А Model for School Subject “AI Proficiency” for Advanced students in Secondary TVET School: Conceptual Framework and Content with Practical Exеmplary Curriculum

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The article proposes a subject model for special preparation for professional education and training for classes in the professional field Computer Science and Technology, as a continuation of the subject „Introduction to AI: General education“ for TVET secondary schools in Bulgaria, proposed by the author in a previous published article. The model is balanced, with an emphasis on active learning with a synergistic and holistic creative approach, where over 80% of the time the student is involved in an active (individual or collective) activity: researching materials, drawing up models, discussions and debates, SWOT analyses, as well as project-based tasks of greater or lesser volume, problem cases. The topics are considered not only in their strict technical context, but also holistically-synergistically: ethics in the matter of AI, strategies for regulations, future perspectives and benefits, personal interests. The curriculum aims to provide vocational high school students with a comprehensive understanding of AI, covering fundamental concepts, practical applications, advanced technologies, ethical considerations and future trends in the field, through a combination of theoretical learning, practical projects, creative activity and discussions, develop critical thinking skills and will prepare for further study or careers in AI-related fields.

Simulated Communities and Online Mentoring

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The advent of digital technologies has reshaped how individuals connect, collaborate, and learn. Simulated communities, virtual environments that replicate real-world dynamics, have emerged as powerful platforms for education, skill-building, and mentorship. These communities allow participants to engage in lifelike experiences, fostering opportunities for experiential learning and meaningful interactions. In the context of online mentoring, simulated communities serve as immersive environments where mentors and mentees can connect, collaborate, and develop skills in a personalized and engaging manner. This paper explores the intersection of simulated communities and online mentoring by analyzing existing platforms, theoretical frameworks, and case studies. The findings highlight the potential of simulated communities to bridge geographical and cultural divides, provide risk-free learning environments, and foster inclusivity and collaboration. However, challenges such as technical barriers, ethical concerns, and scalability issues must be addressed to fully realize their potential. The study concludes with recommendations for future research and practical applications to enhance the integration of simulated communities into mentoring practices.

The Influence of Green Brand Image, Satisfaction, Trust, on Loyalty Moderated by Environmental Ethics on Green Products in Indonesia

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The purpose of this study is to analyze the effect of environmentally friendly brand image, satisfaction, trust, on loyalty moderated by environmental ethics towards green products in Indonesia. The research sample in this study were consumers who used and purchased Aqua gallons in 2024. This research method uses a quantitative approach with data collection techniques through questionnaires distributed to Aqua gallon users in Indonesia. The theoretical framework is built based on the latest literature review on environmentally friendly brand image, satisfaction, trust, loyalty, and environmental ethics on green products in Indonesia. The results of this study are expected to provide new insights into the effect of environmentally friendly brand image, satisfaction, trust, on loyalty moderated by environmental ethics towards green products in Indonesia. The practical implications of this study can help marketers and policy makers in designing effective communication strategies to increase purchases of green products in Indonesia.

Ultra-High Field Nuclear Magnetic Resonance (NMR) Spectroscopy: Applications at 1 GHz and Beyond

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Nuclear magnetic resonance (NMR) spectroscopy is a powerful non-invasive analytical technique with wide applications that can observe multiple nuclear species at a site-resolved level. Despite this, NMR has inherent low sensitivity compared to other analytical techniques. A principal approach to improve the sensitivity and resolution of the NMR experiment is to increase the strength of the external static magnetic field (B0), for which the upper practicable limit has gradually increased over five decades. The relatively recent use of high-temperature superconducting materials, such as Bi2Sr2Ca2Cu3Ox (Bi-2223), Bi2Sr2CaCu2Ox (Bi-2212) or REBa2Cu3O7-x (REBCO, RE = rare earth), has enabled construction of ultra-high field NMR magnets. Over twenty commercial ultra-high field NMR instruments at 1.0, 1.1 and 1.2 GHz (23.5, 25.9 and 28.2 Tesla, respectively) have been installed worldwide in the past several years, with more to come. NMR at ultra-high fields benefits both solution-state and solid-state NMR applications. The potential improvements in sensitivity and resolution in NMR spectra are particularly important for studying the structure, dynamics and ligand interactions of biomolecules, which can suffer from poor sensitivity and prohibitive signal crowding. The benefits of using ultra-high field NMR have begun to be demonstrated on various sample types, including intrinsically disordered proteins, membrane proteins, amyloid fibrils, viral capsids, bacterial chlorosomes, fungal cell walls, and whole human cells. Alongside optimisations in sample preparation, probe design and pulse sequences, and exploitation of dynamic nuclear polarisation (DNP), ultra-high field magnets are contributing to an exciting period for improving the sensitivity and resolution of NMR spectra in the study of more complex biomolecules and other samples.

Comparative Study of the Herbaceous Biomass of 3- and 7-year-old Management Sites in Western Niger

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For years, degraded land in western Niger has been subject to unprecedented reclamation. The aim of this study, carried out in the Ouallam department (western Niger), was to characterise the herbaceous vegetation of two (2) sites of different ages, developed by building sylvopastoral half-moons. Aligned quadrat points and abundance-dominance methods were used. The quantity of fodder was estimated by cutting flush with the ground and weighing, after drying, all the above-ground biomass in the half-moon. The inventory identified 38 and 55 species in the 3 and 7 year-old sites respectively, divided into 20 families dominated by the Poaceae family. The Jaccard index shows that the sites are similar, but the similarity is greater between sites of the same age. The highest rate of herbaceous cover (29%) was obtained on the 3-year-old site. The best values for yield and animal carrying capacity, 350 ± 109.68 and 0.07 ± 0.2 respectively, were recorded on the 3-year-old site. The herbaceous vegetation changed with the age of the site. The development favoured the gradual return of vegetation, thus contributing to the restoration of ecosystem services.

Fuzzy Vertex Range Labeling of Some Graph Families

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The main objective of this paper is to introduce fuzzy vertex range labeling and look at this for some graph families subject to suitable conditions.  In this article, the authors have introduced a new idea in fuzzy graph labeling called fuzzy vertex range labeling.  A graph  G that can be assigned values as the difference between the maximum and minimum values in fuzzy graph labeling strategy and if all the vertex values are distinct,  is called fuzzy vertex range labeling.  If G admits fuzzy vertex range labeling then  G is called fuzzy vertex range graph.  The authors have explored fuzzy vertex range labeling on Fan graph, Double fan graph, Wheel graph and Double wheel graph.

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.