Effective mitigation of geotechnical risk and safety management of underground mine requires accurate estimation of rockburst damage potential. The inherent complexity of the rockburst phenomena due to nonlinear, high dimensional, and interdependent nature of the geological factors involved, however, makes predictive modeling a difficult task. The proposed research is based on the use of the K-Nearest Neighbor (KNN) algorithm to predict the risk of rockbursts with the use of microseismic monitoring data. Several key features like the ratio of total maximum principal stress to uniaxial compressive strength, energy capacity of support system, excavation span, geology factor, Richter magnitude of seismic event, distance between rockburst location and microseismic event, and rock density were applied as input parameters to extract critical rockburst precursor activities. In the test stage, the proposed KNN model recorded an accuracy of 75.50%, a precision of 0.913, a recall value of 0.509, and F1 Score of 0.576. The model is reliable with a significant performance indicating its efficacy in practice. The KNN model showed better classification results as compared to recently available models in literature and provided better generalization and interpretability. The model exhibited high prediction in classified low-risk incidents and had strong indicative capabilities towards high-risk situations, attributed to being a useful tool in rockburst hazard measurement.

Climate diplomacy has become an essential mechanism for fostering international cooperation to combat the global crisis of climate change. This study explores its growth, successes, and challenges, offering understandings into its transformative journey from fragmented environmental determinations to a more cohesive and inclusive global context. Landmark agreements such as theKyoto Protocol, Paris Agreement, and Glasgow Climate Pact have marked important milestones, driving progress toward global climate goals. However, modern experiments geopolitical tensions, economic disparities, and implementation gaps threaten the efficiency of these initiatives. The research highlights the essential role of international association in addressing transboundary climate issues. It postulates the need for just mechanisms of climate finance to enable the developing nations, and integrates science and technology for policy orientation and innovation. The study provides actionable strategies that tackle persistent barriers such as increasing transparency and trust through independent assessments, equitable technology sharing, and building on regional and subnational initiatives. It concludes that climate diplomacy has made considerable strides but needs sustained commitment and inclusive approaches to bridge the gaps in finance, trust, and implementation. This research establishes the need to reimagine climate diplomacy as dynamic and equitable and powerful in global action in the face of escalating climate challenges.

Purpose: The present research aims to establish the involvement of nanotechnology in increasing the rate of oil recovery and the management of reservoirs in the oil and gas business. However, there is still a gap within the state of knowledge on what the practical form of nanotechnologies is, how effective they are in the actual practical context, and how nanotechnologies are viewed by practical practitioners. The study is geared towards establishing the extent to which practising professionals in the petroleum engineering field know about nanotechnology and its applicability, as well as how they appraise the effectiveness of the concept.Objective: In this study; the specific aim will be to indicate the level of knowledge on nanotechnology among professionals working in the petroleum industries, establish their perception of the effectiveness of nanotechnology in enhancing oil recovery and managing hydrocarbon reservoirs, and lastly determine the challenges owing to cost factors, technical considerations and environmental impacts that hinder full adoption of nanotechnology in the petroleum industries.Methodology: A descriptive research design was conducted using a structured questionnaire from 200 professionals working in the petroleum industry. Authorized through another set of close-ended questions with emphasis on awareness, familiarity, and perceived effectiveness of nanotechnology. Statistical analysis was performed by using chi-square tests, Mann-Whitney U tests, ordinal regression, and Spearman correlation coefficients. Descriptive data was presented with graphs and tables and bar charts and box plots were used in displaying the data.Results: The significant value of the chi-square test is 2.34 and its p-value is 0.674. Hence no significant relationship between the variables of awareness of nanotechnology and perceived oil recovery effectiveness can be concluded. The Mann-Whitney U test described no significant difference in the perceived reservoir management effectiveness by participants who applied nanotechnology (U = 5133.00, p = 0.647) and those who did not. The ordinal logistic regression model results did not reveal any significant predictors of the effectiveness of oil recovery about awareness (coefficient = 0.2, p = 0.330) or familiarity (coefficient = -0.5, p = 0.330) had a log-likelihood of - 319.24. After conducting the Spearman correlation coefficient test, it was noted that the relationship between awareness and effectiveness of oil recovery is negative and weak and the results are statistically insignificant (r = -0.063, p = 0.372). These results imply that awareness and familiarity with nanotechnology do not significantly determine their perception of the effectiveness of nanotechnology.Practical Implications: This thesis underlines the necessity of collecting proofs of nanotechnology usage to promote the application of such technology across the world. Education and awareness promotion efforts will not be adequate; instead, stakeholders of the industry should concentrate on actual proof of nanotechnology in large-scale field tests. The current study also suggests the call to reduce the cost of production because the cost is still prohibitive and there is an urgent need to evaluate environmental effects when it comes to the application of nanotechnology in the petroleum industry.Novelty: The present work offers a perspective on the use of nanotechnology in petroleum engineering by focusing the analysis on the practitioners’ point of view, rather than presenting more or less contrived practical examples and equations of filtration theories. In this respect, the study adds to the practicality of existing literature, which is essential for further investigations of nanotechnology applications in oil recovery and reservoir management. Conclusion: The present research also suggests that concerning its applicability in the field of petroleum engineering, awareness and familiarity do not necessarily lead to a positive perception of its effectiveness. From the present study, it is noted that adequate efforts should be made to show the usefulness of nanotechnology for general applications through field applications and explore the challenges associated with its implementation, including cost and environmental aspects. More research should be directed toward following up the effects of this technology in enhanced recovery of oil and the development of guidelines to govern nanotechnology’s use.

The convergence of satellite technology and artificial intelligence (AI) has revolutionized environmental monitoring, enabling real-time, high-resolution analysis of Earth's land, water, and air systems. This paper explores the evolution from traditional remote sensing to AI-driven paradigms, highlighting key orbital platforms such as Sentinel-5P, Landsat-8/9, and GHGSat for data acquisition. Foundational AI methodologies, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Vision Transformers (ViTs), are examined for their roles in feature extraction, time-series analysis, and global context capture. Applications span terrestrial land cover change detection and precision agriculture; hydrospheric water quality assessment, groundwater tracking, and cryospheric dynamics; and atmospheric greenhouse gas detection and air quality modeling. Technological optimizations like edge computing and image restoration address data bottlenecks, while socio-economic implications, including Explainable AI (XAI), the AI digital divide, and environmental footprints, underscore ethical considerations. Through a synthesis of emerging trends, the paper posits that AI-assisted monitoring fosters proactive sustainability but risks rebound effects and inequities, urging balanced policy frameworks for equitable planetary stewardship.