Maasai Mara University

Arid and Semi‐Arid Lands have witnessed a surge in extreme climatic events with devastating environmental and livelihood effects. Understanding the dynamics of these extreme events, including drought, is essential for anticipatory action among resource‐dependent communities. This study utilised Earth Observatory Systems and Google Earth Engine to analyse 24 years of Normalised Difference Drought Index trends in the Narok West landscape of Kenya across six timeframes (2000, 2005, 2010, 2015, 2020, and 2024). It revealed that the Normalised Difference Drought Index ranged from −0.489 (April 2000) to 0.469 (August 2005). Additionally, it established that during June–July–August dry seasons, there was an increase in the proportionate area under severe drought from 11% in 2000 to 24% in 2024 (average 19.17%, SD: 8.43%); and a decrease in the proportionate area under non‐drought (good conditions) from 57.5% in 2000 to 40.5% in 2024 (average 40.5%, SD: 7.43%) respectively. Temporal increase in drought events was observed to be increasing from 2015, with extremes witnessed in 2020. Moreover, we established that season dry season rainfall averages 147.2 mm (95% CI: 100.7–193.8) and is decreasing at a rate of 1.25 mm annually. It is anticipated that the frequency and severity of drought across the landscape might increase due to weather variability, predominantly attributed to climate change. The increase could have a detrimental effect on water quality and quantity, public and ecosystem health, mental health and wellness, peace and protection, and rangeland ecology. Our study contributes to the body of research on future drought scenarios, which could assist with methodological and empirical studies and corrective actions. To adapt to and manage the effects of changing climate, these scenarios necessitate interdisciplinary community and landscape strategies, including the need for communities to develop a comprehensive understanding of the impacts of climate change and plan for the sustainable management of water resources.

The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Thematic Assessment Report on Invasive Alien Species and Their Control presents a “conceptual diagram of management-invasion continuum”, introducing a versatile framework to support decision-making on the management of biological invasions. Drawing on an extensive synthesis of current knowledge, this IPBES invasion-management framework has been developed to broaden the scope of existing invasion curves—which primarily overlay generic management objectives onto a sigmoid curve depicting the expansion of the affected area over time—and to illustrate the applicability of the concept of effective management at different stages of the biological invasion process. To introduce the IPBES invasion-management framework to a wider audience, this paper explains the features of the framework and defines the invasion-stage-based management approaches and the potential outcomes envisaged therein. Reflecting the currently limited management options for biological invasions in marine and other connected-water systems, unlike in terrestrial and closed-water systems, the IPBES invasion-management framework clearly distinguishes between these two groups of systems. For each, it presents management approaches including three key factors that decision-makers should consider concurrently: management objectives, targets, and actions. This framework supports informed decision-making in the management of biological invasions in all ecosystems.

This study aims to prepare an aluminum-citrate ion cell from waste aluminum foils and citric acid extracted from Dovyalis caffra fruits (Kei apples) and further demonstrate their electrochemical performance at varying impedances, electrode thicknesses and discharge times. Aluminum oxide ions were prepared from discarded aluminum foils, and citric acid were produced by co-precipitating macerated Dovyalis caffra extracts by acid infusion. Aluminum foil wastes were utilized in acid co-digestion to produce aluminum oxide nanoparticles. The prepared aluminum oxide was then characterized for surface morphology, composition and phases present. The prepared particles revealed Al2O3 boehmite moieties and ranged between 66.3 and 106.1 nm in size. The synthesized citric acid depicted desirable morphological and electrolytes traits similar to those of commercial citric acid. The cells open and closed current-voltages were directly proportional to the electrode diameter. While the cells were found to be quite Ohmic in nature with increased impedance with electrode distances, discharge times were also proportional to electrode diameters. The cell's energy balance was 477.6–346.3 J/s, with a density of 47.1–53.7 Wh/kg. The results showed that the cells could successfully produce portable energy storage devices from waste materials.

Dopamine is a key catecholaminergic neurotransmitter, and its precise determination in biological fluids is essential for studying neurological dysfunction and developing diagnostic approaches. This study reports a thermally optimized carbon quantum dot‐embedded zeolitic imidazolate framework‐8 (CDs@ZIF‐8) nanocomposite as an electrochemical sensing platform for ultrasensitive dopamine detection. Carbon quantum dots were synthesized via a hydrothermal route using L‐proline and o‐phenylenediamine as carbon and nitrogen precursors and subsequently integrated into the ZIF‐8 framework through pH‐regulated electrostatic impregnation. Structural characterization confirmed the successful incorporation of CDs while preserving the sodalite‐type crystalline structure of ZIF‐8. The introduction of CDs significantly improved the electrochemical properties of ZIF‐8 by reducing the charge transfer resistance from approximately 510 to 55 Ω and enhancing dopamine oxidation performance through improved electron transport and increased accessible active sites. Temperature optimization identified 30 °C as the optimal sensing condition, enabling the CDs@ZIF‐8‐modified glassy carbon electrode to achieve a wide linear detection range from 0.05 to 180 μM, a sensitivity of 0.2156 μA μM −1 , and a detection limit of 18 nM ( S / N = 3). The sensor exhibited high selectivity against common interfering substances, excellent storage stability with 93.8% signal retention after 10 days, and reliable analytical performance in fetal bovine serum samples with recoveries of 97.1%–104.8%. The developed CDs@ZIF‐8 platform provides an effective strategy for improving metal–organic framework‐based electrochemical sensing systems and demonstrates potential for dopamine monitoring in complex biological matrices.

This chapter presents an artificial intelligence (AI)-powered framework for enhancing air traffic surveillance through satellite imagery analysis. The system integrates remote sensing, computer vision, and geo-stamped aircraft location data to improve real-time detection and classification, especially in remote or non-radar-covered regions. A three-phase approach guides the framework: (1) extracting radar coverage from satellite imagery, (2) labeling data using aircraft geo-locations, and (3) applying deep learning models for classification and tracking. Using models such as YOLO and Faster R-CNN, the system distinguishes aircraft from other aerial objects with high accuracy. Experimental results confirm the feasibility of this approach, demonstrating improved monitoring capabilities in high-traffic airspace. The framework enhances situational awareness, supports better flight planning, reduces congestion, and improves aviation security. It also holds potential for disaster response, enabling efficient search-and-rescue operations in unmonitored zones. However, limitations persist under adverse weather and low-light conditions, prompting the need for infrared and radar-based enhancements. This study offers a scalable, cost-effective solution for next-generation air traffic management, combining AI, big data, and satellite technologies for more adaptive and intelligent surveillance.