Renewable energy, regional tourism, and exports to tackle stagnant growth in developed economies

Toxic metal pollution in freshwater ecosystems: A systematic review of assessment methods using environmental and statistical indices

Surface sediments play a crucial role in freshwater ecosystems as sinks for harmful pollutants and indicators of aquatic health. While localized studies have provided insights into pollution levels, a global assessment of freshwater sediments has been lacking. This study addresses this gap by analyzing data from 149 freshwater sites across 32 nations using multivariate statistical methods (PCA and NMDS were used; receptor models such as PMF, APC-MLR, and UNMIX were not applicable due to lack of primary raw data) and established environmental indicators. Results revealed that average metal concentrations, except for cobalt and zinc, exceeded safety standards, with arsenic, cadmium, and mercury being the most prominent contaminants. Severe contamination was observed in 29-69 % of locations, with arsenic, cadmium, chromium, and nickel posing significant risks to aquatic life in 4-31 % of sites. The ecological risk index (CSI > 5, RI ∼ 600) indicated that 65 % of sites face severe environmental threats. Advanced statistical analyses, including principal component analysis and non-metric multidimensional scaling, identified pollution patterns and sources. Four principal components explained 89 % of the variance, with strong linear correlations (R2 = 0.93) and minimal ordination stress (Stress = 0.023), ensuring the reliability of findings. Source enrichment analysis linked contamination to both natural and human activities, including agriculture, mining, industry, municipal wastewater, and fossil fuel emissions. These findings highlight the urgent need for effective sediment contamination management in freshwater ecosystems.

Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data

Regarding the transportation of people, commodities, and other items, aeroplanes are an essential need for society. Despite the generally low danger associated with various modes of transportation, some accidents may occur. The creation of a machine learning model employing data from autonomous-reliant surveillance transmissions is essential for the detection and prediction of commercial aircraft accidents. This research included the development of abnormal categorisation models, assessment of data recognition quality, and detection of anomalies. The research methodology consisted of the following steps: formulation of the problem, selection of data and labelling, construction of the model for prediction, installation, and testing. The data tagging technique was based on the requirements set by the Global Aviation Organisation for business jet-engine aircraft, which expert business pilots then validated. The 93% precision demonstrated an excellent match for the most effective prediction model, linear dipole testing. Furthermore, the "good fit" of the model was verified by its achieved area-under-the-curve ratios of 0.97 for abnormal identification and 0.96 for daily detection.

Analyzing the influence of financial technology and environmental taxation on energy transformation in BRI nations

This research examined the management, financial technology, and environmental taxation elements impacting energy transformation in Belt and Road Initiative (BRI) countries concerning foreign direct investment (FDI). The study aims to analyze data from 2014 to 2022, encompassing a balanced group of 148 BRI member nations-72 from minimal and lower-middle-class countries and 78 from significant and middle-income industrialized nations. Utilizing the two-step systems generalized method of moments (GMM) framework and verifying with the two-stage least squares (2SLS) approach, the study identified critical drivers and barriers to energy transformation in these countries. The findings indicate that effective management practices, advanced financial technologies, and appropriate environmental taxation policies significantly influence energy transformation and FDI inflows. Additionally, the research reviewed the impact of cruise tourism on human health and environmental ecosystems, highlighting severe environmental consequences such as habitat deterioration, marine pollution, and ecosystem disturbances. Human health issues linked to air and water pollution, including respiratory disorders and water-borne illnesses, were also identified. The socioeconomic effects on nearby communities were significant, underscoring the need for stringent regulations and sustainable practices. This comprehensive analysis provides essential insights for policymakers, industry stakeholders, and scholars, advocating for integrated policies to promote sustainable energy transformation and mitigate the adverse effects of cruise tourism.

Assessing the economic viability of prolonged fuel cycles in the agricultural sector: Impacts on productivity, Human capital and sustainability

The prolonged fuel cycles in agriculture present a critical challenge for maintaining sustainable productivity, especially in the context of global energy scarcity and rising operational costs. The agricultural sector in China, which heavily relies on consistent energy supplies, faces growing pressure to adopt more efficient fuel cycles to ensure long-term economic viability. This paper aims to assess the economic viability of prolonged fuel cycles within China's agricultural sector by utilizing a comprehensive econometric model that evaluates the impacts on productivity, cost efficiency, human capital and environmental sustainability. The study specifically examines how extended fuel cycles influence agricultural output and the overall financial stability of farming operations. Results indicate that prolonged fuel cycles significantly reduce operational costs by improving energy utilization efficiency and human capital development increase agricultural productivity by up to 12 %, and mitigate carbon emissions by approximately 15 % due to more efficient fuel use. Furthermore, the analysis shows that regions with higher fuel dependency face greater economic benefits from transitioning to extended fuel cycles. Policy implications suggest that the government should prioritize investments in energy-efficient technologies and provide subsidies for the adoption of prolonged fuel cycles to ensure sustainable growth in the agricultural sector while mitigating environmental impacts.