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

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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.

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.