamwork, are proposed. These insights aim to contribute to the ongoing educational reforms in Azerbaijan, providing a roadmap for fostering a more inclusive and effective learning ecosystem.

Quantitative Analysis of Inventory Record Inaccuracy (IRI): A Case Study on Warehouse Stock Discrepancies

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Inventory Record Inaccuracy (IRI) presents critical challenges to warehouse operations by causing inefficiencies, financial losses, and diminished stakeholder trust. This study examines IRI at XYZ Warehouse through the analysis of Gross Variance and Net Variance, identifying a discrepancy rate of 0.53%, equivalent to 3,089 units or IDR 154,450,000 in potential financial losses. The findings emphasize the importance of accurate inventory management to mitigate these losses and improve operational efficiency. This study serves as a foundation for future research and interventions aimed at addressing IRI and its associated challenges.

A Sociological Perspective on Computer Science in Enhancing Workplace Efficiency: Implications for the Digital Economy and Nation Building

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This paper investigates the intersection of sociological perspectives and computer science in the workplace, emphasizing how technology adoption can enhance efficiency and contribute to the digital economy. Through a qualitative analysis of contemporary case studies, the paper highlights the social dynamics affecting technology integration and argues for a holistic approach considering cultural, organizational, and socioeconomic factors in national development strategies.

Adapting Deep-Learning in Early Yam Disease Detection Using Lenet-5 (Adam Optimizer) Convolutional Neural Network Architecture to Improve Productivity and Enhance Farmers Social Habits in the Digital Age

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The agriculture sector faces significant challenges due to diseases affecting crop yields, particularly in yam cultivation. This study explores the adaptation of deep learning techniques for early detection of yam diseases using a LeNet-5 Convolutional Neural Network (CNN) architecture optimized with the Adam optimizer. The fam sides considered are; Ardokola, Zing and Mutum Biu in Taraba State, Nigeria. By leveraging advanced image processing and machine learning methodologies, we aim to develop an effective diagnostic tool that empowers farmers to identify and manage diseases promptly, ultimately improving productivity. This research not only enhances the technological capabilities of farmers in the digital age but also promotes better agricultural practices, fostering social habits that encourage knowledge sharing and community engagement. The proposed system is tested on a comprehensive dataset of yam leaf images, demonstrating its ability to accurately detect various disease conditions at 17.84%. Results indicate a significant improvement in recognition accuracy, suggesting that the integration of AI-driven solutions can transform disease management approaches in yam farming, contributing to sustainable agricultural practices and improved livelihoods for farmers.