The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI's capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.

In the domain of public management, the concept of agency refers to the capacity of individuals or groups to effectively utilise power and resources to achieve certain goals. The formation of agency is significantly influenced by the external institutional environment and how actors perceive social structures. Thus, the agency to win a game can be generated as players familiarise with the game’s operations and understand the story line. But beyond this, there are also players who make mods on a non-profit basis, modifying the game’s program to meet the needs of others. mods, as a form of patching, are different from other fan-created mediated texts. Therefore, studying the agency in gaming community management, where both players and developers interact, offers valuable insights for understanding how to promote public participation, innovation, and effective governance in the context of public management. This approach bridges the gap between the digital world and real-world public management practices.

Olive production is threatened by a fungal pathogen, Armillaria mellea (Vahl. Fr.) P. Kumm.,causing decline in trees worldwide. Effectiveness of once and twice applications of fungicides hexaconazole, propicoconazole and thiophanate-methyl and application of biological agent (Trichoderma harzianum) to control A. mellea was studied at orchard scale during four years. T. harzianum inhibited the pathogen growth on agar media. This antagonistic fungus provided a 25% control efficiency of A. mellea on olive trees younger than 15 years which was the same as control efficiency of once application of hexaconazole. Control efficiencies as perfect as 100% were determined on younger (<15 years old) diseased olive trees treated with once applications of thiophanate-methyl and hexaconazole, and twice applications of thiophanate-methyl. Moreover, olive tree age was significantly effective on fungicidal control efficiency. Hence, this four-year research advanced our understanding of sustainable olive production in study region and other geographical areas with similar agro-ecological characteristics.

As cities continue to face the increasing demands of urban transportation and the need for sustainable mobility solutions, the integration of intelligent transportation systems (ITS) with smart city infrastructure emerges as a promising approach. This paper presents a novel framework for integrating ITS with smart city infrastructure, aiming to address the challenges of urban transportation and promote sustainable mobility. The framework is developed through a comprehensive literature review, case studies, and stakeholder interviews, providing significant insights into the integration process. Our research outlines the key components of smart city infrastructure that can be integrated with ITS, highlights the benefits of integration, and identifies the challenges and barriers that need to be addressed. Additionally, we propose and apply evaluation methods to assess the effectiveness of ITS integration with smart city infrastructure. The results demonstrate the novelty and significance of this framework, as it significantly reduces traffic congestion, improves air quality, and enhances citizen satisfaction. This paper contributes to the existing literature by providing a comprehensive approach to integrating ITS with smart city infrastructure, offering a transformative solution for urban transportation challenges.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

This review provided a detailed overview of the different synthesis and characterization methods of polymeric nanoparticles. Nanoparticles are defined as solid and colloidal particles of macromolecular substances ranging in size under 100 nm. Different types of nanoparticles are used in many biological fields (bio-sensing, biological separation, molecular imaging, anticancer therapy, etc.). The new features and functions provided by nano dimensions are largely different from their bulk forms. High volume/surface ratio, improved resolution and multifunctional capability make these materials gain many new features.