The cyprinodontiform fish fauna of the Bangweulu–Mweru and Upper Lualaba freshwater ecoregions, situated in the uppermost Congo River drainage, has been reviewed. This study introduces four newly described species of seasonal Nothobranchius killifish and a novel species of lampeye belonging to the genus ‘Lacustricola’. Nothobranchius iridescens, new species, from the Kafila system in the Lufira drainage, is characterized in male colouration by anal fin with irregular red-brown spots and stripes, creating two irregular submedial and medial bands and with broad yellow subdistal band; and a caudal fin with a slender light blue subdistal band, densely marked with irregular red spots, and narrow dark brown distinct distal margin. Nothobranchius katemomandai, new species, from the Kay system in the upper Lualaba drainage, is characterized in male colouration by an anal fin with narrow brown submedial band, followed by a slender yellow band, a slender red-brown band and a slender dark brown distal band; and a caudal fin with brown spots proximally and medially, and with slender white to light blue subdistal band and a narrow dark grey distal band. Nothobranchius marmoreus, new species, from the Lufukwe system in the Lake Mweru basin, is characterized in male colouration by a body with irregular red-brown patches and stripes, forming a marble-like mottled pattern; and anal and caudal fins with slender yellow to amber subdistal band and broad dark brown distal band. Nothobranchius dubieensis, new species, from the Lubule system in the Luvua drainage, is characterized in male colouration by an anal fin with narrow dark brown submedial band, narrow yellow and orange medial bands, narrow white subdistal band, and slender dark brown distal band; and a caudal fin with irregular red-brown spots and stripes proximally and medially, followed by an irregular narrow red-brown subdistal band and slender white distal band, and with interrupted red-brown fin tips. ‘Lacustricola’ gemma, new species, from the Kay system in the upper Lualaba drainage, is characterized by a pattern of iridescent, diamond-shaped, light blue spots in scale centres below mid-longitudinal series on posteroventral portion of flank; median fins yellow to hyaline, with dark grey stripes perpendicular to fin rays; dorsal fin with light blue distinct margin; anal fin with dark grey margin. Analysis of mitochondrial COI gene sequences revealed that the five new species represent phylogenetically distinct lineages. These findings not only underscore their genetic uniqueness but also confirm their placement within the Nothobranchius brieni species group and the genus ‘Lacustricola’. Species of the genus Nothobranchius complete their seasonal life cycle in ephemeral natural habitats within freshwater wetlands, while ‘Lacustricola’ species migrate to breed in flooded areas of shallow, typically seasonal wetland habitats at the onset of the rainy season. These wetlands are highly vulnerable to a variety of human-induced stressors and threats, including agricultural cultivation, water extraction, urban expansion resulting in land-use pressure, and increased pollution, particularly from industrial activities such as mining. Therefore, it is essential to protect the integrity of these unique aquatic habitats throughout all the seasons of the year to maintain healthy wetland ecosystems and safeguard the distinctive seasonal freshwater biodiversity they support.
By the end of the 20th century, the Source Region of the Yangtze River (SRYR) suffered severe ecological degradation driven by the combined effects of climate change and human disturbances. To counteract ecological degradation, the Chinese government implemented multiple ecological protection and restoration measures. Based on a literature review, this study analyzed the entire process of ecological degradation and restoration in the SRYR using the DPSIR (Drivers-Pressures-States-Impacts-Responses) framework. It revealed that climate warming and grazing expansion were the main drivers. Under the dual pressures of natural and anthropogenic disturbances, grasslands experienced severe degradation, accompanied by significant losses of soil nutrients. The decline in grassland quality weakened ecosystem service functions and reduced the livelihood levels of herders. After implementing the ecological protection and restoration projects in China, the ecosystem had been effectively restored. Herders’ income levels had been improved. However, a mismatch persisted between ecological compensation standards and livestock reduction costs for herders. Future efforts should focus on the innovation of the institution and ecological restoration techniques. This study offers critical insights into ecological protection and restoration strategies, providing practical references for decision-makers to accelerate the realization of China’s ecological civilization objectives.
Accurate streamflow prediction is essential for irrigation planning, water allocation, and flood risk management, particularly in water-scarce regions like the Niger River Basin. However, the complexity of hydrological processes and data limitations make reliable predictions challenging. This study optimizes Support Vector Machine (SVM) hyperparameters for daily streamflow prediction using time-lagged climate data and four metaheuristic algorithms—Binary Slime Mould Algorithm (BSMA), African Vulture Optimisation Algorithm (AVOA), Archery Algorithm (AA), and Intelligent Ice Fishing Algorithm (IIFA). Model performance was assessed using eight evaluation metrics, with results showing that AA and IIFA consistently outperform the others, achieving Nash-Sutcliffe Efficiency (NSE) values between 0.986–0.999 and 0.893–0.999, respectively. AVOA and BSMA show less consistent performance, with NSE ranges of 0.524–0.999 and 0.863–0.965, respectively. The study highlights the novel integration of multiple metaheuristic algorithms for optimizing machine learning models, offering insights into their effectiveness for hydrological prediction. By demonstrating the superior performance of AA and IIFA, this research provides a robust framework for enhancing long-term streamflow forecasting. These findings support improved water resource management in West Africa, helping policymakers address climate variability, water scarcity, and hydrological uncertainty.
The detection of drones in complex and dynamic environments poses significant challenges due to their small size and background clutter. This study aims to address these challenges by developing a motion-based pipeline that integrates background subtraction and deep learning-based classification to detect drones in video sequences. Two background subtraction methods, Mixture of Gaussians 2 (MOG2) and Visual Background Extractor (ViBe), are assessed to isolate potential drone regions in highly complex and dynamic backgrounds. These regions are then classified using the ResNet18 architecture. The Drone-vs-Bird dataset is utilized to test the algorithm, focusing on distinguishing drones from other dynamic objects such as birds, trees, and clouds. By leveraging motion-based information, the method enhances the drone detection process by reducing computational demands. Results show that ViBe achieves a recall of 0.956 and a precision of 0.078, while MOG2 achieves a recall of 0.857 and a precision of 0.034, highlighting the comparative advantages of ViBe in detecting small drones in challenging scenarios. These findings demonstrate the robustness of the proposed pipeline and its potential contribution to enhancing surveillance and security measures.
The analysis delves into key strategies for enhancing agricultural productivity in Southeast Asia and South Asia. It underscores the vital role of mechanization, sustainable input practices, and gender-inclusive policies. Customized interventions in these realms hold promise for significantly amplifying agricultural performance in the region. Fertilizer and machinery productivity are pivotal factors that correlate strongly with overall agricultural productivity, as revealed by regression analyses. Notably, male employment in agriculture and agricultural machinery exhibits positive and substantial impacts on agricultural productivity, while female employment and fertilizer consumption indicators show significant yet negative associations. The study highlights systemic issues such as unequal resource access and differing gender roles in agriculture that may impede the immediate productivity gains from increased female labor force participation. Mechanization and efficient fertilizer utilization emerge as critical drivers of enhanced agricultural output, with consistent coefficients across models. Male employment consistently demonstrates a positive influence on productivity, emphasizing the significance of labor force engagement in agriculture. Moreover, the study underscores the imperative of judicious fertilizer management to avert environmental degradation and diminishing returns. The findings affirm the efficacy of the random effects model, supported by the Hausman test, which indicates congruence in results between fixed and random effects models. This methodological choice ensures robust and reliable conclusions regarding the relationships between male and female employment, machinery, fertilizer consumption, and agricultural productivity in South and Southeast Asia.
This study investigates the distinct impacts of electricity and petroleum consumption on economic growth in Eastern Africa. Using a Panel Autoregressive Distributed Lag Model and data for a period spanning 2000 to 2021, the study examines both the short-run and long-run effects of these energy sources on Gross Domestic Product. The findings reveal that petroleum consumption has a statistically significant and positive impact on GDP in both the short run and long run. In contrast, while electricity consumption shows a positive but statistically insignificant effect on GDP in the short run, it exhibits a negative and statistically significant impact in the long run. These results suggest that policymakers in Eastern Africa should prioritize sustainable petroleum management to maximize its economic benefits while mitigating potential environmental risks. While the negative coefficient of electricity implies a corrective response of the variables to long-run equilibrium in the face of short-term shocks. As a result, it is recommended that economic shocks caused by energy consumption be considered in terms of their relationship to economic growth, whether positive or negative in the long or short term, as decision makers need to address their impact and limit such shocks on economic growth.
Offshore wind turbines (OWTs) in cold climate regions have become increasingly significant due to the abundant wind resources with the development of renewable energy. These areas offer considerable potential for the development of OWTs. Generating energy for communities in cold climate regions involves overcoming significant challenges posed by the remote and harsh environmental conditions. This review presents the state-of-the-art research regarding prediction models for ice accretion on wind turbine components. Furthermore, this review summarizes advanced mitigation solutions, such as cold-weather packages and ice protection systems, designed to address icing issues. The present study identifies critical knowledge gaps in OWT deployment in cold climate regions and proposes future research directions.
Green infrastructure (GI) is a growing topic in urban planning, asset management, and climate change adaptation. However, rural regions have been under-represented in the discourse. This paper explores the benefits and challenges associated with the implementation and management of GI through a regional study of rural communities in southwestern Ontario. Our focus concerns the inter-relationships between GI, economic resilience, and the development of rural places. Findings show rural communities benefit from GI initiatives like natural stormwater management, park naturalization, and natural heritage restoration, which provide low-cost municipal services, conserve agricultural soils, and contribute to the amenity appeal of rural places. Challenges surrounding awareness, organizational capacity, and environmental regulation have slowed the uptake of GI and led to inconsistencies across jurisdictions. A mix of supportive policies, funding of demonstration projects with economic monitoring, and training to build professional capacity will advance the use and efficacy of GI across rural regions.
There is a growing awareness of the importance of higher education in Sweden to reduce social differences in society. There are also various mechanisms that individuals relate to that favour either the status quo or change based on an ideal of higher education. Individuals live in a geographical context with a number of ‘key actors’ who influence the perception of higher education with varying degrees of intensity. Paradoxically, despite several reforms to broaden recruitment, it can be seen that relative inequalities persist in terms of residents with higher education in Sweden, not least from a regional perspective. The purpose of this article is to shed light on geographical differences in the higher education level of the population over time from a Swedish perspective. The study shows that higher education has a geographical centre-periphery perspective, but not exclusively. There are thus additional influencing factors that in various ways relate to the social context in which the individual is located. We can conclude from our empirical data that the reforms implemented to broaden recruitment have not had the desired effect, especially for the group of men. We find it likely that what differentiates women and men is who their individual ‘key players’ are and how they interact. From an academic education perspective and as an intermediary of higher education, there is therefore a challenge to be able to identify who these “key players” are in order to be able to be an important actor in contributing to the desired broader recruitment that the government is striving to achieve.