Zero-valent iron is a moderately reducing reagent that is both non-toxic and affordable. In the present work, iron nanoparticles were synthesized using bitter guard leaf extract (Momordica charantia L.) (BGL-Fe NP). Using leaf samples from bitter protectant extract, iron nanoparticles were synthesized with secondary metabolites such as flavonoids and polyphenols acting as capping and reducing agents. Polyphenols reduce Fe²⁺/Fe³⁺ to nanovalent iron or iron nanoparticles. Iron nanoparticles were synthesized by reducing iron chloride as a precursor with bitter protective leaf extract in an alkaline environment. The obtained BGL-Fe NPs were calcined for 4 h at various temperatures of 400 °C, 500 °C, and 600 °C. The obtained samples were coded as BGL-Fe NPs-4, BGL-Fe NPs-5, and BGL-Fe NPs-6, respectively. The synthesized BGL-Fe NPs were systematically characterized by XRD, SEM, FTIR, UV-Vis and TG-DTA analysis. The obtained BGL-Fe NPs were then used as an adsorbent to remove the aqueous solution of basic methylene blue (MB) dye. MB concentration was monitored using UV-Vis spectroscopy.

The global Testing, Inspection, and Certification (TIC) service market is experiencing significant growth, driven by rising demand for high-quality and safety-related TIC services across various industries. This research aims to redesign a position map and strategy for Indonesian TIC State-Owned Enterprises (SOEs) in the Red Ocean competition. This systematic literature review analyzed 17 journals. The results show that the Indonesian TIC SOEs are intensively competing in the Red Ocean competition. In designing the position map in the Red Ocean competition, the SOEs must use technology in their operational activities to implement good corporate governance, collaborative strategies, resource management, and leadership styles aligned with the organizational culture.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

Objectives: This study aims to examine the impact of Sun Tzu’s Art of War Five Virtues Leadership on innovation and the efficiency of the Chinese brand passenger vehicle industry, explore the role of innovation in enhancing industry efficiency, and propose strategies for leveraging the Five Virtues Leadership to improve operational performance and competitiveness in the sector. Methodology: A mixed research method using quantitative research (questionnaire survey) as the main method and qualitative research (in-depth interview) as the auxiliary method. Result: Quantitative and qualitative research results confirm the positive correlation between the Five Virtues Leadership, innovation, and the efficiency of Chinese brand passenger vehicle companies. And through effective data analysis, it explains the importance of the five virtues of leadership in traditional Chinese culture. Further understanding of the effectiveness and competitiveness of China’s passenger car brands, with leadership references. Conclusion: Five Virtues Leadership can foster a favorable environment for innovation, enhance time utilization, optimize resource allocation, and strengthen brand image. By developing and validating a measurement for Five Virtues Leadership, this study enhances the understanding of its role and significance in modern management, paving the way for future research.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.

The detection of urban expansion through digital processing of satellite images provides valuable information for understanding the dynamics of land use change and its spatial relationship with environmental factors. In order to apply or generate effective land-use planning policies, it is essential to have a historical record of the regional distribution of human settlements, an element that is practically non-existent in our country. For this reason, this text aims to determine the urban growth rate during the period 2000–2014 in the state of Hidalgo, Mexico, and to identify potential expansion zones from Landsat images. Six Landsat scenes were used for the spatial analysis of the state urban coverage and their relationship with the road influence area was evaluated. Two maps were obtained as cartographic products: one of urban coverage distribution and another of the municipalities with the greatest expansion, whose areas are located in the Valle del Mezquital region. However, Mineral de la Reforma, Tetepango, Tizayuca and Pachuca de Soto stand out for their growth rates during the study period: 183.44%, 102%, 94% and 68.5%, respectively. In total, the state urban area in-creased 72.3 km² from 2000 to 2014 with an average growth rate of 1.8% per year. Such growth was associated with the areas of influence of important road infrastructure, such as the Libramiento Arco Norte in Hidalgo. Therefore, the Mezquital Valley and the Mexico Basin are considered as potential regions for urban expansion in the state.